Aerospace Booklet: PhD Poster Day - 16th of March 2018

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Content Preface

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Aerospace Engineering

4

Keynote Speaker

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Poster Competition Winners

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Department ASM

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Section Aerospace Structures & Computational Mechanics (ASCM)

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Section Novel Aerospace Materials (NovAM)

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Section Structural Integrity & Composites (SI&C)

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Department AWEP

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Section Aerodynamics (AERO)

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Section Flight Performance & Propulsion (FPP)

72

Section Wind Energy (WE)

82

Department C&O

98

Section Air Transport & Operations (ATO)

100

Section Control & Simulation (C&S)

108

Section Aircraft Noise and Climate Effects (ANCE)

132

Department SPE

142

Section Astrodynamics and Space Missions (A&SM)

144

Section Space Systems Engineering (SSE)

156

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About:

Aerospace Engineering 4

TU Delft is home to one of the leading academic programmes in aerospace technology in all of Europe. The Faculty of Aerospace Engineering draws upon a long history of technical excellence, innovation and teaching performance, preparing graduates to contribute to this dynamic sector with technically imaginative and commercially viable solutions. In preparing engineers for a truly global sector, one of the goals of the programme is to train professionals to be resourceful problem solvers, who are capable of collaborating with colleagues across cultural divides.

About:

Keynote Speaker Patrick Nathen Co-Founder, Head of Calculation & Design Patrick was born in 1986 in DĂźsseldorf. He moved to Munich to study aerodynamics at the Technical University of Munich. He is still enrolled as a PhD candidate in aerodynamics.

He is an expert in numerical simulation and serves as Head of Calculation and design at Lilium. Apart from aerodynamics and design, he is interested in social media management, which he also actively pursues at work.

In 2010 Patrick was awarded the VDI Prize for outstanding academic records and academic engagement. In 2016, he won the highly endowed Grand Prize as well as the jury’s choice of the Hello Tomorrow Challenge for Lilium in Paris. Furthermore, he has been an alumnus of the German National Academic Foundation since.

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Poster Competition Winners

1st & 2nd years:

1st: Vincent Stuber Piezoelectrics & Energy Harvesting “More and more embdedded wireless sensor systems are employed into our daily lives (e.g. medical implants, structural health sensors), normally powered by batteries. Batteries contain limited energy, pollute the environment and are the volumetric biggest component of a sensor system. Using energy harvesting, batteries could be eliminated, which could be achieved using piezoelectrics; harvesting energy from vibrations.

One of the major challenges is to move from brittle ceramics to flexible materials. In our vision, this can be achieved by moving to piezoelectrics.”

See poster on page 32

2nd: Mario Coppola 6

Designing Provable Robotic Swarms “The paradigm of swarm robotics is based on the idea that large teams of simple robots can accomplish complex tasks, far beyond the capabilities of any such robot by itself. [...] Imagine a swarm of satellites that can selfreorganize while in deep space, or a swarm of drones that can coordinate in surveillance and inspection applications, or a swarm of robotic ants that crawls in the tiniest of places and then self-organizes into a larger robot.

These teams exceed the capabilities of any individual robot - all at low cost, high system robustness, and full-blown scalability. However, to really begin using them, we have to make sure that the robots can work together reliably!”

See poster on page 117

3rd: Delphine de Tavernier Vertical-axis Wind Turbines in Double- Rotor Configuration “Vertical-axis wind turbines might take advantage from being in double-rotor configuration, i.e two rotors in close proximity. This configuration has the potential to enhance the power performance. Besides the power increment, other advantages of the double-rotor configuration are fast wake recovery and lower costs with respect to offshore floaters and operations and maintenance.”

See poster on page 88

Poster Competition Winners

3rd & 4th years:

1st: Kimberly McGuire Swarm Exploration with Pocket Drones “Pocket drones are small MAVs that fit in the palm of your hand, extremely light- weight (< 50 g) and are therefore inherently safe for humans (and plants).

