Naval Engineers Journal — Fall 2023 by BonoTom Studio, Inc.

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Capability Modeling for Assessing Mission Effectiveness in Surface Ship Concept and Requirements Exploration 87

Special Section—Promoting Electric Propulsion (PEP) 32

Underwater Long-Range Photonic System for Acoustic Detection of Animal Species 81 Carbon Footprint and Life Cycle Cost Assessment of a Hydrogen-Based Energy Storage System for Ships with a Case Study 107 Shipboard Microgrids and Automation 121

Comparison of Different Approaches for Prediction Self-Propulsion of the Ship Using RANSE Method 133 Theoretical and Experimental Study on the Rapidity of Large Automobile Ro-Ro Ship 8500PCTC 143

www.navalengineers.org

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The appearance of U.S. Department of Defense (DoD) visual information does not imply or constiture DoD endorsement.

TA B L E O F C O N T E N T S

DEPARTMENTS 7 Secretary’s Notes 12 New Members 14 Section Directory 15 Committee Directory 16 Upcoming Events 31 Corporate Supporters 132 Code of Ethics 156 Know Brainer 157 Membership Application

PEP 2023 Awards Presentation

FEATURES & NEWS 5 President’s Page—21st Century ASNE David Lewis

11 From the Editorial Board Leigh McCue

17 Guest Column—How Do You Find a Mentor That’s Worth

Their Value?

Alonzie Scott, III

20 2023–2024 ASNE Scholarship Recipients 22 From the Archives—Planting a STEM Seed: The SeaPerch

Challenge Ten-year Anniversary Introduction by Kelly Cooper

Special Section — Promoting Electric Propulsion 2023 32 Introduction—Promoting

64 Wake Forest University

Electric Propulsion 2023

66 Johns Hopkins University

Mike Briscoe

68 University of

34 The University of Rhode

Wisconsin—Madison

Island

38 Princeton University

50 University of Michigan

42 Florida Atlantic University

54 University of Kentucky

44 Washington College

60 University of Michigan

46 William & Mary 48 Old Dominion University

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Electric Boat

62 University of Pittsburgh

73 University of Georgia 76 North Carolina State

University

78 University of

Wasington—Bothell

80 Florida Institute of

Technology

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ASNE Tidewater Section Dinner Meeting

TECHNICAL ARTICLES 81 Underwater Long-Range Photonic System for Acoustic Detection of Animal Species Martin Holahan, Thomas Plunkett, Harlem Satterfield, Dominic Allizzo, Jazmine Branford, John Suarez

TECHNICAL PAPERS 87 Capability Modeling for Assessing Mission

Effectiveness in Surface Ship Concept and Requirements Exploration David J. Berrow, Mark A. Parsons, Alan Shane, Mustafa Y. Kara, and Alan J. Brown

143 Theoretical and Experimental Study on the

Rapidity of Large Automobile Ro-Ro Ship 8500PCTC

Wang Chi-ming, Wang Yong-guang, Lin Zhuang, Zhang Ying-jie, Yao Kai-han

107 Carbon Footprint and Life Cycle Cost

Assessment of a Hydrogen-Based Energy Storage System for Ships with a Case Study Ibrahim S.Seddiek, Nader R.Ammar

121 Shipboard Microgrids and Automation Isabelle C. Patnode, Michael J. Bishop, Aaron W. Langham, Daisy H. Green, Steven B. Leeb

133 Comparison of Different Approaches for

Prediction Self-Propulsion of the Ship Using RANSE Method Tran Ngoc Tu, Nguyen Thi Hai Ha, Nguyen Huy Hao

ON THE COVER SOUTH CHINA SEA (Feb. 1, 2023) Operations Specialist 1st Class Nancy Ayala, from Orange Cove, Calif., stands watch in the combat information center aboard the Arleigh Burke-class guidedmissile destroyer USS Wayne E. Meyer (DDG 108). U.S. NAVY PHOTO BY MASS COMMUNICATION SPECIALIST 3RD CLASS MYKALA KECKEISEN

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Fall 2023 | Vol. 135 | No. 3

Council of the Society President VADM David H. Lewis, USN (Ret.) Vice Presidents Dr. Leigh McCue

American Society of Naval Engineers, Inc.

RADM Ronald Rábago, USCG (Ret.)

Executive Director and Secretary-Treasurer CAPT Dale Lumme, USN (Ret.)

Council Members

RADM Mark Whitney, USN (Ret.) RADM Brian Antonio, USN (Ret.) Dr. Julie Chalfant, CAPT USN (Ret.)

Editor-in-Chief Dr. Leigh McCue

Mrs. Kelly Cooper CAPT Richard Delpizzo, USN (Ret.)

Managing Editor Ms. Maggie O'Brien

CAPT Stephanie Douglas, USN (Ret.), SES (Ret.)

Executive Directors Emeritus CAPT Dennis K. Kruse, USN (Ret.) Dr. Leigh McCue Associate Editors CDR Dwight Alexander, USN (Ret.), PE Dr. Raju Datla Dr. Norbert Doerry, CAPT, USN (Ret.) Mr. Robert Galway Dr. Harriss C. (Neil) Ganey Dr. Bonnie Johnson Dr. Carolyn Judge Mr. Robert G. Keane, Jr. Dr. Armen Kvryan Dr. Alexander Laun, PE Contributors Mr. Michael Briscoe Ms. Madeline Foresman

Mr. Glen Grogan, PE CAPT James L. McVoy, USN (Ret.) Mrs. Sara H. Skolnick, CAE

Dr. Carolyn Judge Ms. Margaret G. Nate

Dr. Evan Lee Dr. Edward Lewandowski Mr. Peter McCauley Dr. Timothy McCoy Mr. Stephen Minnich Ms. Margaret G. Nate CAPT Tyson J. Scofield, USGG Dr. Daigo Shishika Mr. Kirk Torstenson, PE

CDR Charles G. Pfeifer, USN (Ret.) CDR Renee N. Reedy, USN (Ret.) MIDN Matthew Richardson, USN Student Representative Section Council Representatives Mr. Edward Eastlack Mrs. Allison Hice CAPT Glenn D. Hofert, USN (Ret.), PE CAPT Joseph ‘Ike’ M. Iacovetta, USN Mr. Joseph P. Kosteczko

Mr. Sam Hall Mr. Robert Hand

Ms. Nancy Lackey Mr. Evan Martella

The Naval Engineers Journal (ISSN 0028-1425 [print], ISSN 1559-3584 [online]) is published quarterly by the American Society of Naval Engineers, Inc., 1423 Powhatan Street, Bldg 1, Unit 1, Alexandria, VA, 22314. (Telephone: 703-8366727) Periodical postage is paid at Alexandria, VA and additional mailing offices. The Naval Engineers Journal is printed in Mechanicsburg, PA, by Fry Communications, Inc. CHANGE OF ADDRESS: Society members should send address changes by mail

to: American Society of Naval Engineers, Inc., 1423 Powhatan Street, Bldg 1, Unit 1, Alexandria, VA, 22314. Allow four to six weeks for processing. All other subscribers should send changes to: Naval Engineers Journal, P.O. Box 3108, Langhorne, PA, 19047. POSTMASTER: Send all address changes to: American Society of Naval Engi-

neers, Inc., 1423 Powhatan, Bldg 1, Unit 1, Alexandria, VA, 22314. DISCLAIMER: Statements contained in papers and articles herein are the private

opinions and assertions of the writers and should therefore not be construed as reflecting the views of the American Society of Naval Engineers or any of the organizations with which such writers are affiliated. The Society as a body is not responsible for statements made by individual members. The American Society of Naval Engineers cannot be held responsible for errors or any consequences arising from

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Ms. Makayla Holt

Prof. Michael Rowles, Jr., USN (Ret.) Dr. Gregory R. Thomas, CAPT USN (Ret.)

the use of information contained in this journal. The publication of advertisements does not constitute any endorsement by the American Society of Naval Engineers. COPYRIGHT AND PHOTOCOPYING: ©2022 American Society of Naval Engi-

neers. All Rights Reserved. No part of this publication may be reproduced, stored or transmitted in any form or by any means without the prior permission in writing from the copyright holder. Authorization to photocopy items for internal and personal use is granted by the copyright holder for libraries and other users registered with their local Reproduction Rights Organization (RRO), e.g., Copyright Clearance Center (CCC), 222 Rosewood Drive, Danvers, MA, 01923, USA (www.copyright.com), provided the appropriate fee is paid directly to the RRO. This consent does not extend to other kinds of copying such as copying for general distribution, for advertising or promotional purposes, for creating new collective works or for resale. SUBSCRIPTIONS: Contact USCI (nejsubscribe@uscinsights.com, telephone: 267-

557-3027) for institutional subscription rates and sales. American Society of Naval Engineers members receive a subscription to the Naval Engineers Journal as a benefit of membership. To join or renew, go to www.navalengineers.org. ADVERTISING: nej@navalengineers.org BACK ISSUES: nej@navalengineers.org

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PRESIDENT’S PAGE—21ST Centur y ASNE

PRESIDENT’S PAGE

David Lewis

21st Century ASNE

HEN THE AMERICAN SOCIETY OF NAVAL ENGINEERS (ASNE) was founded in 1888 the U.S. Navy was at a strategic inflection point. Naval technology development and adoption had atrophied after the Civil War and the Fleet was a shadow of its wartime size. However, America was ascendent on the world stage as a growing power, propelled by our commercial and technology-driven successes of the First and Second Industrial Revolutions. Foreign navies were fielding steel ships, naval rifles, cordite and gun cotton propellants, waterside boilers, advanced armor plate and exploding, and armor piercing shells. Nascent shipboard Fire Control systems were replacing Gunner’s Mates firing over open sights. America needed a Navy that could fulfill our national aspirations. America had begun to build that Navy, the New Steel Navy, but we did not fully understand the new technologies, how to implement them, or how to get American industry to build them. Our mission then, as now and as always, has been to help the Navy overcome those challenges, learn the new technologies, and find ways to field them in our Fleet. Since 1888, ASNE has been shoulder-to-shoulder with the U.S. Navy through many subsequent strategic inflection points. America’s new high-tech Steel Navy resoundingly defeated the Spanish Navy. America built the world’s second all-big gun battleship, after Britain’s HMS Dreadnought. The U.S. Navy achieved the status of “second to one” before World War One and then went on to become “second to none” after the war. Naval aviation, undersea warfare, nuclear power, missile, and digital technology was fielded alongside ballistic missiles and phased array radars. The Navy and America’s maritime services thrived during the Third Industrial revolution. And ASNE was right there, helping, leading, educating, collaborating, informing, and advising. We expanded our naval engineering customer portfolio to include the Coast Guard, Marine Corps, Merchant Marine, and commercial ship owners in those halcyon decades. I offer that we, today, are at yet another such strategic inflection point, different in detail but the same in nature. America has the world’s dominant Navy, but America faces true strategic competitors for the first time since the Cold War. New technologies have emerged and continue to emerge that challenge our Navy to successfully implement and integrate them in new and existing platforms. Quantum computing, artificial intelligence, machine learning, “big data,” Cloud computing, Edge to Cloud computing, Multidomain warfare, DEVSECOPS, cyber warfare, autonomy, uncrewed vehicles and weapons, ubiquitous satellite communications, ad hoc networks, and a plethora of other innovations and inventions must be woven into highly integrated and complex shipboard systems. Which should be fielded in the Fleet? When? How? Should the Navy develop systems “in house,” or work with industry and academia to field these new technologies? Or develop an innovative win-win collaborative government-industry relationship? If so, which industries; the existing Defense Industrial Base or America’s emerging Fourth Industrial Revolution-inspired army of businesses? Or a blend of both? Where do America’s future knowledge workers and artisans come from? What Naval industrial base does America need as we advance into the heart of the 21st Century?

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VADM David Lewis, USN (Ret.) President American Society of Naval Engineers president@navalengineers.org

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President’s Page: 21st Century ASNE

It is time for ASNE to renew our commitment to our enduring mission to support the United States’ naval engineering community. It is also time to refresh and communicate the value proposition of what ASNE does for academia, the government, and industry. Specifically, helping our maritime services lead, manage, assess, develop, and most importantly, successfully field today’s maritime technologies through our proven skills in collaboration, connection, communications, and free and open exchange of technical ideas and engineering innovation. ASNE must grow to include these new technologies, these new warfighting paradigms, and this new naval engineering landscape. To do that ASNE must welcome new disciplines, embrace new fields of naval engineering, seek out and recruit new members, engage with new customers, attract new audiences, and implement new ways and means of

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communicating, informing, and collaborating. Strategic inflection points do not always end well for the unprepared. They do not always favor the status-quo leader and, in fact tend to favor the visionary new entrant over the entrenched incumbent. I do not know what we need to do, or how to do what we need to do, but I do know that it must be done. We must do this together, as we always have, as a community of engineers, a Society of naval engineers, committed to improving the practice of naval engineering for, by, and with the U.S. Navy, Coast Guard, Marine Corps, and Merchant Marine. This is both an exciting and challenging time for naval engineering professionals. Please, please join us in finding the road ahead and beginning our journey for our 21st Century ASNE. Together we are better than any one of us. It was true in 1888 and it is still true today. Thank you.

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S E C R E TA R Y ’ S N O T E S

Inspiring the Next Generation of Naval Engineers New Innovative and Collaborative Ideas Requested from Every ASNE Member

INCE MY LAST SECRETARY’S NOTES COLUMN, the American Society of Naval Engineers (ASNE) has hosted very successful MegaRust, MACC and FMMS events featuring senior government and industry maritime leaders. As ASNE President VADM David Lewis, USN (Ret.) states in his first President’s Page column in this issue of the Naval Engineers Journal (NEJ), the Society must welcome new engineering disciplines and embrace new fields of engineering. As former Chief of Naval Operations Admiral John Richardson, USN (Ret.) stated in his remarks at the Fall 2023 Fleet Maintenance & Modernization Symposium (FMMS) and in his article in the September 2019 Naval Engineers Journal article, “Naval Engineering: A Truly Elegant Process”: “I love the links between Naval Engineering, Civil Engineering, Computer Design Engineering, and all other disciplines of engineering…, you’re in the maritime environment which is one of the most challenging environments that you can conceive of, and you have to synergize diverse engineering professions.”

CAPT Dale Lumme, USN (Ret.) Executive Director American Society of Naval Engineers dlumme@navalengineers.org

MegaRust Conference ASNE hosted the MegaRust Conference with senior government, maritime and executives, including keynote, panel speakers, and technical paper presentations. The MegaRust Chair, Dave Zilber, led a volunteer planning committee team that invited and coordinated an impressive program of speakers! Several of the many highlights were an eye-opening presentation by Chris Cavas regarding the state of corrosion control and prevention of U.S. Navy and U.S. Coast Guard ships, and an inspirational Women in Corrosion Mentoring Panel. ASNE is thankful for the support of Courtney Culpepper, not only for being a superb panel moderator, but for providing MegaRust planning committee support for inviting and engaging numerous speakers to be part of the Women in Corrosion Mentoring Panel. The panel of senior women executives received numerous accolades with comments such as: “awesome remarks,” “inspirational role models,” and “superb insights,” from attendees and viewers of the MegaRust proceedings!

Multi Agency Craft Conference (MACC) & Promoting Electric Propulsion (PEP) Grant Competition Dr. Leigh McCue, the Multi Agency Craft Conference (MACC) Chair, led an engaging planning committee and built an informative program with superb innovative speakers from across government maritime combatant and commercial craft builders. Mike Briscoe, ASNE’s Educator-in-Residence, led a superb Promoting Electric Propulsion (PEP) boat race competition in a concurrent event with MACC. The PEP program is funded by a STEM grant from the Office of Naval Research. PEP

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Secretary’s Notes: Inspiring the Next Generation of Naval Engineers

 Former CNO, Admiral John Richardson, USN (Ret.) and ASNE Executive Director CAPT Dale Lumme, USN (Ret.)

 CAPT Glenn Hofert, USN (Ret.), FMSS Chair and RADM William Greene, USN, CNRMC / SEA-21

 RDML Jason Lloyd, USN, NAVSEA-05, Navy Chief Engineer

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Moderator: Courtney Culpepper; Panelists: Kimberly Jordan-Dillard - Superintendent of Coatings for all Trades Newport News Shipyard; Jocelyn Bamford - VP of Automatic Coating Limited; Mary Ellen Bergvinson - Co-Founder Ship 2 Shore Inc; Lt. Andrea Howard, USN - Nuclear Submarine Officer; Dr. Kylee Fazende - Corrosion Control Assistance Teams (CCAT), NSWC, Carderock Division; Brittany Preston-Baker, Materials Engineer, NSWC Carderock Division.

encourages colleges, companies, or individuals to build a boat powered exclusively by electric power, or energize a boat with electrical propulsion and compete in a boat race competition. ASNE is supporting colleges and universities throughout the United States in this competition providing hands-on opportunities for students to get excited about a career in support of all naval engineering disciplines. Sponsor a student or young engineer and help our Society inspire the next generation of naval engineers: www.navalengineers.org/Education/ Promoting-Electric-Propulsion-PEP.

Fleet Maintenance & Modernization Symposium (FMMS) CAPT Glenn Hofert, USN (Ret.) led the FMMS symposium planning committee to superb success with superb keynote, panel, innovation theater speakers, and multiple collaborative partnership events - including National Shipbuilding Research Program (NSRP), Industry Navy Discussion Panel (INDP), Port Engineers Symposium, and Corrosion Control Community of Practice training and discussions. CAPT John Markowicz, USN (Ret.), CDR Tim McCue, USN (Ret.), and CDR Pete Ludwig, USN (Ret.), provided superb FMMS planning committee support in executing a successful FMMS. Cheryl Jordan invited, coordinated, moderated and led a truly awesome innovation theater lineup of senior maritime professionals. In addition, the superb support provided by RADM Mark Hugel, USN (Ret.), RADM Brian Antonio, USN (Ret.), Dr. Scott Tait, CAPT USN (Ret.), Mr. Bill Deligne, SES (Ret.), CAPT Joe Luckard, USN (Ret.), CDR Kirk Burgamy PE, USN (Ret.), CAPT Paul Van

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Benthem, USN (Ret.), CDR Tom Hekman, UNS (Ret.), and Mr. Jonathan Miller as Panel and Technical Paper Session moderators was superb and received high accolades from not only all panel members and presenters but all FMMS audience attendees.

Every Member Recruit a Member The American Society of Naval Engineers is requesting ASNE member assistance to honor the 135-year tradition and history of our Society by informing previous lapsed members of the new and revised benefits and inviting new members to become members of our professional society.The long-term health of the Society is counting on the recruitment of younger members and retention of our current members. PLEASE invite your colleagues, friends, family, and young professionals to join our Society. ASNE needs to grow our Society younger by attracting young naval engineers to sustain our industry and maintain our national security.

Request New Innovative Ideas from Every ASNE member The American Society of Naval Engineers staff is thankful for the superb volunteer leaders of ASNE at the grassroots (Section andChapter), regional (symposia planning committees), and National (ASNE Council and standing committee) levels. Every person reading this issue of the Naval Engineers Journal is requested to bring forth new innovative ideas in collaborating, informing, and supporting the Society’s objective of inspiring the next generation of naval engineers and STEM professionals.

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NOTE FROM THE EXECUTIVE DIRECTOR

135 Years of Tradition & Innovation

S A VALUED SUPPORTER, your unwavering commitment to the American Society of Naval Engineers (ASNE) continues to inspire us. This year, we mark a significant milestone—135 years of fostering innovation, providing professional mentoring and inspirational collaboration among naval engineers. As we celebrate this landmark achievement, we look forward to another century of empowering and inspiring the next generation of STEM professionals. Our work at ASNE is driven by the belief that the future of our maritime industry lies in the hands of today’s young engineers. We are committed to providing them with the necessary resources, mentorship, and guidance to become the innovators of tomorrow. Your contributions have been instrumental in our journey so far, and we are incredibly grateful for your support. Join our other supporters in making a difference in the lives of these young engineers. Your charitable donation, honoring 135 years of sustained professional development, will not only help us continue our mission, but also provide us the ability to provide increased STEM and professional engagements that will inspire others to follow in your footsteps. Your charitable donation helps support STEM engagement events, young professional mentorship programs and provides professional development for our members and symposia attendees. Please consider making a donation today. Your generosity will ensure that we can continue to provide valuable resources and opportunities to our budding engineers. We understand that these are challenging times, and we appreciate your continued support. Your contribution will not only help us sustain our operations but also enable us to continue providing invaluable services to our community of STEM professionals. Thank you for being part of our journey. Your unwavering support and belief in our cause—to seek out new ideas, new innovations, to boldly inspire young STEM professionals—means the world to us. Together, we can shape the future of naval engineering!

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Please Note:

An ASNE Charitable donation of $135 will qualify the donor for recognition and acknowledgement in the print and digital versions of the NEJ, and the first 135 donors of at least $135 will enter the donor in a giveaway for ASNE ‘souvenirs’—including ASNE lanyards, note pads, pens, t-shirts, ballcaps, ASNE books, and polo shirts!

ASNE fun facts:

A number of our supporters give stock, donate vehicles, make a Qualified Charitable Distribution (QCD), or donate their Required Minimum Distributions (RMD). All provide enhanced tax savings over giving cash. By transferring a RMD directly to the American Society of Naval Engineers, a 501 (c) (3) charitable organization, one can exclude the distributions from their taxable income while simultaneously supporting ASNE’s next generation of STEM Professionals. This rollover is a ‘tax-smart’ option for taxpayers who don’t want to pay the additional taxes from their RMDs. We encourage you to explore these options as you consider your contribution. As we look ahead, we are excited about the possibilities the future holds for our Society. ASNE will be releasing a digitally remastered version of the NEJ’s “Story of AEGIS” in 2024. The original publication was distributed at the commissioning of the USS Wayne E. Meyer—15 years ago and is now out of print. A new updated issue on what’s been happening with AEGIS in the last 15 years is being researched and written for future publication. With your support, we can continue to inspire the next generation of naval engineers, contributing to a brighter future for our maritime industry. Warm regards, CAPT Dale Lumme, USN (Ret.) Executive Director, American Society of Naval Engineers

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FROM THE EDITORIAL BOARD

Leigh McCue

HIS ISSUE OF Naval Engineers Journal (NEJ) showcases student work, from the design-build-compete activities that happen under ASNE’s ONRfunded Promoting Electric Propulsion (PEP) program to student research. Even the From the Archives selection by Stephen Michetti and introduction by Kelly Cooper features the rich history of naval STEM outreach, and ASNE’s commitment to shining a light on that outreach throughout the years. That said, a fairly common conversation amongst attendees at any professional society gathering focuses on how we can best support and engage the next-generation workforce. This column summarizes a few of the ways ASNE supports students, with emphasis on how you can help. 1. Naval Engineers Journal—We are committed to continually featuring student work, not just in peer-reviewed format or with the occasional themed issue, but all the time. We recently recruited a new associate editor, Dr. Carolyn Judge, who will be taking the lead on ushering student submissions through the review process, and helping distinguish when content should go through traditional peer review (e.g. undergraduate/graduate research) versus when an expedited process outside peer review might be more appropriate (e.g. capstone projects or internship design reports). So please, encourage your students and interns to submit to the NEJ. We commit to providing them a positive experience on their pathway to publication. For that matter, we talk regularly about establishing a regular student column. If you have talented students you would like to recommend as columnists, please let us know. And if you’re interested in helping review student work for publication, please email nej@navalengineers.org. 2. PEP—In this issue you see many examples of students discovering naval engineering through involvement in ASNE’s Promoting Electric Propulsion (PEP) program. Mike Briscoe has traveled the nation recruiting universities and students to participate. Volunteers are always needed to help recruit and mentor teams or judge the national competition, which is currently slated for April 15-16, 2024 in Virginia Beach. If you are interested in getting involved, please reach out to mbriscoe@navalengineers.org. 3. FLEET—If your outreach interests extend to K-12 students, ASNE’s FLEET program provides a fun and engaging way to teach kids ship design with a video game. I have used this to trick my own tween into being excited about naval engineering. Thanks to ASNE’s long term efforts, partnership with Navatek, and prior ONR funding, this game has cutting edge graphics and hydrodynamic modeling coupled with a robust curriculum for students and educators. You can learn more about how to introduce your child, school group, or club to FLEET at: www. navalengineers.org/STEM-FLEET. 4. Symposia—ASNE events are free for students. Please bring your interns or students and help introduce them to the broader community. I require all my senior mechanical engineering students to attend a professional society event,

NAVAL ENGINEERS JOURNAL

Dr. Leigh McCue Editor-in-Chief, Naval Engineers Journal Chair, Department of Mechanical Engineering, George Mason University lmccuewe@gmu.edu

and many opt to go to ASNE events because the price is right. Even if they went simply to satisfy an assignment, they often walk away with a new appreciation for the value of professional society engagement. If you are really enthusiastic about encouraging student participation at events, please consider donating to ASNE’s General Operating Fund to offset some of the costs of these complimentary student registrations. 5. Scholarships—During the 2022-2023 academic year, ASNE awarded 14 scholarships to undergraduate and graduate students pursing naval engineering and related fields. Rich Celotto and his Scholarship Committee do an excellent job recruiting and reviewing candidate applications. The application period for next academic year will open soon; please help spread the word. While many professional societies require students to join and pay dues prior to submitting a scholarship application, ASNE has taken the inclusive approach of often providing a year of complimentary membership even to those students who are not selected for a scholarship. Here too, donations to the Scholarship Fund make this possible. I hope you enjoy the student showcase in this issue, and huge thanks to the teachers, mentors, sponsors, and donors supporting these students. See you at the 2024 PEP competition!

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A S N E N E W & R E I N S TAT E D M E M B E R S

Central Gulf Coast Section Mr. Larry Ladner Mr. Joseph Sanchez Mr. Andy Speirs Mr. Juan Argueta Mr. Jayson L. Bell Mr. Barry Dreyfus, Jr.

Mr. Gustavo Garza Mr. Brock Ingram Mr. Jason Kalishek Mr. Cagin Karamuftuoglu Mr. Ernesto M. Macias, II

Mr. Jake Ormond Mr. Brian Patchett Mr. Bruce L. Petty CDR William J. Reicks Jr., USCG (Ret.)

Ms. Summer Clark Ms. Franki Curran Mr. Matthew Fink Mr. Scott D Kade Mr. Travis Laws Sponsored by Mr. John York

Mr. Joseph A. McMullen Mr. Nathan Nawalaniec Mr. Vince S. Piesetzkie Mrs. Sarah M. Searles Mr. Stephen Settles

Ms. Tamara Hile Mr. Alexander Johnson Mr. Kedar Kambhampaty Ms. Olivia Kim Mr. Christopher Knox Mr. Michael Leonard Mr. Matthew Livelsberger Ms. Jessica McElman Dr. Theodore Mordfin Mr. Jonathan Mulberg Mr. Travis Niederhauser

Mr. Steve Otto Ms. Lauren Paladino Mrs. Monica Park Mr. Gerald Rapp Mr. Ron Rich Mr. Frank Sabella Mr. Brendan A. Sharp Mr. Joseph Simon CDR Vincent A Stammetti, USN (Ret.) Ms. Katherine A. Tolton Rodriguez Mr. Frank Wells

Mr. Xiaofeng Hu Ms. Kaliah S. Jackson Mrs. Meagan Kerns Mr. Jihwan Kim Mr. Jonathan Lucco Mr. Matthew McCall

Mr. Charles (Mac) E. McKenna LT Scott Payne, USN Dr. Brant R. Savander, PE Mr. Christopher Tipper CWO Jere R. Widhalm, USN

Mr. Jeffery Jacklin Mr. Wim Van Cappellen

Ms. Victoria Wahlig Mr. Kevin Zygadlo

CAPT Richard Braunbeck

RDML Gregory Bryant, USN (Ret.)

Mr. Alan R. Smith

Delaware Valley Section Mrs. Mary Ann Andree Mr. Michael Atcheson Mr. Omar Badran Mr. Christopher Bonatucci Mr. Jeffrey Borowski Mr. Sonam Chheda

Flagship Section Mr. Tim Widing Mr. Paul Basola Mr. Rob Brown Mr. James C Caley PO2 Thomas Copsey Mr. Dajon Daniel SES Mark K. Edelson Mr. Brian Fitzpatrick Mr. Anthony G. Florimbio Ms. Jillian Greatsinger Mrs. Elise S. Hernandez

Metropolitan Section Mr. Jared S Smith

National Ms. Caitlin Dunckel Mr. Dave Evans Ms. Karla V. Fuentes Mr. John R. Funkhouser Mr. Muhammet D. Gokhan Mr. Ken Hasty

Northern New England Section Mr. Alexei Sondergeld Ms. Anita Adams Mr. Benjamin T. Curtis

Pacific Northwest Section Mr. Matthew C. Binsfield

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San Diego Section Mr. Timothy Pridemore Mr. Phong Bach Mr. Brett Balazs Mr. Dionisio Dean Bustria, Jr. Mr. Alexander Echeverri Mr. Faiyaz Farouk

Mr. David J. Gutierrez Mrs. Ayse Nur Kocak Mr. Mark Langner Mr. Gabriel O. Lopez Ms. Catherine Loughran Mr. Omar Maestas

CDR Kimberly Martinez, USN Ms. Shannon L. Pearce Mr. Barton Rierson LCDR William Welch, USNR LT Robert A. Wilkes, USN

SCPO Matthew Duker, USN (Ret.) Mr. Andrew Edwards Ms. Aimee Futreal Mr. Steve Gradine Mr. Jim Hamilton Mrs. Rosemary E. Hobart Mr. Michael P. Hughes Ms. Jessica Johnson Mr. George W. Lauterbach Ms. Beverly Mahoney Mr. Joshua McMillon LCDR Timothy E. Mueller, Jr., USCG Mr. Alden Nelson Mr. Jonathan Parmet Mr. Jerry Parnin Ms. Shante N .Phillips Mr. Anthony Prause Mrs. Jennifer Renne Mr. Jorge Rivera

Mrs. Maryam Salehi CAPT Jeffrey Sheets Mr. Dale R. Shiflett Sponsored by CDR William R. Yoshida, USN (Ret.) Ms. Kelly Smith Mr. Shane Stewart BMC Dave F. Surran, USCG (Ret.) Mr. Kevin Sweeney Mr. Wesley Todt Mr. Jack Vogt Mr. Robert Walling Mr. Jordan Webb Mr. William E. Weimer Mr. Scott Wildermuth Mr. Tim Wise Mr. Ken Wolowitz Dr. Joshua Yamamoto, USN (Ret.) Mr. Lucas Yamamoto

Southern Indiana Mr. Milton Duran

Tidewater Section Mr. Mark Bortfeld Mr. Billie Cook Mr. Thomas Davis Mr. Christopher Marsh Mr. Ryan Underwood Mr. Jared Wall Mr. Mark T. Anderson Mr. Mike Askew Ms. Gul W. Ayaz Mr. Tom Barbour Mr. Jason M. Blair Mr. Joshua Bowen Mr. James Brooks Dr. Giuseppe Brunello Mr. Randall Crutchfield CPT Lee Dingman, USA (Ret.) CDRE Rich Dromerhauser, USN (Ret.) Sponsored by RADM Brian K. Antonio, USN (Ret.)

New ASNE Life Members Central Gulf Coast Section

Metropolitan Section

San Diego Section

CAPT Colin M. Jones, USN (Ret.)

Mr. Robert W. Nunamann

LCDR John M. Green, USN (Ret.)

Flagship Section

National

Tidewater Section

RADM Brian K. Antonio, USN (Ret.)

Mr. Thomas P. Diamant

CAPT James P O’Donovan, USN (Ret.)

CAPT Geoff W. Peters, USNR (Ret)

Mr. John M. Iannetta, USN (Ret.)

Mr. Martin D. Fink

Northern New England Section

Mr. Peter A. Gale

RADM J. Clarke Orzalli, USN (Ret.)

LCDR O. A. “Skip” Schweizer, USN (Ret.)

Mr. Ted Raitch

NAVAL ENGINEERS JOURNAL

Fall 2023 | No. 135-3 | 13

ASNE SECTION DIRECTORY

ASNE Sections and Chapters across the United States offer professional development, networking, and volunteer opportunities. For more information and additional local officers, contact a Section Officer in your area or ASNE Headquarters. Central Gulf Coast CHAIR VICE CHAIR–EASTERN OPS VICE CHAIR–WESTERN OPS SECRETARY/TREASURER

Mr. Edward Eastlack Mr. Terry Mannion CDR Dwight Alexander, USN (Ret.) Mr. Danny Brown

edward@eastlackconsulting.com SeaHorse@Tmannion.com dwight.alexander@ngc.com dbrown@hillercompanies.com

Mrs. Allison Hice Mrs. Patricia McGinn Ms. Nicole Thatcher Ms. Arlene M. Korn

acardarelli88@gmail.com pmcginn@gibbscox.com nthatcher95@gmail.com amillerrn@comcast.net

CAPT Joseph ‘Ike’ M. Iacovetta, USN (Ret.) Mr. Brandon J. Foy, USCG Mrs. Jeannine A. Mantz Mr. Nicholas Abbott

ike_iacovetta@cox.net brandon.j.foy@gmail.com j9vailmantz@me.com nicholas.abbott@aocinc.net

Prof. Michael Rowles, Jr., USN (Ret.) Dr. Raju V. Datla

navalengineer@gmail.com rdatla@research.stevens.edu

Dr. Gregory R. Thomas, CAPT USN (Ret.) CAPT David S. Herbein USN (Ret.) CDR John D. Bowen, USN (Ret.) Mr. David J. Wetherbee

grthomas82@gmail.com dherbein@maine.rr.com bowenjd@alum.mit.edu dwetherbee@comcast.net

CAPT William D. Carroll, USN (Ret.) Mr. Peter H. Thomas Mr. Hugh J. Huck, III Mr. Phillip K Parson

wdcarroll@live.com pthomas@LCE.com hugh.huck@mainsailgroup.com phillip.parson@comcast.net

CAPT Glenn D. Hofert, USN (Ret.), PE CAPT Paul VanBenthem, USN (Ret.), PE Mr. Jeremiah Cox CDR Kirk Burgamy, USN (Ret.), PE

ghofert@qedsysinc.com paulvanbenthem@live.com coxjeremiah1@gmail.com kirk.burgamy@alum.mit.edu

Mr. Milton Duran

miltonduran1@gmail.com

Mr. Joseph P. Kosteczko Mr. Kenneth P. Blair Mr. Neil R. St. Clair CAPT Charles V. Doty, USN (Ret.)

jkostecz@odu.edu Ken.blair757@gmail.com nstclair@fairleadint.com chuckdoty0303@gmail.com

Delaware Valley CHAIR VICE CHAIR SECRETARY TREASURER

Flagship CHAIR VICE CHAIR SECRETARY TREASURER

Metropolitan CHAIR SECRETARY/TREASURER

Northern New England CHAIR VICE CHAIR SECRETARY TREASURER

Pacific Northwest CHAIR VICE CHAIR SECRETARY TREASURER

San Diego CHAIR VICE CHAIR SECRETARY TREASURER

Southern Indiana CHAIR

Tidewater CHAIR VICE CHAIR SECRETARY TREASURER

14 | Fall 2023 | No. 135-3

NAVAL ENGINEERS JOURNAL

ASNE COMMIT TEE DIRECTORY

Much of ASNE’s work is accomplished by Committees, which offer volunteer and leadership opportunities for Members. For more information, contact one of the Committee Chairs listed below or ASNE Headquarters.

Audit

Professional Development

RDML Dale E. Baugh, USN (Ret.) daleebaugh@gmail.com

Awards

Scholarship

CAPT Paul J. Roden, USCG (Ret.) paul.roden@fairbanksmorse.com

Council

CDR Richard C. Celotto, USN (Ret.) richard.celotto@caci.com

Sections

VADM David Lewis, USN (Ret.) president@navalengineers.org

Membership Mrs. Jessica M. Galassie galassie@gmail.com

CAPT Richard D. Delpizzo, USNR (Ret.) rdelpizzo@eagle.org

CAPT David S. Herbein, USN (Ret.) dherbein@maine.rr.com

Ways & Means Mr. Pedram Pebdani pedram.pebdani@ jokell.com

CDR Charles G. Pfeifer, USN (Ret.) charlie@cgpfam.net

Naval Engineers Journal Dr. Leigh McCue lmccuewe@gmu.edu

NAVAL ENGINEERS JOURNAL

Fall 2023 | No. 135-3 | 15

ASNE UPCOMING EVENTS

2023 Technology Systems & Ships (TSS)/ Combat Systems Symposium (CSS) November 28-30, 2023 Van Metre Hall, George Mason University Arlington, VA TSS CHAIR: C APT Richard (Rick) White, USN (Ret.)

rick.white@rwcllc1.com CSS CHAIR: C APT Joe Johnson, USN (Ret.)

asne.combatsystems@navalengineers.org

2024 Arctic Ops

MegaRust

March 4-6, 2024 Maritime Institute of Technology and Graduate Studies (MITAGS) Linthicum Heights, MD

May 21-23, 2024 Marriott Mission Valley San Diego, CA

ARTIC OPS CHAIR: RADM Ronald Rábago, USCG (Ret.)

Advanced Machinery Technology Symposium (AMTS) May 1-2, 2024 Philadelphia, PA AMTS CHAIR: Franki Curran

franki.m.curran.civ@us.navy.mil

MEGARUST CHAIR: D ave Zilber

djzilber@gmail.com

Fleet Maintenance & Modernization Symposium (FMMS) September 16-18, 2024 Virginia Beach Convention Center Virginia Beach, VA FMMS CHAIR: C DR Charles G. Pfeifer, USN (Ret.)

charlie@cgpfam.net

For more information about ASNE Upcoming Events, please visit www.navalengineers.org/Symposia or contact ASNE at (703) 836-6727 or meetings@navalengineers.org.

16 | Fall 2023 | No. 135-3

NAVAL ENGINEERS JOURNAL

GUEST COLUMN—HOW DO YOU FIND A MENT O R T H AT ’ S W O R T H T H E I R VA L U E ?

GUEST COLUMN

How Do You Find a Mentor That’s Worth Their Value?

OLKS, I must confess that I did not write the thought-provoking headline above. I borrowed it from a video cast that inspired me to write my own version using the same headline. I wrote my version to address a question I hear every month, “How Do I Find a Mentor?” This question rears its head like a gopher at every leadership conference, one-onone talk, brown bag, lunch and learns session, forum, workshop, seminar, webinar, podcast, and meeting. So, like a good to a great neighbor, I decided to add to the axiology of finding a mentor worthy of their value. Let me share three simple and practical ideas to help you find your worthy or valuable mentor today. My thoughts derive from “TALK” or KLAT, 13 Behaviors Directly Influence Every Mentor’s Value, and Universal Mentors: Seek One Today Formally or Informally.” Based on this insight, let me share three simple and practical ideas to find your worthy or valuable mentor today. My thoughts derive from “TALK” or KLAT, 13 Behaviors Directly Influence Every Mentor’s Value, and Universal Mentors: Seek One Today Formally or Informally.”

Do You TALK or KLAT? A few years ago, I heard a dynamic senior executive deliver a profound speech instructing you and me to think about the word “TALK” about a mentoring relationship. The speaker then instructed us to think about the word backward, meaning “KLAT,” to find a mentor we value to build a relationship. The premise shifted my brain waterways like a Navy fast boat making a turn at sea.” Yes, my thinking switch was quick, deliberate, and focused. The wise executive said, “Talk” backward now becomes “KLAT,” and that means a mentor needs to

Alonzie Scott, III Director Mission Support Office of Naval Research alonzie.scott.civ@us.navy.mil

NOW YOU or get to know you. Knowing or seeing the mentor can con-

nect you and your mentor professionally to create the value you seek. In short, you start your race for success here.

IKE YOU. You want to work with someone you like, and that person like

you. This link leads to the start of a great relationship. Hence, you create a bond that connects you under the emotional and generational intelligence umbrella. The “Like you” phrase is a great way to set sail in the right direction.

CCEPT YOU as you are today; the small “a” means accountability. It is

a trait one does not ever ignore. You must be acceptable and accountable for yourself and your loyal mentor. Without acceptance and responsibility, your relationship may never produce the results you want, desire, or seek. For example, when the mentor shares knowledge for you to follow and you do not, you create a behavioral block. You did not accept what you heard. The nub is when you choose no action; do not waste the mentor’s time or yours. Why? You did not hold yourself accountable and thus did not value the knowledge imparted to you. Your lack of accountability is not a win-win for anyone going forward.

RUST YOU are paramount to any good to a great relationship. You and

the mentor need trust to build a gregarious relationship to flourish or increase its value for both parties.

NAVAL ENGINEERS JOURNAL

Fall 2023 | No. 135-3 | 17

Guest Column: How Do You Find a Mentor That’s Worth Their Value?

The upfront investment grows when you add critical thinking and listening to converse with a mentor.

13 Behaviors Directly Influence Every Mentor’s Value Now that you have digested the simple KLAT concept. Let us leap forward and feature Steven Covey’s 13 behaviors of high-trust leaders worldwide from his book The Speed of Trust: The One Thing That Changes Everything. For every mentor or mentoring relationship, you should seek Covey’s 13 behaviors as your guidepost. These 13 behaviors below are a surefire way to fuel the worth and value of every mentoring relationship. 1. Talk Straight 2. Demonstrate Respect 3. Create Transparency 4. Right Wrongs 5. Show Loyalty 6. Deliver Results 7. Get Better 8. Confront Reality 9. Clarify Expectations 10. Practice Accountability 11. Listen First 12. Keep Commitments 13. Extend Trust These behaviors are the critical foundations of mentoring success, withstood decades of testing and practical application.

Universal Mentors: Seek One Today Either Formally or Informally Let me begin with a quote by Daniel Kahneman, “nothing in life is as important as you think it is while you are thinking about it.” Hence, mentoring is vital when you decide it offers value to you. Experts suggest you get a mentor to navigate your job, career, and life choices. I strongly agree. I also want to share two key reasons mentoring adds value and worth to your life.

1. Mentors Share Tested and Experienced Information and Knowledge

You can get the gist from a great American founding father, Benjamin Franklin. He says, “Tell me, and I forget; teach me, and I may remember; involve me, and I learn.”

18 | Fall 2023 | No. 135-3

As you start or expand your career, no matter where you live, work, or play, seek or search for your mentor or mentors. You look for a mentor using “Mentor Match” tools to help you find a mentor. Alternatively, you use local resources such as your school, college, community center, church, or places you find people you value and admire. In short, you will find folks with a wealth of knowledge to guide you on your career learning and development journey. Check out Mentor Connector at connect. mentoring.org/en to get you started today! On the other hand, visit these sites below too. ■ MentorLinks—www.aacc.nche.edu/programs/ mentorlinks ■ Mentor—www.mentoring.org ■ iMentor—imentor.org/ ■ MentorNet—greatmindsinstem.org/mentornet/ ■ Find A Mentor—findamentor.com/

Mentors Serve as Communicators, Collaborators, Connectors, Corroborators, and Trusted Advisors People, people, and people—do you need me to say more here? Mentors usually operate within many communities of practice. By the way, your mentors can also come from industry, government, or academia. Hence, you should always open yourself to more than just a single mentor source. I suggest you find multiple sources for your mentors to expand your interest. These gurus or knowledge sharers understand the mission and vision of the work, work process, workload, and workforce. Leverage these gurus’ insight and witness the power of networking and its actual value immediately or as soon as you engage your mentor. Well, this last passage concludes my mentoring message today. Did I spark your interest in obtaining a mentor? If so, do what is necessary to find your valuable mentor. Should the online tools be something other than your thing, now connect with your network of family, friends, associates, or colleagues. Hence, do the right thing and ask for help finding a mentor. The nub here, do nothing, and nothing happens. The action belongs to you. As kids might say in a game of tag, “Tag, you’re it, so get busy making a difference in your life with the right mentors for the modern world you live, work, and plan in today.”

NAVAL ENGINEERS JOURNAL

ASNE SCHOLARSHIP PROGRAM

2023–2024 ASNE Scholarship Recipients 2023–2024 ASNE Scholarship Recipients Announced

HE AMERICAN SOCIETY OF NAVAL ENGINEERS (ASNE) began its scholarship program in 1979 in order to promote the profession of naval engineering and to encourage college students to enter the field. ASNE has since awarded hundreds of scholarships to undergraduate and graduate students interested in pursuing an education and career in naval engineering.

Eligible programs of study for ASNE scholarships include: ■ Naval architecture

■ Environmental engineering

■ Computer science

■ Marine engineering

■ Aeronautical engineering

■ Engineering technology

■ Ocean engineering

■ Electrical engineering

■ Applied mathematics and physics

■ Mechanical engineering

■ Electronic engineering

■ Other relevant professions as accepted

■ Structural engineering

■ Systems engineering

by the ASNE Scholarship Committee

■ Civil engineering

■ Software engineering

The recipients of ASNE Scholarships for 2023–2024 include:

ASNE Tidewater Section Admiral David Donohue Scholarship

ASNE Tidewater Section Captain Paul F. Siebeking Scholarship

George Selden

Aubrey Miles

NAVAL ARCHITECTURE AND MARINE ENGINEERING

MECHANICAL ENGINEERING

University of New Orleans

Old Dominion University UNDERGRADUATE

GRADUATE

ASNE Tidewater Section Captain Joseph Yurso Scholarship

ASNE James J. Convy Memorial Scholarship

Daniel Erdogan

Ethan Amlquist

MECHANICAL ENGINEERING

NAVAL ARCHITECTURE & MARINE ENGINEERING

Old Dominion University UNDERGRADUATE

University of Michigan GRADUATE

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NAVAL ENGINEERS JOURNAL

ASNE James J. Convy Memorial Scholarship

ASNE San Diego Section Scholarship

Sean Hickey

Nicholas Genco

NAVAL ARCHITECTURE AND MARINE ENGINEERING

ELECTRICAL ENGINEERING

University of Michigan

University of Pittsburgh UNDERGRADUATE

UNDERGRADUATE

ASNE Flagship Section James A. Lisnyk Scholarship

Commander Lewis E. Erdner Scholarship

Jacob Zweifach

Joseph Serpa

OCEAN ENGINEERING

NAVAL ARCHITECTURE & MARINE ENGINEERING

Virginia Polytechnic and State University UNDERGRADUATE

University of Michigan UNDERGRADUATE

ASNE Flagship Section Sheldon Johnson Memorial Scholarship

ASNE Headquarters Scholarship

Lily Samoyan

University of Rhode Island

George Washington University MECHANICAL ENGINEERING UNDERGRADUATE

Victoria Reilly

MECHANICAL ENGINEERING AND APPLIED MECHANICS GRADUATE

ASNE San Diego Section Scholarship Juan Cortez

University of Arizona MECHANICAL ENGINEERING UNDERGRADUATE

For more information about the ASNE Scholarship Program, go to www.navalengineers.org/Education/Scholarships or send an email to scholarships@navalengineers.org.

NAVAL ENGINEERS JOURNAL

Fall 2023 | No. 135-3 | 21

FROM THE ARCHIVES

FROM THE ARCHIVES—PL ANTING A STEM S E E D : T H E S E A P E R C H C H A L L E N G E T E N -Y E A R ANNIVERSARY Introduction by Kelly Cooper

Planting a STEM Seed The SeaPerch Challenge Ten-year Anniversary Introduction by Kelly Cooper

OW! Steve Michetti’s article, “Planting a STEM Seed,” brought back some vivid and wonderful memories. Memories of having interns and Girl Scouts assemble the materials for 200 SeaPerch kits that we delivered to the Naval Surface Warfare Center Philadelphia, kicking off their start with this remarkable Navy STEM program; memories of the first regional event hosted at Drexel University with generous support from ASNE’s Delaware Valley Section, NAVSSES, and ONR; and memories of all the young engineers whom I have met over the years who remind me that they were part of this first SeaPerch Challenge event and others since.

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It is has been nearly 20 years since the inaugural Greater Philadelphia SeaPerch Challenge became the standard for SeaPerch regional competitions, and the model for the Annual International SeaPerch Challenge, bringing together a vast community of educators and student innovators. Since this article first appeared in Naval Engineers Journal, the SeaPerch program has expanded (thanks to those early champions) and now in partnership with the non-profit organization, RoboNation, reaches an estimated 250,000 students annually. I encourage readers of this article to consider supporting SeaPerch by becoming a mentor or regional volunteer, and begin your own collection of SeaPerch memories!

NAVAL ENGINEERS JOURNAL

This paper was Fe at ur e originally published in the December 2014 edition of Naval Engineers Journal.

Stephen M. Michetti Head, Cargo/Weapons Handling and Stowage Systems Branch, Naval Ship Systems Engineering Station, Philadelphia

Planting a STEM Seed

The SeaPerch Challenge Ten-year Anniversary

Figure 1. Design modified for Additive Manufacturing.

t was 11 years ago when the Office of Naval Research (ONR) first introduced SeaPerch, an innovative underwater robotics program, to Naval Ship Systems Engineering Station (NAVSSES) and the American Society of Naval EngineersDelaware Valley Section (ASNE-DV). The program provided an underwater Remotely Operated Vehicle (ROV) kit that included a construction manual, list of tools, and all the parts necessary to build an ROV, to be used as a hands-on activity for engineering outreach to middle and high school students. It sparked the interest of our engineers, and we expected an enthusiastic response from students as we introduced the program at several local schools. No one could have predicted the growth and evolution of SeaPerch since then. From initial feedback that “students can’t wait to come to school to work on SeaPerch”, it was clear that this was a fun, educational, and challenging way to get students not only interested in Science, Technology, Engineering, and Math (STEM), but excited about it as well. NAVSSES and ASNE-DV reached out to Drexel University and the School District of Philadelphia to advance the concept of using SeaPerch for outreach, and one year later the Greater Philadelphia SeaPerch Challenge (GPSPC) was conceived and implemented. The Challenge consists of a three-month design, build, and test period that culminates in a four part design and performance competition at Drexel University. The birth of the GPSPC effectively planted the seeds that would yield interest and enthusiasm for STEM by tens of thousands of students across the country over the following 10 years, and its popularity continues to grow.

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December 2014 n No. 126-4 n 37

From the Archives: Naval Ship Engine Exhaust Emission Characterization The SeaPerch Challenge Ten-year Anniversary

GPSPC 10 Year Overview As we approach the 10-year anniversary of the GPSPC, almost 10,000 students from Philadelphia and surrounding Pennsylvania and New Jersey areas have been directly involved in either SeaPerch or the GPSPC. Hundreds of NAVSSES/ASNE-DV engineers, industry engineers, and Drexel faculty, staff, and alumni have served as organizers, mentors, judges, and volunteers. Hundreds of GPSPC students have participated in ASNE Intelligent Ships Symposia or Electric Machines Technology Symposia, where they interact with scientists, engineers, and naval engineering VIP’s. GPSPC participants and student volunteers have successfully pursued employment opportunities with the Navy under the Science and Engineering Apprentice Program (SEAP) at the high school level, as college engineering interns and co-ops, and as naval engineers upon graduation from college. GPSPC has spawned and/ or supported other regional events, including a NAVAIR/ Rowan University SeaPerch Challenge in Southern NJ, a Navy City Outreach/ NYC SeaPerch challenge, a Stockton College/ USCG/ Friends of American Engineering

and Science (AES) Atlantic City, NJ SeaPerch initiative, a NAVSSES/Temple University SeaPerch summer camp and SeaPerch underwater Battle-Bots competition, a NAVSSES/Philadelphia University Girl Scout STEM summer camp, and more. Our partners at the Philadelphia Independence Seaport Museum installed a SeaPerch tank in their lobby, which is one of its most popular attractions. Bus loads of students and museum patrons read about the SeaPerch program on informational boards as they navigate an ROV in a museum tank. GPSPC hands on activities and displays are popular at many other NAVSSES/ASNE/Drexel events, including science fairs, “bring your kids to work” days, multicultural days, symposia, and virtually any event where we can reach students and pique their interest in STEM. Inspired by the success of the GPSPC, ONR initiated a National SeaPerch Challenge program that approaches its fifth year of national competition in 2015. Now managed by the Association for Unmanned Vehicle Systems International (AUVSI), this national program boasts that over 200,000 students have participated in SeaPerch from over 50 states and Puerto Rico. Perhaps

2005: ONR introduces SeaPerch to NAVSSES and ASNE-DV; Drexel University and the School District of Philadelphia brought on as partners and the GPSPC is planned with a naval engineering theme.

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The SeaPerch Challenge Ten-year Anniversary

most importantly, the journey to reach this tenyear milestone has been rewarding and fun for both student participants and engineers, and it has been inspirational to see what these student STEM enthusiasts can accomplish. Hallmarks of the GPSPC The SeaPerch Challenge places significant emphasis on Naval Engineering and the Design Process, includes an integrated and extensive Mentor Program, and includes an effective technical society/industry/academia/government Partnership Model. The attributes of each are discussed below. Each SeaPerch team represents a company competing for a simulated Navy contract by demonstrating that their SeaPerch design best meets Navy performance and design requirements while staying within a preset design innovation budget. Teams are provided a technical requirements document and a realistic mission to perform. They are encouraged to optimize their design based upon the specific mission, which changes each year. Design modifications must meet prescribed technical and cost constraints. 2015 GPSPC Mission: Operation “Top Secret Recovery”

A U.S. satellite, containing highly classified information on its data recorder, has crashed landed at the bottom of the ocean floor in a hostile location. An enemy spy drone was observed surveilling the crash site and was shot down by the Navy. Prior to visual contact, the drone remained virtually undetectable by Navy technology and is considered made of a high

tech stealth material. As the Navy’s most advanced underwater Remote Operated Vehicle (ROV), a SeaPerch—equipped with acoustic technology— is being dispatched to the scene. Its mission is to recover the satellite’s data recorder, activate the satellite self-destruct mechanism, recover the data recorder from the enemy drone, and recover a sample of the drone stealth material amid the debris field for Navy engineers and scientists to analyze. This top secret mission must be carried out quickly and eﬃciently, before enemy reinforcements arrive on the scene. Completion of this mission is imperative and is a matter of national security. Good luck! For SeaPerch Operators: You have been selected to lead this vital mission due to your remarkable skills in navigation and your extraordinary ability to operate the SeaPerch ROV under extreme conditions. Your SeaPerch will be equipped with the advanced acoustic mission package, which includes the Mark 1 Hydrophone. Your SeaPerch video mission package (Underwater Video Camera) will not be used for this mission. You may equip your SeaPerch with other probes or manipulators as you deem necessary. The design notebook and oral presentation competition categories include specific naval engineering elements that must be researched and addressed by the teams. Judges use scoring rubrics that place emphasis on the naval engineering aspects of the design notebook and presentations, and they engage students with follow-up questions during oral presentations. Finally, to punctuate the emphasis on naval engineering, ASNE-DV provides an annual Engineering Process cash award for the composite winners of the design

The mission of the 2015 GPSPC was the “Top Secret Recovery”.