Additionally, their tiny size makes them ideal to maneuver through tight spaces, which is practical for indoor navigation. Therefore they could be used for [e.g.] structure-integrity inspection, greenhouse surveillance & parcel scanning in warehouses. If multiple pocket drones are operational at these locations, they can act as a swarm of movable sensors, deployable anywhere the user wants them to.”

See poster on page 128

2nd: Svenja Woicke A Stereo-Vision Based Hazard-Relative Navigation “Current navigation systems, based on IMUs and altimeters only, are not accurate enough to make use of the data provided by a Hazard Detection and Avoidance system (HDA), to perform a safe and precise landing. The knowledge of hazard locations will only increase the safety of a landing, if the full GNC system is actually able to avoid this hazard. One of the necessities to this end is more accurate navigation capabilities. In our research we are addressing this need by proposing a Hazard Relative Navi- gation (HRN) algorithm, which can improve the localisation accuracy.”

See poster on page 147

3rd: Fabricio Ribeiro Understanding Variable Amplitude Fatigue Effects on Composite Bonded Joints “Variable amplitude fatigue (VAF) loading is known to cause change on the crack growth rate within diverse materials. In metals, it is known that overloads decelerate crack growth due to crack tip plasticity effects. In an adhesive, it is still not clear how this process occurs so VAF loading can lead to inaccurate structural life prediction.

Evaluate the presence of VAF loading effects on the damage evolution of bonded joints and improve the understanding of the disbond growth mechanism.”

See poster on page 42

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Depar Aerospace Struct (AS

tment tures & Materials SM)

Department ASM

Aerospace Structures & Computational Mechanics (ASCM) Head of section: Prof. dr. Chiara Bisagni

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Supervisors Prof. dr. Chiara Bisagni Dr.ir. Roeland de Breuker Dr. Sergio Turteltaub Dr.ir. Coen de Visser

Department ASM

Our mission is to perform research dedicated to advancing the stateof-the-art in the field of aerospace structures and computational mechanics, and to educate our students to be lifetime learners and leaders in academia and industry.

Our research combines engineering expertise with the use of advanced computational tools and experimental activities in order to develop design solutions relevant to industrial needs.

The final product of research can be a scaled innovative prototype pushing technology’s boundaries or computer software for use in industry.

The group provides an open, international and diverse environment for the faculty, researchers, staff and students, to achieve their best and fulfill their professional goals. Our faculty has active collaborations all over the world, and is committed to providing the best possible education and research experiences for our students.

PhD Candidates: -- Inés Uriol Balbín -- Niels Van Hoorn -- Fardin Esrail -- Javier Gutiérrez Álvarez -- Yuqian Tu -- Marco Tito Bordogna -- Jayaprakash Krishnasamy -- Darwin Rajpal -- Paul Lancelot -- Yasir Zahoor -- Tigran Mkhoyan -- Marta Gavioli -- Zhi Hong

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Department ASM

Future Launch Vehicle Structures: Scaling Methodology applied to Buckling Response

PhD Candidate: InĂŠs Uriol BalbĂ­n Department: ASM Section: ASCM Supervisor: C. Bisagni Promotor: C. Bisagni Contact: i.uriolbalbin@tudelft.nl 1

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Sandwich composite materials for launch vehicle structures?