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From the Archives: Naval Ship Engine Exhaust Emission Characterization The SeaPerch Challenge Ten-year Anniversary

notebook/oral presentation categories. Each year, the winning teams for Engineering Process and Oral Presentation are invited to the ASNE-DV Intelligent Ships Symposium or Electric Machines Technology Symposium. Approximately 40 students and their advisors attend the opening ceremony and keynote address, where speakers address their accomplishments and they are asked to stand and be recognized as 500 or so scientists and engineers (S&Es) applaud them. They interact with attendees at the exhibit halls, have lunch with young engineers, demonstrate and discuss their design with attendees at an exhibit table, and interact with S&Es, who can take a turn driving the SeaPerch in a portable tank. Each year, approximately 50 NAVSSES/ ASNE-DV and other local engineers, as well as Drexel engineering students, serve as mentors to support the teams. Mentors go through orientation and training and learn tips on how to help inspire teams to think creatively and problem solve, without giving teams direct help or solutions. They also support team advisors with SeaPerch engineering learning modules developed by Drexel for teaching concepts such

as buoyancy and electrical circuits. A recently implemented mentor tiger team approach ensures that more intense mentor support is provided to the schools that need it vs. even distribution of mentorship time across all schools. A mentor call-in forum is held biweekly during the design and build period for mentors to share experiences, clarify rules, etc. Perhaps most importantly, the mentor program provides a unique opportunity to get engineers into the classroom for direct communication with the students on a regular basis. Most typical mentors are enthusiastic about this job, and share their experiences with students about a career as a naval engineer. Mentors relay information about naval engineering careers, and programs that offer potential employment opportunities for high school and college engineering students and graduate engineers. The GPSPC partnership model is comprised of technical society, industry, academia, and government members. Each plays a critical and distinct role and all share the values and goals of the SeaPerch program, including increasing student interest in STEM-related studies, increasing

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The SeaPerch Challenge Ten-year Anniversary

NAVSSES Engineer and SeaPerch Mentor, Denis Colahan, helps students prepare to compete

in the Pool Performance round at the GPSPC.

student awareness of naval engineering as an interesting and rewarding career field, increasing diversity in engineering, increasing interaction between engineers and students, providing teaming and collaboration opportunities, and exposing students to a college campus environment. Reach and Appeal of GPSPC SeaPerch appeals to a broad range of students because it provides levels of challenge and commitment that can vary based upon the goals set by each team. Teams that are involved in multiple structured engineering or robotics clubs can explore and learn more advanced engineering concepts and innovations as they design and build their SeaPerch. Middle school teams, or teams with little-to-no prior experience with robotics or hands-on activities, can follow the construction manual and learning modules and build a working SeaPerch while learning more rudimentary engineering concepts, and they fare well in the robotic performance challenge. Since almost all of the costs for GPSPC are covered by sponsorship, SeaPerch can also reach those schools that don’t have financial resources to pay for more expensive robotics programs. For those schools that don’t have personnel with experience in robotics, GPSPC provides teacher training and mentors to facilitate their participation. And GPSPC doesn’t just appeal to brick and mortar schools, it includes home school teams, Police Athletic League (PAL) teams, as well as both Boy Scout and Girl Scout teams. These factors have resulted in an increasing interest in the program to the point in which registration has been closed after just two days in several consecutive years

after reaching a maximum capacity of about 55 teams. So, the competition was expanded to two days, middle and high school teams were separated, and GPSPC can now accommodate up to 100 schools each year. SeaPerch, with its mentor program and naval engineering focus, impacts students who have already decided to pursue engineering or STEMrelated studies, as well as those who are considering engineering as a career or those who have not previously considered it as a career choice. Some students who had already committed to engineering now pursue naval engineering careers, some undecided students have been inspired to pursue engineering studies, and some high-achieving students now consider naval engineering as a potential career goal. GPSPC Planning Cycle Planning, organizing, and evolving an event of this size and complexity requires a significant team effort and involves extensive coordination, collaboration, and communication. A typical ten-month planning cycle is outlined in the figure entitled, “SeaPerch Planning Time Line”. GPSPC 10 Year Anniversary Event Student participants in the 10th annual Greater Philadelphia SeaPerch Challenge will be focused on the competition, on demonstrating why their team’s design best meets Navy technical and mission requirements, and on doing well enough to take home a trophy in at least one of four categories. Some may be thinking about designing and building robots as a career, others will just be enjoying the fun of the moment, and those competing on Friday (competition is Friday and Saturday) may be thinking about having a day off while their classmates are in school! They will be mostly unaware that they are part of something much larger than this year’s competition. Many GPSPC sponsors, organizers, mentors, judges, and volunteers reflect on the past 10 years, and the impact that each year has had in helping to shape thousands of youth that are part of what some refer to as “the iGeneration”. After all, while some students were spending after school time on iPhone, Xbox, or on social media, GPSPC participants were committed to something bigger. Kudos to all who have contributed to GPSPC. You have ensured not only the continuation of GPSPC, but December 2014 n No. 126-4 n 41

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From the Archives: Naval Ship Engine Exhaust Emission Characterization The SeaPerch Challenge Ten-year Anniversary

you also contributed to its continued growth and the innovations made along its ten-year journey. While not all of GPSPC student alumni aspire to become engineers or pursue careers as naval engineers, all of them have benefited from the program. They learned teamwork, technical process, business process, time management, presentation skills, overcoming failure, and many more skills that will serve them in any career they choose. So, in commemoration of this great achievement, this year’s students will witness a special event kickoff, intermission, and post competition events, but they will also be left to focus on their competition tasks. Current plans (although not finalized) include invitations to VIP’s from the contributing sponsors, an optional student open class SeaPerch exhibition, a sponsor/VIP SeaPerch exhibition in which engineering organizations will compete with an ROV that they design and build, the expansion of the end-of-competition mascot dance-off (last year we had four), and a special guest speaker.

GPSPC Today and Beyond Today, NAVSSES, ASNE-DV and Drexel University continue a strong ten-year partnership in promoting STEM through the GPSPC. The longevity and success of the GPSPC is made possible through the generosity and/or support of ONR, The National Defense Education Program (NDEP), the National SeaPerch Program, Drexel University, ASNE-DV, NAVSEA/NAVSSES leadership, the GPSPC planning committee, and the many mentors, judges, and volunteers. As the event continues to evolve and improve, one thing that will remain constant is the dedication of the many folks committed to engineering outreach and the promotion of STEM and naval engineering. For more information on the GPSPC, please visit www.phillyseaperch.org. For information about the National SeaPerch Program, visit www.seaperch.org

Perspective of former SeaPerch Student, now NaVSSeS engineer: Amanda Gaetano has been working as a mechanical engineer at the Naval Ship Systems Engineering Station (NAVSSES) since graduating from Rutgers University in 2012. Here is what Amanda has to say about SeaPerch: “I think one of the greatest strengths of the SeaPerch program is the relationship between mentors and their schools/teams. My dad’s an engineer, so I was exposed to engineering from a young age, but my mom always jokes that until she met my dad she thought engineers operated trains. She never knew anyone in the profession, and had no real knowledge of it as a result. I’ve always thought it was interesting that for a lot of kids the real issue isn’t that they think engineering is something they won’t like or won’t be good at, it’s that engineering isn’t even on their radar. The SeaPerch program is great because, even more than hands-on experience, it gives students access to someone working in engineering—who’s gone through the classes, gotten a degree, has a career—who can open their eyes to all of the opportunities in this field. In my case, I knew I was interested in engineering but had never even heard of NAVSSES; I was set on an aerospace engineering degree and planned on working on planes when I graduated. I met Steve through SeaPerch, and the more I talked to him about what he worked on here at SSES, the more interested I became in working here. I was fortunate to obtain an internship here after my freshman year at Rutgers and never looked back. Now, as a mentor, I’m on the other side of things. I’ve had the opportunity to go back to Paul VI High School, and every year I spend a couple minutes giving a short talk on the work we do here. It’s great to see some eyes light up, and to answer the team’s questions. It’s clear that Sea Perch is working.” testimonial from temple university Me major Keith S.: Keith was introduced to SeaPerch while attending Conwell Egan High School. He is now entering his senior year at Temple University and has completed several summer internships at NAVSSES: “SeaPerch gave me the opportunity to see what kind of work is involved in the engineering process, from design to construction. My experience with SeaPerch confirmed my decision to become an engineer.” testimonial from Philadelphia Futures advisor Christine Johnson: A union of White-Williams Scholars and Philadelphia Futures provides Philadelphia’s low-income, firstgeneration-to-college students with the tools, resources and opportunities necessary for admission to and success in college. http://www.philadelphiafutures.org/about-us/about-us. The Futures have participated 42 n December 2014 n No. 126-4 28 | Fall 2023 | No. 135-3

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The SeaPerch Challenge Ten-year Anniversary

in GPSPC since 2011. Christine said “……one student went on to the Naval Academy as a direct result of being exposed to the work of the Navy via SeaPerch. This year, we had two seniors who went on to get internships at NextFab Studio (where we built our ROV) and are applying to Drexel’s School of Engineering this fall. We also had two other students who have sought out opportunities to work at the Naval Shipyard and plan to major in engineering next year, and one junior who plans to major in engineering after previously wanting to go into pharmacy.” testimonial from Little Flower High School advisor Sharon Cornwall: “As a teacher at an all-girls’ high school, I witnessed the transformation from tentative to capable engineers in the making from their participation in SeaPerch. This window into engineering offers possibilities previously not considered. The hands-on nature and problem solving engages these girls and gives them a first time direct interaction with soldering tools, PVC cutters, and offers possibilities that these young adults may not have considered. SeaPerch has resulted in an increase in our graduates studying engineering at Drexel University, Penn State, Widener, University of Pennsylvania, and Rensselaer Polytechnic Institute.” testimonial from Drexel university Dean of engineering, Joseph Hughes “Drexel University is proud to celebrate its 10th year in collaboration with the Greater Philadelphia SeaPerch Challenge. Having been involved with the program from its inception, Drexel is happy to have seen this program grow to the great event it is today. As the program continues to expand, so do the number of students who are positively impacted by GPSC, which has reached approximately 10,000 students over the past 9 years. As the host of GPSC, we take great pride in our role in inspiring middle and high school students to continue their studies in STEM fields.” testimonial from Susan G. Nelson, executive Director, SeaPerch (auVSI Foundation): “Tasked with creating a national SeaPerch program, I learned of Steve Michetti and his team at the Greater Philadelphia SeaPerch Challenge through ONR, and visited the 2007 GPSPC at Drexel. By the end of the day, I had a template of our National SeaPerch Challenge, and it was based on the outstanding event I observed that day. The GPSP folks, beginning with Steve Michetti, have been allies, advisers, friends, and colleagues as the national SeaPerch organization has grown. Our most recent National SeaPerch Challenge, held at the University of Southern Mississippi, had a record 108 teams and 1,000 attendees, and we can thank the Philadelphia organization for helping us get started.” testimonial from LCDr Michael Fourte, esq., Navy City Outreach Northeast, New York: “GPSPC was my inspiration for starting the New York SeaPerch program in 2012-2013. The New York Navy City Outreach program’s success led directly to the expansion of similar Navy City Outreach programs in 2014-2015 in New York, Los Angeles, Houston, Atlanta, Miami, Houston, and Dallas” testimonials from NaVSSeS Science and engineering apprentice Program (SeaP) and former SeaPerch Students Katherine and Cevan: Katherine said, “SeaPerch teaches you about the use of failed designs to create a successful design, how to keep an engineering notebook, how to work toward a common goal as a team, and it teaches time management skills. The SeaPerch experience is one of learning and teaching. Many who begin as team members turn into mentors to help the next generation of competitors. With each successive team, more and more students join. What starts out as a small group of students wanting to try something new turns into a school-wide club. You not only form new friendships with people you would not have met otherwise, you also form connections and gain experience in teamwork as an engineer.” Katherine joined SeaPerch in 2009 as a middle school student. As a high school student, Katherine continues to be involved as a mentor to her former middle school SeaPerch team, and has participated as a judge for the Greater Philadelphia SeaPerch Challenge, (GPSPC). Cevan said, “You realize an important part of engineering in the form of competition through the SeaPerch Challenge. Only one team’s design can win. Many different factors influence this. The team’s engineering notebook, the ROV’s performance, and team presentation skills. From my experience, you have something to gain from SeaPerch if you have no idea what you want as a career, know you want a STEM career, or even if you want to go into anything involving working in a group toward a common goal— you can start

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From the Archives: Naval Ship Engine Exhaust Emission Characterization The SeaPerch Challenge Ten-year Anniversary

with SeaPerch.” In 2013, Cevan entered the Penn State engineering program to pursue a double major degree in Civil Engineering and Computer Science. testimonial from GPSPC Judge CBS3 New York, Director of Broadcast Operations & engineering rich Paleski: “I wanted to tell you how impressed I was with the way the SeaPerch STEM event was organized, and with the skill of the participating schools. The students were all exceptionally focused on the various tasks and it was a joy to share their experience. The students took away so much more from this program than building and navigating an underwater vehicle. It was clear that they are learning project management, the engineering process, and team work. Those life skills will serve them well in a competitive world. In my opinion, one of the noteworthy successes of your program is that your goals were so incredibly attractive to these students. After many of them faced a disappointing setback, all were drawn back into the process of creating, improving, testing, and competing. All of this was happening while they continued to work on their normal load of schoolwork. I attribute this success to the well-organized program that your volunteers have set up and their commitment to the students. I look forward to seeing this program expanding into other areas of the country, and you can count on me to judge the New York area event.” testimonial from Drexel university alumni association Board Member Joseph Maenner, esquire: “As a Drexel engineering alum, as well as a former U.S. Navy civilian employee, I have had the privilege of serving as a judge for the Greater Philadelphia SeaPerch Challenge. It has been a great experience for me to see high school and middle school students take on the engineering challenges of designing, building, and operating their SeaPerch craft, and discussing with me what they were able to accomplish as well as what they learned about both engineering and themselves throughout the several months leading up to the competition.”

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A S N E C O R P O R AT E M E M B E R S / S U P P O R T E R S

✯

One Star Engineering Services Network, Inc.

Tri-Tec Manufacturing

Massa Products Corporation Commodore L.A.B. Industries, a division of the Louisiana Association for the Blind

The SPECTRUM Group

For more information about becoming an ASNE Corporate Supporter, visit www.navalengineers.org/Support-ASNE/Corporate-Support-Program, or contact ASNE at (703) 836-6727 or CorpSupport@navalengineers.org.

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Fall 2023 | No. 135-3 | 31

SPECIAL SECTION

INTRODUCTION—PROMOTING ELECTRIC PROPULSION 2023

Promoting Electric Propulsion

T Mike Briscoe Educator-in-Residence American Society of Naval Engineers education@navalengineers.org

Check out the PEP 2023 Intro Video 

HE AMERICAN SOCIETY OF NAVAL ENGINEERS (ASNE) has managed the Promoting Electric Propulsion (PEP) competition for the Office of Naval Research (ONR) since 2018. The student energy and ingenuity are the cornerstones that make PEP a successful workforce development program. Year after year, teams bring unique designs utilizing local connections for a five-mile race. The excitement starts in the parking lot as teams work seamlessly together to integrate the systems one last time and ensure their craft is race ready. The student articles on the forthcoming pages are an outgrowth of the fourth PEP competition held during ASNE’s Multi-Agency Craft Conference (MACC). Last school year, 24 teams worked on 27 craft. Engineering challenges prevented 12 of the teams from completing their builds, and 12 universities proudly brought 16 competition craft to Portsmouth. By the end of the day, Princeton placed first in the manned division and University of Rhode Island won the unmanned division. The students shared the value of this program, “Without PEP reaching out,” Nathan Yates a three-year member of the Princeton Electric Sports team, “we wouldn’t be here. Their email is why we built these crafts.” “The Vessel “39-R/V Hatsune Miku” is based on a bulk-carrier-type superstructure,” shared Alexei Sondergeld of the URI team and a 2022 Carderock intern, “The new configuration consists of three Blue Robotics T500 thrusters and a rudder, giving multiple redundancies for both propulsion and steering.” In addition to building fast craft, PEP students build engineering careers. Former PEP students currently work at NAVSEA, GD-EB, BAE, Lockheed Martin, Boeing, Battelle, the U.S. Air Force, Tesla, SpaceX, and other small- and medium-sized engineering companies. Returning PEP students completed SEAP and NREIP internships and joined internship programs at Flux Marine, Newport News Shipbuilding, and other organizations. Through PEP, these students worked on a naval problem for at least ten months and know the challenges of integrating HM&E with control systems in robust, reliable ways. PEP judges were continually impressed with these mechanical engineering and electrical engineering students discussing issues like stability, cavitation, thrust direction, and cells’ power density. Finally, a big thank you. We simply could not hold a PEP competition or share this level of student engagement in naval engineering without our supporters. Judges from ONR and NAVSEA give students a taste of professional design reviews. In-water support from our friends at Little Creek made PEP 23 go safely and smoothly—we are so excited that they are keeping our competitors safe. ASNE volunteers handed out food, took photographs and talked with students about their engineering journies. For PEP 2023, the volunteers worked tirelessly to handle multiple roles needed to make this event a success. If you have availability to join us April 15-16, 2024 in Virginia Beach, we need your support. For more information about PEP, and these teams, please visit navalengineers.org/PEP.

Mike Briscoe 32 | Fall 2023 | No. 135-3

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Teams Participating in PEP 23 Manned Division

Unmanned Division

Princeton

University of Rhode Island

Washington College

Florida Atlantic University

Old Dominion University

William & Mary

University of Michigan

North Carolina State University

University of Kentucky

Texas A&M

Wake Forest University

University of Kentucky

University of Pittsburgh

Teams Participating in PEP 24 Michigan Electric Boat

UCONN

Vanderbilt University

Rhode Island

Georgia

University of Tennessee Knoxville

William & Mary

Wisconsin

University of Alabama

Texas A&M

Virginia Tech

Auburn University

Florida Atlantic

Syracuse University

Princeton University

University of Notre Dame

Virginia Commonwealth University

Washington College

University of Buffalo (2 unmanned)

University of Illinois

Northeastern University

Georgia State

University of Central Florida Robotics Club

Madison College

North Carolina State Wake Forest University University of Pittsburgh University of Kentucky Stevens Institute Johns Hopkins

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Howard University

Stony Brook

University of Virginia VICTOR Robotics Research

Arkansas Tech

George Mason University

University of Iowa

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PEP 2023

The University of Rhode Island Sea Tình

HE TEAM had a variety of inspirations in modern hull design, but this first section will focus on the fastest hull we designed, the trimaran. The trimaran our team constructed, “48/Sea Tình”, was designed as a model of a Vietnamese river barge/sand barge design, but with an improved, more efficient Sea Tình hull. (The outrigger hulls were added to address problems with instability caused by adverse yaw and wouldn’t be present on a scaled-up river barge of this design.) These types of vessels travel up stream in extreme currents in Vietnam, often carrying large amounts of cargo. In Vietnam, a country much covered in much of a marsh environment with heavy rainfall, many of the river systems are controlled using dams. Due to this type of environment, it often makes sense to move cargo or merchandise along these many rivers. In addition, the aforementioned dams don’t have locks— when they are open, there are powerful currents that require substantial power to navigate, particularly

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when the barges are loaded such that the decks are awash! Adding to the challenge is that most of the barges have slow-to-respond turbo-diesel engines; the quick response and high power of electric motors on a scaled-up Sea Tình would be ideal for these short trips that require occasional intense bursts of power. This was an attractive factor in our design as we looked to model successful, powerful real-life hulls.

The hull itself has an unusual design and construction method: it is built with popsicle sticks and tongue depressors. It was first modeled in Rhino, which allows the definition of ruled surfaces based on curves in space. For the construction of a hull such as this one, these curves represent the keel, chine, and gunwale shapes that are first constructed as a frame with standard popsicle sticks. Once the frame is built, the larger tongue depressors are used as hull planks. The hull is smoothed with an angle grinder, then thin continuous strand fiberglass mat and epoxy are used to seal and further strengthen the hull.

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Sea Tình

During testing, we realized that this hull exhibits unstable adverse yaw at around 14-16 mph. We tried multiple solutions, such as trim tabs and far-aft weight distribution, but we couldn’t fully eliminate the problem. We assume that this problem is related to the high length to width ratio of the vessel; hulls with a similar ratio used for manned racing in Thailand and Vietnam often crash due to what appears to be adverse yaw. After a capsize induced by our adverse yaw problem during testing, we decided to convert the vessel into a trimaran. Two identical outrigger hulls were constructed using the same wood-and-fiberglass construction method and attached using aluminum box-tube struts bolted to the decks of all of the hulls. The extra hulls eliminated our stability problems, but they reduced our top speed to about 17 mph and, more importantly, put extra stress on our twin motors, which began overheating. Since these motors are drone motors which expect more airflow for cooling, we installed a large computer fan in our engine room to try to direct heat away from the motors. During our race, we decided to maintain a pace somewhat slower than maximum power to prevent

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R/V Hatsune Miku

Sea Tình during towing tank testing

Sea Tình on the course during the race.

overheating. We sustained about 12 mph for about 3 miles, at which point we realized that we were getting no power from our starboard motor. We continued at the same speed with only the port motor at a higher power setting, but the vessel only continued for another half lap before the port motor also quit. When we approached the vessel, we

noticed that the engine room hatch had shifted and, with the choppy conditions, had probably been letting salt spray into the electronics. The starboard motor-toshaft coupling had also broken, which allowed the prop shaft to loosen and let water in. The vessel had completed 3.5 laps, which earned it third place by time/distance.

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The University of Rhode Island

R/V Hatsune Miku

ESSEL “39-R/V Hatsune Miku” is based on a bulk-carrier-type superstructure, though the cargo hold is used as a room for data collection electronics and even a 1/32 scale movie theater. The vessel is also fully equipped in scale with scale-model crew quarters, plumbing, and even a working Peltier-junction-based refrigerator. The hull was designed to be built with plywood on a 2x4 frame, so it has a much more angular hull than Sea Tinh. It also is reinforced with a composite of weed-control fabric and epoxy. The hull was originally built at Boston University and was overhauled with a new, much more powerful, propulsion and control system by our team. The new configuration consists of three Blue Robotics T500 thrusters and a rudder, giving multiple redundancies for both propulsion and steering. It also originally had about 100 pounds of ballast in the bilge because the original batteries were quite small. We removed the aft ballast and replaced it with two sets of two 21 Ah 6S batteries in parallel and one set of 30 Ah 5s batteries

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in parallel. The batteries are mounted in wooden brackets attached to the hull. The batteries were borrowed from a research lab at URI and were equipped with non-standard connectors which we were not allowed to change, so we had to create our own custom parallel connectors. Four of the batteries sit on the floor of the bilge while the other two sit on the ‘shelf ’ above the former propeller cutout (which we filled with a foam fairing we created).

We did not have any way to transport this vessel in a vehicle, so all of our testing of the new propulsion system took place in the URI towing tank. Since the tank is fairly short compared to the speed of the vessel, we could only do short bursts of full throttle operation. We determined that the top speed is likely to be around 10-12 mph. We also found that the three extremely powerful (1.2 hp, 35 lb bollard-pull thrust) thrusters made for an extremely maneuverable vessel: we can control all actuators manually, and being over-actuated in yaw allows

the vessel to even move directly sideways by using the center motor and rudder to produce a sway force and using the outer thrusters to counteract the yaw moment and forward thrust. During the trip to Virginia from Rhode Island, this vessel was strapped into a U-Haul trailer. Since this vessel is so long, it required the largest U-Haul trailer, which has suspension designed for a 1 ton plus load. With only a couple hundred pounds of boats and batteries in the trailer, the suspension was essentially non-existent. This resulted in forces on this vessel, and

R/V Hatsune Miku

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more importantly the ballast in the forward bilge, far beyond the expected hydrodynamic slamming forces that could result during on-water operation. The 50-pound piece of steel ballast hammered its way through the wood-and-composite hull, shattering the forward hull panel. Fortunately, the boat arrived at the competition a day early and a very rapid repair was possible after stopping at a Home Depot to buy an angle grinder, construction adhesive, fiberglass cloth, and the fastest-curing epoxy we could find. On race day, we competed with Sea Tình first, which did not finish. When our race time for Hatsune Miku came up, we realized that none of the unmanned vessels had finished and there were few left to run after us. Therefore, we realized that the race had essentially become a test of reliability and endurance: if our vessel proved to be reliable enough to finish the race, we would likely do well. To ensure that our vessel would have a greater chance of finishing, we decided to drive the vessel at a bit less than half throttle to ensure that our speed controls and

NAVAL ENGINEERS JOURNAL

Construction of the plywood /2x4 hull.

homemade battery connectors would not overheat. During the race, the vessel performed mostly as expected. Interestingly, the roll torque from the center thruster is quite significant—it created a noticeable list to starboard and the rotating prop wash was clearly evident in the wake. The thruster wake’s interaction with the rudder also created a yaw moment that had to be corrected with rudder trim. This was exacerbated by the fact that the forward ballast was removed, so with only a light load of emergency flotation foam in the cargo hold, the bow rode very high and the overall center of mass was much higher. We maintained

The result of the rapid repair of the ballast damage.

about 6 mph, which allowed Hatsune Miku to be the only vessel to finish the race, which it completed in about 50 minutes.

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PEP 2023

Princeton University Safety first!

HERE ARE THREE MAIN PARTS to Papillon Rouge’s mechanical design process: Finite Element Analysis (FEA), Shape Optimization, and Material Selection. FEA is integrated into every step of our mechanical design process in order to predetermine the quality of our in-house fabricated parts and components. All components are simulated under worst-case loading conditions and the variable most concerning to our designers is the minimum safety factor. Minimum safety factor is, in essence, the crux-point of a fabricated part where it is weakest under load. By verifying that the minimum safety factor of each prefabricated part is within spec, this ensures that all our manufactured parts and components will perform safely—even under unanticipated loading cases. Our in-house design standards mandate that fabricated parts maintain a minimum safety factor of 3 during loading simulations. This is concurrent with modern aircraft design standards. The first fabricated component of Big Bird’s design process was the adapter plate used in the outboard to attach the motor to the midsection. The minimum safety factor was 8.97. Ostensibly, the consensus of our mechanical designers at the time was to ensure that the motor “wouldn’t go anywhere” during all modes of operation—having learnt from prior experience. The lay term “over-built” is a proper description, as the design successfully limits deflection during operation. Material selection is undoubtedly one of the most important steps to creating a well-designed powertrain. Prior to any design simulations, the material options were examined. For example, initial designs for the custom drive shaft used in Big Bird’s outboard featured a 316 stainless steel (316SS) selection. Excellent corrosion resistance and a thus decreased maintenance requirement made the material particularly attractive. However, due to its annealed state, 316SS’s low elastic and shear moduli rendered the design unable to yield a minimum safety factor of 3 in repeated software simulations. Even re-running analyses under lighter-than-anticipated loading conditions continued to result in simulated inelastic torsional deformations. Eventual on-water testing would prove the simulation to be correct.

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FIGURE 1. Adapter Plate static load analysis, with minimum safety factor of 8.97 shown. Simulated elastic deflection is also shown under extreme loading.

FIGURE 2. Drive Shaft static load analysis, featuring a minimum safety factor of 1.38, resulting in a design review. In the lower square section of the shaft, viewers can see the simulated inelastic torsional deformation.

Ultimately, a shift to a hardened-grade stainless steel resulted in a safety factor of 5. At PEP in 2023, Big Bird reached speeds of 70+mph in a demo following race completion, confirming the ruggedness of the final design. A final feature used to create key components is shape optimization. This CAD feature allows us to achieve the lowest mass possible during design while optimizing load-bearing capabilities. Specifically, during shape optimization we remove unnecessary material in areas with low stress while bolstering areas under greater stress, creating more organic structures that are optimized to their particular loading case. A newer, shape optimized version of the adapter plate above has allowed for a 60% weight decrease with only a decrease of 30% in safety factor—to be featured in the 2024 PEP competition. We would like to extend our sincerest gratitude to Glenn Northey and Al Gaillard, who run Princeton’s MAE Machine Shop, for their instruction and for helping us make all this fabrication possible—all while maintaining a sincere patience with our learning.

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Big Bird Can Fly

HE DESIGN and construction of our 2023 race boat built on experience gained from our two previous boats. In 2021, our first boat was completed as an all-electric 10ft runabout (monohull), to be raced at Promoting Electric Propulsion (PEP). However, the hull proved too small for the combined weight of the electric propulsion system and the driver. Having been constructed during COVID, the components were fabricated remotely and assembled too shortly prior to the PEP race. The following year, Le Papillon (LP for brevity)—as she would be affectionately named—was created by retaining the original powertrain and replacing the 2021 runabout hull with a 11ft-hydroplane hull (hydro). Featuring a raised center tunnel between two sponsons, hydro hulls ‘pack’ air underneath themselves at higher speeds, lifting the hull out of the water beyond plane and

NAVAL ENGINEERS JOURNAL

FIGURE 1. Papillon Rouge raced two craft, Le Papillon and Big Bird, in PEP ‘23. They are featured here at the starting line.

increasing both speed and efficiency. This “flying” setup would ultimately achieve a top speed of 42 mph and a tested range of 8 miles on plane. Alongside LP’s development in 2022, Papillon Rouge acquired a 12-foot-long hydroplane hull that had successfully raced as a D-Stock gasoline hydroplane—reaching top speeds of 95 mph. We completed

an all-electric conversion shortly after LP and dubbed her Tiger. This hydro has been successfully tested to 45 mph and is currently under refit, expecting to emerge capable of 70+mph. Experience garnered from these two hydros included optimal weight distribution, propeller selection, outboard setup, and high-speed handling.

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Princeton University FIGURE 2. Big Bird getting underway, beginning to pack air under her tunnel and get up on a full hydroplane.

and coolant fixtures. Results were immediate, as the first high-speed testing pass saw the boat reach 83 mph without fault, eclipsing the lake and team records. During the same testing session, another highspeed pass saw a staggering 90.2 mph. This was cause for celebration— the driver and on-shore team were ecstatic to say the least. But even more exciting was that our post-test checklists confirmed this setup yet retains much potential as it included an undersized propeller, under-trimmed outboard, restricted power potential (75% of a conservative max), and unoptimized weight distribution. Additionally, even at 90.2 mph, Big Bird was running FIGURE 3. Big Bird at speed, hydroplaning under the watchful eye of Little ‘fat’—a term used in the professionCreek staff. al racing world to mean she still sat relatively low in the water. As she was Going into this year, we (re)created Big Bird, a newly originally designed for 100+ mph, Big acquired 14ft hydro, formerly raced as gas-powered boat Bird was running much more conservatively than usual up to 130mph. Like her sisters, LP and Tiger, Big Bird and was thus in a very safe running position. The driver, features a raised tunnel, ‘lifting’ the boat out of the water Andrew Robbins, described it as “I just set the cruise and to “fly”, reducing drag and increasing efficiency at high babysat the steering wheel!” It should be noted, that as a speed. Unlike her sisters however, Big Bird’s hull sports result of the “flying” nature of hydroplane hulls there is improved features: an airfoil-shaped cross-section (for almost no wake thrown off at high speeds. We were duly additional lift generation), enclosed canopy, 6-point informed of such by two paddle boarders who witnessed racing harness, onboard air system, driver-controlled the episode firsthand. ailerons, and a foot-powered gas pedal. Papillon Rouge’s This work culminated in our five-mile PEP run in 6 modifications saw the addition of a full touch-screen disminutes and 12 seconds. A conservative run allowed for play, adjustable powertrain mounts for weight optimizasafe completion of the race and a first-place finish. tion, vertically-suspended powertrain systems, dual-loop We at Papillon Rouge would like to extend a sincere cooling systems, and remotely-operated fire suppresthank you to our partners, having made this project and sion systems. Altogether, the hull, including batteries, effort possible. Without the expertise, guidance, and high-voltage components, low-voltage components, safesupport of industry experts, this venture would not have ty components, and driver, totals to a weight of ~850 lbs. been possible. In particular, Papillon Rouge would like Routine testing ensures continual progress, as is par to give a particular shoutout our Title partners, namely: for the course. Initial tests resulted in inverter (motor The American Society of Naval Engineers, The Office controller) errors limiting the boat to only 7 mph. Furof Naval Research, Cigarette Racing, Flux Marine, and ther attempts at tuning that particular inverter came to Danecca Ltd. As your support continues, we will be no avail and thus we opted to change to a slightly heavipleased to unveil even more exciting innovations in the er, but more functionally robust inverter. This course future—already in development and well on the way— correction required that all-new DC and AC-phase that should aid in shaping the course of performance cables be fabricated along with new mounting brackets electric boating.

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FIGURE 4. Papillon Rouge takes home the trophy!

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PEP 2023

Florida Atlantic University FAU WAVESWEEPR Hull Design

HE ASYMMETRICAL catamaran autonomous surface vehicle consists of two pontoon hulls that support the main deck. As displayed in the below figure, the electronics box, which houses the microcontroller and navigation systems, is placed on top of the main deck, and the power and thruster controls are stored within each hull. As opposed to a conventional catamaran where each twin hull has an equivalent shape on both starboard and port sides, the asymmetrical catamaran has disparate shapes on opposite sides of its pontoons. An example of an asymmetrical catamaran hull is the outboard hull meaning that the outside has the traditional catamaran curvature while the inboard side is a flat vertical face. This catamaran design has a very slender hull shape, which is very light. This results in a smaller wetted area which reduces pressure and frictional drag as well as a less power required to get the vessel up on plane and to a higher maximum Froude number. While

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the wetted area of the asymmetrical design does not change greatly, the main advantages that are associated with this particular design include a shift of the centers of buoyancy of each hull further outward, which increases transversal

stability further. Additionally, the outboard asymmetrical design generates far less interference drag between the hulls of the catamaran. Although there are some generic disadvantages of an asymmetrical catamaran hull, such as

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susceptibility to heaving motions and sensitivity to payloads, the many advantages that the asymmetrical catamaran has outweigh the potential risks. As previously mentioned, one of the reasons that the outboard asymmetrical catamaran has been designated as the proposed design is its ability to plane. To determine whether or not a hull design is capable of planning, the Froude number related to the design of a hull is taken into consideration. The velocity of the autonomous surface vessel has been solved to ascertain what minimum speed is required for the vessel to plane on the water conservatively. To solve for the velocity at which the vessel can plane, the Froude number has been taken

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to be a value of 1.1. The waterline length of the vessel is 6 ft, which is equivalent to 1.8288 m. Solving for the displacement velocity value yields a result of 4.66 m/s, which is attainable from our estimated drag calculation and thrust output of our propulsion system. For the design of the hulls, the cross sectional station curves, form lines, and general dimensions of a motorized planing catamaran were used. As seen in the below figure, these designs were imported onto planes in Solidworks. These designs were then turned into splines onto 17 cross sectional stations. These stations were then lofted through using guide curves such as along the deck, keel, and waterline.

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PEP 2023

Washington College Electric Propulsion Leaders

HE WASHINGTON COLLEGE Electric Boat Team operates out of the IDEAWORKS Innovation Center. They continuously work on multiple boats while leading the area’s electric propulsion community. Their wooden monohull race boat placed first at PEP in 2022 and remains a reliable, efficient craft. Their latest boat is a trimaran, a design that allows for a combination of stability, speed, and increased driver safety compared to some traditional boats. Its unique configuration makes it a popular choice for racing, cruising, and exploring the waters of the Chesapeake Bay. “Our goal was not just to build a boat capable of sprinting 5 miles, but to also endure longer marathon races, varying weather conditions, and demonstrate capabilities that would promote electric propulsion and appeal to a broader public audience,” says team advisor and Director of the IDEAWORKS Innovation Center, Brian Palmer. One of the interesting features of the new design is its unique power storage which affords race speeds and marathon distance. “The trimaran’s new motor would run at triple the voltage of the previous, so we decided to build three groups of 16 LiFePO4 cells, each bank separated from the others by using high current contactors. The wooden hulled race boat battery consisted of 16 LiFePO4 cells with a capacity of 280Ah each—this battery would be three times as large. As per ABYC recommendations, breaking it into three groups allowed us to break the 144v nominal battery down into three isolated 48v nominal banks at the flip of a switch or pull of the safety lanyard. To ensure the battery bank has maximum utility, we

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designed it to allow it to plug in to any J1772 Level2 electric car charger, which is becoming more common place in residential driveways and garages,” noted Cole Davis, a rising sophomore on the team. When connected to a 220V Level2 charger, it can sustain up to a 6.6kW charging rate (charging a fully depleted battery easily overnight). In addition to the safety of this battery chemistry, these newer cells have a 6000-cycle lifespan, which further helps convey the reliability and longevity of this design to the public. The 48 cells are well integrated into the trimaran hull. The three banks surround the cockpit with long strings of 16 cells on either side, and a stack of 4x4 cells in the front of the cockpit. This alternate layout was necessary for the center bank to fit in a narrow space. The three battery compartments are integral to the hull and provide structural support as well as protection to the cells. Removable polycarbonate panels provide access to the cells and the ability for visual inspection while still sealed into separate air and watertight compartments. With an expectation of drawing nearly 1C of current continuously (250 amps anticipated), we built aluminum

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heat sinks with copper tubing routed to water cool the cells. The BMS we chose has a modular design, with a main BMS module that connects to three distributed banks, each with 16 balance leads and 5 thermistors. These distributed modules report to the main BMS via CAN bus, further isolating each bank electrically when the high voltage contactors are disconnected. To our team, the future of the electric boat industry is a collaborative effort, open to contributions from all. Together, we can drive the transition towards cleaner, greener boating by supporting research, advocating for sustainable technologies, and embracing electric boats as consumers and competitors. We believe that encouraging students to contribute to the future of the electric boat industry is an essential step towards a sustainable and thriving planet. As the next generation of innovators, researchers, and decision-makers,

students have a unique opportunity to shape the trajectory of this industry. By actively engaging in STEM education and competitions such as this, students can develop the skills and knowledge needed to create innovative solutions that drive the advancement of electric boats. We hope to inspire people to unite in the shared responsibility of creating a more sustainable and enjoyable future on the water. You can learn more about the team at www.WACElectricBoat.com.

Trimaran

Wooden

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PEP 2023

William & Mary Hull Design

N OUR DESIGN MATRIX, a catamaran proved to be the best choice, and a kayak with outriggers came in second. A simple kayak design would be far too unstable to support the weight of the batteries or to turn quickly. We aimed to design a catamaran hull for the race boat, after completing the test platform. The catamaran is more stable and easier to handle in rough waters. For the first prototype, we made a general, adjustable frame out of 80/20 extruded aluminum rails and fastened it to a youth kayak. This design afforded quick in-water testing of our other systems. For the outriggers, we decided to use PVC pipe. It was readily-available, cost-effective, and easily machinable and customizable. Each outrigger was a 3-foot-long pipe with caps glued at each end, suspended 1 foot from the hull using 80/20 rails and 3D printed mounting rings. For thrust, we purchased two Diamond Dynamics TD1.2 integrated underwater thrusters with built-in ESCs. Each thruster was 3 inches in diameter and provided 1.2kg of forward thrust. They were mounted on the same rings that held the PVC outriggers to the aluminum frame. For the rudder, we modified the one included with the kayak. We attached a FeeTech FT5330M 35kg digital servo motor to a 3D-printed rudder mount, which turned through nearly 180 degrees using a pin-slot mechanism. Our PEP race boat, the Colonial Cruiser, is an electrically-powered unmanned catamaran. Each hull is 5½ feet long, 1 foot wide, and 1½ feet tall, constructed out of wood-reinforced fiberglass. The wood frame of each hull is made from 1/5-inch Luan flexible plywood, which was wired and then glued together. This frame is strengthened by three crossbeams of medium density fiberboard (MDF). The wood is covered on the sides and the bottom with 2-3 layers of Fiberlay 6oz fiberglass cloth. The cloth and all exposed wood is coated with a mixture of ortho polyester laminating resin and methyl ethyl ketone peroxide (MEKP) catalyst, both from Fiberlay. The top of each hull has two rectangular portholes. The interior of each hull is filled with TotalBoat 2-part polyurethane marine

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flotation foam, with space left to hold batteries. On top of each hull is a frame of 80/20 aluminum extruded bars, fastened with through-bolts and nuts. These bars extend down the back of the hull and serve as mounting surfaces for a Reacher RT14S300A-BEC electronic speed controller (ESC) and a Reacher D107-125-WP brushless motor. The bars also hold screws that fasten a clear acrylic lid over the portholes. The lid is lined with general weather stripping Between the hulls, fastening them together, is a series of 80/20 cross-bars. On top of these is an acrylonitrile butadiene styrene (ABS) plastic platform, which holds switches, an electrical junction box, and an Elephant B35 waterproof battery enclosure. That waterproof box holds the CubePilot Cube Orange AutoPilot flight controller (FC) and its power supply. Also fastened to the ABS is a carbon fiber rod that serves as a mast. On top is a smaller ABS platform that holds a Taoglas CGGBP.25.4.A.02 GPS unit, an RFDesign 900x ultra long range telemetry radio modem, and a FrSky Rx8R Pro remote control receiver. This hull is powered by two Reacher brushless motors with a maximum of 13 kW and 13,000 RPM utilizing differential thrust steering. The platform provided more than enough stability and was able to generate an attack angle that allowed us to maintain a steady speed for miles in the Elizabeth River.

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PEP 2023

Old Dominion University Strong Annual PEP Competitors

LD DOMINION UNIVERSITY strives to be a safe, prominent figure in every engineering endeavor, and our ASNE/PEP student team also strive to do the same. Our involvement in the Promoting Electrical Propulsion Competition (PEP) has allowed us to pave a new road in electrical propulsion units and water vessels. With over three years of competition experience, ODU’s PEP team has and will always guarantee reliability, functionality, and performance. In this paper, we showcase the latest evolution of our electric propulsion system that offers improved performance, efficiency, and sustainability. For this year’s competition, we prioritized improving the performance of our boat. We allocated a significant portion of our budget to build LiFePo (Lithium Iron Phosphate) batteries consisting of 16, 280Ah cells, which offered an enormous boost in power capacity; drastically improving our total range. In the 2023 PEP competition, we raced at full throttle for the entire five miles and still had 67% of our power remaining. We secure our battery in a metallic box that protects our batteries from the elements while providing rigid structural integrity. This year’s team pushed our power storage solution forward. This year, we also fine-tuned the weight distribution of the boat, enabling a better planing boat allowing us to reach higher speeds. We also experimented with propeller modifications, including higher-pitched propellers and different sizes, seeking optimal performance. Despite the challenges faced each year, our team’s determination and limited resources have pushed us to explore creative solutions and continuously improve our electric propulsion system. From our many testing sessions, we were quick to the

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conclusion that the electric motor was not a tamed beast. The torque output was incomparable to that of a gasoline engine which came both with its benefits and downfalls. After our first couple runs, we noticed that even though our 9inch propeller worked well, the motor was not being completely utilized. We ordered a high pitch 8.5-inch propeller and a regular pitch 9.5inch propeller. Our testing concluded as such; the high pitch 8.5-inch propeller has extreme low end kick, it would take you out of the water quickly, however the motor couldn’t reach the desired rpms to accelerate the vessel beyond 11mph. However; the 9.5-inch propeller performed extortionary well, the larger diameter of the propeller allowed for more water to be pushed, and the high torque of the motor could easily handle a heavier load while spinning at the same rpm. We ordered a 10-inch stainless steel propeller which allowed us to reach 14 mph. This may seem low however paired with out 20-mile estimated range, we are the best of both worlds in speed and range. Continuing to some of the draw backs, the electric motor has proven to a be a challenge to control. Unlike a normal gasoline engine, electric engines do not idle, coupled with the forces required to overcome magnetic fields to spin the motor, electric motors often spin up to 20% throttle at startup. When the prop is underwater, this can cause an unexpected acceleration. Furthermore, electric systems are heavy, ODU’s battery pack alone weighs 252lbs, and motors can weigh anywhere from 45 to 120 pounds, the 4/0 awg cable can weigh up to 50

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lbs. The total mass of the electric system, driver, and vessel can greatly impact top speed performance. Moving forward, there are improvements we would like to make. Starting with the hull, we have concluded that the deep V hull is a jack of all trades, but we would like to switch to something more appropriate to our needs. From our research we have gathered that flat bottom hulls would offer greater efficiency in terms of getting on plane, stability, and top speed. The potential for lower performance in choppy waters would increase however that is a risk we don’t mind taking in pursuit of speed. Additionally, we would design a new and improved propulsion system for our vessel. The 10’-long speed tail is an extremely efficient way of transferring power into prop and water directly from the motor but this greatly effects our tuning capabilities. With a pulley or chain gator tail system, we would have a deeper tunning solution by tuning our drive ratios. Interested in supporting ODU in 2023-2024? Contact the ODU Student Officer (President) Daniel Erdogan derdo001@odu.edu.

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PEP 2023

University of Michigan High Voltage Electronics in Electric Watercraft Challenges, Impacts, Solutions, and Future Prospects By Ryan Needle and Sujay Racha

HIS JOURNAL ARTICLE explores the challenges associated with high-voltage electronics in fully electric watercraft and presents potential solutions for ensuring their reliable operation. Electric watercraft, driven by electric propulsion systems, offer promising alternatives to traditional combustion engine vessels, reducing emissions and dependence on fossil fuels. However, the marine environment poses unique challenges to the performance and longevity of such systems. This article discusses the key challenges faced by high voltage electronics in electric watercraft, explores existing mitigation strategies, and presents future prospects for enhancing the reliability and durability of such systems.

Introduction Electric watercraft have gained significant attention in recent years due to their environmental advantages and potential for sustainable maritime transportation. These vessels rely on high voltage electronics to support critical functions such as electric propulsion, power management, battery systems, communication, and navigation. However, the demanding marine environment, characterized by humidity, saltwater exposure, temperature variations, and vibrations, poses challenges to the reliable operation of electric watercraft.

Challenges and Impacts Presented in a Marine Environment As watercraft increasingly adopt electric propulsion systems, it becomes crucial to understand the unique impacts and potential failure modes these components face in the demanding marine environment. The impact of a marine environment on high voltage electronics is particularly evident in three critical components: the motor, inverter, and battery pack.

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Challenges and Impact on Motors

The electric motor itself operates within the challenging marine environment, subjecting them to the corrosive effects of saltwater. Saltwater can infiltrate motor components, such as the rotor, stator, windings, and bearings, leading to corrosion and degradation.[1] Degradation of these components affects motor efficiency, power output, and overall performance, potentially causing premature failure. Implementing protective coatings, specialized materials, and internal diagnostic tools can help mitigate the impact of corrosion. Additionally, high humidity and moisture in the marine environment can compromise motor insulation. Reduced insulation performance can lead to electrical leakage and increased chances of short circuits. These challenges can result in unexpected motor shutdowns, potentially leaving the watercraft stranded or compromising its maneuverability. Secondly, compromised insulation can weaken the motor’s ability to withstand high voltage stress, increasing the likelihood of insulation breakdown and arcing. This not only affects the motor’s efficiency and power output but also poses a safety hazard to the surrounding electrical systems and personnel on board. Additionally, degraded insulation may result in decreased dielectric strength, making the motor more susceptible to voltage spikes and transient electrical disturbances, further jeopardizing its reliability and longevity. Temperature variations in the marine environment also influence motor performance. Fluctuating temperatures can stress motor components such as the permanent magnets, leading to reduced motor performance. Furthermore, mechanical vibrations and shocks experienced by watercraft can damage motor components, such as bearings, windings, and rotor balance.

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Challenges and Impact on Inverters

Inverters play a vital role in converting and controlling electrical power in the electric watercraft. Corrosion is a major challenge for inverter components exposed to saltwater. Power electronic devices and printed circuit boards (PCBs) are susceptible to corrosion, leading to reduced efficiency, power conversion capabilities, and overall reliability. Moreover, high humidity and moisture can impact inverter circuits, connectors, and cooling systems. Electrical shorts, reduced insulation resistance, and circuit malfunctions may occur in such conditions. Temperature variations pose another challenge for inverters in marine environments. Fluctuating temperatures can stress power semiconductors and capacitors, leading to thermal derating and reduced power conversion efficiency. Additionally, mechanical vibrations and shocks can jeopardize the reliability of inverter components, resulting in cracked connections and potential failures.

Challenges and Impact on Battery Packs

Battery packs are essential for storing and supplying electrical energy in electric watercraft. Corrosion is a significant concern for battery pack components exposed to saltwater. Terminals, connectors, and enclosure materials can be affected, leading to increased resistance, corrosion-related failures, and reduced battery performance.[2] Furthermore, high humidity and moisture can compromise battery pack insulation, terminals, and internal electronics. Reduced insulation resistance, internal short circuits, and capacity degradation are potential consequences. Temperature variations pose challenges for

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battery packs, affecting their performance and lifespan. Fluctuating temperatures accelerate capacity loss, increase internal resistance, and contribute to premature aging. Mechanical vibrations and shocks also affect battery pack components, including cell connections and modules. Increased internal resistance, cell damage, and compromised safety can result from these mechanical stresses.

Mitigation Strategies and Solutions In order to address these challenges, the development and implementation of effective mitigation strategies and solutions are crucial. This section focuses on exploring various approaches and techniques to mitigate the impact of the marine environment on motors, inverters, and battery packs. By implementing these mitigation strategies, watercraft operators and engineers can ensure the reliable operation, longevity, and performance of these critical components. The following subsections delve into specific mitigation strategies and solutions for each component, including corrosion prevention, moisture resistance, thermal management, and mechanical robustness. Through a comprehensive understanding of these mitigation measures, these systems can overcome the challenges presented by the marine environment and reliably operate in demanding conditions.

Corrosion Prevention and Protection Measures

To mitigate corrosion, various prevention and protection measures can be employed. This includes the use of corrosion-resistant materials for components, such as stainless steel or

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The University of Michigan

marine-grade alloys. Additionally, applying protective coatings, such as specialized paints or corrosion inhibitors, can provide an extra layer of defense against saltwater exposure. To reduce the need for regularly-scheduled maintenance and inspection a corrosion sensor system can be implemented. The system incorporates specialized corrosion sensors strategically placed in critical areas prone to corrosion, such as terminals, connectors, and enclosures. These sensors are designed to detect changes in electrical conductivity or impedance, which can indicate the presence and severity of corrosion. The sensors may utilize different technologies, including electrochemical impedance spectroscopy or galvanic corrosion monitoring.

Moisture Resistance and Sealing Techniques

Moisture ingress can lead to electrical shorts, reduced insulation resistance, and malfunctions in high voltage electronics. Effective sealing techniques, such as gaskets, O-rings, and waterproof connectors, should be employed to create a barrier against moisture penetration. Enclosures and housings should be designed to be moisture-resistant, utilizing materials and designs that prevent water ingress. Conformal coatings can also be applied to protect circuit boards and sensitive electronic components from moisture damage. Testing should be done to ensure the integrity of the seal. Conduct a pressure test that involves subjecting the sealed components to increased pressure, typically using air or water. Observe if there are any leaks or signs of pressure loss, indicating potential weaknesses in the sealing. An immersion test can also be performed by immersing the sealed components in a controlled environment, such as a water tank or a simulated marine environment, to evaluate their resistance to moisture ingress. Monitor the components for any signs of water penetration, such as droplets, moisture accumulation, or changes in electrical performance.

Effective Thermal Management Strategies:

The nature of continuous operation in electric watercraft introduces additional challenges compared to land-based vehicles, such as cars. Watercraft often require sustained high-power output for propulsion, resulting in increased heat generation within the electronic systems. Cooling systems, such as fans, heat sinks, or liquid cooling, should be implemented to remove excess heat and maintain optimal operating temperatures.[5] Thermal insulation materials can be used to prevent heat transfer to sensitive components. Thermal modeling and simulation can help optimize the design and placement of cooling systems to ensure efficient heat dissipation. Temperature sensors and monitoring systems should be employed to detect any abnormal temperature rise and trigger appropriate cooling measures. The repeated thermal cycling

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caused by continuous power output and fluctuating environmental temperatures can lead to material fatigue, expansion, and contraction. These thermal stresses can degrade the mechanical integrity of components, such as solder joints, connectors, and insulation materials, over time. Ensuring proper material selection, robust mechanical designs, and thermal expansion considerations are essential to mitigate the potential risks associated with thermal stress. Additionally, watercraft often have limited space for housing components, leading to compact and enclosed compartments. The confined nature of these spaces impede heat dissipation, exacerbating the challenges of heat management. Heat build-up in enclosed spaces can result in temperature hotspots, compromising the overall performance and longevity of the electronic systems. Adequate ventilation, effective airflow design, and thermal insulation strategies are crucial for maintaining a suitable operating temperature range and preventing overheating within confined spaces.

Robust Mechanical Design and Vibration Mitigation:

The marine environment subjects watercraft to mechanical vibrations and shocks, which can lead to component damage and failures. Robust mechanical design practices, including secure mounting, shock-absorbing materials, and proper vibration isolation techniques, should be implemented to minimize the impact of vibrations and shocks. Mounting systems should be designed to withstand the dynamic forces encountered during watercraft operation. Mechanical stress analysis can aid in identifying potential weak points and areas prone to failure due to vibrations, enabling design improvements to enhance mechanical robustness.

Material Selection for Marine Environment Compatibility: Choosing appropriate materials is crucial in a marine environment. Components should be made from corrosion-resistant materials, such as marine-grade stainless steel. Non-metallic materials should be selected based on their resistance to UV radiation, humidity, and chemical exposure. Careful consideration should be given to insulation materials, ensuring they are suitable for marine conditions and can withstand temperature variations and moisture exposure. Material compatibility with the marine environment plays a vital role in ensuring the longterm performance and reliability.

Future Prospects As the industry continues to evolve, there are many future prospects for electric watercraft. Advancements in materials, sealing technologies, sensor systems, thermal management,

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and power electronics are expected to shape the future of these complex marine applications. These developments aim to enhance the performance, reliability, and sustainability of electronic systems. This section delves into the potential future advancements and trends that will drive the evolution of these systems, ensuring the longevity and success of electric watercraft as a viable and sustainable marine transportation option.

Advanced Power Electronics

Advanced Materials

Innovations in battery technologies, such as solid-state batteries or advanced lithium-ion chemistries, will enhance the energy storage capabilities of battery packs in fully electric watercraft. This will result in increased range, improved power density, safer systems, and longer operating times, driving the adoption of electric watercraft.

Ongoing research and development in material science are expected to lead to the emergence of new corrosion-resistant materials that can withstand the harsh marine environment more effectively. These materials will offer improved durability, higher resistance to corrosion, and enhanced thermal and mechanical properties, thereby extending the lifespan of the electric powertrain.

Enhanced Sealing Technologies

Advancements in sealing technologies will focus on improving moisture resistance and durability. Innovations in gaskets, O-rings, sealants, and encapsulation techniques will provide superior protection against moisture ingress, ensuring the integrity of high voltage electronic systems in marine environments.

Integrated Sensor Systems

Future systems may incorporate advanced sensor technologies to continuously monitor the condition of high voltage electronics, including corrosion levels, temperature variations, and moisture ingress. These integrated sensor systems will provide real-time data, enabling proactive maintenance, optimizing performance, and reducing the risk of failures.

Intelligent Thermal Management

The development of intelligent thermal management systems using advanced algorithms and control mechanisms will enable more efficient heat dissipation. These systems will dynamically adjust cooling mechanisms based on real-time operating conditions, ensuring optimal temperature control and maximizing performance while reducing energy consumption.

Predictive Maintenance and Analytics

Utilizing data analytics, machine learning, and artificial intelligence, future systems will be able to predict and prevent potential failures. Predictive maintenance algorithms will analyze sensor data, historical patterns, and operational parameters to identify early warning signs of degradation, enabling proactive maintenance and minimizing downtime.

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Advancements in power electronic devices, such as insulated gate bipolar transistors (IGBTs) and wide-bandgap semiconductors,[3] will contribute to higher power density, improved efficiency, and enhanced reliability of motor drive systems and inverters. These advancements will enable more compact and efficient systems.

Energy Storage Technologies

Sustainability and Environmental Impact

The future of electric watercraft will prioritize sustainability and environmental impact. This includes the development of eco-friendly materials, energy-efficient designs, and recycling programs for electronic components, batteries, and other system elements to minimize the ecological footprint of electric vehicles.[4]

Conclusion In conclusion, the challenges, impacts, and mitigation strategies discussed underscore the importance of understanding and addressing the specific requirements of high voltage electronics in electric watercraft. With ongoing advancements, these systems are poised to play a pivotal role in shaping the future of marine transportation, offering sustainable and efficient alternatives. By embracing innovative solutions, collaboration between industry stakeholders, and ongoing research and development, these systems will continue to evolve, ensuring the continued progress and success of electric watercraft.

REFERENCES [1]

Velev, B. “Comparative Analysis of PMAC Motors for EV and HEV Applications.” Machines. Technologies. Materials. 8.2 (2014): 32-36.

[2]

Crowell, Jon. “Battery arrays, rechargable Li-ion battery power sources for marine applications.” Proceedings of OCEANS 2005 MTS/IEEE. IEEE, 2005.

[3]

Sáiz, Víctor Manuel Moreno, and Alberto Pigazo López. «Future trends in electric propulsion systems for commercial vessels.” Journal of Maritime Research 4.2 (2007): 81-100.

[4]

Emblemsvag, Jan. “The electrification of the marine industry.” IEEE Electrification Magazine 5.3 (2017): 4-9.

[5]

Dincer, Ibrahim, Halil S. Hamut, and Nader Javani. Thermal management of electric vehicle battery systems. John Wiley & Sons, 2016.

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PEP 2023

University of Kentucky Team 10

VER THE PAST FEW YEARS, different senior design teams have been working on an ongoing senior design project to build an unmanned electric boat, and our team is continuing this task to complete and improve the design. The boat is a 10-foot inflatable craft with a custom-made mud motor from a capstone team 2 years ago. The boat is outfitted with six total batteries, four to power the motor, one to power the onboard electronics, and one in reserve. The boat will need to be fully operational by May and be able to compete in the Promoting Electric Propulsion (PEP) competition in the summer.