Sandwich composite materials offer: + Low weight + Good mechanical properties - Buckling design criteria unknown

Ariane 6 concept

Future launch vehicles demand: ď&#x192;&#x2DC;More payload ď&#x192;&#x2DC;Lighter structure ď&#x192;&#x2DC;Low cost

NASA Marshall Space Flight Center laboratory

Full size test - Expensive specimens - Huge laboratory - Few tests

Carbon fiber laminate composite facesheets

Delft Aerospace Structures and Materials Laboratory

Scaled Test

Length ~5m

Thickness ~ 20 mm

+ Overall cost reduction + Standard laboratory + More tests

Honeycomb core

Objective: Obtain scaled structures with same buckling response How? Analytical scaling methodology based on nondimensional buckling equations1

12

1 đ?&#x2018;&#x160;đ?&#x2018;&#x160;,đ?&#x2018;§đ?&#x2018;§ đ?&#x2018;§đ?&#x2018;§ đ?&#x2018;§đ?&#x2018;§ đ?&#x2018;§đ?&#x2018;§ + 2 đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2018;&#x160;đ?&#x2018;&#x160;,đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§2đ?&#x2018;§đ?&#x2018;§2 + 12 đ?&#x2019; đ?&#x2019; đ?&#x;?đ?&#x;? đ??šđ??š,đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1 â&#x2C6;&#x2019; đ?&#x2018;˛đ?&#x2018;˛đ?&#x2018;&#x160;đ?&#x2018;&#x160;,đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1 = 0 đ?&#x153;śđ?&#x153;śđ?&#x;?đ?&#x;?đ?&#x2019;&#x192;đ?&#x2019;&#x192; 2 2 2 2 1 đ?&#x153;śđ?&#x153;śđ?&#x;?đ?&#x;?đ?&#x2019;&#x17D;đ?&#x2019;&#x17D; đ??šđ??š,đ?&#x2018;§đ?&#x2018;§2đ?&#x2018;§đ?&#x2018;§2đ?&#x2018;§đ?&#x2018;§2 đ?&#x2018;§đ?&#x2018;§2 + đ?&#x;?đ?&#x;? đ??šđ??š,đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1 + 2 đ?? đ?? đ??šđ??š,đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§2đ?&#x2018;§đ?&#x2018;§2 â&#x2C6;&#x2019; 12 đ?&#x2019; đ?&#x2019; đ?&#x;?đ?&#x;? đ?&#x2018;&#x160;đ?&#x2018;&#x160;,đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1 = 0 đ?&#x153;śđ?&#x153;śđ?&#x2019;&#x17D;đ?&#x2019;&#x17D; đ?&#x153;śđ?&#x153;śđ?&#x;?đ?&#x;?đ?&#x2019;&#x192;đ?&#x2019;&#x192; đ?&#x2018;&#x160;đ?&#x2018;&#x160;,đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1đ?&#x2018;§đ?&#x2018;§1 +

Similar buckling response (K) can be achieved by keeping these parameters constant throughout the scale changes.

Results and verifications: Obtain scaled structures with same buckling response 2. Finite Element verification

1. Rapid Analytical Calculation

The nondimensional load and displacement are compared and both scales show a similar stiffness

Multiple configurations are possible for each large design Baseline

Scaled 1

Scaled 2 [78/-78]s

[12/-12]s 1250 mm 2305 mm

357 mm

2.29 mm

Scaled 3 [15/-15/0] [Âą30/90/0]s

7.62 mm

Baseline Scaled 1 Scaled 2 Scaled 3 Scaled 4

Îź 1.51 1.52 1.52 1.52 1.49

β 0.79 0.78 0.78 0.82 0.89

Scaled 4

[65/-65/0]

Baseline

2.54 mm

2.48 mm

1250 mm

2.29 mm

Scaled

645 mm

Îąm 0.60 0.60 0.60 0.60 0.60

Îąb 0.60 0.60 0.60 0.60 0.60

Z2 73.9 73.9 73.9 73.9 73.9

K 433 431 431 429 399

Baseline

The solution that better replicates all nondimensional parameters is adopted

Scaled

Radial postbuckling displacement also shows a similar pattern in the baseline and scaled structure

3.Test

Improved Buckling Design guidelines for sandwich composite launch vehicle structures

Introduction Impact damage in thick composite structures (i.e., 2050mm or 80-200 layers) is not completely understood.