Objective Our objective was to: ■ Improve upon last year’s design and make an inflatable 10foot boat operate unmanned using a remote controller ■ Utilize the motor from last year’s capstone project and improve upon its design ■ Create better motor controls

■ Implement a steering system ■ Make the boat fully remote controlled ■ Implement GPS capabilities ■ Integrate a visionary system into the design

Background The background for our project was as follows: ■ ASNE funded us $5,000 to build an all-electric boat for a 5-mile competition. ■ Our project was an ongoing project from previous years; we worked on fixing past issues so that we may compete in this year’s PEP competition. ■ We rebuilt the motor controls, implemented a steering system, and developed a remote-control system. ■ Our budget was $5,000, $2,500 of this money was allocated for travel expenses. ■ The budget was used to create the remote controller, a new steering system, an interfaceable camera, and a water resistance enclosure for all the electronics.

Objective Tree

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Functional Decomposition

 Mud motor at UK Engineering Labs

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The University of Kentucky

Behavioral Model

Component Overview

Costs Subsystem

Total Cost

Power Distribution Steering Mechanics Remote Controller Vision and Broadcast Microcontroller/GPS Miscellaneous Camera/ Transmitter

$236.17 $472.52 $112.43 $88.52 $71.63 $59.45

Budget Breakdown 5.8 GHz Antenna

Eachine Display

Camera Unit

Total Budget Travel Allocation Subsystem Subtotal Shipping Costs Remaining

$5,000 $2,500 $1,045.72 $48.95 $1,405.33

Acknowledgement

Linear Actuator

Motor Driver

HTCC-AB02S Microcontroller

Joystick Module

BBP-32701

AA Battery Holder

Motor Controller

Remote Controller

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Special thanks to ASNE (American Society of Naval Engineers), for sponsoring and funding this project as well as Dr. Hannemann, Dr. Cheung, and Dr. Adams for advising the design process. Special thanks to KORA (Kentucky Organization of Robotics and Automation) as well for mentoring our work and offering their lab for testing purposes.

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Team 13

HE FOLLOWING is the final design review of the remote-controlled boat project by the Starboard Solutions senior design team. This poster outlines design alternatives, implementation, and cost of the project using requirements and constraints laid out by the American Society of Naval Engineers (ASNE). The project is divided into the subsequent subsystems, materials, communication and diagnostics, electric propulsion, and power distribution. The project is laid out on a block diagram to illustrate how each subsystem integrates with one another. The result is an unmanned, remote-controlled, electrically propelled boat that can display onboard diagnostics, operate on battery power alone, and compete in ASNE’s Promoting Electric Propulsion (PEP) competition.

Objective Starboard Solutions is to design an unmanned all-electric waterborne vessel to compete in ASNE’s PEP competition in the unmanned heat.

■ Vessel must be electrically powered ■ No combustion engines or generators are permitted ■ Must complete five laps of roughly one mile each ■ All vessels must abide by United States Coast Guard (USCG)

safety regulations

Background The competition is motivated by the potential benefits of integrated electrical propulsion (IEP) systems for naval and maritime applications. With a hybrid or IEP system, power is generated with diesel generators while the ship is propelled by an electric motor as the prime mover. This allows for optimal performance of the power system and can better keep up with the rising demand for shipboard power capability. IEP offers benefits in: ■ Reduced fuel costs ■ Increased stealth due to quieter operation of electric motor ■ Increased survivability of vessels due to the modularity of integrated electric architectures

Objective Tree

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The University of Kentucky

Block Diagram

Behavior Models

Design Summary

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Propulsion Subsystem

Materials Subsystem

■ Dual Brushless Motor System ■ Custom Dual Electronic Speed Controller (ESC)

■ Fiberglass Catamaran

■ Arm M4 Processor for sensorless BLDC control

■ Custom Controller

■ Water Cooling System

■ Shafts and Propellers

Communication Subsystem

Power Subsystem

■ Arduino Mega and Uno

■ Turnigy 10000mAh 6S 12C LiPo Batteries

■ nrf24l01 Transceivers

■ 150 Amp Fuse

■ 20x4 LCD

■ Battery Charger

■ Joysticks

■ DC-DC Buck Converter Voltage Step Down

■ Thermistors

■ Wire Harness

Cost Subsystem

Cost

Power Distribution

$712.84

46%

Propulsion

$495.89

32%

Mechanical

$185.96

12%

Communication & Diagnostics

$154.97

10%

Total

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Percent of Total Cost

Acknowledgements This project is funded by ASNE. The team would also like to thank the class advisors for guiding our effort this semester.

$1,549.66

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PEP 2023

University of Michigan Electric Boat Snowfinkle

YDROFOIL DESIGN affords adequate lift for the vessel and minimizes drag. The critical aspects of the hydrofoil selection were external layout, hydrodynamic design, structural design, controls, and manufacturing. A design matrix was employed to compare and evaluate eight different hydrofoil layouts: flat anhedral, “T” anhedral, “L” anhedral, connected Aft Foil Vertical Lift/Drag

anhedral, SP “L” anhedral, SP connected anhedral, dihedral, and T-foil. We considered passive stability, manufacturing complexity, control systems, cost, speed flexibility, drag, and ease of tuning. The flat anhedral and T anhedral shapes scored the highest, leading to the decision to implement fore and aft anhedral surface-piercing foils with a T foil on the fore foils.

Fore Foil Vertical Lift/Drag

FIGURE 1. Full Assembly of hydrofoils and propulsion pods.

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Considerations were given to the hydrofoil shape that can reduce the risks of cavitation and ventilation, and expert guidance from Dr. Julie Young led to the selection of the Eppler 1127 foil shape as the basis for hydrodynamic design.[1] Predictions of lift and drag were made using empirically determined formulas for surface-piercing hydrofoils.[2] Lastly, computational fluid dynamics (CFD) simulations were utilized to validate the calculations. This informed the decisions to make the T foils 0.3m long with an attack angle of 55°. The hydrofoils’ structural design balanced material thickness to support the vessel’s weight and drag from

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the motor pods, while allowing internal channels within the hydrofoil for various cables and cooling lines. Each propulsion pod includes the motor, a custom machined gearbox, a thrust bearing and the driveshaft. There are also temperature and water sensors to ensure the cooling system is functioning properly, and the pod maintains a water tight seal. For propulsion, we selected two DHX Peregrine 60 motors, which have 96V nominal voltage, 35 kW continuous power, 55kW peak power (30 s), and run at 44006500 RPM at max power. They are controlled by InMotion ACS96L motor controllers.

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PEP 2023

University of Pittsburgh Pittsburgh Electric Propulsion Electric Boat Design Design by the 2022–2023 Pittsburgh Electric Propulsion Team Written by Nicholas Genco, Luke Sowalski, Conor Fallon, Hayden Feddock, David Marcano, Kevin Gu

Abstract The University of Pittsburgh’s first electric vehicle student design team, Pittsburgh Electric Propulsion (PEP) completed its second design of an all-electric boat to compete in the American Society of Naval Engineers’ (ASNE) Promoting Electric Propulsion for Small Craft (PEP) competition. The objective for this competition is to design an all-electric marine vessel that can travel 5 miles as fast as possible. PEP is competing in the manned division of the PEPSC competition for their second consecutive year. The PEP team has designed their system based on a Zodiac Milpro Emergency Rescue Boat (ERB) 400 with an HPP model floor. In their second year competing in the PEP competition, the PITT PEP team has completely updated their system redesigning their outboard, battery, and low voltage electronics system. This paper will outline the design of the entire system that is targeted for completion in the 2023 ASNE PEP competition.

Background & Introduction Over the last two decades, the rise of electric propulsion has long been associated with the rise of the electric automobile.

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There has been limited attention drawn to the electrification of marine vehicle. Due to the constant torque required to operate a marine vessel on an internal combustion system in the water, the amount of gasoline required is much greater than that of an automobile. One hour of recreational boating results in the same amount of pollution as driving an automobile 800 miles1. In addition to this, the marine industry contributes to direct gasoline pollution in our waterways due to very frequent oil spilling. The ASNE PEP competition draws attention to this issue and has drawn the attention of hundreds of colleges students to work towards advancing solutions to this issue via the development of all electric systems. The PITT PEP team utilized 21700 NMC battery cells, an ME1616 PMAC motor, and a Kelly 8080 IPS motor controller. In addition, the PEP program is a workforce development program and our PEP team is in its second year of existence and has grown to a total member count of 50 members. In a university that has no naval engineering program, the PEP team is focused on introducing their members to the naval engineering industry while developing their skillset and interest in renewable energy solutions. The team’s outboard is developed from a 1998 30-hp

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Evinrude internal combustion engine (ICE) outboard. The team stripped the ICE and replaced it with its electric powertrain. The cooling system is designed as a dual-loop system. The loop that runs through our motor and motor controller is a closed loop system of water glycol. The closed-loop system is cooled through a liquid-to-liquid heat exchange. The openloop system is the second component in the liquid-to-liquid heat exchange. This cools the water in our closed loop system that feeds into our electronics, while the open loop system is constantly refreshed with water from the body of water the boat is running on. The low voltage system helps to control the operation of our entire system. The team has plans to convert to a dc-dc converter for low voltage power, but for the 2022-2023 season our team had to pivot to a 12-volt (V) 60Amp hour (Ah) LFP battery. This low voltage system controls our pre-charge and discharge system, closing our contactors, powering our cooling system, as well as our motor, controller, and battery management system. The entire low-voltage system was designed by a team of underclassmen students on the PEP team. Finally, the aspect of the PEP team design that we believe differentiates our team from others is the complete design of our entire battery system. Our team uses 21700 NMC battery cells configured in three modules of 9 batteries in series (s) and 25 batteries in parallel (p). These modules

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are connected in series giving us a 27s25p battery pack that is rated for 96 V and 84 Ah nominally. The team designed their own busbars from a specialty material called SigmaClad that was donated by Engineered Materials Solution. These busbars feature team designed cell level fusing. The team intended to spot weld their own battery pack but after having their spot welder break just weeks before the competition, they received help from Phoenix Laser Solutions to help them laser weld their batteries to their bus bar. The team also had to manually wire and configure their battery management system, which was donated by Flux Marine, to their custom pack. Finally, the team designed their own battery enclosure to protect all our electronics from risk of damage from the presence of water in a marine environment. The 2022-2023 PEP team unfortunately had issues with low voltage system on the day of the competition. The team tried to quickly reconcile these issues and were able to temporarily solve their issues, however when placing the boat on the water the team lost a connection and their low voltage system and the teams contactors were prevented from closing, resulting in the team not being able to run on race day in 2022-2023 race. Despite this, PITT PEP is extremely proud of the updates they made to their system that largely focus on improving the overall safety and weight of our system.

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PEP 2023

Wake Forest University

HIS PROJECT AIMS TO PRODUCE innovative and cost-effective electric propulsion solutions that promote the use of electric motors, helping to eliminate the negative impacts of gas engine usage in marine biology research, naval study, and recreational boating. The group first investigated various sources, including potential stakeholders and pre-existing electric boat designs. Access to stakeholders such as the ASNE or recreational boaters can benefit the project by forming a mutually beneficial relationship with experienced individuals that share overlapping interests. Research of pre-existing electric boat designs provided proper benchmarking. The iterative nature of the design process required testing, research, refinement, and feedback from the Lead Technical Coach and Review Panel to create the ideal solution. Through preliminary research, the group established a list of system requirements focusing on competition requirements, electric power, hydrodynamics, water tightness, and budget to ensure the design is competition-ready for the race at the Lafayette River. Extensive research on areas such as planing,

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buoyancy, and cavitation was required to understand how the design interacts with the water and increase efficiency. After identifying crucial system requirements, the focus shifted to ordering necessary parts, testing equipment, and researching relevant areas of study. Through this, the group gained a better understanding of the boat’s components and performance metrics such as speed, weight, and steering. The goal is to minimize weight to maximize top speed, allowing the team to complete the race in the shortest time possible. Integrating the components required research and creative design; for example, ultiple iterations were required for the battery box, motor mount, and electric system schematic to meet system requirements. Several key changes were made to the designs, such as adding cages to the motor mount sides for increased stability and changing the plywood type used for the battery box as hazards were identified regarding laser cutting the originally desired plywood. Moving forward, the team will continue to test and iterate, including water-tightness testing, hydrodynamic efficiency tests of the boat in the water, and contactor testing.

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PEP 2023

Johns Hopkins University Power Train

HE POWERTRAIN subsystem consists of a shaft seal assembly, a propeller shaft, support plates, and a shaft coupling assembly. The shaft seal assembly consists of two shaft seals that prevent water from entering the boat, a thrust bearing to mitigate thrust force from the propeller shaft into the engine, and a shaft seal housing to hold the seals and thrust bearing. A ball bearing is added to give additional support to the shaft. The shaft coupling assembly is comprised of two hubs and a spider to allow for misalignment of the motor shaft and propeller shaft, a small step-down shaft to allow for transfer from a 10-mm to 8-mm shaft diameter, and a clamping shaft coupling to connect the powertrain to the motor shaft. For a preliminary test, we tested the boat’s ability to float while keeping water out, as well as its ability to successfully

operate the power train system. This test allowed us to have a better understanding of the progress of our boat. The first round of testing was conducted at Merritt Point Dock to observe any areas of leakage. Afterwards, the electronics were engaged and operated on dry land. The boat successfully floated in the water, and after over 15 minutes of being in the water, there was minimal leakage. Our final powertrain design was the result of many careful iterations and repeated testing. It consists of a single thrust bearing, as well as a radial bearing inside the shaft seal housing to support axial and radial loads. We switched to a single shaft seal to minimize heating issues. The whole shaft seal housing was machined in the professional JHU machine shop. Inside of the hull, we kept the same rigid shaft coupling, spider connector, and propeller shaft design;

however, we removed the internal bearing from our hull, and added a thrust bearing to the shaft collar holding our propeller shaft in place. Finally, we upgraded to an 82mm propeller. This design largely fixed all of the heating and vibration issues we had from before. When we ran the motor “dry” the point at which the shaft seal interfaces with the propeller shaft was still reaching upwards of 200°F after 5 minutes. However, after we ran the system with running water over it for lubrication and cooling, all our remaining issues with the shaft seal were resolved. We were able to run our system for 22 minutes without issue, and we were only limited by the temperature of the motor, as water cooling had yet to be implemented at that point.

FIGURE 1. Exploded view of final powertrain design. View of the whole assembly shown below, and images of partial components are shown above.

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PEP 2023

University of Wisconsin—Madison Bucky’s Electric Engine—Propulsion Team All Electric Boat | 5 Mile Course | As Fast As Possible

Previous Results 1st: Texas A&M—5 miles in 17:42 1st: Washington College —5 miles in 22:23 2st: Old Dominion University—4.75 miles in 25:29 3st: University of Pittsburgh—3 miles in 37:13

Our Angle ■ Convert, rather than ground up ■ Use conventional technologies ■ Minimize spending

Specifications Goal ■ 15 kW ≈ 20 HP ■ 5 miles @ 15 mph ≈ 20 mins run time ■ 15 kW @ 20 mins ≈ 5 kWh battery pack size

Budget ■ $9,000

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enginemass = 135.4 [kg]

engine assembly mass

engineweight = 1328 [N] {298.5 [lbn]}

engine assembly weight

enginecost = 4110 [$]

engine assembly cost

cbW,dot = 10627 [W-hr]

power stored in the batteries

mW,dot = 17280 [W] {23.17 [hp]}

power consumed by the motor

enginerun,time = 0.615 [hr] {36.9 [min]}

time that the engine can run off of a full charge

enginerun,time,adj = 0.5121 [hr] {30.72 [min]}

"" if operating al rnax power 25% of the time

enginerun,time,min = 0.4092 [hr] {24 55 [min]}

"" if operating al rnax power 50% of the time

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University of Wisconsin—Madison

Bucky’s Electric Engine— Hull Team

UCKEY’S ELECTRIC BOAT teamed up with Buckey’s Electric Motor for the Promoting Electric Propulsion (PEP) competition put on by the American Society of Naval Engineers(ASNE). The competition is a 5 mile race in Virginia, consisting of 1 mile laps. The goal of this competition is to foster development of electric boats in America.

Hull Shape

Savitsky’s Method: Resistance Curves for Hull

Savitsky’s Method: Power Curves for Hull

Test Tank for Hull Resistances

Hull Shape CAD Models

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Subfloor & Center of Mass

Center of Mass for Batteries and Driver

Built subfloor

Subfloor Iteration 1

Subfloor Iteration 2

Battery Charging

Battery Charger

CAD Model Battery Charging Setup

Cell Balancing Setup

Safety and Waterproofing

Plate for Battery Fastening

Weather Foam Gasket Replaced Waterproofing Setup

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Noise Making Device

Waterproofing Test

Extendable Paddle

Personal Floatation Device

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University of Wisconsin—Madison

Cooling Loop

Final Cooling Loop Design

Cooling Loop Analysis: Continuous Conditions

COMSOL Cooling Plate Model

Results

Successful Flotation, Propulsion, and Cooling Test

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Successful Planing Test

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PEP 2023

University of Georgia Project Abstract We are the University of Georgia Promoting Electric Propulsion (PEP) team. The American Society of Naval Engineers (ASNE) hosts an all-electric annual boat race among universities across the country. ASNE is promoting the future of all-electric naval vessels, while also pushing young engineers to get real-world experience. Our goal is to create a boat that is completely powered by electricity to compete in a five-mile race. The race will be held in Elizabeth River in Virginia in late June and the rules are as follows: boats must make it five laps around the one-mile loop course. The race has the following stipulations: Failing to complete the race disqualifies a team. Boats must be powered only by electric batteries, with no external charging (i.e., a generator). However, solar panels are an exception. Boats must also be visually acceptable and appear seaworthy (no “Frankenstein” vessels). To accomplish this task, we have decided to use a 14-foot aluminum jon-boat hull, a 10 kW AC motor, a DC brushless motor, tiller steering outboard, and six 12V 100Ah LiFePO4 batteries. We decided to replace last year’s team’s four lead-acid batteries, because they were extremely heavy and weighing down the boat and replace the motor controller with one with a higher voltage. In doing so, our goal was to increase the voltage supplied to the motor to increase the overall power supplied. By replacing the old batteries with new lithium-ion batteries, we decreased the overall weight of the boat. These two upgrades allow us to

achieve a higher speed. Last year’s team also incorporated our electric throttle into the original tiller steering throttle. We also decided to place the batteries in groups of three at the middle and front of the boat to even out weight distribution.

Our Prototype & The Competition There were several in-the-water testing days throughout fall and spring semester at Lake Herrick. There was plans to move to Lake Chapman and its boat ramp, once 8 – 10+ mph speeds were achieved, for safety reasons. However, we were unable to increase the speed of the boat up to these speeds due to problems with the controller acting strange during the configuration process. The ASNE (American Society of Naval Engineers) are in final discussions with the U.S. Coast Guard about the exact racecourse for June 27, 2023. There will be room at the Fairlead docks to put in craft and work with them in the protected slips. The hotel with the vendors and audience are a half mile down the Elizabeth River. Because this waterway is publicly accessible, we do need to abide by public boating regulations: 1. Register your boat in your state and you can use it in the state of Virginia 2. Ensure your race-day captain has a boating license. Virginia will recognize any other state’s boating license

FIGURE 1. Overview Wiring Block Diagram

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University of Georgia

FIGURE 2. Team member Keith driving the prototype for speed and battery length of life testing at Lake Herrick.

FIGURE 3. Map of Elizabeth River, Portsmouth, Virginia

Existing Parts from Last Year’s Team Some of the existing parts from last year’s team that this year’s team is using are the red and black 2 awg wires, ME115 BLDC motor, Curtis ET-126 electronic throttle, LEV200A5NAA High Voltage DC Series Contactor, and the key/ key switch. Some parts we did not use was the four 12V heavy lead-acid batteries.

FIGURE 4. ME115 BLDC (Brushless DC Permeant Magnet) Motor

FIGURE 5. Curtis ET-126 Electronic Throttle

FIGURE 6. LEV200A5NAA High Voltage DC Series Contactor

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FIGURE 7. 48V motor controller, industrial relay/series contactor, electronic throttle, & key switch

FIGURE 8. Four 12V lead-acid batteries (weigh about 300 lbs. together)

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Prototype Development Process

12.8V LiFEPO4 batteries with a battery on/off switch wired to KLS-H sinusoidal brushless permanent magnet motor controller and BLDC ME115 motor being tested in the fabrication lab.

Some of the datasheets for the existing parts given to us did not have adequate information on the electrical connections, so there was some trial an error testing with wiring up the electronic throttle and motor controller. Most of testing was done in the fabrication lab or at Lake Herrick, but some was done with the help of Dr. Jin Ye and Bowen Yang of the Intelligent Power Electronics and Electric Machine Laboratory at University of Georgia. Thank you to Dr. Jin Ye and Bowen Yang for their assistance with this project.

New Parts The Promoting Electric Propulsion Competition run by the American Society of Naval Engineers has rules and safety guidelines we must follow to compete in the competition. The newest rule this year was that there needed to be a batter/motor kill-switch, so that was a new part we purchased this year. We also purchased six 12.8V LiFePO4 batteries that each weigh 25.35 pounds, and in total weigh 152.1 pounds which is about half the weight of the last year’s team’s lead-acid batteries.

FIGURE 12. Six 12.8V 100 Amp-hour LiFePO4 Redodo Batteries

Potential Project Impact

FIGURE 9. Kelly KLS-H Sinusoidal Brushless Permanent Magnet Motor Controller

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FIGURE 11. Ampper Battery Switch FIGURE 10. Outboard Lanyard Motor Killswitch

The Promoting Electric Propulsion Competition by the American Society of Naval Engineers has its potential impact in the name. The purpose of our project is exploring and encouraging the electrification of marine vessels. This could lead to beneficial environmental impact as emission of greenhouse gases would no longer be an essential part of marine vehicles.

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PEP 2023

North Carolina State University First-Year Lessons

UR GOAL FOR OUR FIRST SEASON in Promoting Electric Propulsion was to create a boat capable of competing in the competition while developing a strong foundation for future teams. To do this, we split the project into separate subsystems for each operation in the boat. The major systems were power, controls, hull, and propulsion. Each system had at least a leader and support assigned to them and together we worked as a team to put our subsystems together to make a functioning RC boat. The power system has two lithium-ion batteries in parallel, operating at 25V. There are three primary cutoff switches: the first switch cuts power to everything on the boat, the second switch cuts power output from the flight controller to the gate of our MOSFETs, and the third switch is a remote cutoff that turns off that same flight controller power output. All three switches must be turned on in order for the motors to receive power. This redundancy allows us to turn on the controller and ensure it is connected while being able to safely put the boat in the water with no risk of the motors turning on. Our controller system is an SIYI MK15 remote control with a Kakute H7V2 flight controller. The remote control was chosen because of its range and compatible camera/receiver setup. The flight controller is compatible with Ardupilot Mission Planner which could make the boat autonomous. Our boat has a fiberglass hull (52” x 15”) and is in the semi-Deep Vee shape. There are two internal Lauan bulkheads that divide the boat interior into three sections. The front section houses the pumps and reservoir for the cooling system; the larger, middle section houses the batteries and electronics (flight controller, batteries/BMS, receiver, etc.); the rear section houses the ESCs, motors, servos (controlling rudders). The top side of the cover is Lauan triple

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coated with marine grade epoxy while the bottom side is only single coated. The top cover is secured to the hull via fiberglass cloth tape soaked in marine grade epoxy. There are three waterproof inspection hatches (one for each section) for accessing internal components. Lastly, we have a custom built, carbon fiber antenna mast for mounting the antennas. Throughout our year-long project, we were only able to test on the water towards the end a week before the race. Despite our many attempts to get the boat out in the water earlier, we kept facing breakages in the power system, the waterproofing, as well as unfortunate weather conditions. We were able to test, run, and troubleshoot all of the systems alone and together throughout the year, although not in the water. From an electrical standpoint, we expected the boat to go all five miles. We had never experienced any issues with the current batteries and had built the batteries to supply full power to the system for the 30-minute runtime. From our propulsion calculations, we expected to be able to finish the race in around 20 minutes. Also from our testing, we expected all the electronics to be safe from water because we had tested the waterproofing extensively. Given there were no mechanicals failures, we were hopeful for race day. During our testing we did encounter

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issues with some screws being loose, primarily the ones attaching the propellers to the drive shaft and holding the rudders in place. We tightened these up, but unfortunately during the race some of the screws inside the boat, attaching the motor to the drive shaft came loose. This ultimately resulted in the motor disconnecting, and we could only spin the right motor around half a mile. Although not the result we were hoping for, we were able to keep all parts and electronics intact, there was no water damage to any of the components, and we can pass everything on to the next team.

 Dr. Steve Russell, funder

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PEP 2023

University of Wasington—Bothell HUSK-E Boat Saturn SD260

Goal & Scope To develop a fully electric boat for participation in a 5-mile race within the PEP competition, which aims to promote electric propulsion. A budget of $5,478.59 was allocated by the American Society of Naval Engineers (ASNE) to successfully complete this project.

• Lightweight (Inflatable) • V-Hull • 8.6’ long, 5’4 wide $933.65

Design Methodology

Hangkai Electric Motor

The V-shaped hull reduces drag by slicing through the water and minimizes wave impact, requiring less energy to move forward. With the electric outboard motor market still in its early stages, our choices were limited to expensive options like Torqeedo and E-Propulsion (10hp), both priced over $4k. To fit our budget, we opted for the more affordable Hankgai 8hp motor. Initially, we had four 12V lead batteries weighing ~50 pounds each. However, we were gifted two 25V Li-ion batteries weighing ~ 0 pounds, providing more power and decreasing the weight of our boat.

• Voltage 48V • 2.2KW Engine • 8Hp $544.98 JH3 64Ah battery (x2) • Lithium Ion Battery (25.55V, 63Ah) • LWH: 16.75', 5.0', 4.125' • Weight: 19.6 Pounds • Terminal Size: M5 • High Density Waterproof Battery Container • Name: Husky 12-gal Container • LWH: 26.9", 18.1", 10" • Material: Plastic • Rating: IP67 $31.98 Total Cost

$1,510.61

Testing & Design Dry Test: Check motor and batteries functionality, the motor should display 48V+, and propeller should spin when turned on.

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Wet Test: Test boat's performance on a 5-mile course simulating PEP Competition, with 2 checkpoints 0.5 miles apart. Each round trip counts as 1 mile, to be repeated 5 times.

Location: Lake Sammamish. Utilize the Savvy Navvy app for distance recording and time trials. ASNE 2022 top 3 times are 22:23 minutes (5 miles) – 37:13 minutes (3 miles).

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8Hp Hangkai Electric Motor

Saturn SD260

JH3 64AH battery (x2)

Waterproof Battery Container

Data & Results The HUSK-E boat completed 1 mile in 11:12 minutes. We anticipate finishing 3 miles in 33:36 minutes (approx.), potentially placing among the top 3 times.

0.5 miles

Challenges Waterproof container: Eliminating the probability of water penetrating through the container by using ½ in. Nylon Cable glands for the negative and positive terminal connections. Lithium-ion battery terminal connections: Assuring the terminals are properly connected, by selecting 4 copper lugs SC25-6 AWG 4-¼ 5PCS terminal connectors along with two M5 threaded stud bolts for each terminal and 50 cm long 60Ah long electrical cable.

Conclusion

Text

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Our capstone project successfully implemented an electric motor in an inflatable boat powered by two lithium-ion batteries, overcoming challenges such as budget constraints and utilizing a multi-disciplinary approach involving both mechanical and electrical engineers; the practical testing, weight distribution optimization, and speed improvements showcased not only our individual technical proficiencies but also our collective strength as a cohesive and effective team.

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PEP 2023

Florida Institute of Technology Panther Electric Boat Our Mission We want to promote the study and application of electric and autonomous designs by students so that society can move towards a more sustainable and efficient future, while building a professional development pipeline for intrepid engineers.

Who We Are We are a group of undergraduate and graduate students in the Ocean Engineering field who want to apply the concepts we learn in the classroom to a real-world project.

What We Are Doing We are designing an unmanned, autonomous electric catamaran as a technology demonstrator for transporting underprivileged school children in Guatemala. Our finished boat will compete in a collegiate race sponsored by ASNE in June.

Our Goals We want to develop a 6-foot electric catamaran that can navigate riverine and in-land waterways efficiently and autonomously. This will involve remote control and monitoring as well basic GPS navigation and extensive field testing.

How Can You Help Building a boat (mostly) from scratch and traveling to the competition will not be cheap. Therefore, we will need sponsorship from external partners to help us reach our goals! Any amount will help, and it is even tax-deductible!

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TECHNICAL ARTICLE

Underwater Long-Range Photonic System for Acoustic Detection of Animal Species 1, Dominic 1, Holahan,1,Thomas Satterfield, Allizzo, Jazmine Branford, John Suarez Martin Holahan ThomasPlunkett, Plunkett1Harlem , Harlem SatterfieldDominic Allizzo 1 1 Jazmine Branford , John Suarez

Abstract

I. Introduction

In this paper, we describe the design and experimental test results of an underwater acoustic-noise detection system, implemented using an analog radio-over-fiber optical link. We describe the system’s ability to detect the presence of noise signals within the frequency range of various underwater animal species. Various electronic techniques were used to simulate the desired noise signals; in each case, the system clearly provided an indication of the presence of each disturbance. The system is useful for detecting acoustic noise at a particular point of measurement—called a node— when the node is located far from the processing site.

It is often of interest to identify underwater species by the characteristic sounds they emit while in their natural habitat. For instance, in the course of natural communication, haddock emit sounds of mean frequency 258 Hz, Atlantic cod emit sounds of mean frequency 53 Hz, dolphins emit sounds in the range 200 Hz – 24 kHz, and sea lions emit sounds in the range of 100 Hz – 10 kHz.[5] The literature provides many descriptions of systems for monitoring underwater animal species, with wide variation in their complexity, capability, and sophistication.[2]–[5] In this paper, we present a simple system which may be used for locating and identifying underwater animal species based on the frequencies emitted during normal communication or living conditions. A wide-area detection network may be used for this purpose, where the term “wide area” refers to a rectangle whose dimensions exceed hundreds of meters. We envision the architecture shown in Figure 1, which consists of four acoustic-detection nodes, spread across 100 m2, and a central data-collection unit at the center of the square. A greater or lesser number of nodes may be used, based on necessity. For the basis of this paper a single node was designed to provide evidence that a multi-node network connected by fiber optics would be a viable method of detecting various noise sources. There are at least three challenges to be addressed in the development of such a network. First, it is helpful to identify the type of noise mechanism to be detected. This serves to bound the problem to a particular set of detection methods and narrows the design to a specific set of sensor technologies. Second, the long-distance transmission of data among the various nodes and the central data collector—that is, the physical layer of transmission—must be addressed. Third, the cooperative handling of data among the various sensors—that is, the data communication protocol—can greatly affect the complexity of the system and requires considerable forethought. In this paper, we describe the engineering design of such a network, and discuss its use in detecting simulated noise

1 Widener University

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Underwater Long-Range Photonic System for Acoustic Detection of Animal Species

sources. In Section II, we describe the basic principles of square-wave noise generation using relatively simple electronic circuitry. We have designed circuitry to detect this noise in an underwater environment; this is the subject of Section III. In Section IV, we discuss the transmission of data from individual sensors using fiber optics, requiring the use of electro-optic modulation and photodetection; and in Section V we present the results of our experimental testing. Finally, in Section VI we discuss extrapolation from the single node—whose performance we have experimentally verified—to a larger, more complete photonic network.

II. Generation of Noise Sources Using Square-Wave Signals

In our experimental testing, the goal was to electronically simulate various forms of acoustic noise. Square-wave generation proved useful for this task because it allows controlled amounts of noise to be added easily and economically. As shown in Figure 2, the width of a square waveform—that is, its duty cycle—is increased if the user desires a greater frequency of noise; and the width of the square waveform is decreased if the user desires a lower frequency of noise. When considering the noise output applied to a load resistor, the output voltage across the load resistor will be proportional to the duty cycle of the square waveform. The generation of square waves is a noisy process. That is, the generated waveform contains harmonic-frequency components at odd-integer multiples of the square waveform’s fundamental frequency. (This canbe shown using Fourier analysis.) The harmonic components are the noise. This situation is represented in Figure 3. Figure 3 illustrates the simplified case of FIGURE 1. Left: Diagram of network architecture. Right: Artist’s rendition.[1] an unmodulated square wave. Notice that the fundamental and harmonic frequencies are at fixed locations on the frequency axis. But, when the square wave is modulated, the frequencies of the fundamental and harmonic components change with time. Due to nonidealities, the amplitudes may change also. Nonidealities can occur due to attenuation through the physical properties of the copper wire used to transFIGURE 2. Basic illustration of a modulated square-wave signal. mit the waveform. Nonidealities can also occur due to the amplification process by the transistor pair. The circuit we have designed for generating controlled square waveforms in the laboratory is shown in Figure 4. It is driven by a function generator providing a 5-Vpp square wave at 1 kHz. At the center of the waveform generator is a voltage comparator. The particular comparator in Figure 4 is the LM311 integrated-circuit comparator, which compares the values of its two inputs. If the input at the noninverting FIGURE 3. Harmonic distortion associated with a nonlinear system. (+) terminal is greater than the input

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at the inverting (–) terminal, then the comparator’s output will be driven to a logical HIGH. If the opposite is true, then the comparator’s output will be driven to a logical LOW. In the case of Figure 4, the logical HIGH is +5 V, and the logical LOW is 0 V. But here, we have described the basic operation of the comparator as a mathematical comparator of constant, DC voltage values. Some additions are required in order to use the comparator for square-wave generation.

For square-wave generation, the comparator is now provided with two inputs: (1) a variable DC voltage and (2) a sine wave. In Figure 4, the variable DC voltage ranges from –5 V to +5 V. The means for varying this voltage is a potentiometer—a variable voltage divider connected between the –5-V and +5-V sources. This is connected to the comparator’s noninverting (+) input. A function generator provides the sine wave input; this is connected to the comparator’s inverting (–) input. When the comparator is provided with these inputs, as shown in Figure 4, the resulting output will be a square wave. The duty cycle of this square wave will vary according to the DC voltage provided at the noninverting (+) input. This, of course, is the desired goal—a square wave with a variable duty cycle is precisely the desired noise signal.

III. Design of Audio Circuitry

FIGURE 4. Circuit schematic of the square-wave generator.

To detect acoustic noise, the system employs an analog microphone circuit. The circuit uses a Texas Instruments LM358P operational amplifier as a transimpedance amplifier and takes the current output of the Knowles MB6022APC-0, an electret microphone, and amplifies it to a usable voltage to modulate over fiber-optic cable. A schematic of the design can be seen in Figure 5 below. The

FIGURE 5. Schematic of audio circuit.

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IV. Electro-Optic Modulator and Photodetector The overall design and operation of the noise detection system is illustrated in Figure 7. This system, in effect, functions as a single node of the network shown in Figure 1. The system receives the audio input (i.e., the noise)—presumably from an animal species—amplifies it appropriately, and converts it to an optical signal via a Mach-Zehnder (MZ) electro-optic modulator. The optical signal then propagates over a single-mode optical FIGURE 6. PCB Design of audio circuitry. Left: 3D render. Right: Top and fiber and is then received by a photodebottom layers. tector. This photodetector then converts the optical signal back to a voltage signal to be analyzed. Note that only the audio circuitry and MZ modulator reside in the enclosure and comprise the node. The laser and photodetector are located in the central data-collection unit. The system uses the MZ modulator to transmit the initial voltage signals of the audio circuit over a photonic link. The modulator is essentially an asymmetric Mach-Zehnder optical interferometer, with a lithium niobate (LiNbO3) crystal embedded into one of its arms. FIGURE 7. Diagram of overall system, illustrating a single node of the network The modulator accepts a single optical shown in Figure 1. input and splits it across two internal waveguides. The voltage from the audio circuit is applied to the microphone operates at a range of 100 – 10,000 Hz and has a modulator’s voltage input, and this voltage creates an electric standard sensitivity rating of –40 dB, where 0 dB = 1V/Pa. For field which modulates the optical index of refraction of the our application, the electret microphone was attached to the lid of a waterproof enclosure, which required external terminal LiNbO3 crystal. The result is a modulated optical signal that is blocks for connection. The external terminal blocks permit dif- controlled by the variable electric field created by the acoustiferent microphones to be tested as necessary. The microphone cally influenced voltage signal. The modulator accepts voltage circuitry is powered by a 12-V A23 battery; this is then resignals and converts them into optical signals. The modulator ceived by a Texas Instruments voltage regulator which supplies can be externally biased by an externally controlled voltage the circuit a stable 5 volts. As stated above, the LM358P acts as signal. This biasing allows the attenuation of the input signal a transimpedance amplifier and takes the current signal from to be a constant value. For applications where only threshold the microphone and converts it into a suitable voltage signal detection is needed, such as digital applications, the variable for optical modulation through the fiber-optic cable. attenuation or noise of the modulator can be ignored; and The printed circuit board (PCB) layout of this circuit is biasing at the quadrature point is not needed. For traditional shown in Figure 6. The PCB has been designed to be mountanalog designs, the quadrature-point biasing is used to provide ed inside the waterproof enclosure with 1/8-inch screws and the most linear signal possible. For the purpose of our design— terminal blocks to ensure that both the board and external with the possibility of multiple nodes in mind—we designed connections are secured while being easily serviceable. the base system to function with an unbiased modulator.

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V. Experimental Results

We have presented the design and performance of an analog photonic system for the detection of various noise sources.

We showed that relatively simple analog audio electronics, coupled with a photonic link, can detect a wideband or 1-kHz noise signal; and this detected noise signal could be transmitted—by way of optical fiber—across a long distance to a central data-collection station. Our experiments compared acoustic profiles of wideband noise source and a 1-kHz noise source. Should such a system be deployed in the form of the network shown in Figure 1, our experimental results would pertain to a single node of such a network. The question may arise as to the implementation of several nodes in such a network. Considering the network shown in Figure 1, how would a system monitor know that a noise-emitting animal is over the upper-left node as opposed to the lower-left node, for example? Certainly our results have shown that the presence of an underwater animal species can be determined, based on emitted noise. But how would its location be determined? Which of the four nodes was “triggered”? We will use wavelength-division multiplexing (WDM) to address this question. Referring to Figure 7, note that a single laser is used as the carrier wave, which is modulated by the output of the audio electronics. That is, the noise profile of the communicating animal above the specific node is optically transmitted through the optical fiber. In the network shown in Figure 1, there are four nodes—each of which resembles Figure 7. Suppose that the central data-collection unit contains four laser sources of wavelengths 1520 nm, 1530 nm, 1550 nm, and 1570 nm; and four photodetectors that can accommodate these wavelengths. Using the appropriate optical filtering, the response from one particular node of the network can be isolated and identified. This will be the subject of future experimentation, in which the system illustrated in Figure 7 will be replicated, and a WDM-enabled architecture in the form of Figure 1 will be designed and characterized.

FIGURE 8. Voltage output of the audio circuit, due to the wideband noise signal.

FIGURE 9. Voltage output of the audio circuit, due to the square-wave noise signal.

Experimental testing involved a comparison of the analog acoustic information from (1) a wideband noise signal and (2) a 1-kHz square-wave noise signal. This acoustic information was transmitted over a photonic link and then reconverted to a voltage signal, eventually received by a Tektronix oscilloscope. The voltage output, due to the wideband noise signal, can be seen in Figure 8. The result shows that there was a constant noise detected with an amplitude of 0.2 V, and occasional peaks reaching 0.4 V. Compared to the output due to the 1-kHz square-wave noise signal, shown in Figure 10, it is apparent that the square-wave noise is considerably more noticeable than the wideband noise. The square-wave signal has more frequent peaks of 0.4 V and –0.3 V. After the initial tests of wideband and 1-kHz noise were performed, the output signal of the audio circuit signal was transmitted over single-mode optical fiber, as shown in Figure 7. The resulting signal can be seen in Figures 10 and 11, below. The output due to the wideband noise source (seen in Figure 10) in comparison to that shown in Figure 11 shows that the system properties are consistent even without a bias applied to the MZ modulator. The output due to wideband noise has a consistent amplitude of ~ 0.45 V, while the detected 1-kHz output is still noisier with an amplitude consistently above 0.46 V—with multiple peaks. These results show that there is attenuation of the fiber optic system seen in Figure 7, but the system still is able to differentiate between wideband and 1-kHz noise sources.

VI. Conclusions and Future Work

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FIGURE 10. Optically transmitted output of the audio circuit, due to the wideband noise source.

FIGURE 11. Optically-transmitted output of the audio circuit, due to the square-wave noise signal.

REFERENCES [1]

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Cox, W.; Muth, J. Simulating channel losses in an underwater optical communication system. J. Opt. Soc. Am. 2014, 31, 920–934.

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Mobley, C.D. Light and Water: Radiative Transfer in Natural Waters. Academic Press: Cambridge, MA, USA, 1994.

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Ju, M.; Huang, P.; Li, Y.; Shi, H. Effect of Receiver’s Tilted Angle on the Capacity for Underwater Wireless Optical Communication. Electronics 2020, 9, 2072.

[10] Nababan, B.; Louhenapessy, V.S.; Arhatin, R.E. Downwelling Diffuse attenuation coefficients from in situ measurements of different water types. Int. J. Remote Sens. Earth Sci. 2013, 10, 122–132. [11] Sun, X.; Kang, C.H.; Kong, M.; Alkhazragi, O.; Guo, Y.; Ouhssain, M.; Ooi, B.S. A review on practical considerations and solutions in underwater wireless optical communication. J. Light. Technol. 2020, 38, 421–431. [12] Zhang, M.; Zhang, Y.; Yuan, X.; Zhang, J. Mathematic models for a ray tracing method and its applications in wireless optical communications. Opt. Express 2010, 18, 18431–18437. [13] Li, C.; Park, K.H.; Alouini, M.S. On the use of a direct radiative transfer equation solver for path loss calculation in underwater optical wireless channels. IEEE Wireless. Commun. Lett. 2015, 4, 561–564. [14] Zeng, Z.; Fu, S.; Zhang, H.; Dong, Y.; Cheng, J. A survey of underwater optical wireless communications. IEEE Commun. Surv.Tutor. 2016, 19, 204–238.

[15] Rice J, Creber B, Fletcher C, et al. Evolution of Seaweb Underwater Acoustic Networking[C]//OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No.00CH37158). Piscataway: IEEE, 2002. [16] Yang, P.; Xiao, Y.; Xiao, M.; Li, S. 6G wireless communications: Vision and potential techniques. IEEE Netw. 2019, 33, 70–75. [17] Jaruwatanadilok, S. Underwater wireless optical communication channel modeling and performance evaluation using vector radiative transfer theory. IEEE J. Sel. Areas Commun. 2008, 26, 1620–1627. [18] Hanson, F.; Radic, S. High bandwidth underwater optical communication. Appl. Opt. 2008, 47, 277–283. [19] Cochenour, B.; Mullen, L.; Muth, J. Temporal response of the underwater optical channel for high‐bandwidth wireless laser communications. IEEE J. Ocean. Eng. 2013, 38, 730–742. [20] Gabriel, C.; Khalighi, M.A.; Bourennane, S.; Léon, P.; Rigaud, V. Monte‐Carlo‐based channel characterization for underwater optical communication systems. J. Opt. Commun. Netw. 2013, 5, 1–12. [21] Yang, Y.; He, F.; Guo, Q.; Wang, M.; Zhang, J.; Duan, Z. Analysis of underwater wireless optical communication system performance. Appl. Opt. 2019, 58, 9808–9814.

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TECHNICAL PAPER

Capability Modeling for Assessing Mission Effectiveness Eff ectiveness in Surface Ship Concept and Requirements Exploration 1, Mustafa 1, Alan Berrow,1,Mark Shane, Y. Kara, Alan J. Brown David J. Berrow MarkA.A.Parsons, Parsons1Alan , Alan ShaneMustafa Y. Kara J. Brown1

Abstract

Introduction

This paper describes the development and implementation of mission capability models necessary in the development of a system design framework for Mission, Power and Energy Systems (MPES) in Surface Ship Concept and Requirements Exploration. This system framework includes architectures for ship operations, physical arrangements and for the logical relationship of MPES vital components including simple energy and data models of their function. The mission capability models provide a critical interface between logical and operational architectures, quantifying warfighting capabilities through system measures of performance at specific capability nodes. The operational architecture queries the capability models representing the behavior at these nodes for necessary operational capabilities and implements them in the warfighting environment. The capability model also provides essential mission and vital component priorities used to guide the alignment of system vital components over time using a dynamic architecture flow optimization (DAFO).

Mission, Power and Energy Systems (MPES) are fundamental to the design, mission, and operation of surface ships. MPES design and architecture are critical in determining all aspects of a surface ship’s effectiveness, survivability, and cost. MPES systems are distributed systems, most simply defined as mechanical, electrical and electronic Vital Components (VCs) distributed throughout a ship that are connected to work together. This includes mission systems. These systems, particularly the power and energy systems, are traditionally represented in “one-line diagrams” and “equipment lists”. Over time, distributed systems have become increasingly interconnected and interdependent, particularly in modern surface ships. This complexity makes them more vulnerable to cascading failure and to behavior that may become evident only when the system is in operation if not properly discovered and considered early (Brown, 2020). Parsons et al. (2020) describe a surface ship concept & requirements exploration process developed over two decades at Virginia Tech and MIT (Brown & Thomas, 1998, Brown & Salcedo, 2003, Stepanchick & Brown, 2007, Strock & Brown, 2008, Kerns, et al., 2011a, Kerns, et al., 2011b, Brown & Sajdak, 2015, and Parsons, et al., 2022). Goodfriend & Brown (2018) describe the application of this process to early concept exploration considering vulnerability. Typically, survivability analysis for surface ships is deferred until preliminary design or later, but many important design decisions regarding systems and system architecture are made in concept exploration and are difficult and costly to reverse. Survivability is an important factor in assessing mission effectiveness which is an important objective attribute in concept exploration. Survivability must be considered in these early decisions and in concept & requirements exploration. Goodfriend & Brown (2018); Snyder (2019); Snyder et al. (2019); and Habben Jansen, Kana, & Hopman (2019) use a

1 Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Tech

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FIGURE 1. Architectural Framework for Distributed Ship Systems (Brefort et al., 2018)

traditional system vital-component deactivation diagram approach (or a Markov chain variation) to assess system vulnerability. In these methods, deactivation diagrams are used both to define the system architecture and assess its vulnerability to vital component loss. However, these methods only consider system connectivity; they do not consider power, or commodity flow capacity. Recoverability is also not considered. Stevens et al (2017) consider power, but only electrical power. The total ship system is not modeled.

Working in a Naval International Cooperative Opportunities in Science and Technology Program, the authors began exploring the use of networks and a simple network architecture framework to provide an alternative approach to system architecture and different paradigms for preliminary arrangements, system topology, and for considering survivability in early design decisions. Figure 1 shows how this Architecture Framework decomposes the total system into three primary views: physical, logical, and operational, representing the spatial, connectivity/functional, and temporal relationships of a distributed system, respectively (Brefort et al., 2018). Figure 2 shows examples of these architectures and their relationships. These architectures and their application in concept & requirements exploration using an architecture flow optimization (AFO), particularly working with the logical architecture, are described in Brown (2020), Parsons et al. (2022, 2020) and Shane et al. (2023). The interface between the logical architecture and the operational architecture occurs at capability nodes in the logical architecture and is quantified using Ship Behavior and Interaction Models (SBIMs). The capability nodes also reside in the “Functional Utilization” intersection shown in Figure 1. The operational architecture is illustrated in the lower right corner of Figure 2 and its development, application and associated SBIMs are summarized in the next section of this paper before discussing the capability nodes. This is the second of three papers written simultaneously describing the Ship Behaviors and Interactions Models (SBIMs). The first paper describes the warfighting and blue

FIGURE 2. Ship System Architectural Framework (Shane et al., 2023)

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ship operational models (Shane, et al. 2023). This paper, the second paper in this series, describes the ship capabilities model and presents descriptions of mission system vital components. The third paper describes the ship Dynamics Architecture Flow Optimization (DAFO) model and present descriptions of power system vital components supporting mission system vital components (Parsons, et al. 2023). Each paper in this series presents the methodology and results relevant the respective models described. The OPSITs presented in this paper are referenced from Shane, et al. (2023). The simplified operational architecture, warfighting model, and mission system vital component models presented in this paper are for academic purposes only, adapted from content provided by Friedman (2006), Payne (2010), Hughes & Girrier (2018), and Appleget, et al. (2020) to a very basic level of detail sufficient for fundamental research and the development of new methods, processes, and system framework. An extensive review of the agent-based modeling literature was not performed. This paper uses a methodology providing the sufficient information for this framework.

Ship Operational Architecture, Behavior and Interaction Models Traditionally, early concept & requirements exploration evaluation is performed based on relative effectiveness by comparing ship designs to each other, rather than modeling the ship design in an operational scenario to determine its effectiveness. Shane, et al. (2023) describe an improved assessment process that explicitly considers the ship’s effectiveness during execution of a Design Reference Mission (DRM). This process allows for a ship’s overall measure of effectiveness for a specific DRM to be used to compare designs.

Lexicon and Identification

The following sub-sections detail the language and conventions used to describe the ship operational architecture, operational model, warfighting model, and warfighting environment used in this paper. Identification/Naming ■ Library: Generic source information. Agents and their associated characteristics are described in the Library. ■ Agent: An intelligent operating vehicle, aircraft, missile, ship, boat, land base, etc. in the warfighting model. ■ Blue Ship: “own” ship system of systems represented in the logical and physical architectures. The blue ship is also an agent, in the context of the warfighting environment.

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■ Object: an individual green, red, or gray agent viewed from

the perspective of the blue ship. ■ Threat: an individual red object (or green/gray object prior

to identification) that is being analyzed or acted upon. ■ Team Colors: ● Green: applied to allied objects ● Red: applied to enemy objects ● Gray: applied to neutral objects Mission-Means Framework The definitions for terms in the mission means framework are adapted from the DOD Dictionary of Military and Associated Terms (2020). ■ Mission: a task and an operational objective assigned to a system or system of systems. ■ Task: a clearly defined action or activity performed by a system which requires system capabilities, resources and/or information. Tasks may be decomposed into subtasks. ■ Capability: a state of system function having sufficient system resources and/or information to perform a task. ■ System: a functional (logical architecture), physical (physical architecture), and/or behavioral (operational architecture) group of related interacting/interdependent components. Systems may be decomposed into subsystems. Models The models used in implementing the Operational Architecture are as follows: ■ Warfighting Model: an agent-based behavior model for determining how agents move and interact with each other (events) in the warfighting environment. ■ Ship Operational Model: the behavior model of a single ship agent implemented in a discrete event simulation and able to apply requested ship capabilities to operational tasks. ■ Ship Capability Model: the behavior model of the capability nodes in the logical architecture able to assess the application of available ship systems to provide requested operational capabilities. ■ Ship Dynamic Architecture Flow Optimization (DAFO) Model: the behavior model of the ship’s distributed systems able to align and apply available system energy and data components to provide requested capabilities and maximize effectiveness. This paper focuses on the capability model. Shane, et al. (2023) describe the warfighting model, the ship operational model, and library of allied, neutral, and enemy objects. Parsons, et al. (2023) describe the DAFO model.

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FIGURE 3. Modified Mission-Means Framework (Purdy, 2004)

Warfighting Environment Areas ■ Wargame Area (Total Warfighting Environment): Covers all areas, including all local warfighting environments. ■ Operational Area (Local Warfighting Environment): Area in which operations take place. ■ Area of Responsibility Radius / AoR: The observable radius around and relative to the blue ship.

Mission-Means Framework

Figure 3 shows the Purdy (2004) Mission-Means Framework, which is a modified systems-engineering V-model, that illustrates the relationships between necessary elements in a system’s operational architecture. A mission is comprised of and accomplished by tasks, the clearly defined actions or activities of a system which require system capabilities, resources and/or information to be performed. Each mission is assigned a measure of effectiveness, a quantitative or qualitative metric for how well a mission is accomplished by its completed tasks. Each system capability is assigned measures of performance, quantitative or qualitative metrics describing how well a task is performed by the capability that enables it. Both measures of performance and measures of effectiveness can change with respect to time, system configuration, and availably of resources/information. The necessary and accomplished time-dependent relationships between one system state and the next comprise the operational architecture. This state-based approach to modeling an operational architecture is similar to Stevens et al (2017).

Ship Behavior and Interaction Models

The Ship Behavior and Interaction Models (SBIMs) implement the Mission-Means Framework to accomplish mission(s) and perform tasks specified by the DRM as system responses in a discrete event simulation. The SBIMs and their relationship are represented in Figure 4. The SBIMs model ship behavior throughout an operational situation (OPSIT) which is completed over a series of

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time-steps. A uniform time-step of one second is currently used in the SBIMs. This is consistent with the time scale required for the measurement of system energy usage, heat dissipation, and energy storage charging/discharging. It is not intended to consider transient stability and other shorter time scale phenomena as in the electrical subsystem. A typical detect to engage sequence occurs in less than a minute. A one-second time interval allows for basic operational modelling and operations that occur in less than a second are modelled for the entire time-step (one second) even if they occur faster. Figure 4 is an application and refinement of Figure 3 where the (blue) ship agent operational model requests capabilities from the capability model. Following this query, the capability model assesses the ability of the ship to provide the capabilities if supported by the MPES. The operational model is the blue ship mission/task behavior model implementing the blue ship’s Operational Architecture. It serves as the interface between the warfighting model and the blue ship capability model. It interacts with the capability model through capability requests and responses, as shown in Figure 4. As an example, when the warfighting model requires the blue ship to perform an Anti-Air Warfare (AAW) task, the operational model determines necessary capabilities and sends capability request(s) from the operational model to AAW system’s (or plex’s) capability node(s), where the capabilities are modeled and managed. Capability nodes are indicated by the blue circle in the logical architecture in Figure 5. The outcome of the execution of a task defined in the operational model is determined by whether or not the ship possesses the capability to perform the task. If the necessary capability is available, the task can be successfully performed. This is discussed in more detail later in the paper. The capability model interfaces with the operational model and the DAFO model to answer queries at each time step. The specific query being requested is specified by the operational model based on the operational mode of the blue ship which determines decision capability logic and mission doctrine. The operational mode of an agent is selected during setup based on the OPSIT. The available operational modes of an agent are: loitering, self-defense, offense, and attack. Both the offense and attack operational modes allow an agent to engage other agents. The attack mode is reserved for agents that require an intercept course to engage another agent such as an Anti-Ship Missile (ASM). A system response objective function in the DAFO model is updated at each time step based on priorities specified by the operational model and consistent with the operational architecture. This enables blue ship system alignment by the DAFO.

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FIGURE 4. Ship Behavior and Interaction Models (SBIMs) (Shane, et al. 2023)

FIGURE 5. Anti-Air Warfare (AAW) Mission Plex Vital Components and Circled Capability Nodes with Data Commodity Flows (adapted from Friedman, 2006, p 105 and Chalfant et. al, 2017)

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FIGURE 6: Effectiveness Hierarchy (Shane, et al. 2023)

The DAFO model uses both an energy formulation and a mission system data formulation to support and interface with the capability model and operational model as shown in Figure 4. The DAFO model determines the optimum energy flow and mission system data flow (0, 1) through the logical architecture to support the capability pull, translated by the capability model, from the operational architecture at each time step. The data flow is a DAFO commodity in addition to the energy flow used in earlier AFO formulations to model a functional mission system, as shown in Figure 5. Each of the AAW VCs in Figure 5 requires electric power and cooling which must be provided in the energy solution. The power and energy formulation and the basic AFO is described by Parsons et al. (2022), Brown (2020) and Parsons et al. (2020). The DAFO model determines the mission system usage, data and energy flow using the prioritized capability pulls on the system to determine which VCsare available and active during the discrete event. At each time step, the mission effectiveness for the blue ship system is maximized in the DAFO model using the hierarchy shown in Figure 6, with priorities provided by the operational

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model and capability model, passed through the capability model. Objects approaching the blue ship area of responsibility are assigned a priority between 0 and 1 by the operational model based on: ■ Time to impact the blue ship (or not) ■ Potential damage to the ship (i.e. warhead kg TNT) Objects reaching an engage task capability request are prioritized above all other objects. Object priorities are normalized to sum to 1 for all detectable objects in the warfighting environment. Warfighting tasks in the operational architecture and their corresponding capability requests are structured according to the steps in the kill chain (adapted from Payne, 2010; Office of the Chief of Naval Operations, 2007; and Office of the Chief of Naval Operations, 2014): 1. Detect: Detect 2. Track: Track 3. Identify: Track and Identify 4. Decide: Track and Decide 5. Engage: Track and Engage

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Capabilities are prioritized and sum to 1 for each object. This basic series of tasks is also used as a simple and generic warfighting operational architecture as discussed and illustrated in Figure 7. The temporal implementation of these generic tasks is more completely specified and described by the warfighting model and operational model consistent with the operational architecture. System VCs provide capabilities, but more than one VC may be able to provide a single capability. To determine which VCs are best suited to provide each capability, capability measures of performance are calculated for each applicable VC. Based on these measures of performance, VCs are prioritized between 0 and 1, and normalized to sum to 1 for each capability. For example, the engage capability may be requested for an object, and may be provided by VC 1 or VC 2. For engage, the measure of performance used is the Probability of Kill (PKill), so the PKill against the threat is calculated for VC 1 and VC 2. In this example, if PKill for VC 1 is assessed as being two times greater than for VC 2, then VC 1 would receive a priority of 0.67 and VC 2 would receive a priority of 0.33. The priorities associated with each VC, each capability, and each object (see Figure 6) are used in the DAFO objective function. The DAFO model maximizes effectiveness by selecting VCs to provide the optimum combination of successful capabilities as responses against the objects, as described in Parsons, et. al (2023).

Capability Modelling

FIGURE 7. Kill Chain Operational Architecture (note: AoR is Area of Responsibility) (Shane, et al. 2023)

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Capability modelling is the process of quantifying warfighting capabilities through system measures of performance. Capability nodes provide the interface between the blue ship operational model and blue ship DAFO. The determination of whether a capability can be provided, along with how it is provided, requires a query of the blue ship DAFO, which maintains and modifies the status of the ship’s systems and VCs. At each time step, the operational model determines if a task or tasks must be performed following the operational architecture sequence. If capabilities are required to perform the tasks, the operational model queries the capability model to determine if the capabilities can be provided, which triggers the DAFO to determine which capabilities can be supported by the MPES. The DAFO manages the power and energy status of all ship systems and components at each time step. It also assesses combat system component connectivity between the Command_SYS node and capability nodes within each warfighting plex. Intact connectivity with intact nodes supported by necessary power and cooling are assumed to provide a complete functional system necessary to support

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FIGURE 8. Ship Behaviors and Interaction Models (SBIMs) Execution Process (Shane, et al. 2023)

a specific capability. It is also assumed that all components capable of providing the detection capability are initially operating at their standby power level. Once a capability has been requested and a specific VC has been selected to provide that capability, the selected VC operates at full power (i.e. mission power). The a DAFO time-based AFO method is used to determine feasible and optimal energy and data flows in the total ship system at each time step necessary to optimize the ship response to capabilities requested by the operational architecture through the capability nodes. The DAFO depends on the operational architecture to provide a “pull” on capabilities from the logical and physical architectures through the capability nodes as illustrated in Figure 5. The DAFO integrates the AFO energy formulation with a mission system data formulation and scenario to determine the optimum mission system data flow (0, 1) and simultaneous energy flow through the logical architecture to support the capability pull from the operational model at multiple time steps. The DAFO models mission system usage using prioritized capability pulls (Shane et al, 2023) on the system to determine which prioritized VCs are active during the discrete time step. This data flow is a new commodity in addition to the energy flow, necessary to establish a functional mission system. The SBIM execution process is illustrated in Figure 8.

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SBIM Execution Example

The following process steps describe the execution of a single time-step (at t = 1 second) during an OPSIT that involves three initial objects: 1 x Blue Surface Ship and 2 x Red Anti-Ship Missiles (ASMs). Shane, et al. (2023) present this as OPIST 3: 1. The warfighting model determines that an event occurs: the red ASMs move to the following locations: ● Red ASM 1 = 10 meters above sea level at 20 kilometers away from Blue Ship. ● Red ASM 2 = 10 meters above sea level at 20 kilometers away from Blue Ship. 2. The warfighting model determines the red ASMs are located within the blue ship’s AoR. 3. The operational model determines which tasks the blue ship should perform to respond to each object. ● Red ASM 1: Detect ● Red ASM 2: Detect 4. The operational model prioritizes each object based on a combination of time to impact the blue ship and potential damage to the blue ship: ● Red ASM 1: Detect (50%) ● Red ASM 2: Detect (50%) 5. The operational model prioritizes each task to respond to each object: ● Red ASM 1: Detect (100%)

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Red ASM 2: Detect (100%) 6. The operational model queries the capability model to determine if the blue ship has the capability to perform the requested tasks. 7. The capability model determines which blue ship VCs can perform the requested tasks given sufficient resources/information and prioritizes these VCs based on measures of performance: ● Red ASM 1: ■ S-Band Radar 1 = 11.11% ■ X-Band Radar 1 = 33.33% ■ S-Band Radar 2 = 11.11% ■ X-Band Radar 2 = 33.33% ■ ESM Antenna 1 = 5.56% ■ ESM Antenna 2 = 5.56% ● Red ASM 2: ■ S-Band Radar 1 = 11.11% ■ X-Band Radar 1 = 33.33% ■ S-Band Radar 2 = 11.11% ■ X-Band Radar 2 = 33.33% ■ ESM Antenna 1 = 5.56% ■ ESM Antenna 2 = 5.56% ●

8. The capability model queries the DAFO to determine if the Blue Ship can provide sufficient resources/information to the prioritized VCs providing prioritized capabilities. 9. The DAFO maximizes the provided capabilities by aligning the total ship system to allocate energy and information to the highest prioritized VCs, per requested capability, per object, subject to: capacity constraints, power doctrine, and damage constraints. 10. The DAFO responds to the capability model’s request by providing a list of VCs that are providing each requested capability. All other combat system VCs are assumed to be in standby mode: ● S-Band Radar 1 = Standby ● X-Band Radar 1 = Standby ● S-Band Radar 2 = Standby ● X-Band Radar 2 = Active ● ESM Antenna 1 = Standby ● ESM Antenna 2 = Standby ● All other combat system VCs = Standby 11. The capability model records any multi-time step characteristics about the selected VCs and responds to the operational model’s request by providing a list of capabilities that are being provided.

FIGURE 9. Anti-Surface Warfare (ASUW) Plex (adapted from Friedman, 2006, p 105 and Chalfant et. al, 2017)

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Fall 2023 | No. 135-3 | 95

Capability Modeling for Assessing Mission Effectiveness in Surface Ship Concept and Requirements Exploration

FIGURE 10. Anti-Submarine Warfare (ASW) Plex (adapted from Friedman, 2006, p 105 and Chalfant et. al, 2017)

Blue Ship can detect Red ASM 1 with X-Band Radar 2. Blue Ship can detect Red ASM 2 with X-Band Radar 2. 12. The operational model resolves the tasks: ● Red ASM 1 detected by blue ship. ● Red ASM 2 detected by blue ship. 13. The warfighting model advances to the next time step. ● t = t + Δt ● ●

Warfighting Areas

The MPES mission systems are grouped into three warfighting area plexes: ■ Anti-Air Warfare (AAW) ■ Anti-Surface Warfare (ASUW) ■ Anti-Submarine Warfare (ASW) The remaining systems are the power and energy systems or plexes. Each warfighting area uses specialized systems to provide capabilities and perform tasks unique and inherent to that area. Figure 5, Figure 9, and Figure 10 show the logical architectures for each warfighting plex. In addition to the warfighting systems, systems providing ship propulsion and maneuvering capabilities must also be included. An example mechanical

96 | Fall 2023 | No. 135-3

propulsion system logical architecture is shown in Figure 11. These logical architectures identify which VCs connect to each capability, and therefore, which VCs provide each capability.

Detect Capability

A surface ship must have the capability to detect objects such as aircraft, ships, unmanned vehicles, and missiles. Within the capability model, both AAW and ASUW detect capabilities are provided by the same VCs, which are grouped into one or more of the following: ■ Active Radar ■ Electronic Support Measures (ESM) ■ IR (Infra-Red) ■ Visual The AAW detect-capability nodes and the ASUW detect-capability nodes interface with these VCs and their supporting systems. The operational model provides a named object and its current operating parameters (speed, altitude, Radar Cross Section) and queries the AAW and ASUW detect-capability nodes through the capability model as to whether the ship can detect the object. Fixed data associated with specific objects are available in the library, including

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Detect Capability — Active Radar

Active radar detection performance is complex and involves many factors such as pulse shape, pulse width, pulse repetition frequency, carrier frequency, scan rate, beam width, receiver sensitivity, transmitter power, antenna gain and target radar cross-section. When assessing active radar VCs, the capability model calculates maximum range using the minimum of the two-way radar equation and an Over the Horizon (OTH) test. The two-way radar equation (Weapons Division, Naval Air way radar equation (Weapons Division, Naval Air Warfare Warfare2013, Center, 2013, and p. 4-4.6 and2010) Payne, Center, p. 4-4.6 Payne, is:2010) is:

where: (1) (1)Rmin c way radar equation (Weapons Division, Naval Air Warfare PW where: Center, 2013, p. 4-4.6 and Payne, 2010) is: RmaxR Maximum Detection Range (km) Maximum Detection Range (km) max For an a where: Pt Transmit Power 4 Pt Gt Gr Lt Lr λ2 σ transmit and R P Transmit Power min R = (1) √Gain max Gt t Transmitter (4π)3 kT0 Br Nf SNRmin transmission c Gr Gt Receiver Gain Gain Transmitter period PW of tim where: Lt G Transmit Loss Gain Receiver active radar, r R Maximum Detection Range (km) Lrmax Receive Loss For an a on the distan Transmit P Transmit PowerLoss λ t Lt Wavelength transmit and equation use G Transmitter NF NoiseReceive FactorGain t L Loss r transmission G Receiver Gain kr Boltzman's Constant period of tim λ Wavelength L Transmit Loss Tt0 Temperature active radar, Noise Factor L Receive Loss Brr NF Receiver Noise Bandwidth on the distan where: λSNR Wavelength Minimum SignalConstant to Noise Ratio k min Boltzman’s equation use R object NF Noise σ TargetFactor Radar Cross Section c T Temperature k 0 Boltzman's Constant Dsp Cross Section (RCS) a specific characteristic T0 BRadar Temperature Receiver NoiseisBandwidth r of object that depends on many factors. The Np = 10 Br a reflective Receiver Noise Bandwidth SNR Minimum Signalcross-section to Noise Ratio where: analytical the radar SNRmin mincalculation MinimumofSignal to Noise Ratio is complex Robject All activ and on: Target Section σ σdependsTarget RadarRadar CrossCross Section c returns from  Radar Physical geometry exterior 34). Dsp The fol Cross Sectionand (RCS) is a features specific characteristic Cross Section is radar aon specifi c characteristic within the ca  aRadar Direction of the illuminating of reflective object that(RCS) depends many factors. The of Np = 10 aanalytical refl ective object that depends on many factors. e anathe radar cross-section isTh complex  Radar calculation transmitter of frequency D All activ and depends on: of the radar cross-section is complex and  Active calculation lytical Material  Electroni returns from depends on:  Physical geometry andcapability exterior features 34). The For simplicity, in the model, targets are pre Air andfolM ■ Physical geometry and exterior features with aof notional RCS. An radar OTH test, in addition to within defined Direction the illuminating  Air the andca M ■ Direction of the equation, illuminating radar two-way radar is also completed (Payne, the Radar transmitter frequency  Close-in  Active D p. 24): ■ Radar transmitter frequency 2010, Material Helicopte  Electroni ■ Material  Surface S For simplicity, in the targets are pre Air and M R los = √17 ∙ hcapability 17 ∙ hobject (2) √model, sensor + model, For simplicity, in the capability targets are pre-dedefined with a notional RCS. An OTH test, in addition to Detect Air and M Capa fined with a notional RCS. An OTHcompleted test, in addition where: the two-way radar equation, is also (Payne,to the  Close-in An Elec two-way radarHeight equation, is alsoabove completed (Payne, hsensorp. 24): of sensor waterline (m) 2010, p. 24):gathers 2010, infor Helicopte Height of object above waterline (m) hobject electromagn Surface S R los = √17 ∙ hsensor + √ 17 ∙ hobject (2) (2)An ESM sys Rlos Range (line-of-sight) (m) Detect becauseCapa the The maximum detection range (R max ) for an active where: An Elec unknown be radar is determined asofthe of above thewaterline OTH test(m) (R los ) and hsensor Height sensor above gathers infor using ESM, hsensor Height oflower sensor waterline (m) HeightEquation of object(R above the two-way Radar hobject e ): waterline (m) electromagn equation, rew Height of object above Rloshobject Range (line-of-sight) (m) waterline (m) An ESM sys as shown be R max = min⁡(R los , R e(m) ) (3) Rlos Range (line-of-sight) because the Center, 2013 The maximum detection range (R max ) for an active unknown be to the range, a check is radarInisaddition determined asmaximum the lower detection of the OTH test (R los ) and using ESM, completed forRadar minimum detection the two-way Equation (R e ): range using the equation, rew following equation (Payne, 2010, p. 31): Fall 2023 | No. 135-3 | 97as shown be R max = min⁡(R los , R e ) (3) Center, 2013 In addition to the maximum detection range, a check is 4

Pt Gt Gr Lt Lr λ2 σ (4π)3 kT0 Br Nf SNRmin

R max = √

FIGURE 11. Mechanical System Plex Logical Architecture (Parsons, et al., 2022)

“generic” data to be used before an object is identified. The detect-capability nodes determine, from the logical and physical architectures, which VCs are physically available to provide the requested capability. VCs that can provide the detect capability when intact and supported by the MPES are shown in the logical architecture definition of the AAW plex in Figure 5 and ASUW plex in Figure 9. The physical architecture advises whether VCs are intact. Each intact VC that can provide the capability is assessed to determine its maximum detection range, minimum detection range, and detection standard period for the specific object using the object and VC characteristics. A VC’s detection standard period is the time required for it to detect the object. The methodologies used to determine the detection standard period for a VC are dependent on the detection method employed by the VC (i.e. active radar, ESM, infrared, visual, rotating, fixed array) and are explained in the next four sections. Those VCs with sufficient detection range are prioritized through a weighting function. The weighting function is used to represent warfighting doctrine and is very simplistic, but sufficient to serve the purposes of this research. More sophisticated models could be substituted in a real application.

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Warfare

Warfare (1)

(1)

the two-way radara equation, is alsoAn completed defined with notional RCS. OTH test,(Payne, in addition to  Close-in  Air and Missile Defense Radar - X and C Band Weapon System (CIWS) 2010, 24): thep.two-way radar equation, is also completed (Payne,  Close-in Weapon System (CIWS)  Helicopter 2010, p. 24):  Helicopter  Surface Search Radar System CapabilityRModeling for Assessing Mission Effectiveness in Surface Ship Concept and Requirements Exploration (2) los = √17 ∙ hsensor + √17 ∙ hobject  Surface Search Radar System R los = √17 ∙ hsensor + √17 ∙ hobject (2)Detect Capability – ESM where: An Electronic Support Measures (ESM) system Detect Capability – ESM hsensor where: Height of sensor above waterline (m) gathers information through passive surveillance of An Electronic Support Measures (ESM) system of object above waterline (m) (m) Height of sensor above waterline hobjecthsensor Height electromagnetic radiationthrough to detect and identify an object. gathers information passive surveillance of Height of object above Rlos hobject Range (line-of-sight) (m) waterline (m) An ESM system requires a wide-spectrum electromagnetic radiation to detect andsurveillance identify an object. Relos Range (line-of-sight) (m) the parameters of enemy transmissions are An ESM systemsurveillance requires a wide-spectrum surveillance wide-spectrum because the parameters of enemy Th maximum ) for an active radar because The maximumdetection detection range range (R (Rmax max ) for an active unknown beforehand (Payne, VCs that operate because the parameters of2010). enemyFor transmissions are transmissions are unknown beforehand (Payne, 2010). For VCs is determined as theaslower of theof OTH testmax (R )(R and the tworadar is determined the lower the OTH test ) and The maximum detection range (R )losfor an active los using ESM, the capability(Payne, model uses theFor one-way radar unknown beforehand 2010). VCs that operate that operate using ESM, the capability model uses the one-way waytwo-way RadarisEquation (Reas ): the(Rlower the Radar Equation ): radar determined of the OTH test (R ) and e los equation, solve formodel maximum range using rewritten ESM, thetocapability uses detection the one-way radar the two-way Radar Equation (R e ): radar equation, rewritten tofor solve for maximum detection as shown below (Weapons Division, Naval Airdetection Warfare equation, rewritten to solve maximum range R max = min⁡(R los , R e ) (3) (3) Center, 2013, p. 4-3.8 and (Weapons Payne, 2010): as shown below (Weapons Division, NavalNaval Air Warfare range as shown below Division, Air Warfare R max = min⁡(R los , R e ) (3) Center, 2013, 2013, p. p. 4-3.8 4-3.8and and Payne, Payne,2010): 2010): Inaddition addition to to the the maximum maximum detection Center, In detection range, range, aa check check is is Pt Gt Aesm completed for minimum detection range using the In addition to the detection maximumrange detection a check is Re = √ ⁡ (6) completed for minimum usingrange, the following 4πkT0 Br NPf SNR G Amin following equation (Payne, 2010, p. 31): completed for2010, minimum R e = √4πkT tB tN esm ⁡ (6) (6) equation (Payne, p. 31):detection range using the 0 r f SNRmin following equation (Payne, 2010, p. 31): c(PW) ⁡ 2 c(PW) R min = ⁡ 2

R min =

(4) (4) 12

(4) where: where: Rmin Minimum detection range (m) detection range (m) where: SpeedMinimum of Light (m/s) c Rmin R Minimum detection min Light range (m/s) (m) PWc PulseSpeed width of (m) Speed of Light (m/s) c PW Pulse to width (m) detect an object, it must reliably PW For an active Pulseradar width (m) transmit and receive in the direction of an object. The For radar detect an object, Foran anactive active radarto toreliably reliably anrequires object,ititamust must transmission and reception of thesedetect pulses transmit and receive in the direction of an object. Th e transtransmit and receive in the direction of an object. period of time (i.e. the detect standard period). ForThe each transmission anddetect reception of these pulses requires abased mission and reception of these pulses requires a period of active radar, the standard period is calculated period of the timedetect (i.e. the detect standard period). For each on the(i.e. distance between the object andFor theeach radar. The time standard period). active radar, active radar, the standard period isbased calculated equation used is:detect the detect standard period is calculated on thebased distance on the distance between the object and the radar. The between the object and the radar. Th e equation used is: 2∙Robject equation used is: Dsp = Np ( ) (5) c 2∙Robject

eristic The eristic omplex The omplex

are preion to re prene, on to ne, (2) (2)

tive

os ) and

tive

os ) and

(3)

check(3) is

heck is

Dsp = Np ( c ) (5) (5) where: Robject Range of Object (m) where: c Speed of Light (m/s) where: R Range Object Period (m) (s) Detect of Standard Dobject sp Range of Object (m) object cNpR= Speed of of Light (m/s) 10 Number pulses required to detect Detect Standard Period (s) Dsp c Speed of Light (m/s) NpDAll = 10 activeNumber radars are assumed to require 10 pulse of pulses required detect Detect Standard Periodto(s) sp returns from a point object to detect it (Payne, 2010, p. =active 10 Number ofdetect pulses required topulse detect All radars are assumed tousing require 10 pThe 34).N following systems active radar returns from a point object within the capability model:to detect it (Payne, 2010, p. 34). The following systems detect using active radar active radars are assumed  All Active System (ADS) to require 10 pulse returns within the Denial capability model: from a point object to detect  Electronic Warfare Systemit (Payne, 2010, p. 34). The following Denial System (ADS)  Active Air and Missile Defense Radar S Band systems detect using active -radar within the capability  Electronic Warfare System Air and Missile Defense Radar - X and C Band model:  Air and Missile Defense Radar - S Band Close-in Weapon System (CIWS) Denial System (ADS) ■ Active Air and Missile Defense Radar - X and C Band Helicopter WarfareSystem System(CIWS) ■ Electronic Close-in Weapon Surface Search Radar System and Missile Defense Radar - S Band ■ Air Helicopter ■Detect Surface Search Defense Radar Capability – ESMSystem Air and Missile Radar - X and C Band An Electronic Support ■ Close-in Weapon SystemMeasures (CIWS) (ESM) system Detect ESM passive surveillance of gathersCapability information–through ■ Helicopter An Electronic SupporttoMeasures (ESM) system electromagnetic radiation detect and identify an object. ■ Surface Searchrequires Radar System gathers through passive surveillance of An ESMinformation system a wide-spectrum surveillance electromagnetic radiation detecttransmissions and identify an because the parameters ofto enemy areobject. An ESMCapability system requires a wide-spectrum surveillance unknown beforehand 2010). For VCs that operate Detect —(Payne, ESM because the parameters of enemy transmissions using ESM, the capability model uses the one-way radarinforAn Electronic Support Measures (ESM) system are gathers unknown For VCs that operate equation, beforehand rewritten to (Payne, solve for2010). maximum detection range mationESM, through passive surveillance electromagnetic using the capability usesof the one-way radar radiaas shown below (Weaponsmodel Division, Naval Air Warfare tion to detect and identify An ESM system requires a equation, rewritten to solve forobject. maximum detection range Center, 2013, p. 4-3.8 and an Payne, 2010): as shown below (Weapons Division, Naval Air Warfare Center, 2013, p. 4-3.8 and Payne, P G A2010): R e = √4πkT tB tN esm ⁡ (6) SNR 0 r f

min

0 r f

min

98 | Fall 2023 | No. 135-3 Pt Gt Aesm

R e = √4πkT B N SNR

⁡

(6)

where:

12 Re NF

Maximum Detection Range (km) Noise Factor

Aesm Receiver’s Effective Antenna Aperture where: wavelengths where: Rek Maximum Detection Range (km) the wavelen Boltzmann’s Constant Re Maximum Detection Rangelower (km)bound NF Noise Factor where: wavelengths T0 Standard Temperature NF Noise Factor Aeesm Receiver's Effective Antenna Aperture detect (i.e. λ where: wavelengths R Maximum Detection Range (km) the wavelen A Receiver's Effective Antenna Aperture esm Receiver Noise Bandwidth k eBr Boltzmann's Constant is assumed t R Maximum Detection Range (km) the wavelen NF Noise Factor lower bound k Boltzmann's Constant T0esm Standard Temperature withinbound this λr NF Noise Factor lower Nf Noise fi gure of receiver A Receiver's Effective Antenna Aperture detect (i.e. T0 Bandwidth Standard Temperature Receiver Noise emmissions A Receiver's Effective Antenna Aperture Bandwidthdetect (i.e. λt kBresm Boltzmann's Constant assumed B SNR Minimum Detection Signal Noise to Noise Ratio is r min N0f Noise figure of receiverReceiver band, athis Banrt kT Boltzmann's Constant is assumed Standard Temperature within NGain Noisetofigure receiver fraction of th f SNR Minimum Detection Signal NoiseofRatio Object T Standard Temperature within this r 0rGt min B Receiver Noise Bandwidth emmissions SNRmin Minimum Detection Signalwavelength to Noise Ra Grft Objectfigure Gain B Receiver Noise Bandwidth emmissions N Noise of receiver band, a Band Pt Object Power Gt Object Gain Ptf min Objectfigure Power universal N Noise band, a Band SNR Minimum Detection Signal to Power Noise Ratio fraction ofbla th Pt of receiver Object Scientists, SNR Minimum Detection Signal to Noise Ratiotest is fraction of 1th min to theGain one-way radar equation, an OTH GtIn addition Object wavelength In addition to the one-way radar equation, an OTH test total flux wh G Object Gain wavelength Ptt completed Power universal bla In addition to2010, thep.one-way an OT also forfor ESM VCs (Payne, 2010, 24): isPalso completed ESM VCs (Payne, p. 24): radar equation, variable λT, Object Power universal bla t 1 is also completed for ESM VCs (Payne, Scientists, 2010, p. 24) In addition to the one-way radar equation, an OTH test Scientists, 1 totalThe fluxfoll wh R los to = for ∙ hsensor + √17 ∙ hobject (7) (7)maximum √17 d In addition the one-way an⁡ OTH test is also completed ESM VCsradar (Payne, flux wh variable R losequation, =2010, hobject ⁡ λT, √17 ∙p.h 24): + √17 ∙total is also completed for ESM VCs (Payne, 2010, p. sensor 24): (Federation variable λT, The foll where: Weapons In R los = √17 where: ∙ hsensor + √17 ∙ hobject ⁡ (7) The foll maximum d where: hsensor Height of sensor above waterline (m) R los = √17 ∙ hhsensor + 17 ∙ h ⁡ (7) √ sensor object maximum d Height of sensor above waterline (m) (Federation hobject Height of object above waterline (m) (m) hsensor Height of sensor above waterline where: (Federation hobject Height of object above waterline (m)In Weapons R Range (Line of Sight) (m) los where: hsensor Height of sensor above waterline (m) hobject Height object above waterline (m) Weapons In Rof Range (Line of Sight) (m) los hsensor Height of sensor above waterline (m) object above object RThe Range (Linerange of Sight) (m) detection (Rwaterline ) for an max los maximum hRobject Height of object above (m)ESM (R maxwhere: Range (Line of Sight) (m) los The maximum detection ) for an ES system as theoflower of(m) the OTH test (Rrange RIR los ) Rlos is determined Range (Line Sight) system is determined as the lower of the OTH an ESM system F test (R e maximum detection range(R(R andTh the one-way Radar Equation max) )for e ): The maximum detection range (R for an ESM max where: and thethe one-waytest Radar Equation (R e ): is determined as thedetection lower (Rlos the ρ IR Theismaximum rangeOTH ) for an) and ESM system determined as theoflower of(Rthe test (R los ) one- where: maxOTH R R = min⁡ ( R , R ) (8) max los e AIR system is determined aseEquation the lower(R ofe ): the OTH test (R ) way Radar Equation (R ): and the one-way Radar R los R max = min⁡(R los , R e ) F Ae and For the one-way Radar Equation (R ): F e ρ ESM systems, the detect standard period (i.e. time R = min⁡ (ESM R los ,systems, Re) (8) σperiod (i.e For the detect standard (8) ρ A required for a radarR max to detect an object) is calculated based = min⁡(for R losa, radar R e ) to detect an object) (8) is A Tcalculated maxrequired e on For the between the object and theperiod radar. (i.e. The ESM the detect standard time Fordistance ESMsystems, systems, the detect standard period (i.e. timeand theA ϵ radar. on the distance between the object The e σ equation used is: For ESM systems, the detect standard period (i.e. time required for an required for aa radar radar to to detect detect anobject) object) calculatedbased based on σ AT equation used is:isiscalculated T required for a radar to detect an Robject) is calculated based on the distance between the object and radar. object the distance between and thethe radar. ThThe e equation T ϵSmin Dthe =object Nobject on the distance sp the p ( cand)the radar. The R(9) object equation used is:between ϵ D = N ( ) AT used is: used is: sp p c equation The max A T Robject Smin where: not include a Dspwhere: = Np (Robject ) (9) Smin c Robject Range DofspObject = Np ((m) c ) (9) (9)for atmosph The max Robject(m/s) Range of Object (m) c Speed of Light In additi where: The maxa not include c Speed of Light (m/s) Detect of Standard Period (s) Dobject system in aa where: sp R Range Object (m) not include for atmosph where: Detect Standard Period (s) Dsp Number pulses required to detect is also comp R Range ofof Object (m) p = 10 cNobject Speed Light (m/s) for atmosph In additi N = 10 (m) Number of pulses required 2010, to detect Robject Range ofp Object cDsp Speed of Light (m/s) Detect Standard Period (s) In p. additi system in24) av All ESM systems are assumed to require 10 pulse Detect Standard Period (s) D c Speed of Light (m/s) system in av Nsp = 10 Number of pulses required to detect is also comp p All ESM systems are assumed to require 10 puls returns from aNumber point object to detect it (Payne, 2010, p. R Np = 10 of pulses required to detect is also2010, 2010, p.comp 24) returns from a ESM point object the to detect it (Payne, 34).All TheESM following VCs detect using within systems are assumed to require 10 pulse 2010, p. 24) 34). The following VCs detect using ESM within the capability model: where: R All from ESM are assumed toitrequire pulsep. returns asystems point object to detect (Payne,102010, capability model: hsensor returns a pointVCs object to detect (Payne, 2010, 34). Thefrom following detect usingitESM within thep. R  ESM &2 NAVAL ENGINEERS JOURNALwhere: hobject 34). The 1following using ESM within the capability model: VCs detect ESM 1 & 2 glos capability model: where: hRsensor Detect Capability – Infrared  ESM 1 & 2 hsensor Detect Capability – Infrared

For ESM systems, the detect standard period (i.e. time required for a radar to detect an object) is calculated based on the distance between the object and the radar. The equation used is: Robject

where: Robject c DD spsp NN == 1010 pp

Dsp = Np (

)

(9)

Range of Object (m) Speed of Light (m/s) DetectStandard StandardPeriod Period(s) (s) Detect Numberofofpulses pulsesrequired requiredtotodetect detect Number

All ESM systems are assumed to require 10 pulse All ESM systems are assumed to require 10 pulse returns returns from a point object to detect it (Payne, 2010, from a point object to detect it (Payne, 2010, p. 34). The p. follow34). The following VCs detect using ESM within the ing VCs detect using ESM within the capability model: capability model: ■ ESM 1 & 2  ESM 1 & 2

Detect Capability — Infrared

σ T ϵ AT Smin

Stefan-Boltzmann Constant Temperature Emissivity Target surface area Minimum flux that receiver can detect

The maximum range predicted by this formula does not include atmospheric attenuation. The correction factor for atmospheric attenuation is excluded for simplification. Inaddition addition to to the the maximum maximum detection In detection range rangeof of an an IR IR syssystem in a vacuum equation, a geometric line of sight test tem in a vacuum equation, a geometric line of sight test is also is also completed for each IR system as follows (Payne, completed for each IR system as follows (Payne, 2010, p. 24): 2010, p. 24):

where: where: hsensor hsensor hobject Rglos h

R glos = √13 ∙ hsensor + √13 ∙ hobject

(11) (11)

Height of sensor above waterline (m) Height of sensor waterline Height of object aboveabove waterline (m) (m) Range (Geometric Line of Sight) (m) (m) Height of object above waterline

object Detect Capability – Infrared InfraredInfrared systemssystems allow a surface to detect target abytarget the allow a ship surface ship toadetect RThe (Geometric (m) detection range (RLine isSight) determined glos maximumRange max )of collection processing of its thermal radiation. Thermal raby the and collection and processing of its thermal radiation. as the lower of the geometric line of sight test (R glos ) and Thermal radiators emitover their energy overofawavelengths. wide band ofIt is diators emit their energy a wide band The maximum detection range (Rmax) is determined as the not practical to detect equally over all the wavelengths so infra- the lower of the geometric of sight (Rglos) in and the maximaximum detectionline range of an test IR system a vacuum Track Capa s. It is not practical to detect equally over all bounds on the wavelengths mum red detectors have upper and lower detection range of an IR system in a vacuum (RIR): wavelengths. It is not practical to detect equally over all Tracking (R ): IR ngths so infrared detectors have upper and 13 the maximum detection range of an IR system in a vacuum Track Capa that they are designed to detect (i.e. λ and λ ). For simthe wavelengths so infrared detectors have upper and monitor obje upper lower ds that they are designed s. Itonisthe notwavelengths practical to detect equally over all to Tracking R max = min⁡(R glos , R e ) (12) (12)future (R IR ): lower bounds on the wavelengths that they are designed to positi plicity in this paper, it is assumed that IR detectors can pick up λs.upper λlower ). For simplicity inupper thisover paper, It isand practical to detect equally ngths sonot infrared detectors have and allit monitor obje detect (i.e. λthat identificatio upper and λlower). For simplicity in this paper, it thatonIR detectors can pick up energy that is To account for the fact that any energy isany within this range. ngths so infrared detectors have upper and R max min⁡(standard R glos , R eperiod ) ds the wavelengths that they are designed to For IR systems, the = detect (i.e. time(12) future positi is assumed that IR detectors can pick up any energy that is capability m range. Toλwavelengths account the fact that thermal onand the that they designed to fact thermal emmissions are not all within anthat IR sensor’s detection For IRfor systems, the detect standard period (i.e. time reλds ). Forfor simplicity inare this paper, itthe required the sensor to detect an object) is calculated upper lower identificatio within this range. To account for thermal following V are not all within ansimplicity IR sensor’s detection λthat anddetectors λlower ). For in paper, itIR For IR systems, detect (i.e. timebased oncapability m upperIR can pick any energy is sensor’s on the distance between the objectperiod and the sensor. band, a Bandwidth Factor (F)that is used to quantify the fraction of based quired for the sensorthe to detect standard an object) is calculated emmissions areup allthis within an detection ndwidth Factor (F)can is used tonot quantify the that that IRTo detectors pick up any energy is required for the sensor to detect an object) is calculated range. account for the fact that thermal The equationbetween used is: the object and the sensor. The equation following ActiveVR band, aradiation Bandwidth Factor (F) isa IR used to quantify the the IR that within system’s wavelength band. the distance he radiation that isIR within athat IRisdetection system’s range. Toallaccount for the fact thermal based on the distance between the object and the sensor. areIRnot within anof sensor’s  IR atio fraction the IR radiation that is within a IR system’s Within theIRcapability model, the universal black-body curve used is: band. the the are notWithin all wavelength within an sensor’s detection The equation used is: D = (Robject ) dwidth Factor (F)capability is used to model, quantify the capability model, the  Visual Active R (13)  band. Within the sp c ack-body curve (Federation of (Federation of to American Scientists, 1998) is used to determine dwidth Factor (F) isisused quantify the he IR radiation that within a American IRcurve system’s  IR universal black-body (Federation of American Robject 1998) usedthe tothe determine the is outside of the band from he IR isradiation that is 1998) within afraction IR system’s fraction of the the total flthe uxof which band. Within capability model, Dsp = ( c ) (13) (13) Track-ca Visual where: Scientists, is used to determine the fraction of the hich isWithin outside of(Federation the band from zerothe to the band. the capability model, ack-body curve ofoutside American each VC tha TH test zero to thewhich variable λT, in units ofband μm-K. R Range of Object (m) total flux is of the from zero to the object ,ack-body in units of μm-K. curve (Federation offraction American Track-ca 1998) is used toTh determine of the where: capability is ): c Speed of Light (m/s) variable λT, in the units of μm-K. e following equation can be used to quantify the maxiwhere: lowing equation can be used to quantify the 1998) used toof determine the fraction of the each tha hich isisoutside the band from zero to the R Range of Object (m) detectVC capab object Detect Standard Period (s) D The following equation can be used to quantify the sp mum range of an IR system in a vacuum (Federation detection range of detection an IR system inzero a vacuum Range of Object is outside of the band from capability is ,hich in units of maximum μm-K. c Robject Speed of Light (m/s) (m) track-capabi (7) detection rangeto ofthe an IR system in a vacuum of Scientists, Introduction to Naval of American Scientists, Introduction to Naval Weapons Infra,lowing in American unitsequation of (Federation μm-K. detectstanda capab can be of used to quantify the Detect Standard Period (s) infrared within the track Dsp cThe following Speed of Light (m/s) systems detect using American Scientists, Introduction to Naval nfrared and Detection, 1998): lowing Propagation equation can be used to quantify the track-capabi detection range of an IR system in a vacuum red Propagation and Detection, 1998): need to be th capability Weapons Infrared Propagation and Detection, 1998): Dsp model: Detect Standard Period (s) detection rangeScientists, of an IR system in a vacuum track standa The following systems detect using infrared within the of American Introduction to Naval Track Stand 4 A ϵσT FA e of American Scientists, Introduction to Naval need to be D th capability model: nfrared Propagation and Detection, 1998):  1200 kW Laser 4 FA successful R IR = (10) √ T4πS AT ϵσT e e following systems detect using infrared within the capaR IR1998): = √ 4πS (10) (10)  Th nfrared Propagation andmin Detection, Track Stand CIWS period is the min bility1200 model: kW Laser A ϵσT4 FAe successful D track an obje  ADS R IRwhere: = √AT4πS (10) 4 FA ϵσT SM ■ 1200 kW Laser  CIWS period is the T min e based on a V  IR Search and Track System (IRST) R IR Detection = √ 4πS Range (m) (10) Maximum R los ) minMaximum Detection Range (m) track an obje  CIWS ADS RFactor ■ IR Maximum Detection Range (m) BandwidthR IR based on a V FEfficiency Bandwidth Factor  ADS IR Search and Track System (IRST) Detect Capability – Visual ■ Collection F Bandwidth Factor Maximum Detection Range (m) Efficiency ρ Collection For surface ships, detection capability is usually Physical of the detector opening ■ IR Search and Track System (IRST) Maximum Detection Range (m) (8) size Bandwidth Factor Detect Capability Visual where: Physical size Effi of the detector opening ρA Collection ciency provided by sensors,– but in close-in situations in support of Detection Aperture = ρA Bandwidth Factor Collection Efficiency For surface the ships, detection capability is usually Tsp A Detection Aperture = ρA some weapons, CM also considers visual detection for e Stefan-Boltzmann A Physical size of the detector opening e. Collection time Efficiency Physical size of theConstant detector opening where: Detect Capability — provided by sensors, butVisual in close-in situations in support of σ Stefan-Boltzmann Constant the following systems: Dsp Temperature based Physical size of the = detector openingAperture = ρA Detection Aperture ρA A Detection Tsp somesurface weapons, thedetection CM also capability considers is visual detection for by e T Temperature For ships, usually provided EmissivityApertureConstant e Detection = ρA Stefan-Boltzmann the following systems:  0.50 cal Heavy Machine Gun (HMG) DspIn the ca ϵ Emissivity σ areaConstant Stefan-Boltzmann Constant sensors, but in close-in situations in support of some weapTarget surface Stefan-Boltzmann Temperature A Target surface area track are ass T Minimum flux can detect ons, the CM alsoare considers detection the following T that receiver Temperature Temperature Emissivity HMGs assumed to have a static for detection  The 0.50 cal Heavy Machinevisual Gun (HMG) In the si ca Smin Minimum flux that receiver can detect of objects Emissivity (9) surface range of 1000 meters and can provide the detect capability Target area systems: track are ass ϵ Emissivity actual ship. ximum range predicted by this formula does Thecal HMGs are assumed to have static detection Target surface area Minimum fluxThe thatmaximum receiver can detect for ASUW objects that are Gun within thisa range. A detect range predicted by this formula does of objects si ■ 0.50 Heavy Machine (HMG) atmospheric attenuation. The correction factor A Target area Tinclude range of 1000 and can capability Minimumnot flux that receiver can surface detect standard periodmeters of 1 second is provide assumedthe fordetect the HMGs. atmospheric attenuation. The correction factor actual ship. Track Capa Th e HMGs are assumed to have a static detection range of heric attenuation is excluded for simplification. ximum range predicted by this formulaflux does for ASUW objects that are within this range. A detect Smin Minimum that receiver can detect for atmospheric attenuation is excluded for simplification. VCs tha ion to the maximum detection range of does an IR 1000 meters andofcan provideisthe detect for capability for ASUW Track Capa ximum range predicted byThe this formula atmospheric attenuation. correction factor standard period 1– second assumed the HMGs. Detect Capability Prioritization In addition to the maximum detection range of an IR prioritized a vacuum equation, geometric line ofpredicted sight testby this formula does not atmospheric attenuation. Thefor correction factor heric attenuation simplification. objects within this range. A detect period of 1methodology Thise aexcluded maximum range VCsthat thatare have sufficient detection rangestandard for an object VCs tha system in a vacuum equation, a geometric line of sight test pleted for each IR system as follows (Payne, heric is completed excluded for simplification. Detect Capability – Prioritization ion toattenuation the maximum detection range IR Theascorrection are prioritized against that object through a simplistic prioritizedFoa second is assumed for the HMGs. include atmospheric attenuation. factor for is also for eachofIRansystem follows (Payne, doctrine. ):ion to theequation, maximum detection range of an IR VCs that have sufficient detection range for an object vacuum a geometric line of sight test methodology that is intended to represent warfighting methodolog 2010, p. 24): attenuation is excluded for simplification. is as follows atmospheric vacuum a geometric line of(Payne, sight test are prioritized against object through a simplistic pleted forequation, each IR system as follows doctrine. For the detectthat capability, the following se doctrine. Fo Rpleted = ∙ h + 13 ∙ h (11) √13 √as follows sensor object system methodology of that is intended to represent warfighting ):glos prioritization VCs is implemented: p. for each IR followsR is asActive R glos = √13 ∙ (Payne, hsensor + √13 ∙ hobject (11) ): doctrine. For the detect capability, the following e o If m of VCs is implemented: prioritization Active Radars R glos = √13where: ∙ hsensor + √13 ∙ hobject (11)  Active giveR Height of sensor above waterline (m) (11) R glos = √13 ∙hhsensor 13 ∙ h o If more than one Active Radar is available, priority sensor + √ object o If m Height of sensor above waterline (m)  IR senso JOURNAL Fall 2023 | No. 135-3 | 99 Height ofNAVAL object ENGINEERS above waterline (m)  Active Radars is given to those with higher carrier frequency. give( hobject LineHeight of object  Visual Range (Geometric of Sight) (m) above waterline (m) more than one Active Radar is available, priority Height of sensor waterline (m)  o ESMIf sensors  IR senso Rglos aboveRange (Geometric Line of Sight) (m) is given to those with higher carrier frequency. Height of sensor above waterline (m) object above

Capability Modeling for Assessing Mission Effectiveness in Surface Ship Concept and Requirements Exploration

Detect Capability — Prioritization

■ Active Radars

VCs that have sufficient detection range for an object are prioritized against that object through a simplistic methodology that is intended to represent warfighting doctrine. For the detect capability, the following prioritization of VCs is implemented: ■ Active Radars ● If more than one Active Radar is available, priority is given to those with higher carrier frequency. ■ ESM sensors ■ IR Sensors ■ Visual (i.e., HMG for ASUW objects only)

vacuum (12)

ime ated nsor.

(13)

hin the

y pport of on for

on pability ct Gs.

object ic ng

priority cy.

Track Track Capability Capability

Tracking capability is theisability to continuously monitor object Tracking capability the ability to continuously monitoraft object motion after initial detection, to positions predict of the motion er initial detection, to predict future future positions of the object, andcation to help the based on this object, and to help in the identifi ofin objects identification of objects based on this motion. In the motion. Inmodel, the capability model, track-capability is provided by capability track-capability is provided by the the following types: following VCVC types: ■ Active Radar  Active Radar ■ IR  IR ■ Visual  Visual Track-capability nodes calculate track-capability for each Track-capability calculate track-capability for is VC that is capable of nodes tracking an object. Track capability each VC that capable tracking an object. Track capability, modeled usingis the sameof methodology as the detect capability is modeled using the same methodology as the but once a VC is selected to provide the track-capability, the detect capability, but once a VC is selected to provide the same VC must bethe used forVC the must entirebetrack period. track-capability, same usedstandard for the entire Th e track-capability does not need to beVC thedoes same track standard period.VC The track-capability notas the need to be the same thethe detect-capability but the detect-capability VC,asbut Track StandardVC, Period must start Track Standard Period must start after the end of a after the end of a successful Detect Standard Period. The Track successful Detect Standard Period. The Track Standard Standard period is the amount of time required for the sensor period is the amount of time required for the sensor to to track object. e track standard period is calculated track an an object. TheThtrack standard period is calculated based on a VC’s VC’s detect standard period, as follows: detect standard follows:

where: where: Tsp Dsp Tsp

Tsp = 2 ∙ Dsp

Track Standard Period (s) Detect Standard Period (s) (s) Track Standard Period

(14) (14)

DInspthe capability Detect Standard Period (s)are able to model, all systems that track are assumed to be able to track an unlimited number In the capability model,which all systems are able to track are of objects simultaneously, is notthat possible on an assumed to be able to track an unlimited number of objects actual ship. simultaneously, which is not possible on an actual ship. Track Capability – Prioritization VCs that have sufficient track range for an object are Track Capability — Prioritization prioritized against that object through a simplistic VCs that havethat suffiiscient track to range for anwarfighting object are priorimethodology intended represent doctrine. For that the Track prioritization of VCs that tized against objectcapability, through a the simplistic methodology is intended as follows: is to represent warfighting doctrine. For the Track capability, prioritization of VCs is as follows:  Activethe Radars

If more than on Active Radar available, priority is given to those with higher carrier frequency. ■ IR sensors ■ Visual (i.e., HMG for ASUW objects only) VC’s that were used for a specific object in the previous time step are given a preference over any other equally capable VCs. An example of this would be if X-Band Radar 1 and X-Band Radar 2 were both equally able to track an object, but X-Band Radar 1 was used to provide a detect capability for the object in the previous time step, then X-Band Radar 1 would be slightly prioritized over X-Band Radar 2. ●

Identify Capability

Identify capability is provided through the collation and processing of sensor information. This can be accomplished by a ship’s Combat Management System (CMS), but can also be provided through stand-alone Electronic Support Measure (ESM) systems. ESM systems have the capability to rapidly acquire signals, characterize the signal parameters and identify the signal by comparison with an emitter library. In the capability model, the identify-capability is treated as a mandatory step in the detect to engage sequence. Following a successful track-capability request, the operational model can submit an identify-capability request. A succesful response to an identify-capability request must be provided to the operational model before the decision capability can be requested. The following VCs provide the identify capability within the capability model: ■ Electronic Warfare System ■ Laser Support Equipment ■ CIWS ■ Interrogation Friend or Foe (IFF) System ■ ADS ■ HMGs

Identify Capability — Prioritization

Following an identify-capability request from the operational model, the identify-capability is assessed for each system that is capable of identifying an object. The measure of performance used to prioritize VCs for the identify-capability is the identification standard period, which is the amount of time required for the system to complete the identification process for a single object. VC priorities are assigned in the following order: ■ VCs with lower identify standard periods. VC’s that were used for a specific object in a previous time step are given a preference over any other equally weighted VCs. The selected VC then becomes active for the identify

If more than on Active Radar available, priority is given to those with higher carrier frequency. 100 IR sensors | Fall 2023 | No. 135-3  Visual (i.e., HMG for ASUW objects only)

NAVAL ENGINEERS JOURNAL

Agent/Threat Mode SelfLoiter Defense Offence Attack Agent/Threat Mode

standard period, meaning it is operating at its mission power. Refer to the Track Prioritization section for an example of this.

Decide to Track Decide to SelfLoiterTrack Defense Decide to Self-Defense Offense Track

BlueShip Ship Mode Blue Mode

Decision Capability

Loiter

Decide to

Decide to Decide to Self-Defense Track Track Decide to Decide to Decide to Decide to Track Track Decide Track Engage to Track Decide to to Decide Decide to to Track Decide Trackto Decide Engage Engage Engage

Loiter Track

Blue Ship Mode

On a surface ship, the decision-capability is provided through D sets of formal guidance such as mission doctrine and Rules of D Engagement (ROE) and human intelligence. Mission doctrine is a guide to action rather than an explicit set of rules and Offense Decide to Track Decide to Engage D TABLE 1. Ship Capability Model Decide Rules of ROEs are the military rules or directives that define the cirTable 1: Ship Capability Model Decide Rules of Engagement Engagement cumstances, conditions, degree, and manner in which the use of force, or actions which might be construed as provocative, Decide Capability – Prioritization may be applied. is capable of deciding to engage an object. e measure of Following an decide-capability requestTh from the In the capability model, the decision-capability is a manperformance used to VCs for the decision-capability operational model, theprioritize decide-capability is assessed for where: each that is capable of deciding engage an of time datory step in the detect to engage sequence. It is modeled as is thesystem decision standard period, which istothe amount Phit_factored object. The of performance useddecision to prioritize VCs the ability to decide whether to engage an object or to continue required formeasure the system to complete the process for a Vthreat for the decision-capability is the decision standard period, tracking an object. Following a successful identify-capability single VC priorities assigned a higher which object. is the amount of timeare required forwith the system to priority This for request from the previous time step, the operational model for VCs with decide standard periods. complete the lower decision process for a single object. VC generated fo priorities are were assigned highercpriority fora previous VCs withtime of actual val uses simplified ROEs to assess whether to proceed to submisVC’s that usedwith for aa specifi object in lowerare decide periods. for high-spe sion of a query to the ship’s decision-capability node. These step givenstandard a preference over any other equally weighted were usedPrioritization for a specific section object in previous ROEs are based on the operational mode of the ship and the VCs.VC’s Referthat to the Track fora an example work regard time step are given a preference over any other equally capability m object. They are presented in Table 1. If a decision-capability of this. VCs. Refer to the Track Prioritization section for weighted capability in request is answered with a “yes”, an engage-capability request an example of this. part of this a can be made to the engage-capability nodes in the following Engage Capability exist for wea Engage Capability time step. Engage capability is the ability of the ship to engage a threat. Engage capability is the ability of the ship to engage a  All wea The operational modes listed in Table 1 are defined as It is achieved throughthrough the application of a weapon. To form threat. It is achieved the application of a weapon. degree f follows: the basis the for basis deciding which ofwhich the available weapons to use To form for deciding of the available engagem weapons use to engage a target, of a Pperformance to engagetoa target, a PKill measure ■ Loiter: Does not engage any objects; is used to Kill measure of from an performance is used to determine weapon is most surface determine which weapon is most which likely to neutralize the target. ■ Self Defense: Engage all opposing team objects if they are in likely to neutralize the target. PKill is the probability that a  Soft-kill PKill is the probability that a hit by a weapon will destroy a attack mode; hit by a weapon will destroy a specific threat. PKill is the threa specifi c threat. ■ Offense: Engage all red team objects with no restrictions; based on theaprobability calculated basedPKill on is thecalculated probability of hitting target (Phit)of and hitting target (Phitthat ) anda hit thewill probability a hit will To asses and theaprobability destroy that the target (Pd).destroy provided (as Thetarget PKill equation the (Pd). Theis:PKill equation is: ■ Attack: Engage opposing teams objects except for those in implement a loiter mode. Note: a ship cannot be in attack mode. Attack Pkill = Phit × Pd (15) (15)following pa mode represents a missile or round that is travelling towards Todetermine determinePPdd,,pre-determined pre-determined tables are used based and will impact its target. To  Fire and on the ,, either: In the capability model, the decide-capability is assessed on the threat threat type type and andthe theweapon weapontype. type.To Todetermine determinePPhithit pre-populated tables are generated for each of the ship’s o Fire for each VC that is capable of decisions or at final VCs before a pre-populated tables are generated for each of the ship’s weapweapons based on each threat type and threat range. The syst Command (human) node. In the Figure 5 and Figure 9 plexes ons type and threat range. The data in the Agent/Threat Mode databased in theon Pd each and Pthreat hit tables are estimated based on once this includes: P tables are onwhere publicly available data publicly available dataestimated for each based weapon available d and Phit requ Loiter Self-Defense Offense Attack (2020, p.similar using an weapon approach similar to Appleget 111). for each where available usingetanal.approach ■ Combat Information Center (CIC) Equipment and Displays enga Where publicly is Where notDecide available sensitive, o Ship Loiter Decide to Track Decide to Track Decide to Track toorTrack to Appleget et al.available (2020, p.data 111). publicly available ■ Laser Support Equipment (always assumed in automatic the authors have generated estimates which are reasonable, inde data is not available or sensitive, the authors have generated mode in capability model) but not actual values.toATrack speed factor is used to Phit Self-Defense Decide to Track Decide to Track Decide Decide toadjust Engage ship estimates arespeed reasonable, butfollowing not actualformula: values. A speed ■ CIWS (always assumed in automatic mode in capability based on awhich threat’s using the stan Decide to Track Decide to Engage Decide to PEngage Decide to Engage factor is used to adjust model) Offense hit based on a threat’s speed using the Table 1: Ship Capability Model Decide Rules of Engagement following formula: Decide Capability — Prioritization 16 V Decidean Capability – Prioritization Following decide-capability request from the operational Phit_factored = (1 − threat ) ∙ Phit (16) (16) 10000 Following an decide-capability the that model, the decide-capability is assessedrequest for eachfrom system operational model, the decide-capability is assessed for where: each system that is capable of deciding to engage an Phit_factored Probability of Hit (factored for speed) object. The measure of performance used to prioritize VCs Vthreat Threat Speed (m/s) for ENGINEERS the decision-capability NAVAL JOURNAL is the decision standard period, Fall 2023 | No. 135-3 | 101 which is the amount of time required for the system to This formula is an estimate that the authors have complete the decision process for a single object. VC generated for simplification purposes and is not reflective

Capability Modeling for Assessing Mission Effectiveness in Surface Ship Concept and Requirements Exploration

where: Phit_factored

Probability of Hit (factored for speed)

Vthreat

Threat Speed (m/s)

This formula is an estimate that the authors have generated for simplification purposes and is not reflective of actual values. It is intended to reduce the value of for high-speed threats and serve as a placeholder for future work regarding the generation of values. In the capability model, the ship systems that provide the Engage capability include both hard-kill and soft-kill systems. As part of this analysis, the following assumptions/limitations exist for weapons: ■ All weapons are considered to have unobstructed, 360-degree firing arcs. In the capability model, any engagement VC can engage any threat approaching from any direction which is not true for an actual surface ship; and ■ Soft-kill weapons always engage the threat just before the threat reaches the ship. To assess whether the engage capability can be provided (as described in the steps below) and to implement an engagement in the operational model, the following parameters exist for all weapons: ■ Fire and Forget Indicator (each weapon is classified as either: ● Fire and Forget — Weapon has its own tracking system and does not require input from the ship once launched. A Fire and Forget weapon does not require the ship to provide tracking for the engagement to be successful. ● Ship Illumination — Weapon does not have an independent tracking capability and requires the ship to provide tracking for the engagement standard period of the weapon. If the track capability is unable to be provided by the ship during the engagement, the engagement fails. ■ Engagement Standard Period — This is a parameter that is specific to directed-energy weapons (e.g. ADS and LaWS). It is a time period during which a weapon must be engaging a threat (i.e. directing energy), before an engagement can be considered successful. ■ Engagement Magazine Capacity — This is the current number of rounds remaining for a VC. This value is maintained during the execution of an OPSIT. The Vertical Launch System (VLS) magazine capacity considers the number and type of missiles and rockets loaded into the VLS. ■ VC Salvo/Rounds Per Engagement — This is the number of rounds that a VC would fire for a single engagement. This parameter is required for weapons, such as the CIWS, which fire more than one round during an engagement. ■ Engagement Delay Period — This parameter is intended to account for the time required for a weapon to reach its

102 | Fall 2023 | No. 135-3

engagement speed (e.g. launching a missile from a VLS requires a period of time for the missile to reach its engagement speed). ■ Engagement Speed –The engagement speed is the speed (m/s) at which the munition will travel after completion of the Engagement Delay period. For example: a rail gun must be powered up for a given time period prior to firing (i.e. Engagement Delay period), after which the round is fired and will travel at the engagement speed towards the target. ● Note: Decoys and soft-kill weapons do not work this way, as they do not fire munitions at a target. In capability model, these weapons are assigned an Engagement Speed of -1. This indicates to the OM that, once the weapon is “fired”, the threat is not defeated until it reaches the ship. This effectively indicates that the threat has been lured away from the ship. ■ Engagement Cooldown Period — This parameter is intended to account for the time required for a weapon to cooldown after completing a previous engagement. E.g. A rail gun must cool down for a given time period prior to subsequent firings. Engage capability is assessed through the following steps: 1. PHit, PD and PKill values are calculated for a VCs salvo fired against each threat. 2. Weapons that are unable to achieve the goal PHit are not included in the following steps. Note: In the capability model, the goal PHit is defined as 0.5 or higher. 3. The magazine capacity of each weapon is assessed to confirm that there are adequate rounds available. Weapons that do not have adequate magazine capacity are not included in Step 4 through 7. 4. Weapons are queried to determine if they are currently in-use (i.e. whether they are currently executing any engagements requested in a previous time step). Unavailable weapons are not included in Step 5. 5. Weapons are queried to determine if they are able to complete an engagement before the threat would hit the blue ship. Weapons that require more time to complete an engagement than is available are not included in Step 6. 6. At the completion of Step 5, a list of weapons that: have enough ammunition, can provide a PHit value > 0.5, are not currently executing an engagement, and are able to engage within the required timeframe exists. The VCs are prioritized based on their PKill and are passed to the DAFO as options for providing the engage capability. 7. A DAFO is completed and a weapon is selected to provide the Engage capability. The result is provided to the capability nodes as the firing solution. If the DAFO is unable to

NAVAL ENGINEERS JOURNAL

provide any VCs, the engage capability is not provided. 8. The capability nodes flag the weapon as active for its engagement standard period. This indicates that it cannot be used for additional engagements until this time period has elapsed. If no weapons are able to provide the engage capability, no weapons are activated. 9. The capability nodes provide a yes/no response to the operational model, along with information on the weapon used. This additional information includes: a. Fire and Forget indicator b. Engagement Standard Period c. Engagement Delay Period d. Engagement Speed

Single Object Vital Components (SOVCs)

■ Zero objects

CIWS 1 is not assigned to any object. CIWS 2 is not assigned to any object. ● Laser is not assigned to any object. ■ One object ● CIWS 1 is assigned to the object. ● CIWS 2 is assigned to the object. ● Laser is assigned to the object. ■ Two objects ● CIWS 1 is assigned to the highest priority object. ● CIWS 2 is assigned to the second highest priority object. ● Laser is assigned to the highest priority object. ■ Three (or more objects) ● CIWS 1 is assigned to the highest priority object. ● CIWS 2 is assigned to the second highest priority object. ● Laser is assigned to the third highest priority object. ● ●

In the capability model, the following VCs have additional considerations not described in the previous sections: ■ CIWS 1 Capability Model Demonstration ■ CIWS 2 This section describes the execution of a simple OPSIT (from the perspective of the blue ship). Table 2 shows the event log ■ Laser generated by this OPSIT when events are resolved at each time For each of these SOVCs, prioritization is based on the step as part of the SBIM Execution Process (Figure 8). Shane, et capability range (), which is the range at which a SOVC can be For each of these SOVCs, prioritization is based on the Capability Model Demonstration al. (2023) describe the warfighting model and operational modused to provide Th e capability range was develcapability range a(Rcapability. ), which is the range at which a SOVC c This describes the execution of aShip simple can beasused a capability. The these capability range el andsection Parsons, et al. (2023) describe the DAFO Model. oped parttoofprovide this study to ensure that SOVCs are only OPSIT (from the perspective of the blue ship). Table 2 was developed as part of this study to ensure that these Thisevent OPSIT hasby three 1 x Blue Ship and 2 used to engage objects once they reach the SOVC’s maximum shows the loginitially generated this objects: OPSIT when events SOVCs are only used to engage objects once they reach x Red ASMs. Th e OPSIT begins at t = 1 s when the two Red eff ective engagement range. Th e capability range is calculated are resolved at each time step as part of the SBIM the SOVC’s maximum effective engagement range. The Execution Process (Figure 8). Shane, et al. (2023) describe ASMs enter the Blue Ship’s area of responsibility which is set to using the following formula: capability range is calculated using the following formula: the warfighting model and operational model and Parsons, a radius of 20 km from the blue ship. describe the Ship DAFO Model. R c = R eff + (Vref ∗ t min ) (17) (17)et al. (2023) The first time step of this OPSIT is described in detail in This OPSIT initially has three objects: 1 x Blue Ship Execution Example section in thisthe paper. In where: where: and the 2 x SBIM Red ASMs. The OPSIT begins at tearlier = 1 s when Capability Range (m) summary: Th e operational model requests two detect capabiliR two Red ASMs enter the Blue Ship’s area of responsibility Rcc Capability Range (m) Maximum Effective Engagement Range R eff which is(one set tofora each radiusRed of 20 km from the blue ship. ties ASM). Th e capability model the provides Reff Maximum Effective Engagement Range (m) (m) The first time step of this OPSIT is described in detail prioritized list of VCs that could provide the requested capabilV ReferenceObject ObjectSpeed Speed(m/s) (m/s) Reference Vref in the SBIM Execution Example section earlier in this ref ities (six VCs in total) using the methodology described in the Minimum time required to advance from min ttmin Minimum time required to advance from the paper. In summary: The operational model requests two Detect Capability thisASM). paper. The The DAFO uses these the first kill chain step to the Engage step(s) detect capabilities (onesections for eachofRed capability first kill chain step to the Engage step(s) requests to select the VCs (ESM Antenna 2 in this case) that model the provides prioritized list of VCs that could To ensure that SOVCs are prioritized to engage threats provide the requested capabilities (six VCs in total) using To ensure that SOVCs are prioritized to engage threats once will provide each capability. These selected VCs are provided to once they reach SOVC’s maximum engagement range, the in the Capabilitywhich capabilthe capabilitydescribed model which inDetect turn determines they reach SOVC’s maximum engagement range,track, the SOVCs the methodology SOVCs must be prioritized to provide the detect, sections of this paper. The DAFO uses these requests to ities are provided to the operational model. The operational must be prioritized to provide the detect, track, identify, or decide capabilities based on the timeidentify, it takes or select the VCs (ESM Antenna 2 in this case) that will for an object to reach the on bluethe ship. reference model then resolves These the events and VCs generates the first line in decide capabilities based timeThe it takes for anobject object to provide each capability. selected are provided speed is selected to account for the fastest AAW object thecapability event logmodel shownwhich in Table 2. determines which reach the blue ship. The reference object speed is selected to to the in turn type of the AAW class used by the warfighting model. are provided to (from the operational Theshown in TaThe resolved events each timemodel. step) are account for the fastest AAW object type of the AAW class usedcapabilities This capability model prioritization limits each SOVC operational model then resolves the events and generates ble 2. During the first five seconds of the OPSIT the blue ship by the warfi ghtingcapabilities model. to only providing in response to a single object the first line in the event log shown in Table 2. completes theevents first five stepseach of the killstep) chain: Th is capability model prioritization limits each SOVC to at each time step. If an object is determined to be within The resolved (from time are shown the capability range for the automated VCs, the automated only providing capabilities in response to a single object at eachin Table ■ At 2. t =During 1 s thethe Blue Ship detects theofRed first five seconds theASMs OPSITwith the ESM VCs are against each othertoasbefollows: completes time step.prioritized If an object is determined within the capability blue ship Antenna 2. the first five steps of the kill chain: range for objects the automated VCs, the automated VCs are priori■ At t = 2 s, the Blue Ship tracks the Red ASMs with X-Band  Zero  At t = 1 s the Blue Ship detects the Red ASMs with tizedoagainst each other as follows: Radar 2. CIWS 1 is not assigned to any object. ESM Antenna 2. o CIWS 2 is not assigned to any object.  At t = 2 s, the Blue Ship tracks the Red ASMs with Xo Laser is not assigned to any object. Band Radar 2.  One object  At t = 3 s, the Blue Ship tracks and the ASMs NAVAL JOURNAL FallRed 2023 | No. 135-3 | 103 o ENGINEERS CIWS 1 is assigned to the object. with X-Band Radar 2 and identifies the Red ASMs o CIWS 2 is assigned to the object. with the IFF Antenna. o Laser is assigned to the object.

Capability Modeling for Assessing Mission Effectiveness in Surface Ship Concept and Requirements Exploration

t (s)

Object

Event

1 1 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6-14 6-14 15 15

Red ASM 1 Red ASM 2 Red ASM 1 Red ASM 2 Red ASM 1 Red ASM 1 Red ASM 2 Red ASM 2 Red ASM 1 Red ASM 1 Red ASM 2 Red ASM 2 Red ASM 1 Red ASM 1 Red ASM 2 Red ASM 2 Red ASM 1 Red ASM 2 Red ASM 1 Red ASM 2

Enters AoR Enters AoR Moves Moves Moves Moves Moves Moves Moves Moves Moves Moves Moves Moves Moves Moves Moves Moves Killed by Killed by

Capability

Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Blue Ship Green ESSM 1 Green ESSM 2

Detects Detects Tracks Tracks Tracks Identifies Tracks Identifies Tracks Decides Tracks Decides Tracks Engages Tracks Engages Tracks Tracks

with with with with with with with with with with with with with with with with with with with with

TABLE 2. OPSIT Event Log ■ At t = 3 s, the Blue Ship tracks and the Red ASMs with

X-Band Radar 2 and identifies the Red ASMs with the IFF Antenna. ■ At t = 4 s, the Blue Ship tracks and the Red ASMs with X-Band Radar 2 and decides to engage with Display 2. ■ At t = 5 s, the Blue Ship tracks and the Red ASMs with X-Band Radar 1 and engages the Red ASMs with two ESSMs (one per Red ASM). Then, from t = 6 s to t = 14 s, the Blue Ship tracks and the Red ASMs with X-Band Radar 2 while the Green ESSMs move toward the Red ASMs. Finally, at t = 15, the Green ESSMs kill the Red ASMs. This event ends the OPSIT.

Conclusions and Future Work The capability model described in this paper serves as the critical link between the ship’s distributed systems and the warfighting environment. This link is represented by the capability sink nodes and their incoming arcs in the mission system’s logical architecture (Figure 5). These capability nodes enable the Ship Behavior and Iteration Models (SBIMs) to simulate an OPSIT and determine a ship mission effectiveness during concept & requirements exploration. The capability and operational models that support these nodes provide essential mission and vital component priorities used to guide the alignment of system vital components over time, ultimately using a Dynamic Architecture Flow Optimization (DAFO). The ultimate objective of coupling ship

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X-Band Radar 2 X-Band Radar 2 X-Band Radar 2 X-Band Radar 2 X-Band Radar 2 IFF Antenna X-Band Radar 2 IFF Antenna X-Band Radar 2 Display 2 X-Band Radar 2 Display 2 X-Band Radar 2 ESSM 1 X-Band Radar 2 ESSM 2 X-Band Radar 2 X-Band Radar 2

system architecture and function to the operational architecture is that it enables time-stepping operational scenarios to be implemented that are fully coupled to ship system design, function, damage and even alignment for optimum mission performance. The most important take-away from this paper is the concept of using capability nodes to pull function from the ship energy and data systems thus enabling the whole framework. This is not a paper about combat systems, but about enabling a total ship system approach and framework. These methods are fully scalable to force-level operational situations with separate system models for all operational units using parallel computing. Pseudo-intelligence is provided by an agent-based approach in the warfighting model and by a doctrine-based network energy and data flow optimization in the ship systems. These algorithms are efficient and effective. This approach is also adaptable for machine learning. The most critical future work includes completion of the fully-functional DAFO including energy flow and data flow responding to capability requests from the operational architecture through the capability nodes. These two commodities (energy and data) provide a sufficient level of detail to assess mission effectiveness in concept & requirements exploration. Another area for future work is to modify this methodology allowing the DAFO to select more than one VC to respond to each capability request. For example, tracking an object with a single VC is sufficient but, tracking that same object with two or more VCs may provide a higher level of confidence in the track. The final area for future work is to add additional time-marching functions to the DAFO algorithm for properly modeling energy storage and the ramping up and down of mechanical and thermal-fluid systems, particularly for damage control with system realignment.

Acknowledgements The authors would like to thank Ms. Kelly Cooper, ONR Code 331, for supporting this work under contract N00014-15-12476. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Office of Naval Research. DCN# 43-8235-21

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AUTHOR BIOGRAPHIES DAVID J. BERROW is currently employed with the Department of National Defence (Canada). He received his M.S. in Ocean Engineering from Virginia Tech in 2021 and his B.S. in Ocean Engineering and Naval Architecture from Memorial University of Newfoundland in 2011. DR. MARK A. PARSONS is currently employed as a naval architect in the public sector. He researches network-based methods to assess surface ship distributed system vulnerability, survivability, and deactivation recoverability in concept stage design. He received his Ph.D. in Aerospace Engineering (Ocean Engineering Specialization) and his M.S. in Ocean Engineering from Virginia Tech in 2021 and 2019 respectively and his B.S. in Naval Architecture and Marine Engineering from the University of New Orleans in 2016. He is a lifetime member of ASNE, the co-chair of SNAME’s SD-08 Naval Ship Design Panel, and a member of USNI. ALAN SHANE is currently employed as a naval architect in the public sector. He is interested in developing ship concept development and assessment tools that supports a path towards MBSE and DE in those processes. He received his M.S and B.S. in Ocean Engineering from Virginia Tech in 2020 and 2010 respectively.

DR. MUSTAFA Y. KARA is currently a postdoctoral associate at the Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Tech. He researches network-based methods to assess surface ship distributed system vulnerability, survivability, and deactivation recoverability in concept stage design. He received his B.S. in Naval Architecture and Marine Engineering from the Turkish Naval Academy in 2006. He received his S.M. in Naval Architecture and Marine Engineering from MIT in 2010. He received his Ph.D. in Aerospace Engineering (Ocean Engineering Specialization) from Virginia Tech in 2022. He is a student member of ASME and SNAME and alumni of MIT (Educational Counselor). DR. ALAN J. BROWN, CAPT USN (RET), is currently NAVSEA Professor of Ship Design, Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Tech. He was Professor of Naval Architecture and directed the Naval Construction and Engineering Program at MIT from 1993 to 1997. Dr. Brown was the 2021 recipient of the ASNE Harold E. Saunders Award (Lifetime Achievement), the 2007 recipient of the ASNE Solberg Award (Research), and the 2015 recipient of the SNAME William H. Webb Medal for outstanding contributions to Education in naval architecture, marine or ocean engineering. As an Engineering Duty Officer from 1971 to 1998, he served in ships, fleet staffs, shipyards, NAVSEA, and OPNAV. He received his Ph.D. in Marine Engineering in 1986 from MIT. He is a member of ASNE and a fellow of SNAME.

REFERENCES Appleget, J., Burks, R., & Cameron, F. (2020). The Craft of Wargaming. Naval Institute Press. Brefort, Dorian, Shields, Colin, Habben Jansen, Agnieta, Duchateau, Etienne, Pawling, Rachel, Droste, Koen, Jasper, Ted, Sypniewski, Michael, Goodrum, Conner, Parsons, Mark A., Kara, Mustafa, Roth, Mark, Singer, David J., Andrews, David, Hopman, Hans, Brown Alan J., Kana, Austin (2018). An architectural framework for distributed naval ship systems. Ocean Engineering, 147, 375–385 Brown, Alan J. (2020). Ship and Marine Engineering System Concept Design. In Michael G. Parsons (Ed.), Marine Engineering (4th ed.). Alexandria, VA: SNAME Brown, Alan, Salcedo, Juan. (2003). MultipleObjective Genetic Optimization in Naval Ship Design, Naval Engineers Journal, 115(4), 49-61 Brown, Alan J., Thomas, M. (1998). Reengineering the Naval Ship Concept Design Process” in From Research to Reality in Ship Systems Engineering Symposium, ASNE Brown, Alan J., Sajdak, John. (2015). Still ReEngineering the Naval Ship Concept Process. Naval Engineers Journal, 127(1), 49-61

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Chalfant, Julie, Chryssostomidis, Chrys, Snyder, Daniel, Parsons, Mark A., Brown, Alan (2017). Graph Theory Applications in FOCUS-Compliant Ship Designs, in Electric Ship Technologies Symposium 2017, Alexandria, VA: IEEE Department of Defense. (2020). DOD Dictionary of Military and Associated Terms. Washington, D.C., USA. Goodfriend, David, Brown, Alan J. (2018). Exploration of System Vulnerability in Naval Ship Concept Design, Journal of Ship Production and Design, 34(1), 42-58. Federation of American Scientists, “Introduction to Naval Weapons Infrared Propagation and Detection”, accessed 16 November 2019, https://fas.org/man/dod-101/navy/docs/es310/ IR_prop/IR_prop.htm. Friedman, N. (2006). Naval Institute Guide to World Naval Weapon Systems (Fifth Ed.). Naval Institute Press. Hughes, W., & Girrier, R. P. (2018). Fleet Tactics and Naval Operations (Third Ed.). Naval Institute Press.

Kerns, C., Brown, A. J., & Woodward, D. (2011). Application of a DoDAF Total-Ship System Architecture in Building Naval Ship Operational Effectiveness Models. MAST Americas, 2011. Kerns, L. C., Brown, A. J., & Woodward, D. (2011). Application of a DoDAF Total-Ship System Architecture in Building a Design Reference Mission for Assessing Naval Ship Operational Effectiveness. Naval Engineers Journal. Parsons, M. A., Kara, M. Y., & Brown, A. J. (2022). Refinement of a Mission, Power, and Energy System Architecture Flow Optimization Method and Tool for Surface Ship Concept Design. Naval Engineers Journal, 134(3), 109-128. https://bt.editionsbyfry.com/ publication/?m=63540&i= 762944&p=110&ver=html5 Parsons, M. A., Kara, M. Y., Shane, A., Berrow, D. J., & Brown, A. J. (2023). Dynamic Architecture Flow Optimization for Assessing Mission Effectiveness in Surface Ship Concept and Requirements Exploration. Naval Engineers Journal, in press.

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Parsons, M. A., Robinson, K. M., Kara, M. Y., Stinson, N. T., Snyder, D., Woodward, D., & Brown, A. J. (2020). Application of a Distributed System Architectural Framework to Naval Ship Concept and Requirements Exploration (C&RE). Naval Engineers Journal, 132(4), 105–124. https:// bt.e-ditionsbyfry.com/publication/?m= 63540&i=694411&p=106&ver=html5 Payne, C. M. (2010). Principles of Naval Weapon Systems (Second Ed.). Naval Institute Press. https://books.google.com/ books?id=MUUmQwAACAAJ Purdy, E. M. (2004). “Test and Evaluation WIPT.” Proceedings of the 20th Annual Test & Evaluation Conference & Exhibition. Reno, NV, USA.

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Shane, A., Parsons, M. A., Berrow, D. J., Kara, M. Y., & Brown, A. J. (2023). Operational Architecture and Framework for Assessing Mission Effectiveness in Surface Ship Concept and Requirements Exploration. Naval Engineers Journal 135(2), 101-117. https://bt.e-ditionsbyfry.com/publication/? m=63540&i=798580&p=102&ver=html5 Stevens, John D., Opila, Daniel F., Oh, Eun S., Zivi, Edwin L. (2017). All-Electric Warship Load Demand Model for Power and Energy System Analysis Using Exogenously Initiated Threats, in Electric Ship Technologies Symposium 2017, Arlington, VA: IEEE Stepanchick, Justin, Brown, Alan. (2007). Revisiting DDGX/DDG-51 Concept Exploration. Naval Engineers Journal, 119(3), 67-88

Strock, Justin, Brown, Alan. (2008). Methods for Naval Ship Concept and Propulsion System Technology Exploration in a CGX Case Study. Naval Engineers Journal, 120(4), 95-122 Snyder, Daniel J., Parsons, Mark A., Brown, Alan J., Chalfant, Julie. (2019). Network Architecture Framework Applications with FOCUS-compliant Ship Designs, in Electric Ship Technologies Symposium 2019, Alexandria, VA: IEEE Weapons Division, Naval Air Warfare Center (2013). Electronic Warfare & Radar Systems Engineering Handbook. Point Mugu, California.

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TECHNICAL PAPER

Carbon Footprint and Life Cycle Cost Assessment of a Hydrogen-Based Energy Storage System for Ships with a Case Study Ibrahim S. Seddiek,1Nader NaderR. R.Ammar Ammar2,3

Abstract

Introduction

Applying different methods to reduce the negative effects of vessel emissions presents one of the priorities of bodies concerned with maritime industry, the most important of which is the trend towards relying on alternative fuels, such as hydrogen. The present research establishes an environmental and economic comparison between conventional and hydrogen powering systems in maritime application. Different ship types are selected to evaluate the proposed system. The environmental effects are described in terms of their ability to cause photochemical reactions, global warming (GWP), particulate matter, eutrophication, and acidification. The findings indicated that using hydrogen fuel has distinct environmental advantages. Comparing the reduced carbon emissions to the traditional marine fuels, the total carbon footprint will be reduced by 81.5% to 83.1%. The economic results reveal that using hydrogen causes increment of ships’life cycle cost assessment by about 30 percent. However, hydrogen of 4.0 $/ kg will be an eco-friendly alternative fuel.

(Lee et al., 2020, Yang et al., 2018) pointed that the transported goods by ships worldwide in tons showed a significant growth during the last ten years, which led to a considerable increment in both ships’ number and the corresponding amount of consumed fuel. International Maritime Organization (IMO) announced that the total annual fossil fuel consumption by ships all over the word reached approximately 300 million tons (Zincir et al., 2019). IMO announced in 2020 that greenhouse gasses (GHG) emitted from ships have increased by 0.13% during years 2012-2018 (IMO, 2020). In addition to GHG emissions, nitrogen oxides (NOx) and sulfur oxides (SOx) emissions from shipping increased from 19 and 10.2 mega tons in 2014 to 20.9 and 11.3 mega tons in 2018, respectively (Wei et al., 2018, Liu et al., 2021, Deng et al., 2021). The above-mentioned statisticsmake ship emissions a focus point in all meetings of Marine Environmental Protection Committee (MEPC), which belongs to IMO. The international requirements target by 2050 is to decrease GHG emissions by 50%compared with 2008emissions(Gössling et al., 2021, Joung et al., 2020, Ölçer et al., 2018). Achieving a sensible energy efficiency improving is one of the major goals of IMO with respect to the maritime environment (Ammar and Seddiek, 2021, Sadek and Elgohary, 2020). Energy Efficiency Design Index (EEDI), launched in June 2013, was used as a measure to reduce GHG from ships, but unfortunately it is not enough as it applies only on newly built ships (Ammar and Seddiek, 2020, Lindstad et al., 2019). Recently,IMO lunched specificemissions’regulationsthatbelong to GHG for workingvesselsstarting from 2023 (Psaraftis, 2021, DNV, 2021). IMO policy is to attain carbon intensity reductions of 40% and 70% by 2030 and 2050, respectively and phase them out by the end of the century (Antonopoulos et al., 2021, IMO, 2021).

KEYWORDS: Hydrogen, ship emissions, international regulations, Ship cost analysis Ship environmental assessment.

1 College of Maritime Transport & Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria, Egypt. 2 Faculty of Maritime Studies, King Abdul-Aziz University, Jeddah, Saudi Arabia Kingdom. 3 Department of Naval Architecture and Marine Engineering, Faculty of Engineering, Alexandria University, 21544 Alexandria, Egypt

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Carbon Footprint and Life Cycle Cost Assessment of a Hydrogen-Based Energy Storage System For Ships

On the other side, at interval periods, maritime transport stakeholders pay attention to find suitable methods that can achieve the international maritime requirements. Those strategies may be classified into three main procedures, namely design, operational, and technical (Seddiek, 2015, Atilhan et al., 2021, Sazali, 2020, Wang et al., 2019, Gilbert et al., 2018, Khojasteh Salkuyeh et al., 2017). Moreover, there are certain factors that must be taken into consideration in case of studying the applicability of hydrogen as fuel onboard ships instead of diesel oil. Both life cycle cost assessment (LCCA) and life cycle assessment (LCA) are two main factors that affect the choice of the suitable fuel onboard ships. The objective of this paper is to evaluate the economic and environmental effectsof using renewable-hydrogen onboard vessels. A comparative life cycle assessment and life cycle cost assessment, during vessel life time, is performed with diesel-powered ships as a baseline. LCA comparison is evaluated using differentsoftware’s for assessing the environmental impacts. LCCA will consider the lifetime costs for the conventional diesel and renewable-hydrogen operated ships. Three different ships with three routes are investigated as a case of study.

Literature review and background of LCA LCA is a method used for evaluating the environmental effects of systems and products. This approach studies the total emissions released during the whole life cycle of a product(Kairies-Alvarado et al., 2021). The main principles of LCA are provided in ISO 14040 and 14044 standards(ISO 14040, 2006, ISO 14044, 2006). The aim of the current LCA in the present paper is to investigate GHG emissions from two different ship engines operated with diesel and hydrogen fuels over their total life cycle. The different LCA processes and systems are evaluated using different software packages illustrated in Fig. 5. Thesesoftware systems investigate two phases of the

LCA: the well-to-wheel (WTW) and well-to-pump (WTP), based on the input structure boundaries. Carbon footprint is calculated for these two options expressed in CO2-eq. WTW phase presents the emissions released from the different processes during engine manufacturing, while WTP phase presents the emissions released during the processes involved in crude oil recovery, transportation, refinery, and its transportation to the pump. The third LCA phase is pump-to-wheel (PTW). It presents the tailpipe emissions released during fuel burning. The input for the current life cycle assessment is the energy systems used in the life-cycle stages while the outputs represent the released GHG emissions. From the literature review, (Hienuki et al., 2021) investigated the LCA for a passenger vehicle using hydrogen fuel produced from fossil fuels. The results showed a specific environmental and energy efficiency benefits for reducing the power required for hydrogen production. (Wu et al., 2021) performed LCA and LCC for hydrogen/methanol manufacturing systems for fuel cell vehicle operated with hydrogen fuel. (Gupta et al., 2019) studiedthe well-to-wheel investigation for hydrogen production from natural gas for vehicles. The results showed specific benefits for natural gas pathways, compared to biomass gasification method. (Chen et al., 2019) premeditated the LCA of a fuel cell operated vehicles in China investigating different methods for producing hydrogen fuel. Hydrogen fuel produced from renewable sources showed a dual benefit from energy requirements and GHG emissions. (Hwang et al., 2020) presented a comparative LCA for a Korean coastal ferry operated by natural gas, marine gas fuel, and hydrogen. The authors suggested a plan for using hydrogen fuel for small coastal vessels. (Perčić et al., 2020) considered a comparative LCA for alternative marine fuels, including hydrogen gas in Croatia. Electric vessel showed the best environmental option among the options investigated.

FIGURE 1. Different LCA processes for diesel-powered vessel.

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FIGURE 2. Components of ship propulsion system using renewable hydrogen fuel.

FIGURE 3. Different LCA processes for hydrogen-powered vessel.

Life-cycle assessment of a diesel-powered vessel

The life cycle assessment (LCA) of a diesel-powered ship takes into account all emissions produced over the course of that vessel’s operation, from emissions produced during engine production and WTW to emissions produced during fuel combustion and PTW. The various phases of the LCA are depicted in Fig. 1. In addition, WTW emissions are significantly influenced by the proportions of the various metals used in machine production.

Life cycle assessment of a hydrogen-powered ship

One of the renewable energy alternatives that can be produced on board vessels is hydrogen fuel. The current study assumes hydrogen production in land and transported and stored onboard the ship in the liquid form.In order to produce hydrogenin land, electricity is first generated using onshore renewable energy resources. This power is used to operate electrolysers, which produce the amount ofhydrogen fuel used to operateProton Exchange Membrane fuel cell (PEMFC), as revealed in Fig. 2. The vessel’s propulsion is provided by an electric motor that is driven by the fuel cell’s output electric power. The details of the system operation and components can be found in (Ghenai et al., 2019).

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Fig. 3 depicts the primary elements of a renewable hydrogen-powered ship’s LCA. The major components of the LCA are the WTP emissions. These emissions include the emissions released during electricity generation using renewable energy,hydrogen production, liquefaction, and distribution processes. WTW emissions include the emissions released during electrolysis and fuel cell manufacturing processes. The third phase of the LCA, PTW, is the emissions released during PEMFC operation onboard the ship. These are no tailpipe emissions due to the zero emissions for fuel cell operations operated with pure hydrogen fuel.

LCCA for maritime applications

LCC refers to the sum of all the cost factors related to the asset during its operational life, which is considered a challenge for practitioners trying to calculate costs (Woodward, 1997).(IEC, 2017)refers that LCCA typically includes the cost of acquisition, operating costs, maintenance costs, and costs of disposal at the end of items’ functional life. This concept is very important for most industry fields, especially the maritime sector. Unfortunately, shipping industry witnesses a challenge that appears in the form of life cycle management challenge, as maritime

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transport has a long process that includes many activities until the product delivery (Favi et al., 2017). The last century showed some practical and academic studies to support the concept of LCCA in maritime transport.(Favi et al., 2017) had established a life cycle cost model to assess costs and environmental impact of different maritime vessels.(Jeong et al., 2018b)discussed SHIPLYS project, which is presented by European Union, regarding the life cycle and cost assessment.(MAN, 2019)carried out an LCCA study to estimate the building cost of spreading of electric thrust system on sea going ships for a specific capacity. In addition to the general researchstudies concerning LCCA for ships, due to the priority of hydrogen from the environmental perspective environment, some of the studies handled the costs of hydrogen powered ships. The Eco-REFITEC project with the concern of LCCA was one where the European Union emphasized on the applicability of using green power, especially from hydrogen, on board ships(Blanco-Davis and Zhou, 2014). (Zincir and Deniz, 2016)studied the economic assessment of alternative fuels. They concluded that more studies should be carried out on hydrogen fuel application as an alternative fuel from the economic perspective. (Svemark and Dervishllari, 2020) studied the LCCA of an electrified inland waterway vessel usinghydrogen-fueled concept. Their study revealed that the electric concept vessel will be more expensive in both construction and operational costs with current prices and industry standards compared to a similar size vessel installed with a diesel propulsion system.(Aarskog et al., 2020)studied the energy and cost assessment of a hydrogen driven high speed passenger ferry. The main outcome of this study was that the hydrogen solution has 28% higher annual costs than a conventional diesel solution. In 2020,a technical reportwas carried out by one of the interested companies to demonstrate the progress on the world’s first Sea-going H2-powered ROPAX ferry called HySeas III. The project revealed that the life cost of hydrogenis affected with the nature of the prime mover (Trillos et al., 2019). (Kopasz et al., 2021)discussed the potential role of hydrogen as a fuel onboard a ferry, a harbor tug, and river towboat and concluded that the cost of hydrogen in $/kg presents a barrier toward its applicability onboard those ships. Most of the previous studies support the concept of LCCA in maritime industry and open the door for more specific studies, especially the technical issues, such as different energy sources and fuel types.Researches showed that LCCA of any ship is the sum of technical and non-technical items, as shown Fig. 4. The figure classifies the costs as groups of the related functions. Group (A) represents the initial ship’s cost, group (B) states the ship’s powered costs during the life time, group (C) displays the crew related costs, and, lastly, group (D) displays the support and services costs.

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FIGURE 4. The main elements of ship's LCC

For holding an LCCA comparative, this depends on what the repeated costsare. For example,as for establishing a comparative from view point of ship powered system, only group (B) will be affected. However, the costs of the other groups are still the same for the case study. Hence, the presented paper will concentrate on LCCA for group (B), in case of diesel and hydrogen-powered ships.

LCA framework description To make good LCA, the International Standards Organization (ISO) presented a proceduraloutlinetitled ISO 14044 for carrying out LCA studies.The frameworkincludes four stages: Goal and Scope, Inventory Analysis (IA), Life Cycle Impact Assessment (LCIA) and Interpretation(Loubet et al., 2017, Dong et al., 2022). Fig. 5 explains the route of LCA, with the specific current case study.

Goal

The focal objective of the current research is to perform an environmental evaluation of the proposed hydrogen operated fuel cell, which is planned to be fitted on board three different ships at the Red Sea Area (RSE). This alternative was compared with diesel engines’ propulsion systems. The purpose of this investigation is to evaluate the advantages and disadvantages of using hydrogen and fuel cells in marine applications. Theprograms used for the calculations were GREAT2021, CML 2001, TRACI 2.1, and Footprint 2.0. Principal information in terms of modeled power use and engine performance was obtained from the ships’ operator companies. The information gaps not fulfilled by the program were covered using literature sources.

3Scope

This work focuses on inherent function, which is presented in the form of machinery as it constitutes the different propulsion

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cells. LCA considers all types of variousconstituents used in the manufacturing method in their data base as described by(Jeong et al., 2018a). The main and auxiliary engine consumptions for the fuel and lube oils are included in the environmental analysis with a sea margin of 20% (ITTC, 2017, Magnussen, 2017). The main global warming emission species include CO2, N2O, and CH4 through the vessellifetime mileage (LTM). GHG emission factors for CO2, CH4, and N2O are shown in Table 1. IPCC 2020is used as the foundation for the GWP’s values (IPCC, 2021).

Environmental Assessment

FIGURE 5. Framework of life cycle assessment for ships alternative fuels

systems and varies between the considered alternatives. The functional unit used for this study depends on the emission types evaluated, and they are related to reference elements and materials depending on the studied impact throughout the lifetime considered 20 years.

Inventory Analysis (IA)

In this stage of the life cycle analysis, the data used in the analysis, system boundary and the calculation procedures used to calculate the inputs and outputs of the system are illustrated(Schrijvers et al., 2021). The data used in the LCA includes the different properties of diesel and hydrogen fuels and the emission factors for the used marine diesel engines and fuel

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In general, there are eighteen environmental impacts that could be assessed using life cycle assessment, which are classified under midpoint impact categories. Those categories have linkage with eight damage pathways. Consequently, the pathways will cause specific damage to human health, damage to ecosystem, and damage to resource available (Huijbregts et al., 2017). Among the eighteen environmental impacts, five impacts will be assessed, which have a direct relation with ships’ activities. These impacts are Acidification Potential (Aci. pot.),Global Warming Potential (GWP), Particulate Matter (PM),Photochemical Ozone Creation Potential (Photo. Chem. Pot.), and Eutrophication Potential (Eut. Pot.). Aci. pot. is an index that defines the acidifying consequence of constituents. Their acid effects are estimated and related to a reference material, sulfur dioxide (SO2) (Dincer and Abu-Rayash, 2020). Photo. Chem. Pot. index shows the possible capacity of an organic composite to generate ozone in the troposphere. Ethene is used as a reference element for this index (Pettersen and Song, 2017). Eut. Pot. is an index for the evaluation of the extreme biological action of living matters as a result of over-nutrification compared to the reference elements,such as nitrogen (N). PM indicates the dust released into the troposphere or formed by photochemical responses, evaluated as a single index with reference to PM2.5. The environmental effects are assessed in the current paper by means of three methods. CML 2001method is used to estimateSO2 emissions and the global warming potentialexpressed inSO2 equivalent and ton CO2 equivalent, respectively. TRACI 2.1 program is used to calculate PM emissions expressed in PM2.5 equivalent. In addition, Eut. Pot. impacts and Photo. Chem. Pot. are assessed using Environmental Footprint program. The results of Eut. Pot. impacts and Photo. Chem. Pot. are being described inkg N equivalent and kg of non-methane volatile organic compounds (NMVOC) equivalent. Lastly, Aci. pot. impact is estimatedby means of CML 2001method expressed in SO2 equivalent(Dreyer et al., 2003).

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Interpretation

The final section in LCA framework is the interpretation. It demonstrates environmentalconsequences due to using an alternate fuel, providing anargument of the results with reference to the objective as well as scope and setting.

Methodology and modelling The methodology of this paper depends on using different LCIA software packages to evaluate the emissions related to well-to-wheel (WTW) and well-to-pump (WTP) phases. The main focus of the current analysis is comparing the environmental impactsof the two different power systems. Therefore, the software package is set on the power system. The emissions related to vessel hull and other ship schemes are not considered.In order to calculate the PTW emissions for a ship, the energy and fuel consumption per distance should be estimated.The second part of the methodology appears in the form of building a simple model that simulates the ship powering system from an economic perspective.

Environmental modelling

For a single voyage, the onboard diesel-powered ship’s energy consumption per distance (CE), represented in kWh/nm, can be computed as follows: (1)

where, PME is the power of the main engines, Vde and Vav are the vesseldesign and average speeds, respectively. A ship’s fuel consumption per nautical mile can be calculated as a function of both the specific fuel consumption and the energy consumed per distance (be). For each greenhouse gas species, pump-to-wake emissions (PTWi) can be calculated using be, CE, and the associated emission factor (Fi). As indicated in Eq. 3, PTWi can be estimated in kg emission/nm. (2)

The tailpipe GHG emissions (TotalGHG) for a vessel calculated in CO2-eq can be premeditated as illustrated in Eq. 3. (3)

where, (GWPi) stands for the global warming potential for greenhouse gas emission types, i, (CO2, N2O, and CH4) during the vessel lifetime mileage (LTM).

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LTM for a ship is estimated using a number of trips (Z) and distance sailed (D) throughout the vessel lifetime (T), as shown in Eq. 4. (4)

On the other hand, the weight of the materials required for a particular engine is an important factor in evaluating emissions released during the manufacturing process. The engines weights (We) for an average ship power (Pav), expressed in tons, can be calculated using Eq. 6(Jeong et al., 2018a). (5)

The equal energy consumption for hydrogen fuel per nautical mile (CEH), expressed in kilograms per nautical mile for a vessel powered by diesel machinery at specific energy consumption (CE), can be computed as follows: (6)

where ηFC is the efficiency of fuel cell system, assumed 48% for Polymer Electrolyte Fuel Cell. CVH is the hydrogen calorific value (Rivarolo et al., 2020). The electric load (ELC) required for hydrogengasproductiononboard ship isestimatedby means of Eq. 8. (7)

where ηFLE is the efficiency of the electrolyser (90%), and FOS refers to the safety factor for hydrogensystem (Ghenai et al., 2019).

LCCA Modeling

Estimation of LCCA for a certain engineering system is composed of the initial costs (CapEx), the operating costs (OpEx), and decommissioning cost (DiEx). LCCA can be calculated as shown in Eq. 8. (8)

CapEx component for any power source includes the cost of power source and its installation cost onboard ship. With respect to diesel engine, value of CAPEXiD is cost of ship powering system, which is estimated as follows: (Ammar and Seddiek, 2021, Sadek and Elgohary, 2020):

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(9)

where (PME, D) is the power of prime mover in kilowatt, (Cinsde) is the cost of one kilowatt in US dollar, is corresponding installation costs in $, ( consumed power of auxiliary engines, (CGE) is the cost of generation one kilowatt by auxiliary engine, (Cinsge) is the fitting cost for auxiliary engines. With reference to hydrogen fueled system, CAPEXiH could be calculated as follows: (10)

refers to the required expenditure of the power system or the scrap price at the final stage of its life cycle. With reference to the diesel-powered ships, this cost will be variable according to the actual condition of the machinery and the scraping market. Depending on the practical experience of the authors, it may account for 20% of the engine cost. However, in case of fuel cell the gain scraping cost will only be from electrical generators. For more specific economy comparison between the two different fueledsystems, the net present value (NPV) is used and estimated as follows: (Žižlavský, 2014, Gaspars-Wieloch, 2019): (15)

where (CFC) the cost of fuel cell unit, at specific power (PFC) in kW. (Cinsh) the installation cost which includes cost of fuel cell installation, and hydrogen fuel piping arrangement. (LH2SC) is the cost of liquid hydrogen storage vessels. Elements of OpEx simply reflect the all-ship’s activities throughout a certain period and are expressed as follows:

Moreover, the levelized cost of energy (LCOE) is used to evaluate the priority of the above-mentioned fuel system per energy production unit, is estimated as follows in Eq. 16 (Seddiek and Ammar, 2021):

(11)

(16)

where (FCj) is ship’s fuel expenditure, which are estimated for both conventional and hydrogen fueled system, as shown in Eqs. 12 and 13(Qiu et al., 2021): (12)

(13)

where (be,D), (TS), (be,DG), (TGE), (K), (PGE), (N), (PRD), and (PRH) is the amount of fuel burning by the prime movers , the annual vessel traveling period , auxiliary engine c consumed fuel per power unit for each running hour, , the annual running hour of auxiliary engines, number of auxiliary engine in working mode, ship’s expected life time, diesel fuel oil cost, and hydrogen fuel cost, respectively. The term (MCj) refers to planned maintenance activities that are carried out onboard ship for prime mover and auxiliary engines. With regard to diesel fueledsystem, maintenance cost includes lubricating oils, spare parts for both the prime mover and auxiliary engines, and any other related activities. However, in case of hydrogen fueled system, this includes the same activity for the electrical generators in addition to replacement of fuel cell units themselves (Inal and Deniz, 2020). (DiEx) term

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where (k) refers to rate of interest, and (Et) expressesthe cost of energy production unit per year.

Case Study Data All the selected case study vesselsoperate at the Red Sea area, which has countries at its long sides, with high probability of effective ship emissions. M/V ALKAHERA travels between port of Duba at king of Saudi Arabia and the Egyptian port of Safaga with 100 annual trips. M/V AL HURREYA sailing between Jeddah port and Suez portcarried out about 33 trips in year. M/V NAMMA expresses travels between Jeddah port and port Sudan, carried out 90 trips yearly. The basic data of the case study are summarized in Table 2(Vessel Finder, 2021, Marine Traffic, 2021). A period of 20 years will be considered during the calculations of LCA and LCC. Some assumptions are set with reference to the used marine fuel type, including specification, price, and maintenance cost. On the other side, assumptions of hydrogen fuel system include its production way, cost, and the unit cost of fuel cell. With regard to marine diesel engines, the cost of prime mover in per power unit is 360 US dollars (Kistner et al., 2021), specific fuel consumption for main engines and electrical generator are 180 and 200 g/kWh (Banawan et al., 2010), respectively. The installation cost is 10% of the engine price (Sadek and Elgohary, 2020). According to(Bunkerworld, 2021), the cost

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Results and discussions

Electric engines should run on the power generated by fuel cells. The ship will receive all of the meanenergy needNAMMA EXPRESS ed for the prime mover and generator Roll on/Roll off systems from this electric powerplant. 21 Onboard vessel 1, vessel 2, and vessel 121.48 3, proton exchange membrane fuel cell 4734 with output powers of 6,565, 23,370, and 5.2 4,565 kW, respectively, shall be employed 12.11 to provide the necessary propulsion and auxiliary powers. The higher hydrogen 154 consumption rate in the PEM fuel cell, 770 the higher the electricity needed for 5250 hydrogenproduction, Vessel 2has the 91 highest hydrogen consumption rate (27.31 kg/nm) and electricity required (1011 kWh/nm) as a result of its high total average power (11,130 kW) compared with two ships. The amount of electricity consumption for producing hydrogen are 483.3, 1011, and 560 kWh/nm for vessels 1, 2, and 3, respectively. However, consumed energy per distance for diesel powered ships is 208.4, 436.5, and 241.5 kWh/nm, respectively. Therefore, the electricity consumption for the operation of the electrolyser is higher than consumed energy onboard diesel-powered ships by 56.87%. Comparative LCA for hydrogen- and diesel-powered vessels during a 20-year lifespan are shown in Figs. 7(i) and (ii). Compared to diesel-powered ships, hydrogen-powered ships have a less carbon impact. For hydrogen-powered vessels, there are no PTW emissions, and the WTP emissions make up the majority of the LCA. Well-to-pump emissions for hydrogen-powered

According tothe case study’smain specifications and assumptions, theaverage ship speed and power are derived. PTW emission outputs also take into account expected working distance and fuel consumption per km. It should be highlighted that despite having the greatest lifespan mileage (839,520 nm), ship 1 has the lowest average energy consumption per distance (208.4 kWh/nm). This is as a result of the lifetime average ship power and speed. The highest total average power and energy consumption from the studied vessels are 11,130 kW and 436.5 kWh/nm, for ship 2. Eq. 2 and the information in Table 4 are used to compute the tailpipe or PTW phase emissions. The amount of electricity needed for hydrogen consumption and the vessel’s battery capacity determine the well to pump emissions for hydrogen vessels. The necessary electric load and hydrogen consumption for the three vessels is shown in Fig. 6.

FIGURE 6. Hydrogen and electricity consumptions for the case studies

Specifications

Vessel 1

Vessel 2

IMO number

9266487

9441788

Vessel name

AL HURREYA

ALKAHERA

Vessel category

Roll on/Roll off

Passenger

Breadth (m)

23.69

LBP (m)

139.5

DWT

6000

562

Draft (m)

6.8

3.2

Ship speed (kn)

17.10

34.12

Sailing distance (nm)

635

104

Generator power (kW)

410

1,200

Prime mover power (kW)

8,640

31,200

Trips/year

101

TABLE 2. Ships, main particulars

of one metric ton of diesel fuel is 720$. In most cases, a cost of 0.04 $/kw is put for activities of preventive and predictive maintenance, including oils and spare parts, of the engines (Iannaccone et al., 2020, Banawan et al., 2010). However, in case of hydrogen fueled system, the cost of production one kilowatt using fuel cell is 380 $/kW (Hydrogen Council, 2020), assumingly adding 20% of this cost as a result of system arrangement and storage. The market of hydrogen fuel indicates that its price lies between 6 to 8 $/kg (Perna et al., 2022). It is assumed that the fuel cell stack will need to renew every 1000 operating hours (Eudy and Post, 2018). Throughout the present study, 6 US dollars is taken per kg of LH2.

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Vessel 3

8009064

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(i)

(ii)

FIGURE 7. GWP for hydrogen and diesel-powered vessels.

(i)

(ii)

FIGURE 8. Eutrophication and acidification potentials for hydrogen and diesel-powered ships.

ships are 240-ton CO2-eq, 507-ton CO2-eq, and 233-ton CO2eq for vessels 1, 2, and 3, respectively. On the other hand, PTW emissions are the highest components of the LCA, for diesel operated ships. These values are 1200-ton CO2-eq, 2817-ton CO2-eq, and 1227-ton CO2-eq, respectively. Therefore, the hydrogen fuel total carbon footprint is lower than that of diesel fuel by 81.5%, 83.1%, and 82.2%, respectively. Figure 8 displays the findings of the effect assessment of Eut pot and Aci pot for ships using hydrogen as well as diesel fuels. Compared to hydrogen-operated ships, diesel-operated ships produce the highest Aci pot. This is because vessels that run on diesel fuel have a high concentration of substances that have an acidification impact. Due to its high speed and power usage, vessel 2 also has the largest Aci. pot. when compared to the other two vessels. After vessel 2, the emissions from vessel 3 were at the next level. The majority of these emissions are

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produced by engines’ tailpipe emissions, which are emitted when fuel is burned (PTW emissions). The trend for Eut. pot. emissions aresimilar to that for Aci. pot. impact emissions. The effect evaluation of particulate matter and ph. chem. pot. emissions for diesel and hydrogen propelled ships are depicted in Figure 9. Compared to vessels fueled by hydrogen, the life cycle consequences of diesel-powered ships are greater in volatile organic compounds (VOCs), which cause ground level ozone. Comparing the two vessels, Vessel 2 has the highest Ph. chem. pot. emissions. After Vessel 2, Vessel 3 depicts the second level up. The majority of these exhaust gases are displayed during the pump to well period of vessel life. The trend of PM emissions is similar to that of Ph. chem. Pot. effect emissions. With regard to the economic issue for the mentioned case study, the terms of cost are estimated as described in Figure

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10. Figure (10-i) presents M/V ALHURIA economic characteristics, as the CAPEX, fuel cost, and maintenance cost are 3.45, 63.48, and 6.36 million dollars, respectively, for marine diesel engines. However, those values showed a considerable change in case of hydrogen fueled ship to be 4.76, 75.75, and

(i)

16.78 million dollars for the CAPEX, fuel cost, and maintenance cost, respectively. Along the same lines, Fig. (10-iii) shows ship 3 cost expanders elements trend. Based on its data, the CAPEX, fuel cost, and maintenance cost are 2.15, 43.06, and 4.543 million dollars,

(ii)

FIGURE 9. Particulate matter and photochemical potential emissions for hydrogen and diesel-powered ships.

(i)

(ii)

(iii)

(iv)

FIGURE 10. Ships’ Life cycle cost elements

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respectively, for marine diesel engines. Those values showed an obvious change in case of hydrogen fueled ship to be 2.92, 60.68 and 11.12 million dollars for the CAPEX, fuel cost, and maintenance cost, respectively. From the previous results, FCj for the vessels are 87.44, 81.82 and 87.30 % for conventional fueled system and 77.86 %, 76.13 % and 81.20% of LCC in case of hydrogen felled system. This indicates the role of fuel system in the economy study. In addition, in order to reach realistic and optimum economy benefit, hydrogen production cost needs to show an attractive and competent price at 4 $/kg or below. Moreover, Fig. (10-iv) illustrates the corresponding ratio of LCCA as a comparison between the two studied systems for the studiedvessels. The figure indicates that applyinghydrogen will make the vessels’ LCC higher than the conventional system for the three vessels. The increment percentage of LCC due to shifting to hydrogenfueled system may reach 33.2 % to 51.4%. It can be noted that ship 1 is considered the best economic selection; however, ship 3 will be the worst one with regard to economic issue. It can be deduced that the main drawback of hydrogen-powered vessels is the renewable hydrogen fuel price. Lastly, Fig.11 shows a comparison among the studied ships with reference to LCOE. The figure illustrates the conventional systems will have LCOE lower than the hydrogenfueled systems. Ship 1 of diesel fueled system achieves the lowest LCOE wit 0.183 $/ per kWh. However, the same ship proposed to be converted to hydrogen powered with energy production of 0.243 US dollars per kWh. The results imply that the ships’ working hours play a role with respect to LCOE estimation as the highest ship working hours the lower LOCE, with a positive effect on ship’s LCCA.

Conclusions LCA and LCCA comparison is examined over a 20-year lifespan for diesel and hydrogen propelled ships. The environmental findings are provided in Acidification Potential (Aci. pot.), Global Warming Potential (GWP), Particulate Matter (PM),Eutrophication Potential (Eut. Pot.),and Photochemical Ozone Creation Potential (Photo. Chem. Pot.). A simplified cost assessment model for both hydrogen as well as diesel-poweredships is also provided. The life cycle cost assessment included the CAPEX, OPEX, and ENVEX as the main cost components of ships powering systems.Three vesselsworking in the Red Sea areawere examined as a case study. The following is a summary of the paper’s main findings: From environmentalpoint of view,the electricity consumption for operating the electrolyser is higher than

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FIGURE 11. Ships, Levelized Cost of Energy

consumed energy onboard diesel-powered ships by 56.87%. The hydrogen-powered vessels emit no PTW emissions,and the WTP emissions is the highest component of the LCA. Well-to-pump emissions for hydrogen-powered ships are 240ton CO2-eq, 507-ton CO2-eq, and 233-ton CO2-eq for vessel 1, 2, and 3, respectively. On the other hand, PTW emissions are the highest components of the LCA, for diesel operated ships. These values are 1200-ton CO2-eq, 2817-ton CO2eq, and 1227-ton CO2-eq, respectively. Consequently, the hydrogen fuel GHG is lower than traditional fuel by 81.5%, 83.1%, and 82.2%, respectively. Finally, the Photo. Chem. Pot., Aci. pot., PM emissions, andEut. Pot. arereduced in hydrogen operatedships, compared with diesel operated vessels. With respect to the economic analysis, the results revealed that using hydrogen as an alternative fuel with fuel cell units increases the value of LCCA when compared with the conventional ship’s propulsion system, regardless of the ship type and specifications. Operating cost sharing by the upmost portion of total ship’s cost life either with using of diesel or hydrogen powered ships. Among the OPEX elements, fuel cost presents the highest cost, with up to 80% and 75 % for conventional and hydrogen systems, respectively. Reducing the price of hydrogen to the half will open the way to increasing the possibility and applicability of fuel cell hydrogen onboard ships. The foremost recommendations of this research are as follows: environmentally, establishing onshore stations for electrolyze operation near seaports is needed, as hydrogen production from renewable energy onshore improves its life cycle impact, compared with offshore production. Economically, the mass production and searching for new methods for hydrogen production are needed to reduce the hydrogen price.

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AUTHOR BIOGRAPHIES IBRAHIM S. SEDDIEK is a marine expert with significant research experience in maritime environmental regulations, renewable energy, port energy management, marine alternative fuels, shipshore connection, ship energy efficiency, and fuel-saving onboard ships. He currently holds the position of Senior Marine Lecturer and Vice Dean of Training Affairs and Community Services at the College of Maritime Transport & Technology (CMTT), Arab Academy for Science, Technology & Maritime Transport (AASTMT). Dr. Seddiek has authored approximately 30 research papers in the field of marine engineering and ship emissions.

NADER R. AMMAR is an Associate Professor in the Department of Naval Architecture and Marine Engineering at the Faculty of Engineering, Alexandria University. Currently, he is on secondment to the Marine Engineering Department at the Faculty of Maritime Studies, King Abdulaziz University. He has actively participated in several research projects as a principal investigator at the Deanship of Scientific Research, King Abdulaziz University. His current research interests revolve around fuel cell applications in the marine field, renewable energy, ship emission reductions, and modern marine power plants.

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Khojasteh Salkuyeh, Y., Saville, B. A. & Maclean, H. L. 2017. Techno-economic analysis and life cycle assessment of hydrogen production from natural gas using current and emerging technologies. International Journal of Hydrogen Energy, 42, 18894-18909. Kistner, L., Schubert, F. L., Minke, C., Bensmann, A. & Hanke-Rauschenbach, R. 2021. Techno-economic and Environmental Comparison of Internal Combustion Engines and Solid Oxide Fuel Cells for Ship Applications. Journal of Power Sources, 508, 230328. Kopasz, J. P., Ahluwalia, R. K., Papadias, D., Wang, X. & Krause, T. 2021. Potential Role of Hydrogen and Fuel Cells for Maritime Applications: Ferries and Towboats. Transportation Research Board 100th Annual Meeting, Washington DC, United States, Date: 2021-1-5 to 2021-1-29. Lee, H.-J., Yoo, S.-H. & Huh, S.-Y. 2020. Economic benefits of introducing LNG-fuelled ships for imported flour in South Korea. Transportation Research Part D: Transport and Environment, 78, 102220. Lindstad, E., Borgen, H., Eskeland, G. S., Paalson, C., Psaraftis, H. & Turan, O. 2019. The Need to Amend IMO’s EEDI to Include a Threshold for Performance in Waves (Realistic Sea Conditions) to Achieve the Desired GHG Reductions. Sustainability, 11. Liu, J., Law, A. W.-K. & Duru, O. 2021. Assessment of COVID-19 pandemic effects on ship pollutant emissions in major international seaports. Environmental Research, 112246. Loubet, P., Tsang, M., Gemechu, E., Foulet, A. & Sonnemann, G. 2017. Life cycle assessment for green solvents. John Wiley & Sons, Ltd, pp. 131-148. Magnussen, A. K. 2017. Rational Caluclation of Sea Margin. Norwegian University ofvScience and Technology (NTNU).

Ölçer, A., Kitada, M., Dalaklis, D. & Ballini, F. 2018. Trends and Challenges in Maritime Energy Management. Cham, Switzerland: Springer. https://doi. org/10.1007/978-3-319-74576-3. Perčić, M., Vladimir, N. & Fan, A. 2020. Lifecycle cost assessment of alternative marine fuels to reduce the carbon footprint in shortsea shipping: A case study of Croatia. Applied Energy, 279, 115848. Perna, A., Minutillo, M., Di Micco, S. & Jannelli, E. 2022. Design and Costs Analysis of Hydrogen Refuelling Stations Based on Different Hydrogen Sources and Plant Configurations. Energies, 15. Pettersen, J. B. & Song, X. 2017. Life Cycle Impact Assessment in the Arctic: Challenges and Research Needs. Sustainability, 2017. Psaraftis, H. N. 2021. Shipping decarbonization in the aftermath of MEPC 76. Cleaner Logistics and Supply Chain, 1, 100008. Qiu, Y., Schertzer, D. & Tchiguirinskaia, I. 2021. Assessing cost-effectiveness of nature-based solutions scenarios: Integrating hydrological impacts and life cycle costs. Journal of Cleaner Production, 329, 129740. Rivarolo, M., Rattazzi, D., Lamberti, T. & Magistri, L. 2020. Clean energy production by PEM fuel cells on tourist ships: A timedependent analysis. International Journal of Hydrogen Energy, 45, 25747-25757. Sadek, I. & Elgohary, M. 2020. Assessment of renewable energy supply for green ports with a case study. Environmental Science and Pollution Research, 27, 5547-5558. Sazali, N. 2020. Emerging technologies by hydrogen: A review. International Journal of Hydrogen Energy, 45, 18753-18771. Schrijvers, D. L., Loubet, P. & Weidema, B. P. 2021. To what extent is the Circular Footprint Formula of the Product Environmental Footprint Guide consequential? Journal of Cleaner Production, 320, 128800.

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Seddiek, I. 2015. An overview: Environmental and economic strategies for improving quality of ships exhaust gases. Transactions of the Royal Institution of Naval Architects Part A: International Journal of Maritime Engineering, 157, A53-A64. Seddiek, I. S. & Ammar, N. R. 2021. Harnessing wind energy on merchant ships: case study Flettner rotors onboard bulk carriers. Environmental Science and Pollution Research, 28, 32695-32707. Svemark, H. & Dervishllari, E. 2020. Life Cycle Cost Analysis of an Electrified Inland Waterway Vessel Concept. Master’s thesis in Mechanics and Maritime Science. Department of Mechanics and Maritime Science Chalmers University of technology Gothenburg, Sweden. Trillos, J. C. G., Wilken, D., Brand, U. & Vogt, T. HySeas III: The World’s First Sea-Going Hydrogen-Powered Ferry@ A Look at its Technical Aspects, Market Perspectives and Environmental Impacts. 2019.

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Vessel Finder. 2021. AL HURREYA, Ro-Ro Cargo Ship, IMO 9266487. [Online]. Available: https://www.vesselfinder.com/vessels/ALHURREYA-IMO-9266487-MMSI-622139001 [Accessed 22 December 2021]. Wang, M., Wang, G., Sun, Z., Zhang, Y. & Xu, D. 2019. Review of renewable energybased hydrogen production processes for sustainable energy innovation. Global Energy Interconnection, 2, 436-443. Wei, L., Cheng, R., Mao, H., Geng, P., Zhang, Y. & You, K. 2018. Combustion process and NOx emissions of a marine auxiliary diesel engine fuelled with waste cooking oil biodiesel blends. Energy, 144, 73-80. Woodward, D. G. 1997. Life cycle costing— Theory, information acquisition and application. International Journal of Project Management, 15, 335-344. Wu, W., Pai, C.-T., Viswanathan, K. & Chang, J.-S. 2021. Comparative life cycle assessment and economic analysis of methanol/hydrogen production processes for fuel cell vehicles. Journal of Cleaner Production, 300, 126959.

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STUDENT TECHNICAL PAPER

Shipboard Microgrids and Automation Isabelle C. Patnode, Michael J. Bishop, Aaron W. Langham, Daisy H. Green, Steven B. Leeb

Abstract

Introduction

Electric power systems for marine vessels provide the beating heart for survivability, serviceability, movement, and mission. Fleet modernization for USN and USCG vessels, both existing and planned, builds critically on electric power systems. Unfortunately, automation and feedback control, hallmarks of many modernization efforts, can actually increase the challenge of maintaining mission readiness by masking developing or impending fault conditions in critical systems. This paper begins with a survey of electrical power system configurations common on ships. Opportunities for power monitoring are identified. In particular, nonintrusive power monitoring can augment or provide automatic watchstanding, logging and usage tracking, energy scorekeeping, and indicators that prognosticate impending faults. Nonintrusive power data is naturally collated in an easily accessed and securable monitoring system. This data can support ship design by providing up-to-date electric plant load analysis (EPLA) load factors. This paper demonstrates techniques across several types of marine power systems and demonstrates support for automated or semi-automated ship operation.

In 1880, the SS Columbia became the first ship to implement electric lighting.[1] Since then, electric power has become vital to shipboard operation. Designers have continuously experimented with a variety of electrical systems and controls. The United States Coast Guard (USCG) and United States Navy (USN) operate and maintain a diverse ship and cutter fleet. There are significant differences between how each ship or cutter generates, distributes, and regulates electrical power. Ships moored to a pier typically rely on ac “shore” power. Ships underway rely on their onboard or “ship” microgrid. Generally, shipboard power systems are designed closer to the requirements of shipboard loads in comparison to conventional land-based utilities and facilities. Therefore, the ac power quality aboard a ship, especially the ability to maintain a constant voltage amplitude and frequency, tends to be poorer than that of the terrestrial, shore-based electrical grid. For example, Fig. 1 shows the instantaneous supply frequency onboard a USCG cutter for an example day at sea on generator power versus an example day on land-based shore power. The shipboard generation struggles to maintain operating conditions in comparison to the land-based power feed. Although nearly all US military ships supply electrical power with marine diesel generators or gas turbines, their microgrids have several varying design characteristics. Maintaining electrical continuity in the event of a failure is vital. Unlike USN counterparts, whose generators are commonly each installed in separate spaces, USCG cutters’ ship-service marine diesel generators (SSDGs) are often all located in a single space known as the generator or engine room. In the interest of marine survivability, some of these cutters have an emergency diesel generator (EDG) located in a separate space. The EDG is commonly smaller and only designed to maintain vital loads and must be located above the damage control deck level. Operation of the ship microgrid depends on the availability of controls and user display interfaces. Both the age and the size of a ship contribute to the variety of electrical controls, configurations, and watchstander procedures. Generators aboard older military ships must be paralleled manually using an installed synchroscope, which shows the phase angle and frequency difference between the two generators. Newer

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This paper serves two roles. First, Section II provides a tutorial reviewing common ac shipboard microgrids. Section III then discusses applications of power monitoring in these microgrids. Perhaps surprisingly, nonintrusive power monitoring can be extended to all of these microgrids including ring bus systems, providing actionable monitoring information for recording ship operations and for performing condition-based maintenance.

Shipboard Microgrid Configurations

FIGURE 1. USCGC MARLIN supply frequency for a typical day at sea on generator power and in port on land-based utility power.

ships and power systems can automatically parallel generators to meet load power demand. Ship size and mission affects the choice of power system topology and bus configuration. Power systems are the vital arteries for energy flow on ships. It is no surprise, therefore, that power monitoring has a proven, but still not fully exploited, record of assisting with ship operation and maintenance. One approach for power monitoring, nonintrusive power monitoring, uses a single or small set of voltage and current sensors to monitor a collection of loads, disaggregating individual load behavior from measurements on an aggregate power stream, e.g., at a feeder to a panel.[2] A nonintrusive load monitor (NILM) samples the voltage and current (in this work at 8 kHz) at the utility point, and then computes real and reactive power, harmonic content, and system operating frequency.[3] A “NILM Dashboard” can present vital information to ship operators, providing a summary of equipment status and metrics about historical load operation and impending soft faults.[4] Feedback loop controls often mask “soft faults” which are failures in system performance that do not result in a complete shutdown of the system.[5] Examples of soft faults include slipping belts, vacuum leaks, and low refrigerant charge. In these cases, the system will continue to operate within predetermined set points such as temperature and pressure, giving no obvious indication of failure. If a soft fault is left unresolved, it will eventually become a “hard fault,” i.e. a completely broken system coupled with feedback loop controls that indicate as such. These soft faults are difficult to detect, but they cannot hide their power consumption. A NILM is thus a valuable tool in identifying soft faults that can be applied to all types of shipboard microgrids.[5]

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Typically, ships require redundancy to ensure the availability of vital loads under a range of conditions.[6] In some cases, having multiple generators in one space is satisfactory. However, some ships require further redundancy, for example, by spreading generators and switchboards over several compartments and requiring multiple power routes for certain equipment. In such cases, more complex electrical distribution systems may be desirable.[7] The configuration and operation of any particular electrical plant determines the necessary configuration of power monitoring equipment for ship-wide diagnostics, load monitoring, and life-cycle planning. This section explores a representative set of ac power grid configurations with examples from the USCG including the 87’ Marine Protector Class Patrol Boat (WPB), 140’ Large Ice Breaking Tug (WTGB), 154’ Fast Response Cutter (FRC), 270’ Medium Endurance Cutter (WMEC), and the 418’ National Security Cutter (NSC). Additional review is made of the ring bus power system employed on the DDG-51 Arleigh Burke destroyers. Modern electrical microgrids implement controls that attempt to increase safety and overall power availability, with the ultimate hope of a decreased dependence on user input. Grid architectures used on USCG and USN ac microgrids are outlined in this section. Approaches for control, including paralleling generators, vary with ship type, size, and mission. Protection methods also vary in sophistication to accommodate different microgrid configurations and mission demands.

Electrical Distribution

The centerpiece of a shipboard power system is an electrical generator (or a set of multiple generators). Typically, this is a diesel or gas turbine generator. However, alternative fuels such as liquefied natural gas or hydrogen, and the use of fuel cell technology, are continually being assessed.[8], [9] Once electricity is generated, a distribution system must reliably bring power to loads. Electrical distribution systems on ships, particularly those with hybrid or electric propulsion, face a unique set of challenges that distinguish them from terrestrial power systems. These issues include but are not limited to the

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FIGURE 2. Single bus with two SSDGs and two load centers.

variable frequency associated with shipboard microgrids, load sharing, load dynamics, and ungrounded or high impedance grounded systems.[10] Power systems deliver power through a conductor or set of conductors referred to as a “bus” or “bus bar.” Several bus bar configurations exist, and the configuration depends on the size, age, and design requirements of the ship. A bus bar may be considered part of a switchboard, which consists of a frame that houses protective devices and controls. Load centers (LCs) are circuit breaker panels connected to the bus bar; connections from LCs energize smaller breaker panels. Breaker panels and individual loads may also be connected directly to the bus bar without an LC. We consider five example ships that illustrate typical arrangements of these distribution components. The 87’ WPB, 154’ FRC, 270’ WMEC, 418’ NSC, and USN DDG-51, presented in increasing size, demonstrate that the distribution complexity generally increases as ship length increases, in addition to other constraints. Additionally, we will discuss ship designs that do not quite fit more conventional arrangements. The 87’ WPB uses a single bus configuration, as illustrated in Fig. 2. Here, the main switchboard feeds power directly to several loads, as well as to two load centers, which distribute power to subpanels throughout the ship. The single bus configuration is relatively simple, but a failure of the main bus leads to an outage throughout the entire ship. The 87’ WPB incorporates two SSDG sets, both connected to the main switchboard where the main bus is located. The 154’ FRC and 270’ WMEC use a modified form of a sectionalized bus configuration with two main switchboards and one emergency switchboard, illustrated in Fig. 3. The two main switchboards are often identified as the 1S/2S switchboards or starboard/port switchboards, and are connected with a circuit breaker known as a “bus tie.” The bus tie breaker prevents damage on one side of the bus from disrupting the other

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FIGURE 3. Sectionalized radial bus with two SSDGs, two main switchboards and an emergency switchboard.

side. This setup also allows for maintenance on individual electrical panels without de-energizing the entire ship. In addition to having a bus tie to sectionalize the main switchboard, both the 154’ FRC and 270’ WMEC have an EDG (located above the waterline in a separate space), emergency switchboard, and automatic bus transfers (ABTs), which protect the overall grid and vital loads such as navigation controls, emergency lighting, and fire pumps. In the event of a power loss, these vessels use the EDG to supply 100 percent of the vital load for at least 30 minutes. Larger ships generally incorporate more complex power distribution networks. Unlike the “radial” distribution systems discussed so far, the 418’ NSC uses a “ring bus” configuration, in which switchboards are connected in a ring. Fig. 4 illustrates a ring bus schematic with three SSDGs, similar to that found on an NSC. A load can receive power through more than one path, increasing possibilities for power flow continuity. Also, unlike the previous USCG examples, the generators onboard the NSC are all located in separate spaces, and there is no installed EDG. Newer USN ships have a Zonal Electric Distribution System (ZEDS), which is a ring bus design with added redundancy.[7] Fig. 5 illustrates an example ZEDS configuration, such as that found on the DDG-51 Arleigh Burke class destroyers, hull number 79 and above.[7] This flexible configuration allows a

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FIGURE 4. Ring bus with automatic bus transfer for vital loads. [7]

FIGURE 6. Closed-loop generator control example.

system. The WTGB incorporates a single bus configuration for its electrical plant. The WTGB also employs electric propulsion, still a relatively rare feature on USCG and USN vessels. In addition to the two installed SSDGs, there are two main propulsion diesel generator (MPG) sets for electric propulsion. The SSDG electrical plant provides excitation for the MPGs and the main electric propulsion motor via the main switchboard. The throttle position on the bridge commands the MPG and main motor armature voltage, main motor field current, and diesel speed. These relationships translate to the output shaft and propeller speed. Regardless of the specific type of electric propulsion, e.g., from a dc bus with power electronics on a DDG-1000, or on the more classical multi-machine drive on the WTGB, electric propulsion brings important changes to a ship’s microgrid and, therefore, new electrical monitoring opportunities.

Control Methods FIGURE 5. Zonal Electric Distribution System (ZEDS).[11]

user to conduct maintenance or lose one section of the bus without impacting the other sections.[12] Zonal systems also allow the electrical buses to be separated into port and starboard bus rails. This is important aboard USN ships, where continuing to operate in spite of battle damage is vital. However, significant concerns with ZEDS and multi-ring bus configurations are complexity and protection schemes. For this reason, multi-ring architectures are less practical for many USCG ships with smaller hull sizes. The 140’ WTGB, a Coast Guard icebreaking vessel, is an example of a rather unique electrical power distribution

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Ship microgrids generally provide enough electrical power generation to meet all of their normal and emergency needs. For certain missions, ships may supply needed power with multiple generators using a process known as “paralleling.” The architecture of a shipboard microgrid includes generator controls and control methods to reliably distribute power for different plant setups, such as varying number of paralleled generators. These controls generally consist of two primary components: governors and automatic voltage regulators (AVRs). Load sharing governors use a feedback loop to control their generator’s respective prime mover.[13] The load sharing is dependent on the installed generators’ horsepower or kilowatt (kW) rating.[13] If similarly sized generators are

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61 100 kW Load

Frequency (Hz)

27 kW

73 kW

70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 Generator 1 Load (kW) Generator 2 Load (kW)

FIGURE 7. Graphical representation of load sharing between two similar sized generators with differing droop characteristics.

installed—typically the case onboard military ships, except for EDGs—the load control will nearly equally balance the load between the generators.[13] However, when ships parallel both a SSDG and EDG, which often have different ratings, the load will be shared unevenly. AVRs control the excitation of a generator in order to maintain constant terminal voltage.[13] Fig. 6 shows a schematic of closed-loop generator load control that can be found on a USCG ship. The load control is capable of automatic paralleling of generators and balancing the load for parallel operation. With droop control, the load is balanced between two paralleled generators based on the generators’ rating and their “droop” characteristics.[14]–[16] Droop is defined as the percent difference of generator frequency between no-load and fullload conditions.[13] As more load is drawn from a generator, the system frequency will decrease slightly based on the droop. Automatic governors can account for the generators’ droop characteristics in order to balance the load.[13] Similarly, AVRs are equipped with droop compensation to share the reactive component of the load.[13] If two paralleled generators have the same droop, the load will be split in proportion to their power ratings. Fig. 7 shows an example of a 100 kW load being split between two 78 kW generators with different droop settings. Fig. 7 demonstrates that the load is split in proportion to the generators’ droops. Isochronous control, in contrast to droop control, maintains a constant frequency of the electrical grid regardless of load on the generator. This is typically only employed for single generator operation due to the added complexities associated with isochronous load sharing.[17] Older

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FIGURE 8. US Coast Guard cutter switchboard and synchroscope.

USCG cutters such as the 270’ WMEC allow the watchstander to switch between automatic and manual modes of synchronization in order to parallel generators. In manual mode, additional switches allow the engineering watchstander to choose between droop and isochronous control. Automatic control refers, for example, to a load sharing control method which uses droop control. Other cutters such as the 87’ WPB and 154’ FRC have both automatic and manual control of the generators and do not allow watchstanders to switch between isochronous and droop controls. Additional controls that allow watchstanders to manually adjust a generator’s speed and voltage can be adjusted with reference to dials on the switchboard. Depending on platformspecific guidelines, crew members may be relied on to continually balance the paralleled generators while on engineering rounds of the ship.

Synchronization and Paralleling of Generators

Generators need to be paralleled for a variety of reasons, most often to prevent the overloading of a single generator when operating high-power loads. This could occur while operating advanced weapon systems or maneuvering thrusters. Highrisk evolutions, such as transiting close to shallow water, may also motivate the paralleling of generators for redundancy. For any of these scenarios, generators are paralleled to decrease the likelihood of a total power loss. To parallel generators, the generator being brought online must have voltage amplitude, frequency, and phase angle synchronized to that of the online generator. This process can be performed automatically or

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manually, but in both cases requires input from the shipboard watchstander. As an example, automatic paralleling is performed on the 154’ FRC using the installed load control. The process begins by selecting “e-Power Plant.” The watchstander uses the control panel and ensures the offline and online generators are both available. From here the watchstander follows a series of steps on the control panel to start the paralleling process. The oncoming SSDG is selected and started. Once the SSDG is verified to be on and both generators have the same frequency, voltage, and phase angle, the oncoming generator breaker will close, paralleling it to the bus. The FRC also has the ability to parallel an online generator with the EDG. Manual, or “permissive,” paralleling of the generators involves a synchroscope, an instrument that measures the phase angle and frequency between two ac systems. The synchroscope onboard a USCGC is shown in Fig. 8. The initial equipment verification is the same as the automatic process, where the oncoming generator must reach 450 V ac and 60 Hz. From here, the operator ensures that the synchroscope indicator has moved to the 12 o’clock position. At this point, the operator may attempt to close the generator circuit breaker. If closing the breaker is successful, the associated switchboard indication lamp indicates that the two generators are in parallel. These same basic concepts are true for both radial and zonal electrical networks. Ring bus or ZEDS microgrids require that generators synchronize prior to closing interconnect bus tie breakers.

Microgrid Protection

Several methods exist to ensure the overall protection of the grid including automatic bus transfers (ABTs), panel networks, overspeed trips, overpower protection, and multifunction monitors (MFMs). In addition, real-time electrical plant properties such as oil temperatures, jacket water temperatures, voltage, and frequency can warn watchstanders of abnormal conditions. On many Coast Guard cutters, limits for the aforementioned properties are set through the Machinery Control Monitoring System (MCMS) or a similar system. The complex nature of ring buses and Zonal Electric Distribution Systems (ZEDS) may complicate the detection and isolation of faults. Currently, the Navy has installed multifunction monitors (MFMs) on many Navy ZEDS to minimize the number of unavailable sections of a ring bus during a fault [18]. The MFM can control a contactor or breaker which can interrupt power flow to a specific section of the switchboard. MFMs are placed throughout the zonal distribution system and compare the voltage magnitude and angle as well as power to recognize a fault condition. If a fault is detected by the MFMs,

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they will collectively determine the location of the fault by examining the change in power flow. This is a challenge because power can flow in either direction around a ring or section. Emergency switchboards can be a component for survivability on smaller ships that do not have space for a ring bus or ZEDS. The EDGs installed aboard USCG ships have switch inputs that process auto start and auto stop commands. In case of a generator failure, the emergency generator system senses a fault and automatically starts the emergency generator. On the emergency switchboard the generator and bus tie have motor-operated breakers. The switchboard only allows for the remote closure of the motor-operated circuit breakers if one side of the circuit breaker does not have power (indicating a power loss) or if both sides are synchronized, indicating that the EDG is ready to be paralleled. The ABTs ensure the continued operation of all emergency systems onboard. The ABTs will power the systems on the vital power panels when the main bus is supplying power to the ship. If power from the main bus is interrupted, the ABTs will switch to the emergency switchboard. Ideally, this process is nearly immediate, ensuring the availability of vital loads.

Power As Predictor MCMS systems installed on Coast Guard ships allow watchstanders to identify certain faults. For example, sensors such as temperature sensors can inform a watchstander that a system is not cooling or heating a space or machinery system properly if the temperature falls out of set limits. However, soft faults— faults that degrade but do not disable operation—often go unnoticed, as feedback control works to maintain commanded output levels by altering net energy consumption and run times. Power monitoring can identify soft faults that would otherwise go unnoticed, potentially preventing “hard faults” that lead to equipment failure. A NILM can not only provide an easily installed platform for fault detection and diagnostics, but can also provide data to supplement design processes such as electric plant load analysis (EPLA) conducted for ship designs. Fascinatingly, power monitoring, including nonintrusive power monitoring, can be implemented on all of the microgrids described in the previous section, including ring bus and dc distribution systems.[11] Power monitoring can augment existing monitoring and control systems and watchstanders, flag soft faults that feedback systems and MCMS may miss, and provide sustained data that informs ship operations, alterations, and future designs. We have installed nonintrusive load monitors on five US Coast Guard Cutters (USCGC): ESCANABA (WMEC-907), SPENCER (WMEC-905), THUNDER BAY

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FIGURE 9. NILM Dashboard timeline view showing SSDG operation based on load status. Colored blocks represent periods equipment is online.

FIG. 10: USCGC MARLIN fuel oil transfer pump turn-on power transient.

(WTGB-108), MARLIN (WPB-87304), and STURGEON (WPB-87336).[19], [20] Among many other applications, field results demonstrate that a NILM can track plant lineup using only the plant frequency. A NILM can also identify signatures that prognosticate faults and provide basic data for energy scorekeeping and automatic watchstanding.

Automatic Watchstanding

Crew members aboard US military ships perform multiple assigned roles. In many cases, watchstanders are tasked with typing or writing up changes in shipboard operation in either paper or electronic logs while simultaneously physically performing the watch requirements. These logs include plant status, liquid transfers, on-loads or off-loads, failure reporting, casualty control steps, and any other pertinent engineering needs. Safety requirements and operational tempo may interrupt the exact timing and recording of events, which may be estimated at later times by watchstanders. High-bandwidth data coupled with automatic transient recognition in a power monitor like a NILM enables a machine learning system to generate an

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automatic log of shipboard load operation. This automatic log can supplement or corroborate the accuracy of the manually generated log, which can shift some or all responsibility from the watchstander to the NILM, allowing a heightened vigilance from human operators and more accurate records from power monitoring throughout a watch. Strictly considering cost within the US Coast Guard, the hourly cost to employ an E-5 for watchstanding is $70 [21]. In some instances, three engineering watchstanders are billeted at a time. If shipboard equipment logs are generated automatically by a NILM, all three watchstanders may not be necessary, which could amount to significant cost savings over the ship’s life cycle. As an example of automatic watchstanding possibilities through power monitoring, consider the main diesel engine (MDE) lube oil (LO) heater on a 270’ WMEC. The operation of this heater is a tell-tale that reveals the operating schedule of the engine itself. The MDE lube oil heater cycles actively when engine temperature falls below a setpoint, and secures when the active engine’s temperature rises above the setpoint.[4] By observing the power consumption of the lube oil heater, a NILM can infer and extract the operating times of the engine (a non-electrical device in and of itself). NILM Dashboard can display both the observed operating characteristics of the lube oil heater and also the extracted or inferred operating schedule of the engine, all for quick review by the crew on a “timeline view.” Similarly the SSDG operating schedule on a 270’ WMEC can also be captured from the associated jacket water (JW) heater and lube oil heater.[22] Fig. 9 shows the operation status of a 270’ WMEC’s SSDG and SSDG JW and LO heaters. We have found that, on an 87’ WPB, the MDE jacket water heater can provide similar operating data. In addition, the operation of a fuel oil transfer pump can provide information about the ship’s MDE operation. Fig. 10 shows the turn-on power transient of the MARLIN transfer pump. Relatively constant fuel transfer indicates increased fuel consumption of the engines and a higher ship speed. With analysis considering the transfer pump’s electrical consumption in light of the transfer pump’s fuel flow rate, a ship’s patrol fuel consumption can be estimated and broken down into shorter periods of operation. This type of analysis provides redundant or backup estimates even on ships that track fuel consumption in other ways. The electrical loads on different ship microgrids provide a variety of both direct and inferred monitoring opportunities that can document ship operation directly or as a backup monitor. In addition to turn-on and turn-off events, dynamic or continuous changes in power demand during active operation can expose details of physical tasking. For example, some loads draw power under operating demands that are

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Power (kW)

12 25

8 6

62 64 66 68 70 72 74

10 5 0 0

40 50 Time (min)

FIGURE 11. Power consumption of a controllable pitch propeller pump with a zoom-in on power “surging.” FIGURE 13. Frequency data on USCGC MARLIN showing generators paralleling at minute 4.

FIGURE 12. Power consumption reflecting driving data of an electrically propelled icebreaking ship.

reasonably modeled as stochastic. In such cases, even when deterministic events may ultimately govern operation, analysis of the statistical properties of power consumption can provide valuable summary insights into load operation and ship operation. For example, power surges seen in a hydraulic pump connected to a controllable pitch propeller (CPP) can indicate increased demand for pump actuation while maneuvering the ship, as shown from the WMEC data presented in Fig. 11. Analogously, on electrically-propelled ships, the power demand of generator exciters monitored by a NILM can provide a direct history of driving patterns, as shown in Fig. 12. Analysis of seemingly random power variations, appropriately tailored for particular loads like CPP or drive propulsion, can provide an automatic logbook of ship handling to complement manual logs. Shareholders in a data-driven world seek tools to increase project efficiencies and savings. “Savings” here does not strictly refer to reduced monetary costs, but also availability and accident avoidance. An example within the US Coast Guard is the operational availability metric that tracks the amount of time per year a cutter is fully operational. Hourly rates of ship operation are closely tracked for cost-cutting and accounting

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considerations. For example, the 87’ WPB’s hourly operational cost is estimated to be $4,410.[21] A significantly larger vessel with different operational capabilities, the 418’ WMSL, has an hourly operational rate of $30,859.[21] Power monitoring coupled with machine learning can allow engineers to more easily see operation profiles and opportunities for increased efficiency and savings. For example, power monitoring on a USCG “Famous”-class cutter identified a vacuum pump that, due to a clogged pressure sensor, was consuming over 7 times more power than expected.[23] The pump was still operational and providing adequate vacuum service. The fault therefore went unnoticed, wasting energy and risking leading to a “hard” fault that crippled the system. Power monitoring can therefore potentially shift equipment repair to planned maintenance periods instead of operational periods, providing a unique savings opportunity in cost and availability.

Frequency as a Generation Metric

Variations found in a ship’s microgrid frequency data provide various automatic diagnostic and logging opportunities. Additionally, as computing power has become abundant and cheap, onboard monitoring systems can run scientific computing software for real-time analysis of data streams. To illustrate this, a NILM installed on the USCGC MARLIN captured over three months of frequency data, including ten underway periods where the ship relied on its marine diesel generators for electrical power. Analysis revealed correlations between the ship logs and observed phenomena in the captured frequency data. If exploited, these correlations suggest that ship plant status can be automatically logged from power data. Sharp transitions in operating frequency were correlated with changes in the MARLIN plant lineup. An example is shown in Fig. 13, in which

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the plant status changes from one to two generators at minute 4. We concluded that an increase in steady-state frequency of between 0.04 and 0.08 Hz indicates a transition from single to dual generator operation. The physical explanation for this increase lies in the droop characteristics of generators, shown in Fig. 7. With a constant total load, adding generators in parallel reduces the individual load on each generator, increasing the operating frequency. Furthermore, periods of shore power exhibited much smaller variance in system frequency compared to the periods of onboard ship power, as shown in Fig. 1. To explore the use of frequency variance as a metric for ship plant status, we calculated the standard deviation (i.e. the square root of the variance) of ship frequency for 10 periods of shore power and ship power. The average frequency standard deviation across the periods of shore power was 3.44 mHz; the same metric for the periods of ship power with one generator was 24.3 mHz. This difference in frequency is explained by the poorer ac power quality associated with ship generation when compared with terrestrial grid power. Combining these techniques allows an automatic logbook of ship plant status to be generated in real time. A NILM can observe large changes in frequency variance to identify changes between shore and ship power. A NILM can also identify discrete steps in steady-state frequency to track number of generators online. It is worth noting that these techniques may not generalize across the variety of plant equipment and controllers. Careful analysis of electrical data can facilitate similar metrics for individual power systems.

Equipment Diagnostics and Condition Based Maintenance Changes in load power demand provide valuable indicators to augment frequency analysis and also provide unique diagnostic and prognostic indicators on their own. For instance, the WMEC includes several three-phase jacket water (JW) heaters. The fleet has observed nagging failures on these from corrosion. This is a difficult fault to detect because the heaters are concealed in the engine manifold. Such a fault is extremely concerning because it is an electrical interaction around water that in certain instances has almost resulted in a fire.[24] This particular fault is a perfect example that illustrates how power can be used for shipboard diagnostics. Although the heater suffered physical degradation, it was still able to maintain the temperature commanded by the automatic controller by increasing the duty cycle. This soft fault was therefore able to hide from watchstanders through feedback control. A NILM, however, clearly observed the heater degradation by detecting

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a decrease in steady-state power consumption once corrosion caused a heating element to open circuit.[24] Furthermore, power monitoring can also identify soft faults from characteristics of observed on and off events. A drift in run time of systems may also indicate a fault. With these types of diagnostic insights, maintenance and repair can be shifted towards a condition-based, rather than scheduled, strategy. It is rare for US Coast Guard platforms to have a 100 percent operational availability, and these methods can help close the gap, having positive effects on the Coast Guard missions.

Updating Design Data

In addition to improving shipboard microgrid understanding and safety, power monitoring can be used to improve ship alterations and future designs. Electric plant load analysis (EPLA) is the method for calculating the ship loads over standard operating conditions and ambient environment as described in Design Data Sheet (DDS) 310-1.[25] EPLA is used for calculating fuel requirements in DDS 200-1.[26] In conducting EPLA, the US Navy sets standards for the estimated electrical plant deterioration and modernization. EPLA is done in the design phase and then will typically be adjusted during one or two sea trials prior to delivering the vessel to the US Navy.[25] A NILM can augment the EPLA process in multiple ways. First, EPLA requires each load to be individually monitored.[27] In some cases specified by DDS 310-1, multiple loads can be monitored at once.[25] A NILM can replace individual sensors and instead incorporate a single set of sensors at each panel, disaggregating the individual loads from the power stream of the panel. EPLA requires load factor information for its calculations. [25] Load factors are based on historical data that will have varying degrees of accuracy in any situation. By intent, EPLA design factors are conservative for proper equipment sizing, and changes to these factors may evolve slowly. However, having a way to check these factors on any particular hull type for different missions, situations, and ages offers an obvious opportunity for maintaining confidence in design factors. A NILM or similar power monitor is capable of generating historical logs of ship operation and computing load factors from power data much faster than humans can sift through paper logs. The NILM can serve as a semi-permanent component of an electrical grid, creating an accurate EPLA profile for loads of a ship or class of ship. This continuity can provide industry data that can be used to create models that include opportunities to track deterioration or mission variability.

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Shipboard Microgrids and Automation

Automating the Future Although automation alleviates the need for constant human oversight, applying automation to machinery plants cannot be approached simplistically and without nuance.[28] Automation can introduce feedback loops that hide faulty behavior. Although “soft faults” can hide in feedback loops, they cannot hide from their power consumption. Power monitoring goes deeper into the plant’s health and gives credence to whether or not machinery is operating optimally. Ship design requires an accurate picture of individual load energy consumption. In addition to equipment diagnostics, nonintrusive load monitoring provides long-term power data for new ship modeling, regardless of plant configuration. Ship power consumption evolves over the life-cycle of each ship as newer, more advanced weapon systems and navigation packages become available. The record of energy consumption provided by a NILM gives designers in the seagoing

services a continuously evolving picture of present and future power requirements. We also observe that quick access to power information provided by a NILM may present a unique training opportunity. Electric power systems are frequently the “mysterious” parts of a ship to maintainers and watchstanders. Power monitoring creates a clear window into the electromechanical systems of a microgrid. The power profile provided by a NILM and its relation to the ship’s missions provides valuable training opportunities for new users and operators.

Acknowledgments The authors gratefully acknowledge the U.S. Coast Guard and the crew of USCGC MARLIN for access to their ship. This work was supported by the Office of Naval Research NEPTUNE program and The Grainger Foundation.

AUTHOR BIOGRAPHIES ISABELLE PATNODE received the B.S. degree in naval architecture and marine engineering from the U.S. Coast Guard Academy in 2016 and the M.S. degree in mechanical engineering from the Massachusetts Institute of Technology in 2023. She has served on board USCGC ESCANABA (WMEC 907), as a port engineer for the IBCT product line, and as an engineering instructor at the U.S. Coast Guard Academy. She is a Lieutenant with the United States Coast Guard. MICHAEL J. BISHOP received the B.S. degree in mechanical engineering from the U.S. Coast Guard Academy in 2019 and the M.S degree in mechanical engineering from the Massachusetts Institute of Technology in 2023. He has served as a Deck Watch Officer and an Operations Officer of USCGC NATHAN BRUCKENTHAL (WPC 1128). He is a Lieutenant with the United States Coast Guard. AARON W. LANGHAM received the B.E.E. degree in electrical engineering from Auburn University, Auburn, AL, USA in 2018, and the M.S. degree in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, MA, USA in 2022. He is currently pursuing the Ph.D. degree in electrical engineering and computer science at the Massachusetts Institute of Technology, Cambridge, MA, USA. His research interests include signal processing, machine learning, and computer systems for microgrid energy management.

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DAISY H. GREEN received the B.S. degree in electrical engineering from the University of Hawai‘i at Mānoa, Honolulu, HI, USA, in 2015, and the M.S. degree and the Ph.D. degree in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 2018 and 2022, respectively. She is currently a Postdoctoral Associate at MIT. Her research interests include the development of signal processing algorithms for energy management and condition monitoring. STEVEN B. LEEB received the Ph.D. degree from the Massachusetts Institute of Technology, in 1993. Since 1993, he has been a member on the MIT Faculty with the Department of Electrical Engineering and Computer Science. He also holds a joint appointment with the Department of Mechanical Engineering, MIT. He is concerned with the development of signal processing algorithms for energy and real-time control applications.

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REFERENCES [1]

E. Skjong, E. Rødskar, M. Molinas, T. A. Johansen, and J. Cunningham, “The marine vessel’s electrical power system: From its birth to present day,” Proceedings of the IEEE, vol. 103, no. 12, pp. 2410– 2424, 2015.

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J. Paris, J. S. Donnal, and S. B. Leeb, “NilmDB: The non-intrusive load monitor database,” IEEE Transactions on Smart Grid, vol. 5, no. 5, pp. 2459–2467, 2014.

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J. Paris, J. S. Donnal, Z. Remscrim, S. B. Leeb, and S. R. Shaw, “The sinefit spectral envelope preprocessor,” IEEE Sensors Journal, vol. 14, no. 12, pp. 4385–4394, 2014.

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A. Aboulian, D. H. Green, J. F. Switzer, T. J. Kane, G. V. Bredariol, P. Lindahl, J. S. Donnal, and S. B. Leeb, “NILM dashboard: A power system monitor for electromechanical equipment diagnostics,” IEEE Transactions on Industrial Informatics, vol. 15, no. 3, pp. 1405–1414, 2019. S. B. Leeb, P. Lindahl, D. Green, T. Kane, J. Donnal, and S. Kidwell, “Power as predictor and protector,” Society of Naval Architects and Marine Engineers (SNAME) Marine Technology, 2019. E. Skjong, T. A. Johansen, M. Molinas, and A. J. Sørensen, “Approaches to economic energy management in diesel–electric marine vessels,” IEEE Transactions on Transportation Electrification, vol. 3, no. 1, pp. 22–35, 2017. B. Mills, “Solving Time-Alignment Challenges in Shipboard NonIntrusive Load Monitoring,” Master’s thesis, Massachusetts Institute of Technology, 2021. S. Allen, E. Ashey, D. Gore, J. Woerner, and M. Cervi, “Marine applications of fuel cells: A multi-agency research program,” Naval Engineers Journal, vol. 110, no. 1, pp. 93–106, 1998. M. M. E. Gohary and I. S. Seddiek, “Utilization of alternative marine fuels for gas turbine power plant onboard ships,” International Journal of Naval Architecture and Ocean Engineering, vol. 5, no. 1, pp. 21–32, 2013.

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[10] N. Doerry, “Naval power systems: Integrated power systems for the continuity of the electrical power supply.” IEEE Electrification Magazine, vol. 3, no. 2, pp. 12–21, 2015. [11] B. T. Mills, D. H. Green, J. S. Donnal, and S. B. Leeb, “Power monitoring beyond radial distribution networks,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–9, 2022. [12] J. D. McDonald, Electric power substations engineering, 3rd ed., ser. Electrical engineering handbook. CRC Press, 2012. [13] M. M. Islam, Shipboard power systems design and verification fundamentals. Piscataway, New Jersey: IEEE Press, 2018. [14] M. D. A. Al-Falahi, T. Tarasiuk, S. G. Jayasinghe, Z. Jin, H. Enshaei, and J. M. Guerrero, “Ac ship microgrids: Control and power management optimization,” Energies, vol. 11, no. 6, 2018. [15] F. Chen, R. Burgos, D. Boroyevich, J. C. Vasquez, and J. M. Guerrero, “Investigation of nonlinear droop control in dc power distribution systems: Load sharing, voltage regulation, efficiency, and stability,” IEEE Transactions on Power Electronics, vol. 34, no. 10, pp. 9404– 9421, 2019. [16] N. Doerry, “Optimal generator set loading for energy efficiency,” Naval Engineers Journal, vol. 134, no. 2, pp. 101–111, June 2022. [17] R. E. Cosse, M. D. Alford, M. Hajiaghajani, and E. R. Hamilton, “Turbine/generator governor droop/isochronous fundamentals a graphical approach,” in 2011 Record of Conference Papers Industry Applications Society 58th Annual IEEE Petroleum and Chemical Industry Conference (PCIC), 2011, pp. 1–8. [18] U. Orji, C. Schantz, S. B. Leeb, J. L. Kirtley, B. Sievenpiper, K. Gerhard, and T. McCoy, “Adaptive zonal protection for ring microgrids,” IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1843–1851, 2017.

[19] P. A. Lindahl, D. H. Green, G. Bredariol, A. Aboulian, J. S. Donnal, and S. B. Leeb, “Shipboard fault detection through nonintrusive load monitoring: A case study,” IEEE Sensors Journal, vol. 18, no. 21, pp. 8986–8995, 2018. [20] D. H. Green, S. R. Shaw, P. Lindahl, T. J. Kane, J. S. Donnal, and S. B. Leeb, “A multiscale framework for nonintrusive load identification,” IEEE Transactions on Industrial Informatics, vol. 16, no. 2, pp. 992– 1002, 2020. [21] U. S. C. G. Commandant, Reimbursable Standard Rates, Department of Homeland Security, March 2017. [22] T. Kane, “The NILM Dashboard: Shipboard Automatic Watchstanding and Real-Time Fault Detection using Non-intrusive Load Monitoring,” Master’s thesis, Massachusetts Institute of Technology, 2019. [23] J. Paris, J. S. Donnal, R. Cox, and S. Leeb, “Hunting cyclic energy wasters,” IEEE Transactions on Smart Grid, vol. 5, no. 6, pp. 2777– 2786, 2014. [24] D. Green, T. Kane, S. Kidwell, P. Lindahl, J. Donnal, and S. Leeb, “NILM dashboard: Actionable feedback for condition-based maintenance,” IEEE Instrumentation Measurement Magazine, vol. 23, no. 5, pp. 3–10, 2020. [25] Naval Sea Systems Command, DDS 310-1 Electric Power Load Analysis (EPLA) for Surface Ships, Department of Defense, Sep 2012. [26] ——, DDS 200-1 Calculation of Surface Ship Endurance Fuel Requirements, Department of Defense, Oct 2011. [27] T. Deeter, D. H. Green, S. Kidwell, T. J. Kane, J. S. Donnal, K. Vasquez, B. Sievenpiper, and S. B. Leeb, “Behavioral modeling for microgrid simulation,” IEEE Access, vol. 9, pp. 35 633–35 645, 2021. [28] J. V. Amy Jr., N. H. Doerry, T. J. McCoy, and E. L. Zivi, “Shipboard controls of the future,” Naval Engineers Journal, vol. 109, no. 3, pp. 143–152, May 1997.

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TECHNICAL PAPER

Different Comparison of Diff erent Approaches for Prediction Self-Propulsion of the ShipShip Using for Prediction Self-Propulsion of the RANSERANSE Method Using Method Tran Huy Hao Tran Ngoc Ngoc Tu, Tu1,Nguyen NguyenThi ThiHai HaiHa, Ha1Nguyen , Nguyen Huy Hao1

Abstract

Introduction

The paper deals with simulation results of self-propulsion parameters based on the RANSE method with two different propeller models, including body force propeller and discretized rotating propeller methods. The first method using virtual disk, while the second one using sliding mesh technique. The difference between two methods from the point of views: numerical setup, the accuracy of numerical results compared to the experimental data, thrust and torque values in real-time, computational time and the distributions of the flow around the stern region are shown and analyzed. Finally, the assessment and using recommendation of each approach are given. The Japanese Bulk Carrier ship model was used in this research as the case study to verify and validate the accuracy of two different approaches.

Prediction of self-propulsion point of the ship is one of the most challenging aspects in the process of ship power estimation. Obviously, the accuracy in determining this parameter affect accurate power estimation. Nowadays, there are two popular approaches for predicting of self-propulsion point of the ship, that include experimental and Computational Fluid Dynamics (CFD) methods. Although towing tank experimental method provides the most reliable data for self-propulsion point, but it requires higher cost and time-consuming. Hence, this method is impractical during the ship hydrodynamics optimization process, because numerous designs have to be considered in a short period of time. Currently, CFD approach has been widely used in prediction of the ship hydrodynamic in ship design process due to their rapid calculation, low cost, and give relatively accurate results compared to the experimental method. Otherwise, Post-processing CFD can provide detailed information about the flow field around the ship, which may enable the designers to improve their designs. Within CFD method, the most popular approach used to solve ship hydrodynamics problems is the Reynold Averaged Navier-Stokes equation (RANSE) as sufficient accuracy and reasonable computational time for engineering purposes.[1, 2] Therefore, this article focuses on investigating the self-propulsion parameters using RANSE approach. There are two different approaches for evaluating self-propulsion points using RANSE method, which consists of the discretized rotating propeller (DRP) and body force propeller (BFP) methods. These methods are employed in the works reported in the literatures.[3-14] The first method was employed by Tu T.N. et al. [4] in evaluating the self-propulsion factors for JBC ship at model scale. Its results indicated that employing the DRP method gives reliable results in comparison to experimental data. Castro, A.M., et al. [9] used DRP method to

KEYWORDS: RANSE, Self-propulsion, discretized rotating propeller, body force propeller method

1 Vietnam Maritime University, Vietnam

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Comparison of Different Approaches For Prediction Self-Propulsion of the Ship Using Ranse Method

evaluate the self-propulsion parameters for containership KCS at full-scale. The obtained numerical results are agreed well with the available data. Besides, using the DRP method can provide reliable information about the flow field in the wake regions. However, due to the small time steps, this method requires more time-consuming computations. Chuan, T.Q., et al. [11] applied the BFP method to conduct the self-propulsion simulation for cargo ship 12500DWT at full-scale. The obtained results are in good agreement with sea trial data. In the research of Jasak, H., et al. [7] BFP method has been used to carry out the self-propulsion simulation for two self-propelled ships at full-scale. Grid sensitivity has been studied to estimate the grid uncertainty. The predicted results are in good agreement with sea trial measurements. Instead of the discretized actual propeller, Win, Y.N., et al. [15] applied BFP method to carry out the self-propulsion simulation for Series 60 at the model scale. Numerical results indicate that this method has the capability on studying hull-propeller interaction with less computational time. Both of those methods were used by Villa, D. et al. [3] to perform the self-propulsion simulation. And the obtained results indicate that both methods have the capability of evaluating the self-propulsion parameters. However, each method has its own pros and cons. The advantage of BFP method is simple, easier, and faster in calculating the self-propulsion factors, but unable to provide a detailed flow field around the propeller. While the DRP method can provide all the flow characteristics in the wake regions efficiently and reliably. However, it is hard to model and compute as well as takes a significant computational time. Sezen, S., et al. [14] has investigated the well-known benchmark submarine DARPA Suboff model numerically. The self-propulsion of DARPA Suboff model has been predicted by two different propeller models for different velocities. It has been found that BFP method has estimated lower propulsive efficiency and higher delivered power as compared with DRP method, while DRP method gives higher hull efficiency compared with BFP method. Gaggero, S. et al. [12] have studied the propeller-hull interaction by using different propeller models. The obtained numerical results indicated that the differences in the thrust deduction factor and the wake fraction between measurements and predictions with the BFP method and with the BEM/RANSE coupling are almost negligible. The works mentioned above provided accurate representations of a propeller within RANSE method in predicting the self-propulsion parameters. However, the difference in thrust, lateral force, and torque values in real-time produced by using two different propulsion models, was neglected in those previous researches. Thus, this paper employed both of the BFP and DRP methods in simulation the self-propulsion to present

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differences in the numerical results including propeller’s thrust, torque, lateral forces values in real-time, and the flow distributions in the wake region. A model of Japanese Bulk Carrier (JBC) was used in this work as the case study to verify and validate the accuracy of those two approaches.

Computational Methods Flow model

The three-dimensional incompressible viscous unsteady RANSE is amended with theincompressible body force Fvviscous for the coupled The three-dimensional unsteady simulations. is represents thebody propeller acting on coupled the fluid as RANSE is Th amended with the force Fv for the simulations. This1. represents the propeller acting on the fluid as given in Equation given in Equation 1.

 ui 1   ui  ij p u    Fv   j  x j   x 2j xi xi  2

(1)

Ax 

105 8

A 

105 8

(1) Where Δ, T, a

actuator disk, r Where ρ is the fluid densit, μ is the dynamic viscosity, τij is the Where ρ is the fluid densit, μthe is mean the dynamic is the Employing u i presentsτijthe Reynolds stress, p presents pressure,viscosity, actual propell ui presents the requires defini Reynolds p presents the mean averagedstress, Cartesian components of thepressure, velocity vector. The three-dimensional three-dimensional incompressible incompressible v averaged Cartesian components of RANSE theThe velocity vector. with the body force Fvv is(DRP) amended - force The pos 2.2. Discretized rotating propellerRANSE methodis amended with the body Fv simulations. This represents the propeller acti Specific simulations. This represents the propeller acti The DRP method is the direct simulation method of given in in Equation Equation 1. - Directio Discretized rotating propeller given method (DRP) 1. interaction between the propeller and ship hull based on actual - Specific Thepropeller DRP method is the direct simulation intergeometry. This simulation methodmethod is similar uui of to 1 self-22uui  ij for pexam  1 ij  p  F  u   i i  propulsion testthe inpropeller the towing j this action between andtank. shipFollowing hull based on method actual u F   x22  x- Specific j x    twox jj xii xxii x jj into setup, the computational domain should be split propeller geometry. Th is simulation method is similar to From BFP regions: one surrounded the whole ship hull called the Where is the the fluid fluid densit, is the the dynamic dynamic vv self-propulsion test in Following thisdensit, method ρρ is μμ is shortcomings i stationary region, andthe thetowing other tank. isWhere a cylindrical sub-region Reynolds stress, p presents presents theinmean mean pressur this model. surrounded the propeller only (Fig.1). The rest of the volume Reynolds stress, p the pressur setup, the computational domain should be split into two reaveraged Cartesian components components of the the veloci veloci separated by the intermediate surface (interface) that is averaged Cartesian setup gions: one surrounded the whole ship hull called the stationary ofThe providing a connection between boundaries during the creation of a s 2.2. Discretized rotating propeller method region, and the other is a cylindrical sub-region surrounded 2.2. Discretized rotating propeller method simulation. The sliding or overset mesh techniques were used (Fig. 2). The t model theonly rotating propeller [4, 10, Thevolume DRP method method is the the direct direct simula simul thetopropeller (Fig.1). The rest of 16]. the separated The DRP is computations ( interaction between the propeller and ship hu interaction the propeller shipand hu shipand speed by theThis intermediate (interface) that isbetween providing a method issurface generally too computationally expensive propeller geometry. geometry. This This simulation simulation method method due to it requires small time step during topropeller improve convergence. connection between boundaries the the simulation. Th e 2.4. propulsion test in the towing tank. Follow Self-prop propulsion test[17] in isthe tank. Follow The time step size that is recommended by ITTC 0.5towing to setup, the computational computational domain should should b sliding or overset mesh were used to model the domain the In the bs 2 degrees per time step.techniquessetup, regions: one one surrounded surrounded the the whole whole ship ship regions: rotating propeller.[4, 10, 16] method, the sa stationary region, and the other is a cylind Using the DRP method requires defining the following stationary region, and the other is athe cylind Setting des This method is generally too computationally expensive surrounded the propeller only (Fig.1). The re items: actual propeller geometry, propeller position at the hull, surrounded the propeller only the (Fig.1). The re propeller separated by the intermediate surface (in and rotational speed of propeller. duedirection, to it requires small time step toseparated improve by the the convergence. intermediate surface and (in resistance providing aa connection between bounda bounda [17] is 0.5 to between The2.3. time stepforce sizepropeller that is recommended by ITTCconnection 2revolution is v Body methodproviding (BFP) simulation. The The sliding sliding or or overset overset mesh mesh tech tech (thrust of prop degrees perBFP timemethod step. is employed simulation. to model the rotating propeller [4, 10, 10, 16]. 16]. The tomodel modelthe therotating effectspropeller of a self-propulsion to [4, propeller thrust requires and torque and thereby creating should be con Using thesuch DRPasmethod defi ning the following This method is generally generally too too computati computati This method is propulsion without actually resolving the propeller geometry. items: actual propeller geometry, propeller position at the hull, Friction Corre due to it requires small time step to improve improve due to it requires small time step to In this method, a uniform volume force fb is distributed over The time step size that is recommended by IT IT direction, and rotational speed of propeller. The timeforce step varies size that recommended by 3. NUMERIC the cylindrical actuator disk. The volume in isthe degrees per per time time step. step. 22 degrees radial direction. 3.1. JBC geom Using the DRP DRP in method requires definin definin The force radial propeller distribution method of the body force components two requires Using the method Body (BFP) The refere items: actual propeller geometry, propeller po po f are defined as directions, including axial fbx and tangential bθ items: actual propeller geometry, propeller without ESD Thefollows BFP method the eff ects of a prodirection, and rotational speed of propeller. [18]: is employed to model direction, and rotational speed geometry of propeller. of J * thereby creating propeller such as thrust and and  f bx torque Ax r * 1  r2.3. Figure(BFP) 1, respe Body force force propeller propeller method (2) method 2.3. Body (BFP)

pulsion without actually resolving the propeller geometry. * The BFP method is employed to model r * 1  rforce Thef BFP method isover employed to model In this method, a uniform distributed fb A  volume (3) and torque and b is such as thrust thrust  * ' propeller such as and torque and r (1  rh )propeller  rh' propulsion without actually resolving the the pro pro the cylindrical actuator disk. The volume force variesactually in the resolving propulsion without ' ' r  r In this method, a uniform volume force f b is * h radial direction. In this method, a uniform volume force fb is r 

the cylindrical cylindrical actuator actuator disk. disk. The volume fo the (4) The volume fo radial direction. direction. radial RH r radial distribution of the body force co The  rh'  and r ' The radial distribution of the body force co and tangential tangential directions, including axial axial ffbx and RP R P directions, including (5) bx NAVAL ENGINEERS JOURNAL follows [18]: follows [18]: in axial and component Where fbx and fbθ are the body force  Ax rr** 11   rr** tangential directions, respectively; r is the radial coordinate,  ff bx A

1  rh'

In the the self-propulsion computations using RANSE method, thedesired same procedure in of towing tank test. 2 degrees per time step. Setting ship speed,was an adopted initial rate revolutions of Using thepropeller DRPstep. method requires defining the atfollowing In the the self-propulsion computations using RANSE actual geometry, propeller position the hull, method, 2items: degrees per time Setting ship speed, ancomputing initial rate of revolutions of thedesired same was adopted in towing tank test. the propeller is procedure given, then, both total ship Using the DRP method requires defining the following items: actual propeller geometry, propeller position at the hull, direction, and rotational speed of propeller. method, thedesired same procedure was adopted in towing tank test. Setting the ship of speed, ancomputing initialthe rate of revolutions of the propeller isthrust given, then, both total ship resistance and propeller, rate of propeller Using the DRP method requires defining the following direction, andpropeller rotationalgeometry, speed of propeller propeller.position at the hull, Setting the desired ship speed, an initial rate of revolutions of items: actual the propeller isthrust given, computing bothofistotal ship resistance propeller, the rate propeller isand varied untilofanthen, equilibrium condition obtained items: actual propeller geometry, 2.3. Body force propeller method (BFP) position at the hull, revolution direction, and rotational speed of propeller propeller. the propeller isthrust given, then, computing bothofistotal ship resistance propeller, the revolution isand varied untilofship an equilibrium condition obtained 2.3. Body force propeller method (BFP) (thrust of propeller equal resistance). Asrate a result,propeller the ship direction, and rotational speed of propeller. and thrust ofan propeller, the As rate ofis propeller BFP method is employed to model the effects of a resistance revolution is varied until equilibrium obtained (thrust of propeller equal ship resistance). a result, the ship 2.3.The Body force propeller method (BFP) self-propulsion point is found. For modelcondition scale simulation, it The BFP method is employed to model the effects of a revolution is varied until an equilibrium condition is obtained propeller such as thrust and torque and thereby creating 2.3. Body force propeller method (BFP) (thrust of propeller equal ship resistance). As a result, the(Skin ship self-propulsion point is found. For model scale simulation, it should be considered the applied towing force SFC The BFP method is employed to the model the effects of a (thrust propeller such as actually thrust and torque and thereby creating propeller equal ship resistance). As a result, the(Skin shipit propulsion without resolving propeller geometry. should ofbe considered applied towing force SFC self-propulsion point is the found. For model scale simulation, Friction Correction) according to the ITTC procedure [20]. The BFP method is employed to model the effects of a propeller such thrust volume and torque and creating propulsion without actually resolving the geometry. pointaccording is the found. Forthemodel it isthereby distributed over self-propulsion In this method, aasuniform force fbpropeller should considered applied towing forcesimulation, SFC[20]. (Skin Frictionbe Correction) to ITTCscale procedure propeller such as thrust and torque and creating propulsion without actually resolving the geometry. isthereby distributed In this method, a uniform volume force fpropeller b force be considered the applied towing force SFC (Skin 3. NUMERICAL SIMULATIONS the cylindrical actuator disk. The volume varies inover the should Friction Correction) according to the ITTC procedure [20]. propulsion without actually resolving the fpropeller geometry. and Correction) thrust of propeller, rateITTC of propeller is varied Th ethis radial distribution of volume the body force in 3. NUMERICAL SIMULATIONS the cylindrical actuator disk. The volume varies inover the Friction iscomponents distributed In method, a uniform force b force accordingthe to the procedurerevolution [20]. radial direction. 3.1. JBC geometry SIMULATIONS is distributed over In this method, a uniform volume force f b force 3. NUMERICAL the cylindrical actuator disk. The volume varies in the radial direction. an equilibrium condition is obtained (thrust of propeller two The directions, including of axial andforce tangential fbθ areindefi radial distribution the fbx body components twoned 3.1.until JBCreference geometry The vessel used in this work is a JBC ship SIMULATIONS the actuator of disk. The volume force varies in two the 3. NUMERICAL radial direction. Thecylindrical radial distribution body force components in [18] including tangential fbθ are defined as 3.1.The directions, axial the fbx and equal ship AsThe a result, the ship as follows: reference vesselscale. used in this is a self-propulsion JBC JBC geometry without ESD atresistance). model mainwork particulars and ship the radial direction. The radial distribution of the body force components in two and tangential f are defined as directions, including axial f bx bθ follows [18]: 3.1. JBCreference geometry without ESD at model scale. main particulars and the be considThe vessel used The in this work is Table a it JBC ship geometry of JBC with propeller are shown in 1 and The radial distribution of the body force components in two point is found. For model scale simulation, should fbθ are defined as directions, including axial fbx* and tangential follows [18]: The reference vesselpropeller used The in are this work is Table a JBC ship * without ESD at model scale. main particulars and the geometry of JBC with shown in 1 and  f A r 1  r (2) Figure 1, respectively. and tangential f are defined as directions, including axial f bθ bx x bx follows [18]: (2) without ered the SFC (Skin at model scale. force Theare main particulars the f bx Ax r * 1  r * geometry of applied JBC withtowing propeller shown in Friction Tableand 1 Correction) and Figure 1,ESD respectively. follows [18]:  (2) geometry ** ** [20] of JBC with propeller are shown in Table 1 and  f bx Ax rr* 11rr* according to the ITTC procedure. Figure 1, respectively. (2) Figure 1, respectively. fb  x r* r * 11' rr * ' (3)   f bx A A (2) fb A  r *r(1* 1rh' ) r* rh' (3) (3)  * rh h )r fb A  rr*r'(1*r1'r (3)  Numerical Simulations ' ' fb Ar* r*r(1 (3) ' hr ' h )  rh  ' ' * r (1r'hr )  r 1  r h r  r '  rh' ' h (4) r**  r1'  rrhh'h' r (4) (4) JBC geometry R H  1  r' r h'  rh'' R r and (4) r 1  rh' R H The reference vessel used in this work is a JBC ship without ESD R  rh R (4) P and r P 5 T (5) ' ' H R RrP  P and r  r at model scale. The main particulars and the geometry of JBC R (5) r (6) h' and fbθ rare the ' H body force (3RH  4 RP )(Where RP  RfHbx) R RPcomponent in axial and (5)  P and r h and fbθ are the component axial and Where fbx directions, (5) tangential respectively; the radialincoordinate, with propeller are shown in Table 1 and Figure 1, respectively. RP body force Rr Pis (5) the of body component incoordinate, axial and Where tangential directions, respectively; is the bxP and bθ are 5 Q R are fthe radius theforce hubr and tipradial of the propeller, H and fR  the body component incoordinate, axial and Where fbx and bθ are tangential directions, respectively; is the are fthe radius of (7) theforce hubr and tipradial of the propeller, R respectively. )(HRPand  RHR)Pdirections, RP (3RH  4 RPtangential respectively; r and is thetipradial coordinate, where and body component axial and tanNumerical setup and RPfbθ areare thethe radius offorce the hub ofinthe propeller, R respectively. Hfbx arehub defined as follows: Thethrust, coefficients Ax and R thetorque radius and tip of the propeller, R θthe H and P areand respectively. and Q are thegential thickness, ofofAthe directions, respectively; r is the radial coordinate, R and Computational study in this research was conducted using a H The coefficients Ax and Aθ are defined as follows: respectively. Figure 1. JBC hull shape and its propeller respectively. Aθ are defined as follows: Theradius coefficients RP are the commercial soft ware solver Star-CCM+. of theAhub and tip of the propeller, respectively. x and Figure 1. JBC hullCFD shape and its propeller The setting was and A are defined as follows: The coefficients A x θ g the actuator disk in place of the discretized Figure 1. JBC hull shape and its propeller conducted following case 1.5a in reference [21] as follows: The coefficients Ax and Aθ are defined as follows: Figure 1. JBC hull shape and its propeller ler to perform the self-propulsion simulation ing the following issues: viscous unsteady unsteady 105 T viscous 105 T Ahull;  (6) x for the the coupled (6) A  (3R  4 R )( R  R ) of the actuator disk at x 8 vvsition for coupled (6) (3RHH  4 RPP )( RPP  RHH ) 8curve; ing on the fluid as cation of a propeller performance ing on the fluid as on and rotation rate of actuator disk; 105 Q Q A 105   (7) A  cation of an operating point speed, (7)  (rotational 8 R (3 R RP )( )( RP  RH )    (7) 8 RPP (3RHH  44R  P RP  RH ) mple);  Fv  (1) F (1) Where Δ, Δ, T, T, and and Q Q are are the the thickness, thickness, thrust, thrust, and and torque torque of of the the cation v   of an inflow method. Where  actuator disk, respectively. where T, anddisk, Qthat arerespectively. the thrust, and torque of the actuator P model, it must beΔ, mentioned one thickness, of the viscosity, ττij is is the the respectively. viscosity, Employing the actuator actuator disk disk in in place place of of the the discretized discretized is the lack ijofactuator propellerdisk, lateral forces component Employing the re, uu i presents presents the the actual propeller to perform perform the self-propulsion self-propulsion simulation re, Employing the actuator disk in place of the discretized actui actual propeller to the simulation ity vector. vector. requires defining the following issues: ity requires defining the following issues: simulation requires of the BFP approach does not require the al propeller to perform the self-propulsion The position ofused the actuator actuator disk disk at at hull; hull; separate which model is issues: thenof d (DRP) region -- following The position the defitoning thethe d (DRP) Specification of a propeller performance curve; time step was selected as-- inSpecification case of resistance FIGURE 1. JBC hull shape and its propeller of a propeller performance curve; lation method of e position Th of the diskrate at hull; ation method of - Where Direction and rotation rotation of actuator actuator disk; disk; (ΔT=0.005 –■0.01L/U [19]; U actuator [m/s] is the Direction and rate of ull based on actual ull Specification of an an operating point point (rotational speed, speed, d Lbased [m] isona actual ship lengthcation value). ■ Specifi of a propeller performance curve; -- Specification of operating (rotational is similar similar to to selfselfDescriptions Value is for rotation example);rate of actuator disk; for example); ■ Direction and wing this this method pulsion procedure wing method -- Specification Specification of of an an inflow inflow method. method. Length between perpendiculars L [m] 7.00 PP be split split into into■two two Specifi cation ofusing an operating point (rotational speed, for be self-propulsion computations RANSE From BFP model, it must be mentioned that one of the hull called the Maximum moulded breadth of From model, it must be mentioned that one of the hullprocedure called the example); ame was adopted in BFP towing tank test. 1.125 BWL [m] shortcomings is is the the lack lack of propeller propeller lateral lateral forces forces component component drical sub-region sub-region shortcomings design waterline drical sired ship speed, an initial rate of revolutions of ■ Specifi cation of an inflow method. in this model. est of the volume in this model. est the volume Design Draft T [m] 0.4125 is ofgiven, then, computing both total ship nterface) that that is is From BFP model, itofmust be mentioned that one ofrequire the the The setup the BFP approach does not dnterface) thrust of propeller, the rate of propeller 3 The setup of the BFP approach does not require the [m ] Displacement volume 2.7870 aries during during the the aries creation of separate region to to which which the model is then then used used shortcomings isof the lack of propeller lateralthe forces component varied until an equilibrium condition is obtained creation aa separate region model is hniques were used Propeller center, longitudinal hniques wereship used (Fig. 2). 2). The time the stepship was selected selected as as in in case case of of resistance resistance peller equal resistance). AsThe a result, 0.9864 x/LPP in this model. (Fig. time step was location (from FP) computations (ΔT=0.005 it – 0.01L/U 0.01L/U [19]; [19]; Where Where U U [m/s] [m/s] is is the the n point is found. For computations model scale simulation, (ΔT=0.005 – The setup of the BFP approach not require the creation ship speed and LSFC [m] is aa ship shipdoes length value). nsideredexpensive the applied towing force (Skin Propeller center, vertical location ionally expensive ship speed and L [m] is length value). ionally -z/LPP 0.040421 of a separate region to which the model is then used (Fig. 2). ection) according to the ITTC procedure [20]. the convergence. (below waterline) the convergence. 2.4. Self-propulsion procedure 2.4. Self-propulsion procedure TTC [17] [17] is is 0.5 0.5 totime step was selected as in case of resistance computations Theto TTC Propeller particulars CAL SIMULATIONS In the the self-propulsion self-propulsion computations using using RANSE RANSE [19] Where U computations In (ΔT=0.005 – 0.01L/U; [m/s] is the ship speed and Diameter of propeller DP [m] 0.203 method, the the same same procedure procedure was was adopted adopted in in towing towing tank tank test. test. metry method, ng the the following following ng [m]inisSetting a ship length value). Setting the desired ship speed, an initial rate of revolutions of ence vessel L used this work is a JBC ship the desired ship speed, an initial rate of revolutions of Expanded area ratio AE/A0 [-] 0.500 osition at at the hull, hull, osition the propeller is given, given, then, computing computing both both total total ship ship at modelthe scale. Thethe main particulars and thethen, propeller is Pitch ration P0.7/DP [-] 0.750 resistance andTable thrust of propeller, propeller, the the rate rate of of propeller propeller JBC with propeller are shown in 1 and resistance and thrust of Self-propulsion revolution is isprocedure varied until until an an equilibrium equilibrium condition condition is is obtained obtained ectively. Boss ratio Dh/DP [-] 0.180 revolution varied In the self-propulsion computations using RANSE method, (thrust of of propeller propeller equal ship ship resistance). resistance). As aa result, result, the ship ship equal As the the effects effects of of aa (thrust Number of blades Z [-] 5 ll the self-propulsion point is found. found. For model model scale simulation, it the same procedure was adopted in towing tank test. Setting self-propulsion point is For scale simulation, it thereby creating creating should be considered the applied towing force SFC (Skin thereby Direction of rotation Clockwise should considered the rate applied towing forceofSFC (Skin opeller geometry. geometry. the desired shipbe speed, an initial revolutions the[20]. opeller Friction Correction) according toofthe the ITTC procedure procedure Correction) according to ITTC [20]. distributed propeller over Friction TABLE 1. Principal Particulars of JBC in model scale is given, then, computing both total ship resistance ss distributed over 3. NUMERICAL NUMERICAL SIMULATIONS SIMULATIONS orce varies varies in in the the 3. orce

3.1. JBC geometry omponents in in two two 3.1. JBC geometry omponents The reference reference vessel vessel used used in in this this work work is is aa JBC JBC ship ship The ff bθ are are defined defined as as without ESD ESDJOURNAL at model model scale. scale. The The main main particulars particulars and and the the bθ NAVAL ENGINEERS without at geometry of JBC with propeller are shown in Table 1 and geometry of JBC with propeller are shown in Table 1 and Figure 1, 1, respectively. respectively. (2) Figure (2)

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Comparison of Different Approaches For Prediction Self-Propulsion of the Ship Using Ranse Method

■ Self-propelled at ship point in calm water condition with

Fr = 0.142; ■ The vessel is free to trim and sink.

Computational domain, boundary conditions, and mesh As mentioned in section 2, the computational domain for the self-propulsion simulation employing the DRP method contains two regions: a stationary region and a cylindrical sub-region. The stationary region surrounded the whole JBC hull, while the cylindrical sub-region surrounded the propeller. Sliding interfaces are used to connect two regions (see Fig.2). Based on the recommendations given by ITTC (7.5-03-0204),[17] the stationary region was the rectangular parallelepiped with dimensions, expressed as multiples of ship length between the perpendiculars (LPP). The inlet boundary is positioned at 1.5LPP from bow of the ship, the outlet boundary is extended to 2.5LPP from the ship stern. The right and left side boundaries are positioned at 1.5LPP at 2.5LPP from the side of the ship in order to avoid any wave reflections influences. The top and bottom boundaries are extended to 1.25LPP and 2.5LPP from the free surface, respectively. The rotating sub-region is a cylinder with a diameter equal to 1.65 times of the propeller diameter, the downstream and upstream boundaries are located at 1.0DP and 0.625DP from the aft perpendicular of the ship, respectively. The computational domain for using BFP method contains only one region named as stationary region, in which there is a hull of the ship and actuator disk (Fig.3). For both methods, the boundary conditions were set up on the boundaries computational domain as well as on the hull of the ship and its propeller as follows: The inlet, top, and bottom boundaries were considered as velocity inlet, pressure condition was used on outlet, and side boundaries were set as symmetry plane; no-slip wall condition was applied on ship hull surface. Besides, for DRP method, the propeller is taken as considered with no-slip wall boundary conditions. The mesh generation directly influences the numerical results. The trimmed hexahedral mesh was applied for both methods. In order to correctly capture the Kelvin wave pattern generated by the ship hull, the mesh is refined in the free surface. To reduce the computation time, the local volume refinements were applied for the stern and the bow regions of the ship hull. The mesh is further refined in the cylindrical sub-region and around the propeller (for DRP method) and in the near actuator disk field (for BFP method) in order to capture correctly flow income and outcome propeller/ actuator disk. To capture the exact flow behavior near the walls of the boundary layer, the prism layers were applied. The number of prism layers

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FIGURE 2. Computational domain in DRP method

FIGURE 3. Computational domain in BFP method

at the wall for both propulsion models was defined so that the average Y+ value of the first grid space from the submerged part of the hull in the wall coordinates in the log-law region and in the viscous sublayer region for the propeller, as suggested by the adopted wall-treatment.[4], [22] To compare the level of accuracy of simulation obtained results and computational time between the two methods, the number of mesh will generate is the same between the two methods with a number of cells equal to 9.82 million cells. Physical model The physical model used in this study was three-dimensional incompressible viscous unsteady RANSE. The Volume of fluid method was applied for tracking and locating free surface. The turbulence model used is SST K- ω as this model provided accurate ship hydrodynamics prediction.[23] The vessel was allowed to move with two degrees of freedom with the pitch and heave motions. For DRP method, the propeller rotation was introduced by DFBI Superposed rotation model, which allowed propeller attached to the ship hull. To reduce the resistance force fluctuation due to wave reflections in the computational domain, wave damping set in the region distanced about 1.25LPP away from the ship hull. The first order temporal

NAVAL ENGINEERS JOURNAL

Time step (s)

Number of iterations per time step

DRP method

0.00035

BFP method

0.03

Approaches

TABLE 2. Time step setup for self-propulsion prediction using different approaches n [rps]

RT(SP) - SFC

DRP method

7.8

22.89

22.60

8.0 23.92 24.80 Table 2. Time step setup for self-propulsion prediction using BFP method different approaches 7.6 21.60 21.35 Time step Number of iterations Approaches (s) per time step 7.8 22.38 23.10

FIGURE 4. Mesh generation results for DRP method

DRP 0.00035 TABLE 3. method Computed results of two rps. cases5for both methods BFP method 0.03 5 The computation of the simulation is performed on a

workstation Xeon CPU with 40 cores at 2.8GHz and Results andcomputer Discussion

Figure 4. Mesh generation results for DRP method

GB of RAM. computations were performed with the The 64 self-propulsion RESULTS AND DISCUSSION total4.number of cells was 9.82 million cells for both methods. The self-propulsion point computations is determined thatperformed the pointwith at which The self-propulsion were the total number cells wasand 9.82 million cells for methods. the thrust of the of propeller ship resistance areboth in equilibriThe self-propulsion point is determined that the point at which um. the Forthrust simulations in the and model we have consider the of the propeller shipscale, resistance are into equilibrium. SFCFor (Skin Friction in Correction which into account simulations the modelForce), scale, we havetakes to consider the SFCerence (SkininFriction Correction Force),between which takes into the diff skin friction coefficients the model account the difference in skin friction coefficients between the scalemodel and the full-scale ship.[25] ship [25]. scale and the full-scale  T RT ( SP )  SFC

FIGURE 5. Mesh generation results for BFP method

(8) (8)

Where SFC = 18.20 N (the value obtained from experimental where SFC = 18.20 value thrust obtained from experimental data [21]); T – is N the(the computed of propeller; RT(SP) is the [21]ship total at self-propulsion. data); T –resistance is the computed thrust of propeller; RT(SP) is the

total ship at self-propulsion. In resistance practice, obtaining a self-propulsion point in one run is difficult, so normally should execute atpoint least in two constant In practice, obtainingwe a self-propulsion one run is scheme is used for temporal discretization due to less comspeed runs with different rates of propeller revolution n. In this difficult, so normally we should execute at least two constant putational time and shows no oscillation of the result, while a case study, two constant speed runs with two rates of propeller speed runs withwere different rates of n. In this second order up-wind scheme is used for the discretization of revolutions conducted, andpropeller the linearrevolution interpolation was [24]generation results for BFP method to determine self-propulsion point.rates Theof calculated caseapplied study, two constantthe speed runs with two propeller convection termsFigure in RANSE. 5. Mesh results for both methods are displayed in Table 3 and Figure 6. 3.2.2. Physical model revolutions were conducted, and the linear interpolation was The rate of the propeller revolution for the self-propulsion The physical model used in this study was three-applied determine therps self-propulsion point. e calcuTime step pointtowas 7.85, 7.65 for RDP method and Th BFP method, dimensional incompressible viscous unsteady RANSE. The respectively. lated results for both methods are displayed in Table 3 and One of the major differences in the setup of two approaches Volume of fluid method was applied for tracking and locating Table 3. Computed results of two rps. cases for both methods rate of the propeller revolution for the self-profor self-propulsion prediction are the time step size free surface. The turbulence model used is and SST the K- ω as thisFigure 6. Thne[rps] RT(SP) - SFC T model provided accurate ship hydrodynamics prediction [23]. pulsion point was 7.85, 7.65DRP rps method for RDP method and BFP number of iterations per time step. This setup directly imThe vessel was allowed to move with two degrees of freedom method, respectively. pacts the time consumption during simulation. The reason for 22.89 22.60 7.8 with the pitch and heave motions. For DRP method, the between the computed self-propulsion selecting thispropeller time step (which mainly aff ected computational rotation was introduced by DFBI Superposed rotation The comparison 8.0 23.92 24.80 model, in which allowedsection. propeller attachedsetup to theofship hull. Topoint (CFD) for both methods and measured data (EFD) are time) is discussed the above Detailed time BFP method seen from Table 4, the computstep in Tablereduce 2. the resistance force fluctuation due to wave reflectionstabulated in Table 21.35 7.6 4. As can be21.60 in the computational domain, wave damping set in the region 22.38well with the measured 23.10 both methods agree data. The computation the simulation is performed onhull. a workfrom the ship The ﬁrsted results for7.8 distanced of about 1.25LPP away order temporal scheme used for at temporal discretization dueWith the same number of cells, the DRP method provides station computer Xeon CPU withis40 cores 2.8GHz and 64 GB of RAM.to less computational time and shows no oscillation of theslightly better results compared to BFP method. The similar result, while a second order up-wind scheme is used for the discretization of convection terms in RANSE [24].

3.2.3. Time step NAVAL ENGINEERS One JOURNAL of the major differences in the setup of two approaches

for self-propulsion prediction are the time step size and the number of iterations per time step. This setup directly impacts

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Comparison of Different Approaches For Prediction Self-Propulsion of the Ship Using Ranse Method

FIGURE 7. Time histories of propeller thrust at n=7.8 rps. a) DRP method

FIGURE 8. Time histories of propeller torque at n=7.8 rps

b) BFP method

FIGURE 6. Results interpolation to determine the selfpropulsion point

results were also reported in the recent analysis of Gokce M.K. et al. [26] for JBC ship model. Otherwise, the significant difference between the two methods is that, the BFP method only provides the mean value of thrust and torque, while DRP method provides thrust and torque values in real-time (see Figures 7 and 8). Obviously, this reflects the actual working condition of the propeller behind the ship hull, as the propeller is working in a non-uniform wake behind the hull. Thus, the thrust and torque of propeller will vary in real-time with the magnitude and frequency of oscillation depending on

EFD[21]

Parameters

Total ship resistance, [N]

FIGURE 9. Time histories of propeller side force at n=7.8 rps using DRP method CFD DRP method

E%D

BFP method

E%D

RT(SP)

40.760

41.40

1.57

40.00

1.86

Propeller thrust, [N]

22.560

23.20

2.84

21.80

3.37

Self-propulsion point [rps]

7.800

7.85

0.64

7.65

1.92

TABLE 4. Computed self-propulsion point based on two different methods in comparison with measured data

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NAVAL ENGINEERS JOURNAL

FIGURE 10. Non-dimensional lateral flow velocity field on the propeller/virtual disks

FIGURE 11. Non-dimensional lateral flow velocity field

the number of propeller blades, the rate of propeller revolution, and ship hull form. The evaluation of propeller thrust and torque oscillation frequency in real time has played a crucial role as those are the input parameters in estimating the shaft resonance oscillation. Another issue related to the major difference between two propeller models is that, the DRP method generates a lateral force, while the BFP method lack of lateral force component. Figure 9 shows the propeller lateral force value in real-time using DRP method. It found that propeller lateral force is about 0.86% of propeller thrust. The evaluation of propeller lateral force is very important in ship maneuvering predictions due to this force is one of the

important factors that cause asymmetry in ship motions in the horizontal plane.[13, 27] So, the DRP method offers a more accurate prediction of ship maneuvering compared to the BFP method. To address the cause of propeller lateral force. The non-dimensional lateral flow velocity field (Vy/V) at the propeller/ virtual disks and at the symmetry plane on the propeller/ virtual disk are visualized in Figures 10 and 11, respectively. The yellow parts show the flow in +y direction while the blue parts show fluid motion in -y direction. It is clear in Figures 10 and 11 the differences in the lateral flow velocity field between two propeller models. Higher asymmetry in the lateral flow and the greater lateral velocity generated by the DRP method compared to the BFP method contributes to these differences. For DRP, the yellow parts in these Figures are dominant which results in lateral force in +y direction (the mean value of this force is equal to 0.2N). For BFP method, the yellow and blue parts in Figures 10 and 11 are almost the same. These blue parts partially suppress the lateral motion of the propeller which results in lateral force is equal to zero. The influence of propulsion models on the effective wake distribution at the aft perpendicular is depicted in Figure 12. It can be seen from Figure12, the effective wake for both propulsion models can be divided into two different regions, including the flow inside the propeller/virtual disk region and the flow outside the propeller/virtual disk region. The first region showed an asymmetric configuration due to the propeller/virtual disk rotation, while the other region is an almost symmetric configuration. The wake inside virtual disk region is smaller than inside propeller region and measured data while the wake outside virtual disk region seems to be higher than outside propeller regions and measured data. Besides, it can

FIGURE 12. Axial velocity distributions at the aft perpendicular: a) DRP method, b) BFP method, c) EFD

NAVAL ENGINEERS JOURNAL

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Comparison of Different Approaches For Prediction Self-Propulsion of the Ship Using Ranse Method

Propulsion models

Time elapsed per time step [s]

Total computational time [h]

BFP method

4.5

60.1

DRP method

21.1

270.2

TABLE 5. Computational time of two different propulsion models FIGURE 13. The effect of propulsion models on dynamic pressure distribution at ship stern

FIGURE 14. Axial velocity field in symmetry plane

be observed clear differences in the axial velocity distribution between two propeller models inside the propeller/virtual disk region. Higher unsteadiness and greater non-uniformity in the flow generated by the DRP method compared to the BFP method contributes to these differences. Greater asymmetry in the axial velocity contours between the port and starboard side of the DRP method compared to the BFP method could be an indication of larger temporal and spatial variations of the flow (see Figure 12). Propeller working behind the ship will introduce pressure pulses on the ship hull above the propeller region, which may have effects on noise and ship structure vibration. Figure 13 shows the influence of propulsion models on dynamic pressure distribution at ship stern. As can be seen from Figure 13, the dynamic pressure field at the ship stern is strongly affected by propulsion models, especially at the regions of the hull above the propeller/virtual disk region. Using the DRP method gives higher dynamic pressure and greater asymmetry in the dynamic pressure contours between the port and starboard side at the region of the hull above propeller/virtual disk region than using the BFP method. These differences can be partly explained by the effect of propeller lateral force. So, DRP method offers a more accurate prediction of noise and ship structure vibration in comparison with the BFP method. The influence of propulsion models on the axial velocity field on the symmetry plane is presented in Figure 14. It can be observed from Figure14, there is clearly a difference in the axial velocity field due to propulsion models. Using the DRP

140 | Fall 2023 | No. 135-3

method gives the higher axial velocity than using BFP method at downstream of propeller/virtual disk region. In terms of computational time, the simulation for two different propulsion models is conducted in the same workstation computer and with the same total number of cells. Table 5 shows the time elapsed per time step and the time-consuming to obtain convergence results for two different propulsion models. It can be seen from Table 5 that, the BFP method takes a minor time elapsed per time step and total computational time, by less than 75% compared to the discretized rotating propeller method. The difference in time elapsed per time step between two methods can be explained by that, discretized rotating propeller method using sliding mesh technical, so the solver has to calculate the flux through the sliding interface for each time steps,[28] when the BFP method does not.

Conclusions The self-propulsion prediction of the JBC ship model is presented and solved based on two different propulsion models, including the BFP and DRP methods. Both methods indicated their capability for prediction of the self-propulsion parameters and showed good agreement with the measured data. However, each method has its own pros and cons, the BFP method indicates to be simple, easier, and faster to predict the self-propulsion parameters. However, this method is unable to give the thrust and torque values in real-time and real flow characteristics downstream the propeller, dynamic pressure distribution on propeller blade surfaces, and the cavitation occurrence. Besides, lack of propeller lateral force in BFP method is one of the drawbacks lead to lower accuracy of the BFP method compared to DRP method in prediction of ship maneuvering, noise, and ship structure vibration at the ship stern. The DRP method can overcome the shortcomings mentioned above of BFP method. However, it requires considerable computational time and is the most complex to model and compute.

Acknowledgment The authors are grateful to the Vietnam Maritime University for providing necessary research facilities during current research work.

NAVAL ENGINEERS JOURNAL

AUTHOR BIOGRAPHIES TRAN NGOC TU got his specialist degree at State Marine Technical University of St. Petersburg, Russian in 2009; in the same year, he started working for Vietnam Maritime University (VMU) as a lecturer and researcher. He got his Ph.D. degree in 2013 in Naval Architecture at State Marine Technical University of St. Petersburg, Russian. His current interests include Ship design, CFD and ship hydrodynamics. NGUYEN THI HAI HA received her Bachelor degree (2006) from Vietnam Maritime University; M.Sc. degree (2010) from

the cooperative program between Vietnam Maritime University and University in Liège, Belgium. She is a Lecturer, Shipbuilding Faculty, Vietnam Maritime University. Her current interests include ship design and Computational Fluid Dynamic. NGUYEN HUY HAO lecturer of Vietnam Maritime University, graduated from Vietnam Maritime University in 1994, got the Technical Doctor Degree at Saint-Petersburg State University of Waterways Communication, Russian Federation in 2007. His current interests is ship propulsion design.

REFERENCES [1]

Le, T.-H., et al., Numerical investigation on the effect of trim on ship resistance by RANSE method. Applied Ocean Research, 2021. 111: p. 102642.

[2]

Choi, J., et al., Resistance and propulsion characteristics of various commercial ships based on CFD results. Ocean engineering, 2010. 37(7): p. 549-566.

[3]

Villa, D., S. Gaggero, and S. Brizzolara. Ship Self Propulsion with different CFD methods: from actuator disk to viscous inviscid unsteady coupled solvers. in The10th International Conference on Hydrodynamics. 2012.

[4]

Tu, T.N., et al., Numerical prediction of propeller-hull interaction characteristics using RANS method. Polish Maritime Research, 2019.

[5]

Song, K., et al., Simulation strategy of the full-scale ship resistance and propulsion performance. 2021. 15(1): p. 1321-1342.

[6]

Soares, C.G. and T.A. Santos, Progress in Maritime Technology and Engineering: Proceedings of the 4th International Conference on Maritime Technology and Engineering (MARTECH 2018), May 7-9, 2018, Lisbon, Portugal. 2018: CRC Press.

[7]

Jasak, H., et al., CFD validation and grid sensitivity studies of full scale ship self propulsion. International Journal of Naval Architecture and Ocean Engineering, 2019. 11(1): p. 33-43.

[8]

Hu, J.-m., et al., Prediction of ship power and speed performance based on RANS method. 2017. 64(1-2): p. 51-78.

[9]

Castro, A.M., et al., Full scale selfpropulsion computations using discretized propeller for the KRISO container ship KCS. 2011. 51(1): p. 35-47.

[10] Carrica, P.M., A.M. Castro, and F. Stern, Self-propulsion computations using a speed controller and a discretized propeller with dynamic overset grids. Journal of marine science and technology, 2010. 15(4): p. 316-330. [11] Chuan, T.Q., et al. Full-Scale Self-propulsion Computations Using Body Force Propeller Method for Series Cargo Ship 12500DWT. in International Conference on Material, Machines and Methods for Sustainable Development. 2020. Springer. [12] Gaggero, S., et al., Ship self-propulsion performance prediction by using OpenFOAM and different simplified propeller models, in Progress in Maritime Technology and Engineering. 2018, CRC Press. p. 195-203. [13] Kinaci, O.K., et al., Free-running tests for DTC self-propulsion–An investigation of lateral forces due to the rudder and the propeller. Applied Ocean Research, 2021. 116: p. 102877. [14] Sezen, S., et al., Investigation of selfpropulsion of DARPA Suboff by RANS method. Ocean Engineering, 2018. 150: p. 258-271. [15] Win, Y.N., et al., RANS simulation of KVLCC2 using simple body-force propeller model with rudder and without rudder. 日本船舶海洋工学会論文集, 2016. 23: p. 1-11. [16] Bekhit, A. Numerical simulation of the ship self-propulsion prediction using body force method and fully discretized propeller model. in IOP Conference Series: Materials Science and Engineering. 2018. IOP Publishing. [17] ITTC 2014. Recommended Procedures and Guidelines 7.5-03-02-04. Practical Guidelines for Ship Resistance CFD. Available from: https://www.ittc.info/ media/8169/75-03-03-01.pdf.

[19] ITTC 2011. Practical guidelines for ship CFD applications. In: Recommended procedure and Guidelines, ITTC 7.5–03-02–03. https://ittc.info/ media/1357/75-03-02-03.pdf. [20] ITTC 2008. ITTC – Recommended Procedures and Guidelines 7.5-02-0301.01. Testing and Extrapolation Methods Propulsion, Performance Propulsion Test. https://ittc.info/media/1587/75-02-03-011. pdf. [21] https://t2015.nmri.go.jp/Instructions_JBC/ instruction_JBC.html. [22] Tu, T.N., Numerical simulation of propeller open water characteristics using RANSE method. Alexandria Engineering Journal, 2019. 58(2): p. 531-537. [23] Yong, Z., et al., Turbulence model investigations on the boundary layer flow with adverse pressure gradients. Journal of Marine Science, 2015. 14(2): p. 170-174. [24] Farkas, A., N. Degiuli, and I. Martić, Assessment of hydrodynamic characteristics of a full-scale ship at different draughts. Ocean Engineering, 2018. 156: p. 135-152. [25] https://ittc.info/media/1587/75-02-03-011. pdf. [26] Gokce, M.K., O.K. Kinaci, and A.D. Alkan, Self-propulsion estimations for a bulk carrier. Ships Offshore Structures, 2019. 14(7): p. 656-663. [27] Kinaci, O.K., Straight-ahead self-propulsion and turning maneuvers of DTC container ship with direct CFD simulations. Ocean Engineering, 2022. 244: p. 110381. [28] Tu, T.N. and N.M. Chien, Comparison Of Different Approaches For Calculation Of Propeller Open Water Characteristic Using RANSE Method. Naval Engineers Journal, 2018. 130(1): p. 105-111.

[18] Simcenter STAR-CCM+ 2020.1 User Guide.

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TECHNICAL PAPER

Theoretical and Experimental Study on the Rapidity of LargeofAutomobile Ro-Ro Ship on the Rapidity Large Automobile 8500PCTC Ro-Ro Ship 8500PCTC 2, Zhang 3, Yao Wang Zhuang, Ying-jie, Yao Kai-han Wang Chi-ming, Chi-ming1,Wang WangYong-guang, Yong-guang1Lin , Lin ZhuangZhang Ying-jie Kai-han1

Abstract A parametric transformation method based on the parameters of the 8500PCTC mother ship can be used to improve the hull lines of a large Roll-on/Roll-off (RoRo) ship and meet the required speed. The CFD software SHIPFOLW is used for the numerical simulation analysis, and the simulations are verified by ship model test. The results show that the optimized hull has good flow field characteristics after theoretical analysis with a low wave resistance and gentle pressure gradient of the hull. The fast performance test under the design draught shows that the optimized ship’s lines have high resistance performance and propulsion efficiency, and the experimental performance of the ship model is consistent with the theoretical results.

KEYWORDS: Rapidity, Resistance performance, PCTC, Propulsion efficiency

Nomenclature ■ Ro-Ro ship: Using the roll-on/roll-off loading method,

is a type of ship specifically designed for transporting vehicles. ■ Parametric transformation method: Based on the hull form parameters of the parent ship, the Lackenby method is used in combination with the primary geometric features of the hull, and the search parameters include the longitudinal position of the center of buoyancy and either the prismatic coefficient or the block coefficient. This process involves moving the columns fore and aft, while not changing the section shapes (unless scaling them) i.e. all y-coordinates move by ratio of beams, all z by ratio of drafts etc. ■ Model test: The ship hull form designed is scaled down based on the similarity theorem to create a ship model, which is tested in a water tank to evaluate the ship’s resistance, propulsion efficiency, and other factors. ■ Resistance analysis method: The experimental method for obtaining the forces acting on a ship model at various speeds through ship model tests. ■ Self-navigation analysis method:The sea trial is carried out according to the UK sea trial method. The method involves varying five ship speeds within a certain range that includes the design speed. For each ship speed Vm, four propeller speeds are varied. Real-time sampling is performed using a computer to measure the propeller thrust Tpm, torque Qm, rotation speed nm, and the force F acting on the ship model.

1 School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024 2 School of Shipbuilding and Marine Engineering, Harbin Engineering University, Harbin 150001 3 Naval University of Engineering, Hubei 430030 China

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Theoretical And Experimental Study on the Rapidity of Large Automobile Ro-Ro Ship 8500PCTC

Introduction With the rapid development of the global automobile industry and the impact of COVID-19 on the way people travel, the demand for automobiles has dramatically increased. As a popular tool for automobile transporting, the automobile Ro-Ro ship (PCTC) has the advantages of high efficiency, good economic benefits and extensive automobile loading. Moreover, it is favored by shipowners because of the transportation advantages of the routes extending in all directions. Ultra-large ships of 7500PCTC and 8500PCTC with large capacity, high speed and high performance have attracted much attention due to the demand for automobile transportation [1-4]. Therefore, based on the actual large ship 8500PCTC’s parameter and the shipowner’s requirements, the hull lines are optimized according to criteria of rapidity and efficiency provided by this research. Furthermore, under the condition that the primary dimension of the ship (Total length, Displacement, Waterline Length, Beam, Draft, and so on ) remains unchanged, the CFD method is used to compare the resistance performance of the ship to get the better hull form parameters, and the ship model test verifies the rapidity of the actual optimized ship and the feasibility of the theoretical optimization of the hull form.

1. Main parameters of 8500PCTC 8500PCTC is currently the largest automobile Ro-Ro ship in the market. It has the characteristics of a sizeable block coefficient and a high GM value. The total length is 199.90 m, the length between perpendiculars is 193.11 m, the molded breadth is 36.5 m, and the moulded depth is 38.54 m. The design draught is 9.35 m, the scantling draft is 10.3 m, and the speed of the ship is 16.0 kn. According to the shipowner’s requirements, the parameters of the parent ship are optimized to improve operational efficiency.

Ship length

The length of the lane generally determines the total length of the ship. The length must meet design requirements for general layout, which are restricted by practical factors such as load, speed, seaworthiness, and port conditions. The main dimensions are shown in Table 1. The optimized ship length is between 199.90 m and 199.99 m. The initial ship length is set to be 199.95 m by considering the primary dimension of the parent ship.

Molded breadth

The molded breadth of the ship mainly affects the stability, automobile loading quantity, rolling period, etc., but it is

144 | Fall 2023 | No. 135-3

not conducive to rapidity. In the optimization design, when automobile loading is not reduced as much as possible, the ship width is kept consistent with the parent ship, and the value is 36.5 m.

Draught

The design draught of a ship is primarily determined by factors such as the port, channel depth, and ship speed. The relationship between the deadweight and the draught is outlined in Table 1. Based on the requirements for ship displacement and loading capacity, the preliminary design draft has been set to 9.2m.

Ship’s speed

The speed of a Ro-Ro ship is influenced by factors such as the route, freight volume, turnover rate, and economy. Based on existing ship statistics, large ocean-going Ro-Ro ships with a deadweight of more than 15,000t have a service speed ranging from 18kn to 25 kn, while the average speed of 6,000-8,000 car carriers is around 20 kn. Ro-Ro ships with a deadweight between 7,000t and 15,000t typically have a service speed of 15 kn to 25 kn, while those between 3,000t and 7,000t have a service speed of 15 kn to 23 kn. Most Ro-Ro ships with a deadweight less than 3,000t are used for short-distance transport or serve as centralized and evacuation containers, with service speeds averaging around 13 kn to 16 kn. Therefore, the Ro-Ro ship’s Froude number “Fn” falls between 0.21 and 0.3. Considering operational efficiency, the optimized ship speed is 19.5kn.

Endurance

The ship’s endurance is mainly determined by the area and route of navigation and the shipowner’s requirements, and the endurance mainly affects by the design of the oil tank. Considering the parent ship and other current mainstream ships’ endurance (shown in table 1), the endurance is set to be 28,000n miles.

Parametric transformation of hull lines based on numerical simulation According to the content described in section 1, based on the parent ship, the main parameters of the initial optimization target ship and propeller are shown in Table 2:

Parametric transformation and analysis of hull lines

For reducing the resistance of the ship and improving the propulsion performance, under the premise that the main dimensions remain basically unchanged, using the search parameters for a parametric transformation(the software is MAXSURF,

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Fall 2023 | No. 135-3 | 145

150.00m 28.00m

7.00m / 7.50m

abt.43.76m 26

129.60m

24.40m

23.22m

13.13m

6.00m / 6.60m

29.15m based on folding radar mast

LBP

Breadth (moulded)

Depth, moulded (Upper deck)

Depth, moulded (Freeboard deck)

Draught, Design & Scantling

Air Draft (from B.L.)

CSR: 4233 kW × 117.5 r/min

SMCR: 4980 kW × 124 r/ min CSR: (0. 85MCR) 2 × 4590kW × 750r/min

MCR: 2 × 5400kW × 750r/min

MAN B&W: 9L32/44CR B.2 Tier II × 2sets

TABLE 1. Main parameters of different ship types

Main engine

10 000n.mile

8000n.mile

Endurance

MAN B&W: 6S40ME-B9.3TierII

abt.39.0 t/d

17.14 t/d

Fuel Consumption at M/E CSR

18.0kn

16.0kn

Service Speed (at design draught, M/E C.S.R)

7300t / 9000t

4000t / 5500t

9.80m

Deadweight, Design & Scantling

Complement

159.60m

141.20 m

26.68m

3000PCTC

2100PCTC

Project

LOA

CSR: 5285 kW × 106.5 r/min

SMCR: 7550 kW × 120 r/ min

MAN B&W: 6S50ME-C8.5TierII

12 000n.mile

20.17 t/d

16.0kn

8000t / 11500t

44.92m

7.60m / 8.50 m

13.02m

28.59 m

28.00 m

158.40m

169.10 m

3800PCTC

MAN B&W: 9S50MC-C MCR: 14220kW × 127.0 rpm CSR: 12798kW × 122.6rpm

MCR: 12460kW × 117.0r/min CSR: (0.80MCR) 9968kW × 108.6r/min

20 000n.mile

51t/d

MAN B&W: 7S50ME‐B9.3‐ GI ‐Tier ‐II, 1set

Gas mode: 33.08 t/d HFO at 18.5 8 knots 14,000 nm LNG at 16.5 knots 4,000 nm

Fuel oil mode: 39.63 t/d

20.0kn

6300t / 12000t

10,500t / 14,700t 18.8kn

48.69m

Max air draught at Ballast draught 38.0 m 36

7.70m / 9.00m

12.80m

31.80m

31.50m

170.20m

182.80m

4900PCTC

8.2m / 9.2m

12.92m

28.80m

30.00 m

170.00 m

abt.181.90 m

4000PCTC

49.2m

9.0m / 10.0m

14.35m

35.68m

32.26m

190.00m

199.90m

6800PCTC

43.88m by tilted mast and antenna

9.35m / 10.3m

8.70m / 10.50 m 46.00m

14.40m

36.19m

36.50m

193.11m

199.90m

7800PCTC

14.30m

35.68m

35.50m

190.00m

199.90m

7400PCTC

46.18m by tilted mast and antenna

9.35m / 10.3m

14.40m

38.49m

36.50m

193.11m

199.90m

8500PCTC

CSR: (0.9CMCR) 10980kW × 94.6r/min

C.MCR: 12200kW × 98r/min

MCR: 14280kW × 105r/min

MAN B&W: 6S60ME-C8.2 Tier II

20 000n.mile

43.7t/d

19.35kn

S MCR: 11735kW × 92.0 rpm CSR: 7830kW × 80.4rpm

S MCR: 14840 kW × 102.5 r/ min

SMCR: 11735kW × 92 r/min

CSR: 10561kW CSR: 12614 kW × 88.8 r/min × 97.1 r/min

MCR: 14280kW × 105.0 rpm

MCR: 19040 kW × 105.0 r/ min

MCR: 14280kW × 105 r/min

MAN B&W: 6S60ME-C8.2 Tier II

30 000n.mile

30.5t/d

16kn

MAN B&W: 8S60 ME-C8.5 Tier II

22,000 nmile

48.68 t/d

19.5kn

MAN B&W: 6S60ME-C8.5 TierII

30,000 n.miles

41.8 t/d

18.3kn

7830kW × 80.4rpm

SMCR: 1735kW × 92.0 rpm

MCR: 14280kW × 105.0 rpm

MAN B&W: 6S60ME-C8.2 Tier II

30 000n.mile

30.5t/d

16kn

13500t / 18500t 14500t / 19500t 10700t / 21000t 15900t / 21500t 15200t / 20800t

N/A

9.00m / 10.00m

14.95m

36.68m

32.26m

190.00m

199.90m

6700pctc

146 | Fall 2023 | No. 135-3

2100PCTC

10 with 1 liftable deck

Deck Number

3000PCTC

8 with 1 liftable deck

1000 kW × 2 set 25000 m2

E/G: 150 kW × 1 set

A/E: 700 kW × 3sets

S/G: 1400 kVA × 2sets

TABLE 1. Main parameters of different ship types

18000 m2

600 kW × 1 set

E/G: 150 kW × 1 set

A/E: 700 kW × 3sets

Capacity Deck Area

Bow thruster

Power supply

Project

3800PCTC

10 with 2 liftable decks

1000 kW × 1 set 31200 m2

E/G: 150 kW × 1 set

A/E: 900 kW × 3sets

10 with 2 liftable decks

33 100 m2

E/G: 150 kW×1 set 2000 kW × 1set 1800 kW × 1 set 56600 m2 12 with 4 liftable decks

1100 kW × 1 set 40000 m2 12 with 3 liftable decks

E/G: 150 kW × 1 set

12 with 5 liftable decks

1800 kW × 1 set 56500 m2

E/G: 220kw × 1set

13 with 4 liftable decks

1800 kW × 1 set 61300 m2

E/G: 200kw × 1set

SGT: 2000kW× 1 set

E/G: 180kw × 1set

740kW × 1 set

7400PCTC

A/E: 1 × 900 kW, 2 × 1500 kW

6800PCTC A/E: abt1650 kW × 3sets

6700pctc A/E: 1250kW × A/E: 1980kW× 3 sets 2 sets

4900PCTC A/E: 990kW × 2 sets;

4000PCTC S/G: 1200 kW × 1set

7800PCTC

8500PCTC

SGT: 950kW × 1 set E/G: 240 kW × 1 set 1800 kW × 1 set 71400 m2 14 with 5 liftable decks

SGT: 950kW × 1 set E/G: 240 kW × 1 set 1800 kW × 1 set 65300 m2 13 with 5 liftable decks

A/E: 1980kW × A/E: 1980kW × 2sets; 1000kW 2sets; 1000kW × 1 set × 1 set

Theoretical And Experimental Study on the Rapidity of Large Automobile Ro-Ro Ship 8500PCTC

Total length: Loa (m) Design draught: d (m) molded breadth : B (m) Block coefficient: Cb Displacement volume:Δ(m3)

Hull

199.95 9.2

36.5

0.581

36930

Pitch ratio at 0.7R:P Number of blade: Z Diameter: Dp (m) Direction of turning Number of propeller

Propeller

0.836 4

Right

Nominal power: SMCR Number of thrusters Shafting efficiency Ship speed (knot)

Main engine

1 Endurance (nm)

17500 1

0.99

19.5

TABLE 2. Main parameters of actual ship propeller

FIGURE 1. Parametric transformation of MAXSURF

FIGURE 2. Comparison of parent ship and preliminary design ship (Blue lines for parent ship, Red lines for preliminary design ship)

FIGURE 3. co-rotational coordinate system

NAVAL ENGINEERS JOURNAL

Figure

28000

2.2. CF Ac

moves that the

Neuma system

W W

W boun W bounda bounda

An An

Fr expre Fro express express

And:

 2  Fr 2 22 n  0  z  0 Figure 1. Parametric transformation of MAXSURF And: x  zx  Fr 2 n 2  0  z  0 z (3) 2x 2   n  Fr z  0  x  1 0  2   nx oz   nx R  x0 (3)   x, y, zn   1  R   x0  onx 1  ( n OR 1  x0  R o   x, y, z        R   ( [7]andxHavelock Fig.1). Specify the new LCB position and either a new Block or From Kochin freeamplitude  R x  0 0 1  function   x, y, z  Oo 1[8] R    1,the  [7]R  00 Prismatic Coefficient, followed by constraint values forFrom a maxexpression of xxfiresistance rst-order Kochinwave free resistance amplitude expression function wave expression[8], the   OR  and  Havelock   , ,  x y z R        imum of three of Displacement, Waterline Length, Beam and wave-making resistancecoeffi cient can be obtained x  0as: as: 1coefficient  R  expression of first-order wave-making resistance can be obtained O [7] Fromed,Kochin free amplitude function and Havelock wave resistance expression[8], the  Draft. Once these values and constraints have been specifi  1 R  2 1/2 Rfunction [7]  From Kochin free amplitude and expression[8], C  = coefficient A  tHavelock  t 2 be dt resistance  modeler will iterate to achieve the required parameters and will 1can  wave expression of first-order wave-making resistance obtained as: (4)  1 (4)  2 2 [7] L display the results when it is finished. Based on the expression iterative From of Kochin free amplitudeRU function Havelock wave resistance 2 first-order resistance coefficient can be obtained as: expression[8], 1 and e 2. Comparison of parent ship and preliminary design ship (Blue lines for parent ship, Red wave-making 2 2 1/2  C  =  A  t  12  t  dt calculation results, we can change the cross-sectional curve of of first-order wave-making  1/2 obtained as: 1 R (4)  1-  coefficient 2resistance 2 be expression 2  U L   texpressed C = system A  tcan dt as:  1becan  The wave elevation of the far-field free wave   lines for preliminary ship)and then analyze the hull and the shape of the bowdesign and stern, The wave elevation the far-fi eld free wave system can be  ( 2 1 R of 2 1/2 U 2 L2 1  C  2 1  =  *A t  1  t22 1/2 dt and evaluate the results of multiple CFD calculations[5]. By expressed as: y   1 free Re 2 Asystem t -E t 1be dt as:  ,can  texpressed  (5)(  x,far-field The wave elevation ofthe  FD numerical evaluation results comparison U 2 Lwave comparing theand hydrodynamic performance of the optimized 2 1 free wave system  can be The wave elevation of the far-field expressed as: 1/2 is the ccording to hull, the preliminary design parameters of hull lines, is Where assumedE *that the conjugate ship  x, yof  exponential Re  A  t function E*  , t E: 1  t 2 dt an optimized ship type with better flow fielditcharacter(5)  the (5)   1  free * 2 1/2 The wave elevation ofthe system can be expressed as: x,far-field y   2 1/2 Re wave A t E , t 1 t dt   (    istics, smaller wave-making and lower pressure gradient are 1/2 uniformly on the free surface of the stationary unbounded water at speed U, which satisfies   t 2 z  i  x  ty1/2 1 Fr 2  texponential E  expof 1the  1function Where E * iswhere the conjugate E: * 2   finally obtained. Figure 2 shows the comparison hull lines E* is the conjugate of the exponential function E: x, y    Re   A  t E  , t 1  t dt ( *  ship is generated by e water is an incompressible ideal liquid and the wave making of the Where is the conjugate of the1/2 exponential function E: 2  2 2 1/2 between the parent ship and the preliminary design. Where A Et  is the amplitude  so t E  exp function, 1 t Fr the 1first-order z amplitude i  x  ty function is: 1/2 1/2   Where E *coordinate is the conjugate of the exponential ann-Michell theory[6]. Furthermore, the coordinate plane of the co-rotational  t 2 z E:  i  x  ty  E  exp 1  t 2 2 Fr 2  1function A t   F En dS  En t dl     x x y 1/2 the A  t  is the amplitude function,2 rso CFD and as results function is: 2 first-order coincides with thenumerical unperturbedevaluation horizontal plane, showncomparison in Figure Where 3.  1  tC2 1/2 amplitude  SF   E  exp 1  t z i x ty   r      is theA(t) Where A  twhere amplitude function, so the first-order amplitude function is: According to the preliminary design parameters of hull lines, is the amplitude function, so the fi rst-order ampliA  t    Fr 2  Enx dS   Enxt y dl Where S is the wetted surface area of the hull, and C is the intersection of the hull surface and it is assumed that the ship moves uniformly on the free surface function is: Afunction, 2 the first-order  is the Where A  ttude amplitude function is: Enx dSC  Enxamplitude t y dl  t    FrSso  of the stationary unbounded water at speed U, which es surface. the zsatisfi =0 water C 2 S Where S is the wetted surface area of the hull surface and A  t of  Frhull, Enxintersection t y dl  the S Enandx dSCisCthe that the water is an incompressible ideal liquid and the wave During the optimization of hullarea lines, under theand condition of a 9.2 m of design draught, Where S is the wetted surface of the hull, C is the intersection the hull surfacethea [6].surface. the z =0 water making of the ship is generated by Neumann-Michell theory shipping 19.5 andwetted thearea minimum wave-making are designed as the Where S isof the wetted of the hull, andthe C ishull, the resistance intersection of the hull surface a the zcoordi=0speed water surface. Furthermore, the coordinate plane of the co-rotational where Sknis surface the surface anda C the During the optimization of hull lines, underarea theofcondition of 9.2is m design draught, the nate system coincides with the unperturbed horizontal intersection the surface andthe thecondition z = 0dimensions water objectives, and the ship model isofof free tohull sink and trim. The main ship is fixed a the zplane, =0 water surface. During optimization hull lines, under of asurface. 9.2ofmthe design draught, shipping speed of 19.5 kn and the minimum wave-making resistance are designed as the Figure 3. co-rotational coordinate system as shown in Figure 3. During the optimization of hull lines, under the condition theshipping constraint function, and the type with under the smallest wave-making resistance is selected During the optimization of hull the condition of a 9.2 mare design draught, speed of 19.5 kn ship and the lines, minimum wave-making resistance designed asa objectives, and the of ship model is free draught, to sink and The main dimensions the ship is fixed a The velocity potential a 9.2 m design thetrim. shipping speed of 19.5 knofand he velocity potential of the flow field is:of the flow field is: theshipping final optimization of19.5 hull form by comparing different calculated values. of speed of ship kn and the wave-making resistance as objectives, and the model iswave-making free tominimum sink and trim. The main dimensions ship is fixed the minimum resistance designed asresistance thearethedesigned the constraint function, and the ship type with the smallest are wave-making is selected a (1)   Ux   (1) potential flow calculation results different positions areand shown Figures objectives, andfunction, the ship model freeship to at sink and The main dimensions ship is fixed objectives, the model istrim. free to sink trim.inTh eof the 4-6 theThe constraint and and theisship type with the smallest wave-making resistance is selected the final optimization of hull form by comparing different calculated values. main dimensions of the ship is fi xed as the constraint function, where ϕ, is the perturbation velocity potential. It is the expressed constraint function, ship with the smallestcalculated wave-making resistance is selected final optimization ofand hullthe form bytype comparing different values. The potential and flowthe calculation results are shown in Figures ship type with at thedifferent smallestpositions wave-making resistance is 4-6 in the theperturbation dimensionless formpotential, as: Where  , is velocity It is expressed in the dimensionless form as: the final ofcalculation hull form by comparing different calculated values. Theoptimization potential flow results at different positions are shown in Figures 4-6 Where  , is the perturbation velocity potential, It is expressed in the dimensionless selected form as: as the final optimization of hull form by comparing y, z    XIt, Yis, expressed Z/ L  x,potential, Where  , is the perturbation velocity in the dimensionless form as:calculation The potential flow at different positions are shown in Figures 4-6 diff erent calculatedresults values.  x,y, z /LU  X ,Y , Z  / L The potential flow calculation results at different positions   X ,Y , Z  / L x,y, z/ LU are shown in Figures 4-6  the  /dimensionless LU Where L is the length of ship, velocity potential satisfies the following The 3D model of the optimized hull and its details are where L is the length of ship, the dimensionless velocity Where L is the length of ship, the dimensionless velocity potential satisfies the following ndary conditions in thesatisfi flow field: shown in Figure 8. potential es the following boundary conditions in the Figure 8. optimized hull line Where L is the length of ship, the dimensionless velocity potential satisfies the following ary conditions in the flow field: The (2) resistance performance of the ship is qualitatively anaflow field:  =0 ary conditions in the flow field: from wave-making and pressure distribution hull is qualitativ The resistance performance of on thetheship  =0 (2) lyzed(2) And:  =0 (2) surface. The results are as follows: pressure distribution on the hull surface. The results are as 2 nd: 1. Cp is the hydrodynamic pressure coefficient (visualised in And:  2   nd: Fr 2 2  0  z  0 is the hydrodynamic pressure coefficient (visu a range from1.CpCp = -0.2 (blue) to Cp = +0.5 (red) in discrete  x z Fr 2  22    0 z  0  to0.02). Cp = +0.5 (red) in discrete steps of 0.02). steps of z  0 Fr 2 x2   z  0 xnz nx P  P  Phyd (3) n  nx Cp   nx   1  1 2 , Phyd   gz , where P is the press  vs n (3) o1 R  2 0 x       (3) (3)   x, y, z o 1  R   0 x  R 0 x  1      o   x, y, z    OR  R  x  0 where hydrostatic P is the pressure on the ship Phyd is the hy-or far-field pr P surface, pressure, is the free stream   x, y, z   O 1 R  R   x  0 drostatic pressure, P is the free stream or far-fi eld pressure, ∞   x0 O  1R    ρ is the fl uid density, v is the ship velocity, g is the gravity [7] [8] the ship velocity, g is the gravity acceleration, and z is the d s From Kochin free amplitude function   R  and Havelock wave resistance expression , the acceleration, and z is the depth value of the water. [7] [8] rom Kochin free amplitude function and Havelock wave resistance expression , the ession of first-order wave-making resistance coefficient can be obtained as: 2. Compared with optimization hull lines a, the pressu om Kochin free amplitude function[7] and Havelock wave resistance expression[8], the 2 can be obtained sion of first-order wave-making resistance coefficient as: R 1  2 1/2 C  resistance =  A  t  can dt 1 bet1/2obtained sion of first-order wave-making as: 2 1R 2 2 1 coefficient (4)stations of the bow and fourth to sixth stations of the stern w  - 2  U L C  RJOURNAL = 1   A  t  2 1  t 1/2 dt NAVAL ENGINEERS Fall 2023 | No. 135-3 | 147 (4) 3. Figures 4 and 5 show the pressure coefficient distr C  1 2U 2 L2 =  -  A  t  1  t 2  dt  12 (4)  2 U 2 Lfree The wave elevation of the far-field wave system can be expressed as:

        

              



 



 



 

Theoretical And Experimental Study on the Rapidity of Large Automobile Ro-Ro Ship 8500PCTC

FIGURE 4. Surface pressure distribution of ship’s bow and hull (a. Preliminary design parameters of ship; b.Optimized hull lines)

FIGURE 5. Surface pressure distribution of ship’s stern and hull (a. Preliminary design parameters of ship; b.Optimized hull lines)

FIGURE 6. Pressure distribution of water surface waveform (a. Preliminary design parameters of ship; b.Optimized hull lines)

148 | Fall 2023 | No. 135-3

2. Compared with optimization hull lines a, the pressure distribution at the 14th to 16th, 12th stations of the bow and fourth to sixth stations of the stern was improved in optimized hull lines b. 3. Figures 4 and 5 show the pressure coefficient distribution values for the bow, middle, and stern of the roll-on/ roll-off ship. From the color changes in the pressure coefficients shown in the figures, it can be seen that the pressure coefficient values on the hull surface are smoother in Figures 4.b and 5.b, indicating a more uniform pressure distribution resulting from optimization. Figure 6, ship waves on both sides of the hull (Blue represents the trough of the wave). The color change of the bow and stern waveform in Figure 6.b is more uniform than that in Figure 6.a, and the color change of the ship traveling wave on both sides of the hull of Figure 6.b is more moderate, thus indicating that the resistance performance of the optimized hull line b is better. Figure7.a red line is the optimized hull lines, Figure7.b black line is the preliminary design hull lines, optimized bulbous bow is 180mm above the design waterline. Figure8 is the 3D model of optimized hull lines. 4. The ship’s propulsion power of optimized hull lines (b) under the condition of design draught is shown in table 4. Main points of the optimized hull lines: 1. Bulbous bow: Optimizing of the bulbous bow mainly depends on the design ship speed. When the design draught of the ship is 9.2m, Fn = 0.23, it belongs to medium-speed ship (0.2 < Fn < 0.25). For medium-speed ships, the wave-making is mainly generated at the bow and stern ends of the ship, and the wave-making resistance becomes the main resistance. Therefore, during the design process, favorable interference between the bow and stern wave systems should be created to reduce this portion of the resistance. The smooth transition between the bulbous bow and the hull lines, as well as the precise design of the bow’s waterline, play a critical role in determining the resistance performance of the ship at its designed speed. To avoid generating a large number of vortices on the upper surface of the bulbous bow, this design adopts a rectified bulbous bow. Additionally, the upper surface of the bulbous bow is concave down to the vertical bowline to act as a diversion surface. The optimized hull lines feature a bulbous bow with its top extending 180mm above the design waterline (as shown in Figure 7).

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Name and Code

Preliminary hull form parameters(a)

Optimized hull form parameters(b) (CFD Optimized)

Length between perpendiculars: LPP (m) molded breadth : B(m) Draught of bow: TF (m) Draught of stern: TA (m) Displacement volume: N (m3) Block coefficient: CB Ratio of length to width: LPP/B Breadth draft ratio: B / T Longitudinal center of buoyancy: Lcb(% LPP) Height of shaft: Hps (m) Design ship speed: Vs (kn) Froude number: Fn

189.28 36.5 9.2 9.2 36930 0.581 5.186 3.967 -1.518 3.7 19.5 0.231

189.28 36.5 9.2 9.2 36911 0.581 5.186 3.967 -1.348 3.7 19.5 0.231

TABLE 3. Parameter comparison of hull lines Ship speed: Vs(kn)

Delivered power: Pdt

Ship speed: Vs(kn)

Delivered power: Pdt

17.500 18.000 18.500 19.000 21.500

8746.955 9588.974 10504.665 11497.638 17911.259

19.500 20.000 20.500 21.000 22.000

12570.951 13731.108 14992.051 16376.164 19624.287

TABLE 4. Theoretical calculation of ship’s propulsion power under condition of design draught

FIGURE 7. Comparison of the ship’s lines and bulbous bow lines (Black line for preliminary design hull lines, Red line for optimized hull lines, the bulbous bow is 180mm above the design waterline)

(i) FIGURE 8. optimized hull lines (3D model)

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(ii)

2. Angle of entrance: the half entrance angle of the design hull lines has a decisive influence on the shape of the bow’s waterline. Its size is related to resistance, mainly depending on ship speed, and the half-angle of entrance decreases with the increase of ship speed. For medium-speed ships, the half angle of entrance is about 15° to 25°, and the half-angle of entrance in the Optimized hull lines is 15.9°. (Of which the service speed of 6800 PCTC is 20.5 kn, the width is 32.26 m, the half angle of entrance is 14°, and the service speed of parent ship 8500 PCTC is 16 kn the half-angle of entrance is 18°). 3. The contour of stem post: the raked bow is adopted, and the degree of forwarding inclination is matched with the degree of outward drift of the transverse section of the bow. 4. After body lines: the mainline characteristics that affect the flow separation are the angle between the waterline and the ship’s central axis and the curvature of the waterline. The design should avoid flow separation as much as possible. When the angle between the water flow and the hull surface reaches 15° in the forward direction, the water flow separation begins. Therefore, to limit the water flow separation, the obvious protruding shoulder should be avoided, and the half flow angle between the oblique section line and the middle line of the optimized hull on the oblique section is within 11.5°. In addition, in the design lines, the main parameters of the ship, the main features of other mark lines, such as the curve shape of the cross-sectional area, the length of the parallel middle body, the shape of the design waterline, and the coordination of rudders, propellers and side thruster, are as consistent as possible with the original parent design to reduce the subsequent design workload.

Fall 2023 | No. 135-3 | 149

Theoretical And Experimental Study on the Rapidity of Large Automobile Ro-Ro Ship 8500PCTC

Model test analysis odel 3. test analysis

Model Test Analysis

Model[4] test method odel test3.1method

[4]

3. Model test analysis [13, 14] [4] formula [13, 14]. 3.1 Model test method [13, formula [13, 14] 14]. . formula

formula

The total coefficient ship is: In the pool test, the pool blocking correction formula of of the resistance test and [13, 14] The total resistance coefficient of the ship model model is: self-navigation formula . resistance

The resistance coefficient of model is: 3.3 Self [4] The total total resistance of the the ship ship model [13, Model testtest, method Thcoefficient e total resistance coeffi cient is: of the ship model is: [13, 14] 14]. 3.3 Self Self In the pool the pool blocking correction formula offormula resistance formula [13, test 14] . and self-navigation 3.3 is shown as: 3.3 S formula . the pool test, the pool blocking correction test formula of resistance test and self-navigation R total resistance coefficient of the ship model is: tm R WS In the pool test, the pool blocking correction formulaThe of resisR 3.3 tm  C R tm [13, 14] W(( tm Cship 2 The coefficient of the is: tm 3.3 (7) Self formula test is shown as: tm  model The total total. resistance resistance coefficient of C the ship model is: 2 W C tm S V 0.5  2 (7) (7) m  V tm m m 2 0.5  S V The total resistance coefficient of the ship model is: shown as: tance test and self-navigation test is shown as: 1 S V 0.5  F m m , the SmmVmm 0.5Rtm  (6) Fdd , W th  model The total resistance the is:  m1  Fnh2 of C V 1coefficient R m1 F V F (7) , th tm ship , 2 d R tm d tm  C 0.5  SR m1  V (6) Fd ,((  C tm 2 mV obtaine tmm 22 The residual resistance coefficient is: tm The residual resistance coefficient is: V 2 1  m1  Fnh F  C  0.5  S V obtaine , th The residual resistance coefficient is: Th e residual resistance coeffi cient is: (6) (6) d tm S V 0.5  m m The residual resistance coefficient is: obtaine obtai(7 a 0.5area RStmmmof Vmm2ship model, A is V 1  m1  Fnh  Where m1  is the blocking ratio, a is the mid-ship crossCsection obtai(( tm 2 0.5 C SC The residual resistance coefficient is: C C fmm obtaine  CrrC Ctm C A a mV tmC fm (8) (8) C C C rr  tm fm (8)( The residual resistance coefficient is: tm fm m  Where is the blocking ratio, a is the mid-ship cross section area of ship model, A is The residual resistance coefficient is: 1 where blocking ratio, a is the mid-ship cross seca A is the resistance Where m1  is the blocking ratio, a is the mid-ship cross section areaThe of Vresidual ship model, A is coefficient is: Cr is:Ctm resistance  C fm (8) of resistance tion area ofofpool Th efrictional coeffi cient of frictional is: A) FnhThe  coefficient A area of ship model, A is thethe The residual resistance coefficient coefficient of frictional resistance is: area poolsection, section, The is theof number, and his: is the water depth. C C  C frictional resistance is: A(( C test Ctm C Cfm rr  The coefficient coefficient ofFroude frictional resistance is:C tm  fm V gh A  C (8 r tm fm is the Froude number, and h is the test water depth. F  the area of pool section, is the Froude number, and h is the test water depth. nh 0.075 V ItA( The coefficient of frictional resistance is: C 0.075 Ctm0.075  C fm gh includes resistance, self-navigation, C r 0.075 It F  f Th e model test method 2 C  a of pool section, nh is the Froude number, and h is test the test water depth. The coefficient of resistance is: C It The coefficient of frictional frictional resistance is: Cand fis: ff  The model method includes resistance, self-navigation, R 22 2 Rlg gh lgnwater. R The coefficient of frictional resistance lg nn2 22 open followi lg0.075 R and open water. n 2 followi It Cand The model test method includes resistance, andthe open water. In self-navigation, the resistance test ship model is of towed by a trailer, The coefficient frictional resistance is: computer 0.075 f the followi follow 0.0752 records the force C  In the resistance test the ship model is towed by a trailer, The Reynolds number  2  22  C ff is: lg Rn0.075 follo The Reynolds number is: he model test method includes resistance, self-navigation, and open water. C f   lg In the resistance test the ship modelacting is towed a trailer, and at the computer records the force [9] The Reynolds followi R Reynolds is: on by the each innumber real-time lghot-film Rn   22 2anemometer is The Reynolds number is: is: . A 1mm diameter 0.075 and the computer records the force acting onship the model shipThe model atspeednumber Rnn  2  C f   lgVL [9] the computer records the force WL 2 VL the resistance the ship model is [9] towed byinareal-time trailer, and VL actingeach ontest the ship model at each speed . A 1mm diameter hot-film anemometer is  Rturbulence. The Reynolds is: laminar flow into installed at station 19lm ofanethe ship modelnumber to convert lgWL RnWL  2According to the speed in real-time. A 1mm diameter hot-fi WL nnVL R Rnn R   The Reynolds number is: [9] The Reynolds number is:  on the installed ship mometer model at each speed in real-time . A 1mm diameter hot-film anemometer is  at station 19 of the ship model19 to convert laminar flowtointo turbulence. to the VLWL speeds, and the test is installed at station of the ship model convert The Reynolds number is: Ac tests resistance test requirements, complete 10According sets of as experimental Due to such the wetted surface area of the ship's hull, the speed of the ship, and different Ratnarea Ac VL Due to factors factors such as the wetted surface area of the ship's hull, the speed of the ship, and VL WL Due to factors such as the wetted surface of the hull, the speed of the ship, and the Ac The Reynolds number is: WL ship's Due to factors such as the wetted surface area of the ship's hull, the speed of the ship, and the  R  laminar fl ow into turbulence. According to the resistance test Due to factors such as the wetted surface area of the ship’s VL  R n tests atspeed different speeds, and the test resistance complete setsinclude of experimental ed at station 19 oftest therequirements, ship model to convert10laminar flowthe into turbulence. According to the WL V R n density and viscosity of the fluid medium, the frictional resistance coefficient of a ship may va water c data ship model and the total resistance at this speed. m tm the   R density and viscosity of the fluid medium, resistance coefficient a ship may va n frictional the density and viscosity of the fluid medium, the frictional resistance coefficient aof ship may vary water Ac density and viscosity of the the fluid medium, thearea frictional resistance coefficient of athe ship may vary Due toerfactors such as the wetted surface ofVL ship's hull, the speed of ship, and the requirements, complete 10Vsets of experimental tests at diff hull, speed of the ship, and the and viscosity of WLdensity  water cc water R  R data include the ship model speed and the total resistance at this speed. tm test tests at different speeds, and the test nce test requirements, complete 10 sets ofmexperimental n Due to factors such as the wetted surface area of the ship's hull, the speed of the ship, and The self-navigation is to retain the torrent device and appendage installed in the torque Due to range factors such as thevalues wetted surface area resistance of the ship's hull, thecoefficient( speed ofship the ship, andc wate )) is The typical of for the ship's frictional resistance C of ent speeds, and the test data include the ship model speed Vm the flempirical uid medium, the frictional coeffi cient ofof a aof density and viscosity of theas fluid medium, the frictional resistance coefficient may vary is betwe betw The typical of empirical values for the ship's frictional resistance C)) fis torque Due to range factors such the wetted surface area the ship's hull, thecoefficient( speed the ship, andccc water between The typical range of empirical values for the ship's frictional resistance coefficient( C fis between The typical range of empirical values for the ship's frictional resistance coefficient( C ff ship torque torqu density and viscosity of the fluid medium, the frictional resistance coefficient of a may va Themodel the torrent andDue appendage installed in the Vm test Rtm[10]device density and viscosity of the fluid medium, the frictional resistance coefficient of ship may va clude the ship speedresistance and is the resistance this speed. .atThe self-navigation test issuch carried out according tothe the law of of the theresistance Britishhull, (forced resistance test to viscosity factors asvary. the wetted surface area ship's the for speed of aathe ship, and ship may Th e typical range of empirical values theof and self-navigation the total Rtmtototal atretain this speed. the ship torqu density and of the fluid medium, frictional coefficient ship may vac the ship ) is between The typical range of empirical values for the ship's frictional resistance coefficient( torque C 0.002 and 0.006. f [10] 0.002 and 0.006. the ship the sh 0.002 and 0.006. . The self-navigation testself-navigation is carried according to the0.006. lawrange of ship’s theof British resistanceTh test density and viscosity of the(forced fluid medium, frictional resistance coefficient va ) ismay betw The typical empirical values the ship's frictional resistance coefficient( frictional resistance coeffi cient(C e test self-navigation to retain the out torrent device and between 0.002 of a C 0.002 and method). ship speeds are changed in a for certain range containing the design he self-navigation is to retaintest theis torrent device and appendage installed the TheFive typical range ofin empirical values for thethe ship's frictional resistance coefficient( f ) is Cship ff ) is betw the s ) is betwe The typical range of of empirical values for the ship's frictional resistance coefficient( C the ship f The coefficient the total resistance of ship is: [10] installed in the resistance test. and 0.006. Th The coefficient of the total resistance of ship is: and 0.006. method). Five speeds changed ine0.002 aself-navicertain range containing the design The coefficient the total resistance of ship is: [10] appendage Vm . The The coefficient the total resistance ofthe ship is: The typical range of empirical values ship's frictional resistance computercoefficient( C f ) is betw ship speed, andtofour propeller speeds areof changed under eachfor shipping speed . The self-navigation test isship carried outare according the law of the British (forced nce testself-navigation 0.002 and 0.002 and 0.006. 0.006. The coefficient of the total resistance of ship is: gation test is carried out according to the law of the British 0.002 and 0.006. V S  S   computer ship speed, and four propeller speeds arereal-time changedsampling under each shipping Omis:, rotational speed nm of The coefficient of. The the ofbk ship TpmSS,SStorque is carried outspeed to measure the total thrustresistance vigation method). ship speeds method). are changed a certain containing them design ( C  SbkbkSbk  C C C f C C of The of total resistance ship is: (forcedFive self-navigation Fiveinship speedsrange are changed 0.002 andcoefficient 0.006. fs resistance The coefficient of the theC total of ship is: Ctsts resistance CAA (9) C rrCAAC fs f Cr C AA (9)( C  CfsfsC  Cff C ts The coefficient of the total of ship is: S O n T torqueF acting speed real-time sampling is carried out to measure the thrustandpmthe, force tsm of r  CAA m , rotational S the propeller on the ship model. S  S SSbk  Vm . The computer in propeller a certain speeds range containing design ship speed,speed and The four peed, and four are changedthe under each shipping coefficient of theCtotal resistance is:C  C  C (9) (9) Sbkofship  SS  C fs  ts  f r AA the propeller andspeeds the force acting on under the shipThe model.  C SSbk(( is selected based on experience, Where the roughness subsidy propeller areF changed each shipping speed V . open water test is that the propeller model is installed the unit's horizontal S on SSbkbk coefficient  propulsion C   C  C  C  C  f   C m thespeed is selected based on experience, Where the roughness subsidy coefficient C   C   C  C  C ts fs f r AA  C n S f bkis is selected based on experience, Where roughness subsidy coefficient of me sampling is carried out to measure the thrust Tpm , torque Om , rotational f r based AA on experience, Sbk Th Cf fs m selected is Where the roughness subsidy Ctsts coefficient bk (9 f  Cr  CAA  S SSSSbk   Cffs isC Th Theopen computer real-time sampling is model carriedThe to measure theimmersion The water test is that the propeller is out installed on the propulsion unit's horizontal driveshaft. propeller shaft's depth is 1.5 times the diameter of the propeller Th wetted surface area of the keels, is the wetted surface of ship mode or real ship. S ( C   C   C  C  C  C wetted surface area of the keels, is the wetted surface of ship mode or real ship. S S is selected based on experience, is Where the roughness subsidy coefficient ts fs f r AA surface of the is surface of ship mode or real SSroughness peller and the forceTF acting on O the ,ship model. speed n of thewetted f bk wetted surface area of Where the keels, keels, is the the wetted surface ofiscient ship mode or real ship. ship. thrust , torque propeller andarea the subsidy ΔCf isbased selected Swetted  C selected Where the subsidy coefficient m rotational C Ccoeffi driveshaft. Thepmpropeller shaft's immersion ofthethe propeller [11] m is 1.5 times the ff STh bk isspeed selected based on on experience, experience, SS Where the roughness roughness subsidy coefficient bk . The load changesWhere bydiameter changing advance speedcoefficient to keep the shipping constant. modeldepth The coefficient of air resistance is:  is selected based on experience, the roughness subsidy f The coefficient of air resistance is: bk surface areahorizontal of the keels, of ship mode or of real ship. S is The coefficient of air resistance is: he open water is that the propeller model is installed on thewetted propulsion unit's thetest force F acting on the ship model. based on experience, Swetted is wetted surface area the The coefficient ofspeed airthe resistance is:the bk falsesurface  C wetted surface area of keels, is the wetted surface of ship mode or real ship. S S the advance speed to keep the shipping constant. model[11]. The load changes by changingWhen is ofselected based on experience, Where thearea roughness subsidy coefficient wetted surface of limit, the keels, is thespeed wetted surface ship mode or real ship. f Abe bk the range of trailer speed reaches the thewetted shipping also The 0.001 The open test is that theispropeller model iswetted installed keels, Sresistance iskeels, the surface ofcan ship or real ship. 0.001 ATT ofchanged. surface theCwetted surface ship mode or real ship. SSis:is 0.001 A The coefficient ofofairthe  haft. The propeller shaft'swater immersion depth 1.5 times the diameter of thearea propeller ATT mode AA 0.001 C  C  AA Whenon thethe range of trailerunit’s speedhorizontal reachespropeller thedriveshaft limit,thrust the .shipping can also beTh changed. Theship The coefficient air resistance CAA Vm and Srotational wetted surface area ofof the mode S ofisis: Tpme propelOthe nmor real ship. Th andspeed propeller torque atkeels, each speed speed The coefficient of air resistance is: AAwetted propulsion Th coeffi cient is: of ship SSsurface Th The coefficient ofemair resistance is:air resistance 11] 0.001 AST . The load changes by changing the advance speed to keep the shipping speed constant. Th C  A T O V n Where is the projection area of the hull and superstructure above the waterline on 0.001 A at speed and rotational speed propeller thrust’s immersion ler shaft depth torque is 1.5were times theeach diameter of the m m coefficient m T AA the hull pm and propeller A 0.001 A T The of air resistance is: measured inship open water test. Where is the projection area of and superstructure above the waterline on A Where hull and above C  S superstructure Where ATT Tis is the the projection projection area area of of the the hull and superstructure above the the waterline waterline on on the the AT CAA  0.001 Th [11] the AA  the range trailer speed reaches limit, the shipping speed can also be changed. The CAA SS AT model. e load changes by changing the advance wereofpropeller measured in open waterTh test. mid-transverse section. 0.001 A mid-transverse section. T S Where is the projection area of the hull and superstructure above the waterline on the mid-transverse section. T 3.2 Resistance analysis method C  AA hull and superstructure above the waterline on speed keep thetorque shipping constant. When the range of section. A Om speed Vmid-transverse is andtopropeller at each ship speed rotational er thrust Tpm ATspeed m and Where Where is the thenmprojection projection area area of of the the hull and superstructure above the waterline on S superstructure ATT speed Where isWhere theis: projection area ofbehind the hull above the waterline on Actual ship 3.2 Resistance analysis method mid-transverse section. Actual ship speed is: The shipspeed model testActual is used to verify the rapidity of the hull theand optimized trailer speed reaches the limit, the shipping can also be A is the projection area of the hullhull andlines superstrucship speed is: Ac T Actual ship speed is: mid-transverse Where ATsection. is the projection area of the hull and superstructure above the waterline on measured in open water test. Ac mid-transverse section. Ac mid-transverse section. The ship model test is usedthrust to verify of the hull behind theperformance. optimized hull lines changed. The propeller Tand ture above the waterline on the mid-transverse section. and propeller torque O at pmthe m itsrapidity resistance and propulsion The actual ship resistance is converted by the V  V   Actual ship speed is: s m or meth V  V   V V   mid-transverse section. or meth meth Actual speed is: Actual speed is:Vss sVmm m  each ship speed rotational speed nm were meaActual ship ship speed is: shipthe or m false and sistanceand analysis method or Ac me its resistance and V propulsion performance. ship resistance is speed converted [12] Actual is:thebyair , and resistance and bilge keel resistance are resistanceThe dataactual of the ship modelship test or m Vs  Vm   sured in open water test. Total resistance of ship is: or meth [12] Actual ship speed is: Total resistance of ship is: V  V   Total resistance of ship is: , of andthe thehull air behind resistance and bilge of the test considered he shipresistance model testdata is used to ship verifymodel the rapidity the optimized hull lines Vss is  Vcalculated   m Total resistance ofresistance shipresistance is: are coefficient simultaneously. Then, thekeel friction by the ITTC m

     

     

Vs  Vm 1  2 1

2S 1 22V R V C Total resistance of ship is: ITTC ofRship considered simultaneously. Then, theThe friction resistance coefficient calculated by  resistance and propulsion performance. actual ship resistance is is converted by thethe tstsV1m2V S sC Resistance analysis method Total resistance Rtsts RtstsC Cis: Vss V Sssss Sss ts Total ts  2  2 Total resistance resistance of of ship ship is: is: 12 Total ofare ship is: is used verify the rapidityand of the , andtothe air resistance bilgehull keel resistance resistance nce data of Th thee ship ship model model test test[12] Rts  Cts  11Vs2 Ss2 Effective power of actual is: Total resistance of ship is:ship Effective power of actual ship is: R  C Effective power of actual ship is: R  C 2 1  V V 2 SS power of actual ship is: behind the optimized hull lines and its resistance and Effective proRtststs  Ctststs R ts22VVs sss2 Ssss ered simultaneously. Then, the friction resistance coefficient is calculated by the ITTC 1 R  V R 2ssVs 2 S P ts tsV R V pulsion performance. The actual ship resistance is converted Effective power of actual ship is: RP P s s Ptsee  eeCtsts 1000 Effective power of actual ship is: 2 1000 Effective power of actual ship is: [12] 1000 by the resistance data of the ship model test, and the Effective air 1000 of actual R ship is: powerEffofective actualpower ship is: ts  V s Pe  R R  V V Effective power of actual ship is: resistance and bilge keel resistance are considered simultaneP Rtsts Vss Pe  1000

ously. Then, the friction resistance coefficient is calculated by the ITTC formula.[13, 14]

150 | Fall 2023 | No. 135-3

ts s Pee  1000 1000 R1000 ts  Vs Pe  1000

NAVAL ENGINEERS JOURNAL

b) b) b) Op b) Op

Op followi followi Op followi follow follo followi







The acceleration coefficient J s and torque coefficient KQs are obtained from The acceleration coefficient J s and torque coefficient KQs are obtained from

and other physical quantities are calculated from table 5 as follows: and other physical quantities are calculated from table 5 as follows: Table 5 Calculation of design parameters Table 5 Calculation of design parameters Parameter Computational formula Parameter Computational formula Parameter Computational formula Self-navigation analysis method 1  w V   ts When the force F applied on the ship model is equal to the ns  1  wts Vss Rotational speed  n f-navigationfriction analysis method Rotational speed J D Rotational speed s resistance f-navigation analysis analysis method correction value Fd , the rotational speed, Jss Dss f-navigation method Self-navigation analysis method When the force F applied on the ship model is equal to the friction resistance correction value thrust and torque of the self-navigation point of the actual ship Self-navigation method (7) When the force force analysis F analysis appliedmethod on the ship ship model model is is equal equal to to the the friction friction resistance resistance correction correction value value f-navigation (7) When the F applied on the When the force F applied on the ship modelTh is eequal to the friction resistance correction value 2 D 55ns33kqs are obtained interpolation. friction resistance correction eWhen rotational speed, thrustby and torque of theisself-navigation pointresistance of the actual ship are the force F applied on the ship model equal to the friction correction value 2 D ns kqs P  Delivered power Delivered power ds he rotational rotational speed, thrust andship torque of is the self-navigation self-navigation point of the the correction actual ship ship are When forcespeed, Fspeed, applied on the model to the friction resistance value Pds  r Delivered power he and torque of point of actual are the the rotational and torque oftheequal the self-navigation point of the actual ship are value Fd thrust is:thrust k (7) F ed by rotational interpolation. Thethrust friction resistance correction value Fd is:point of the actual ship are the speed, and torque of the self-navigation 2 r 4 2 T (7) Ts  kT2 J s2 D 4ns2 is: ed by interpolation. The friction resistance correction value Fd Fpoint Thrust he rotational speed,The thrust and resistance torque of correction thecorrection self-navigation areof actual propeller 7) by ed interpolation. friction value ined by interpolation. The friction resistance value d dis:is: of the actual ship Ts  kJTT2 J 2s2  D44 n2s2 Thrust of actual propeller 1 resistance F (7) 2 is: ined by interpolation. The friction correction value T Th rust of actual propeller Thrust of actual propeller d   1 ss  J 22 J ss  D nss (8)     F  V S C C C F (10) (10) 2 is: ed by interpolation. The friction resistance correction value   fm  C fs  C f  (8) d J  21  Fdd  1mmV Vmm2 SSmm2  C C (10)  fm fsC  fC     F C C  k (10)     F V S C (10) d d 2 1m m mm f f  m fm fm fs fs Qs  kqsqs  D55ns22 Fd 12 2 m2Vm2 Sm  C fm  C fs  Cf  Torque (10)of actual propeller Fd  2mVm Sm  C fm  C fs  C f  Q  kqs  D n (10) of actual propeller Torque ) propulsion factor (8) A) propulsion factor factor Qsss  qsrr  D55ns2ss2 (8) Torque propeller Torqueofofactual actual propeller A) propulsion A) propulsion factor factor 2 8) Propulsion rr A)assumed propulsion factor is that the thrust decrement has no dimension effect and is determined by the (8) A) propulsion factor is assumed that the thrust decrement has no dimension effect and is determined by the It is assumed that the thrust decrement has no dimension eff ect Pe  0.5CTs Vs33S Active power is is assumed that thethe thrust decrement hashas nono dimension effect and is is determined byby the It assumed that thrust decrement dimension effect and determined the Pe  0.5CTs Vs33S Active power ing formula: It is assumed that the thrust decrement has no dimension effect and is determined by the determined by the following formula: effect and is determinedActive Pe  0.5CTsTs Vss S power Active power ing formula:and is assumed thatisthe thrust decrement has no dimension by the

ing formula: wing formula: owing formula: ing formula:

Pe Rtm  Fd d  Pe Rtm  F Fd Total propulsive power t  1 R (11)   R  F Pe P  11   Total tm d d (11) propulsive power (11) tt  t  1 tmT m F ddd  Pdsds (11) R (11) Total propulsive power Total propulsive power T tm F d m R  t  1 T T Pdsds (11) t  1  tm m Tmd (11) m According tomethod, the equal thrust method, theKthrust coefficient on the open T ccording to the equal thrust the thrust coefficient is interpolated Tm 1 t m the K Tm is interpolated on the open ccording to the the equal equal thrust thrust method, method, the the thrust thrust coefficient coefficient K the h  1  t eccording K Tmis curve Hull efficiency to interpolated onon thethe open to theisequal thrust method, thrust coefficient is interpolated open Tm eAccording KTm interpolated on thethe open water characteristic of   1 1 wt Hull efficiency Hull ciency J m on K Tm coefficient ary. and the characteristic of the propeller to obtain the speed According to curve the equal thrust method,model the thrust coefficient is interpolated theeffi open ary. hhh  1  wtsts Jon K y. andopen the characteristic curve ofthrust themodel propeller model tocoefficient obtaincoeffi the speed speed coefficient Hull efficiency to the equal method, the thrust interpolated m the Tm is y. eccording the propeller to obtain the speed cient J and the J J and the characteristic curve of the propeller model to obtain the coefficient r characteristic curve of the propeller model to obtain the speedm coefficient m m and the 1  wtsts the K J m behind coefficient incoeffi theofopen The thrust coefficient the torque coefficient the and the er characteristic the water. propeller model towater. obtainThthe speed coefficient een Qmcurve Kcurve torque cient KQm in the open eand thrust coeffi cient y. ween J coefficient in the open water. The thrust coefficient and the torque coefficient behind the and the characteristic of the propeller model to obtain the speed coefficient n Qm P  C P m K n KQmin in coefficient thethe open water. The thrust coefficient and thethe torque coefficient behind ue coefficient Qm open water. The thrust coefficient and torque coefficient behind ary. Pdtdt  Cpp  Pdsds ary. Forecast of test Forecast of power test power the K and the torque coeffi cient behind the ship are obtained from pue are obtained from the following formula: coefficient in the open water. The thrust coefficient and the torque coefficient behind ary. Qm P  C  Pdsds 60 KQm Forecast of test power dt  C pp  n are obtained obtained frominthe the following following formula: Nt dt coefficient water. The thrust coefficient and the torque coefficient behind ppnary. are from formula: hip are obtained fromthe theopen following formula: Forecast of test power Nt  Cnn  nss  60 ween the following formula: ween ship are obtained from the following formula:T Nt  Cnn  nss  60 peen are obtained from the following formula: TABLE 5. Calculation of design parameters T2mm 4 KTm  T ween T  N 2mDm4 KTm  3.4 Forecast of actual ship speed K  Nm2 D KTm  3.4 Forecast of actual ship speed Tm Tm  (12) ship TmmmNDm22mmm4Dm44 KTm  NQ 3.4 Forecast ofexperimental actual speed parameters and pool test are shown in Table 6 and Figu (12) The ship  K (9) KTs  KTm  KTTT m (12)  N D Tm (12) (12)ship experimental model (9) Q22mDm45 m Tm K Qm   NQ The model parameters and test are shown in Table 6 and Figu KTs TsTs KTm Tm  KTpool Q (1 m m  K (12) mDm5  N Qm 2 The ship experimental model parameters and pool test are test shown Table 6 and Figu K  K self-propelled  K  K Qm m m N2Q K Qm  (14) (12) (14)out, Qs Qm Q K  K  K The propeller open water test, resistance test and are in carried respecti D 5 2 5 Qs Qm Q m Qs K Qm Q K    K D QmmN D K Qm  N The propeller open water test, resistance test are carried out, respect Qs testQmand self-propelled Q K Qm  2mNmm2m5Dmm5 (9) is respecta The propeller water test, resistance and self-propelled are carried respecti (9) s9) TheWhere: results areopen as follows (Exciting flowtest mode:1 mm diametertest exciting wire isout, installed  N m Dm s is he wake fraction is: TheWhere: results areWhere: as follows (Exciting flow mode:1 mm diameter exciting wire is installed a he wake fraction is: Th e wake fraction is: Where: he wake fraction is:is: wake fraction (9) The as ship follows (Exciting mode:1 sThe 19th results station are of the model, and theflow propeller hasmm four diameter blades). exciting wire is installed a The wake fraction is: J N D 19th station of the ship model, and the propeller has four is m m m ccblades).  P   he iswake fraction is: J N D P  1 w   m m m m  1 J J 19th station of the ship model, andthe is D P four cof blades). CDDDhas KTTT propeller =-0.3   Z model w mN mmN mmDm Table 6 Parameters ship m  V wmwm 11  JVmmN m Dm =-0.36Parameters C D    KTTable D ofDZship  model is Dof ship model VmVD w  1J N  D  Name Symbol Actual ship Ship mode KTs  KTm Table KT 6 Parameters wm m 1  m mmVmm   c Name Actual ship Ship mode (14) Symbol Z  K  0.25  C Vm m c K  K   K   Q D   he relative rotative efficiency is: 0.25  K  C Z Qs Qm Q Q D Name Symbol Actual ship Ship mode Waterline length of ship (m) K Q 0.25C D Lwl 193.920 6.5296 D Z he relative relative rotative rotative efficiency efficiency is: is: Q D  Lwl  D Waterline length of ship (m) 193.920 6.5296 he The relative rotative efficiency  DLwl  the e relative rotativeis: Waterline lengthofofship ship(m) (m) 193.920 6.5296 Vertical length Lpp 189.280 6.3733 the relativeTh The rotative efficiency is:efficiencyKis: Where: e e QMO Vertical length of ship (m) Lpp 189.280 6.3733 he relative rotative efficiency is: K QMO r  K Theof diff erence ofcoefficient resistance  coeffi K QMO The difference of resistance is:Lpp C CDD cient Vertical length ship (m)(m) 6.3733 molded breadth of ship B ΔCD is: 189.280 36.500 1.2290 r   KQMO  P  c   C D Dis: B The difference of resistance coefficient e  QmB molded breadth of ship (m) 36.500 1.2290 K r r  K KT =-0.3C D    Z QMO QmB the K QmB  K  QmB molded breadth of ship B 36.500 1.2290 DC Bow draught of ship (m) (m)  D   TfC 9.200 0.3098 D  C the Dm  Ds  C  C  C  r r QMO D Dm Ds the D Dm Ds Bow draught of ship (m) Tf 9.200 0.3098 K Ds cC   D  C Dm  CTf K QmBQmB to the 1978 ITTC recommended ctual ship wake correction is calculated according formula Bow 9.200 0.3098 Sterndraught draughtof ofship ship(m) (m) Ta KQ  0.25CD   Z the ship wake correction is calculated according ctual to the 1978 ITTC recommended formula Stern draught offormula ship (m) Ta 9.200 0.3098 ctual ship wake correction is is calculated according to to thethe 1978 ITTC recommended formula D Actual ship wake correction calculated according 1978 ITTC recommended Where: Where: Where: Stern draught offormula ship (m)(m22) Ta 9.200 0.3098 Wet surface area of ship S 7569.00 8.5815 Ei ship as: wake correction is calculated according to the 1978 ITTC hod Actual recommended Where: Wet surface area of ship (m ) S 7569.00 8.5815 Ei as: hod ctual ship wake correction is calculated according to the 1978 ITTC recommended formula Actual ship wake correction is calculated according to 1978of resistance coefficient 22C D is: Thethe difference  hod ethodEi Eias:as: Wet surface area of ship S 7569.00 8.5815 Displacement volume of(m ship) (m33) Disv   36911.0 1.4091   method Ei as: t 0.044 5 Displacement volume ofship (m33)C Disv  36911.0 1.4091 2 C f formula or methodC Eias: C C hod Ei as: ITTC recommended 5 222 Dm2 1Dsship t2 0.044 T CDDm Dm C fsfs  C Cf Displacement ship Disv 36911.0 1.4091 Wind receivingvolume area onofC waterline 1290.00 1.4626 ws   t  0.04   ( wm  t  0.04) C Dm 2))1 1161A    (m  2shipcc(m 2 1)of C   C fs f Dm w  t   w  t  0.04 ( 0.04)   fs f 2 3 Wind receiving area on waterline of (m A T 1290.00 1.4626     66 33 wss ws t  c  2(m  0.04  0.04) CC  (w(mmwm tt0.04)  t0.04  C f Where: Cfsfm Wind area of onthe waterline of ship 1290.00 1.4626 Wettedreceiving surface area bilge keels (m ) 22)6Rnn A S RT3Rnnn  160.00 0.1814 fm (13) C fs C C 2 Rn n bk C ws   t  0.04   ( wm  t  0.04) f n  fm fm Wetted surface area keels (m ) Sbk 160.00 0.1814 (13) of the bilge 2.5 2.5  w  1 tw0.04  ( wm  t  0.04) 2.5  keels (m  22 (13) C Wetted surface of the S 0.1814 Dimension ratioarea of(13) the shipbilge Dimension 29.6988 bk bk w wss  Eis  11  t model 5 t )    0.044 C fm fm c 2.5 160.00   w 1 (13) Dimension ratio of the ship model Dimension 29.6988    C 2 1 2 Ei   s C    2 1 2 1.89 1.62 lg s Dm   1 1 t22 w c Ds  2    1.89   The ITTC formula for 1957 CcDs EiEi  11 (13)   16  Ds Dimension of theship model Dimension 1wm w The (13) formularatio of friction resistance Cf1.62 C 2Rn3 cc1.89 lg lg Kp  29.6988    1.62 Rn coefficient Ds  2  Ei 11wmw s The formula of friction resistance coefficient c    2.5 Cf K pKpp  The ITTC formula for 1957  Ei  1 mswmm The formulacorrection of friction resistance coefficient Cf The ITTC formula for 1957 Roughness △Cf 0.0000500 wm ) Open water correction 1of actual propeller t   chord length c  of blade Roughness △Cf section, blade 0.0000500 Where c, tcorrection andCDsR thickness and Reyno Rnnn2 1are  2 the lg )) Open Open water water correction correction of of actual actual propeller 1.89  1.62  blade  R   Where c, t and are the chord length of section, blade thickness and11.9℃ Reynold Roughness correction △Cf 0.0000500 propeller Experimental b) Open water correction of actual propeller n K p chord  c, t cand   R are the  Where length of blade section, Actual propeller correction ITTC n Experimental 11.9℃ P b) Open water correction ofmodified actual propeller Actual propeller correction ITTC pen water characteristics are from the model to the actual propeller using the P ITTC )pen Open water correction of actual number the Rblade 0.7R, is thethe pitch ratio, and the roughn water Experimental 11.9℃ Where c,of t and are section the chordatlength of respectively, blade section,number blade thickness andblade Reynolds nusing water characteristics are modified from propeller the model model to to the thenumber actual propeller the blade thickness Reynolds section at surface Open water correction ofpropeller actual Actual correction D of thepropeller blade section at 0.7R,and respectively, theofpitch ratio, and the surface roughnes water pen water characteristics are from propeller using the Open water characteristics aremodified modified fromthethe model to theactual actual propeller using the Discorrection P D temperature water correction ing method: Open waterOpen characteristics are modified theedmodel to number the actual propeller using the of the blade at 0.7R, is the ratio, and theand surface 0.7R, respectively, water characteristics arefrom modifi from to the section thepitch pitch ratio, theroughness surface roughing method: temperature pen water characteristics are modified from the model to the the model actual propeller using therespectively, D is correction ing method: wing method: .. of the actual propeller is k pppactual  30m temperature of the actual propeller is actual propeller using the following method: ness of the propeller is k = 30μm. p owing method: of the actual propeller is k p  30m . of the actual propeller is k p  30m .

ing method:

b) Propulsion performance of actual ship

b) Propulsion performance actual ship b)b) Propulsion performance of actual ship Propulsion performance of of actual ship NAVAL ENGINEERS JOURNAL

The of actual The load the actual propeller is: propeller Theofload load of the the actual propeller is: is:

Fall 2023 | No. 135-3 | 151

The load of the actual propeller is: KTS Cts  S s  KTS Ctsts  S ss TS 2 J 2 2 Ds2  KTS 1  tTS 1 WTS 2 Cts tsS s s 22

(15)

Theoretical And Experimental Study on the Rapidity of Large Automobile Ro-Ro Ship 8500PCTC

Name

Symbol

Actual ship

Ship model

Waterline length of ship (m) Vertical length of ship (m) molded breadth of ship (m) Bow draught of ship (m) Stern draught of ship (m) Wet surface area of ship (m2) Displacement volume of ship (m3) Wind receiving area on waterline of ship (m2) Wetted surface area of the bilge keels (m2) Dimension ratio of the ship model The formula of friction resistance coefficient Roughness correction Actual propeller correction

Lwl Lpp B Tf Ta S Disv AT Sbk Dimension Cf ΔCf ITTC correction

193.920 189.280 36.500 9.200 9.200 7569.00 36911.0 1290.00 Where: 160.00 29.6988 The ITTC formula for 1957 0.0000500 Experimental water temperature

6.5296 6.3733 1.2290 0.3098 0.3098 8.5815 1.4091 1.4626 0.1814

TABLE 6. Parameters of ship model

KTs  KTm Th K

KQs  KQm  

P KT =-0.3C D   D c KQ  0.25CD  D

11.9ºC

The difference of resistance coefficient  C D is:

C D  C Dm  C

Where:

 t  0.044  W CDm  2 1  2   1 c    6  Rn

number

t   CDs  2 1  2  1.89  1 c    Where c, t and Rn

of the a

are the chord length of bla

number of the blade section at 0.7R, respectively, of the actual propeller is k p  30m .

P i Db)

b) Propulsion performance of actual ship The load of the actual propeller is:

KTS Cts  S  J 2 2 Ds2  1  t 

The acceleration coefficient J s and torque co

and other physical quantities are calculated and from tabl oth

Table 5 Calculation of d Parameter Rotational speed

Delivered power

FIGURE 9. Model test on pool (Design draught is 9.2m, ship speed is 19.5)

152 | Fall 2023 | No. 135-3

NAVAL ENGINEERS JOURNAL

KTs  KTm  KT

(14)

KQs  KQm  KQ

Where:

Ship speed Vs(kn)

17.5 18 18.5 19

Active power

Resistance

Ship speed

Active power

Resistance

P  c  Pe

Rts (KN)

Vs(kn)

Rts (KN)

722.67 770.09

19.5 20

9393.9 10324.3

936.43 1003.45

11382.3 12588.6

1079.29 1165.26

KT =-0.3C D    Z 6506.6kw  D  D  7131.0kw c KQ  0.257815.7kw CD   Z D

821.22 20.5 8565.2kw 876.29 21  C D of KT differenceTABLE he of resistance coefficient data is:hull resistance under design draught conditions 7. Experimental (14)

KQ

Where:  c 

  Z  D  c Z D

C D  C Dm  C Ds

Ship speed

Delivered power

Rotational speed

Thrust deduction fraction

Wake fraction

Vs(kn)

Pdt

Open water efficiency

Propulsion efficiency

0.1596 0.1609 0.1671 0.176

0.2444 0.2442 0.2471 0.2516

0.691 0.6901 0.6877 0.6842

0.7782 0.7772 0.7725 0.7658

0.2561 0.2592 0.2597 0.2572 0.2518

0.6808 0.6771 0.6741 0.6722 0.6716

0.7592 0.7533 0.75 0.75 0.7534

17.5 18 18.5 19

8360  1.558  9175t   0.044 5 1.606  2 CDm  2 1  2  1   10116c   6 R Rn3 1.654  n 11184

1.703 2.5

t  c  19.5 C  2  112372 1.754 0.1847  Ds   2  1.89  1.62 lg K p  20 1.807 0.1905  13705c    20.5 15177 1.864 0.1916 5 c, t and Rn are the chord length of blade section, blade thickness and Reynolds Where  2  21 16784 1.924 0.1871 Rn3  P 21.5 18520 1.987 0.1773 r of the blade section at 0.7R, respectively, is the pitch ratio, and the surface roughness 2.5

C Ds

1.62 lg

c   K p 

TABLE 8. Experimental data of ship propulsion efficiency under design draught conditions actual propeller is k  30m . ade section, blade thicknessp and Reynolds

is the pitch ratio, and the surface roughness

) Propulsion performance of actualofship Propulsion performance actual ship

The load of the actual propeller is:

he load of the actual propeller is:

KTS Cts  S s  J 2 2 Ds2  1  t 1  WTS 2

1  WTS 

(15)

KT e acceleration coefficient Js and torque coefficient KQs are he accelerationThcoefficient , KT J s and torque coefficient KQs are obtained from oefficient KQs obtained are obtainedfrom from , Js2 J s 2 , and other physical quantities are calculated from table 5 as follows: le 5 as follows: quantities are calculated from table 5 as follows: her physical

design parameters

Forecast of actual ship speed Table 5 Calculation of design parameters

omputational formula

The ship experimental model parameters and pool test are

Parameter Computational formula 1  wts Vsshown in Table 6 and Figure 9. The propeller open water test, n  s

Pds 

J s Ds

resistance test and self-propelled test are carried respec1  wts Vout, s n 

Rotational speedare as follows (Exciting s 2 D 5 ns3 ktively. The results flow qs J Dmode:1 mm r

diameter exciting wire is installed at the 19th station of the ship model, and the propeller has four blades). 2 D 5 ns3 kqs P  Delivered power ds The open water test of the backup propeller  r SSR4-0583 was carried out to obtain the open water characteristic curve of the propeller as shown in Figure 10 . The hull resistance of the ship under the design draught conditions is shown in Table 7. The theoretical and experimental comparison data are shown in Figure 11. The effective power under different draught conditions is shown in Figure 12. Ship propulsion efficiency is shown in Table 8. According to the ship model experimental data, the propeller received power is14726.75KW under the condition of common power CSR: = 14875kW, and shafting efficiency is 0.99. Given the consideration of 10% of the sea wave reserve,

NAVAL ENGINEERS JOURNAL

FIGURE 10. Open water characteristic curve

FIGURE 11. Effective power of ship under design draught condition

Fall 2023 | No. 135-3 | 153

Theoretical And Experimental Study on the Rapidity of Large Automobile Ro-Ro Ship 8500PCTC

The study conclusions are as follows: 1. Under the conditions of design draught, with 19.5kn target speed, the CFD method is used to optimize the hull lines, especially the bow, stern, bulbous bow and other parts. By comparing different design hull lines, the surface pressure gradient of the ship is optimized, and the flow field wave is smaller. As a result, the optimized ship type reduces the theoretical hull resistance and improves the propulsion efficiency. 2. Fn = 0.23 at the design draught of 0.2 m, and the bulbous bow is 180mm higher than the design waterline surface, which can better reduce the wave resistance. 3. Under the design conditions, draught is 9.2m, ship speed is 19.5kn, and the half-angle of the entrance is 15.9°, which can better reduce the ship’s resistance. 4. Under the design conditions, draught is 9.2m, and ship speed is 19.5kn, and the diversion angle is 11.5°, which can better limit the separation of water flow. 5. The experimental results of the ship model show that the resistance performance of the ship in design draught and structural draught is at a “good” level, and the theoretical research has a good guiding significance for the development of ship type.

Funding FIGURE 12. Ship effective power under three drought conditions in a model experiment Design draught

Scantling draught

Ballast

Delivered power Pdt(kW)

13387.5

Service speed Vs(kn)

19.87

18.87

20.7

TABLE 9. Forecast of actual ship speed

the propeller received power is 14875 × 0.99 / 1.1 = 13387.5 kW. Under the ideal trial conditions of the deep sea, the seawater temperature of 15°, no wind, no wave, no current, and no pollution on the bottom of the hull, the actual ship speed prediction results are shown in Table 9.

Conclusion In this paper, based on the parent ship of 8500CEU PCTC, the CFD software SHIPFOLW is used to perform numerical simulation analysis according to the invariant characteristics of the main dimensions and further experiments are designed to verify the results.

154 | Fall 2023 | No. 135-3

The authors are grateful for the financial support provided by the Xiamen Ocean and Fishery Development Special Fund Project (No. 21CZBO14HJ08) and Ship Scientific Research Project-Key Technology and Demonstration of the Type 2030 Green and Intelligent Ship in the Fujian region (No. CBG4N21-4-4) AUTHOR BIOGRAPHIES AUTHOR NAME net eventem inus moluptatem dolo odi optas molupta tibusda erferci eniate lam re repel illam dolorum suntore, sinimusa pos ab iment, omnisi volupta tiscitas comnita tionem ulluptaquiam haribus dus magniam videles tiorepe lab id que archit veri dit ipitatius quo excerum doloreh entiaest, utas dem. Ximin nonseque eum, sit, il et aut harcit, odici corum que iuntint expliquae di aut ant volorit aturepe rnatur? Um haruptinus idus inctate caercia erferspis aute mo exped magnatum cus sus alicabo. Me excea iscipid enturibusam, consequ idusant. AUTHOR NAME net eventem inus moluptatem dolo odi optas molupta tibusda erferci eniate lam re repel illam dolorum suntore, sinimusa pos ab iment, omnisi volupta tiscitas comnita tionem ulluptaquiam haribus dus magniam videles tiorepe lab id que archit veri dit ipitatius quo excerum doloreh entiaest, utas dem. Ximin nonseque eum, sit, il et aut harcit, odici corum que iuntint expliquae di aut ant volorit aturepe rnatur? Um haruptinus idus inctate caercia erferspis aute mo exped magnatum cus

NAVAL ENGINEERS JOURNAL

AUTHOR NAME net eventem inus moluptatem dolo odi optas molupta tibusda erferci eniate lam re repel illam dolorum suntore, sinimusa pos ab iment, omnisi volupta tiscitas comnita tionem ulluptaquiam haribus dus magniam videles tiorepe lab id que archit veri dit ipitatius quo excerum doloreh entiaest, utas dem. Ximin nonseque eum, sit, il et aut harcit, odici corum que iuntint expliquae di aut ant volorit aturepe rnatur? Um haruptinus idus inctate caercia erferspis aute mo exped magnatum cus sus alicabo. Me excea iscipid enturibusam, consequ idusant. AUTHOR NAME net eventem inus moluptatem dolo odi optas molupta tibusda erferci eniate lam re repel illam dolorum suntore, sinimusa pos ab iment, omnisi volupta tiscitas comnita tionem ulluptaquiam haribus dus magniam videles tiorepe lab id que archit veri dit ipitatius quo excerum doloreh entiaest, utas

dem. Ximin nonseque eum, sit, il et aut harcit, odici corum que iuntint expliquae di aut ant volorit aturepe rnatur? Um haruptinus idus inctate caercia erferspis aute mo exped magnatum cus sus alicabo. Me excea iscipid enturibusam, consequ idusant voluptibea ventem quat voluptatur. AUTHOR NAME net eventem inus moluptatem dolo odi optas molupta tibusda erferci eniate lam re repel illam dolorum suntore, sinimusa pos ab iment, omnisi volupta tiscitas comnita tionem ulluptaquiam haribus dus magniam videles tiorepe lab id que archit veri dit ipitatius quo excerum doloreh entiaest, utas dem. Ximin nonseque eum, sit, il et aut harcit, odici corum que iuntint expliquae di aut ant volorit aturepe rnatur? Um haruptinus idus inctate caercia erferspis aute mo exped magnatum cus sus alicabo. Me excea iscipid enturibusam, consequ idusant.

REFERENCE [1]

[2]

[3]

[4]

Wang Yanchun, Wan Zhengtian, Zheng Zuzhong, Zhang Zhuo. Structural Design and Analysis for Dual Fuelled 7 500 CEU PCTC Vessel [J]. Ship Engineering, 2021,43 (03): 40-43 +129(Chinese literature). Gao Jing, Zhao Bing-Qian. Analysis and Verification of EEDI for the 1 000 Units Pure Car Carrier [J]. Ship and Ocean Engineering, 2019,48 (06): 31-35(Chinese literature). Degan, Germano, et al. “LCTC Ships Concept Design in the North EuropeMediterranean Transport Scenario Focusing on Intact Stability Issues.” Journal of Marine Science and Engineering 9.3 (2021): 278. Raven, Hoyte C. “Shallow-water effects in ship model testing and at full scale.” Ocean Engineering 189 (2019): 106343.

NAVAL ENGINEERS JOURNAL

[5]

Song, Soonseok, et al. “Validation of the CFD approach for modelling roughness effect on ship resistance.” Ocean Engineering 200 (2020): 107029.

[10] Villa, Diego, et al. “An efficient and robust approach to predict ship self-propulsion coefficients.” Applied Ocean Research 92 (2019): 101862.

[6]

Zha, Le, et al. “Numerical implementation of the Neumann-Michell theory incorporating with the analytical panel integral for ship waves.” Ocean Engineering 236 (2021): 109566.

[11] Feng, Dakui, et al. “Improved body force propulsion model for ship propeller simulation.” Applied Ocean Research 104 (2020): 102328.

[7]

Yasukawa, Hironori, et al. “Evaluations of wave-induced steady forces and turning motion of a full hull ship in waves.” Journal of marine science and technology 24.1 (2019): 1-15.

[8]

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Liang, Hui, and Xiaobo Chen. “Viscous effects on the fundamental solution to ship waves.” Journal of Fluid Mechanics 879 (2019): 744-774. Hino, Takanori, et al., eds. Numerical Ship Hydrodynamics: An Assessment of the Tokyo 2015 Workshop. Vol. 94. Springer Nature, 2020.

[12] Yang Youzong, Chen Hao. Research on technique of ship speed prediction [J]. Shipbuilding of China, 2000 (01): 1-10. (Chinese literature) [13] Sheng Zhenbang, Liu Yizhong. Ship Principle (Part II). Shanghai: Shanghai Jiao Tong University Press, 2004(Chinese literature) [14] ITTC. Procedures for Resistance, Propulsion and Propeller Open Water Test [C].26th ITTC Proceedings Rio de Janeiro, Brazil.

Fall 2023 | No. 135-3 | 155

KNOW BRAINER

Anchor Chain Part II—The Largest Link Dr. E. Michael Golda Chief Technology Officer Naval Surface Warfare Center Philadelphia Division

IE-LOCK ANCHOR CHAIN was invented by members of the Boston Naval Shipyard Forge Shop in the mid-1920s.1 The patented, “high grade,” heat-treatable alloy steel design was less expensive to produce and 50% stronger than the low-carbon steel, cast chain the Navy was using.2 The Forge Shop was re-appointed as the Navy’s chain supplier in 1934 and production increased with the shipbuilding programs of the 1930s. Forge Shop working conditions were difficult. The shop floor was dirt. Sulfur dioxide levels could be 300% above allowable limits. Roofing tar dripped on workers when the shop temperature combined with summer heat. Paid piecework (by the link), workers often produced more during cooler morning hours, but overtime was often needed to meet production goals. During World War II, the Forge Shop (Building 105) was enlarged until it covered an entire block and housed more than 140 pieces of production machinery. Working three shifts, 550 personnel produced up to 1,285 shots (115,650 feet) of chain per month ranging in size from ¾ inch to 3 ¾ inches. Even then, additional civilian companies were needed to meet demand. After World War II, the Forge Shop’s future was uncertain as civilian companies using the die-lock process met much of Navy peacetime demand. Albert Leahy and Carlton Lutts, two of the original die-lock inventors, developed the 4 ¾ inch die-lock link for the new Forrestal-class aircraft carriers.3 No civilian companies, including Chrysler, Ford, and GM, bid to produce the chain. To them, a low rate production line with the possibility of delays or cancellation of the Forrestal-class acquisition was a poor return on investment. In 1949, the Forge Shop was authorized to build the 4 ¾ inch chain production line. A concrete shop floor enabled the use of fork lifts. The ventilation system was improved. A new 25,000 pound drop hammer, 24 feet high, weighing 664,000 pounds, nicknamed “Mighty Monarch,” was installed. Production began in 1953. Formed from 4 ¾ inch diameter stock, each link was 28 ½ inches long, 17 ¼ inches wide, and weighed 360 pounds. Nine drop hammer blows, 35,000,000

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Forge Shop 4 ¾ inch anchor chain production line, Boston Naval Shipyard BOSTON NATIONAL HISTORICAL; PARK, BOSTS 15908-18

pounds of force each, created the interlocking, mechanical joint between the stem and socket halves.4 A single 90 foot shot of chain weighed 13,680 pounds (6.8 tons). The live, Sundaymorning children’s television show Let’s Take a Trip broadcast from the Forge Shop in 1956. (It can be viewed on YouTube.5) When the shipyard was closed in 1974, the Chain Forge had produced the anchor chains for the nine Forrestal-class carriers and the first two Nimitz-class carriers, (USS Nimitz (CVN 68) and USS Dwight D. Eisenhower (CVN 69)). Boston Naval Shipyard Yard Forge Shop personnel created and refined new production techniques to meet Navy needs for anchor chain. Their innovative spirit, the use of patents to protect their intellectual property, and a willingness to do what industry wouldn’t, enabled the Forge Shop to retain its role as the Navy’s supplier of anchor chain for over 90 years.

ENDNOTES [1]

Located in Charlestown, Massachusetts across the Charles River from Boston, the shipyard had several official names since its founding in 1801 including the Charlestown Navy Yard and the Boston Navy Yard.

[2]

Michael Raber et al., Special Resource Study, Chain Forge Machinery in Building 105, Boston National Historical Park, Charlestown Navy Yard (Boston Preservation Alliance) July 2014, 12

[3]

Carlton G. Lutts and Albert M. Leahy. “Chain Link and a Nonlink Chain Made From a Plurality Thereof.” Filed March 4, 1950, awarded November 9, 1954. Patent No. 2,693,673

[4]

Christianson, Justine. Historic American Engineering Record Charlestown Navy Yard, Chain Forge (Smithery) (Building 105, HAER No. MA-90-3, (National Park Service, U.S. Department of Interior) 2014, 35 and 65

[5]

P. Ivas, 2018 Let’s Take a Trip 1956. Video. youtube.com/ watch?v=0saccAiWbO8, accessed 1 August 2023

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