One part is the effect of material properties impact response. The goal is to determine the global sensitivity of material properties on the peak force during an impact event. This study is part of a research project that investigates the impact damage in thick composite structures with a fabric material [1].

PhD Candidate: Niels van Hoorn Department: ASM Section: ASCM Supervisor Dr. Christos Kassapoglou Promotor: Prof. Dr. Chiara Bisagni Contact: N.vanHoorn@tudelft.nl 1

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3

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Department ASM

Sensitivity of Material Properties on the Impact Response of Thick Composite Structures

of the input parameters on the peak force is determined.

The first-order Sobol indices indicate the main-effect and the total-order indices include the variance due to interactions between input parameters.

Preliminary results

Impact response model Based on the work of Christoforou [2] a Hertz contact law (Eq. 2) is used together with an assumed plate deflection (đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;?đ?&#x2018;? , Eq. 1) in terms of a series expansion with m,n terms. Here đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161; are the unknowns and đ?&#x2018; đ?&#x2018; đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161; = sin đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x153;&#x2039;đ?&#x153;&#x2039; 2 sin đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x153;&#x2039;đ?&#x153;&#x2039; 2.

13 Fig. 1: Sensitivity of input parameters on the peak force for a 50J, 0.04kg impact on a 40mm thick laminate.

Conclusions For a 50J, 0.04kg impact on a 40mm thick laminate: â&#x20AC;˘ The fibre longitudinal modulus ( đ??¸đ??¸đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;11 ) and in-plane shear modulus (đ??şđ??şđ?&#x2018;&#x201C;đ?&#x2018;&#x201C;12 ) have almost no effect on the peak force. â&#x20AC;˘ The total-order sensitivity is almost equal to the firstorder sensitivity indicating small interactions between parameters.

Eq. 2 describes the force đ??šđ??š as a function of the contact stiffness đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x203A;źđ?&#x203A;ź and indentation đ?&#x203A;żđ?&#x203A;ż. Other inputs are the plate natural frequencies đ?&#x153;&#x201D;đ?&#x153;&#x201D;2 đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161; , plate mass đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;? , impactor mass đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;? , and impactor velocity đ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;&#x2013;đ?&#x2018;&#x2013; . As output the indentation, force, plate deflection, and impactor displacement are given as a function of time.

Global sensitivity analysis To determine the effect of the material properties a Sobol global sensitivity analysis is performed. For the 8 input parameters in Fig. 1 a lower and upper bound is given with an interval of 2000. Using the Saltelli sequence [3] 36000 samples are generated and evaluated by the impact response model. The sensitivity

Future Work Evaluate small-mass (0.04kg) and large-mass (4kg) 50J impact events on 12, 20, and 40mm thick laminates.

References

[1] N. van Hoorn, C. Kassapoglou, and W.M. van den Brink. Impact response of thick composite structures. In J.J.C. Remmers and A. Turon, editors, sixth ECCOMAS Thematic Conference on the Mechanical Response of Composites. Eindhoven University of Technology, 2017.

[2] A.P. Christoforou and A.S. Yigit. Characterization of impact in composite plates. Composite Structures, 43:14â&#x20AC;&#x201C;24, 1998.

[3] A. Saltelli, P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola. Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index. Computer Physics Communications, 181(2):259â&#x20AC;&#x201C;270, February 2010.

Department ASM

Impact Damage in Composites: Efficient Analytical Methods for Preliminary Design

PhD Candidate: Fardin Esrail Department: ASM Section: ASCM Supervisor: Dr. C. Kassapoglou Promotor: Prof.dr. C. Bisagni Contact: f.s.esrail@tudelft.nl 1

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Objective: To provide industry with efficient and accurate analytical models to predict impact damage and its effect on the residual strength of composite structures. Analytical models are needed for fast calculations and trade-offs between laminate configurations during aircraft preliminary design.

1. Impact problem

Impact modelled as a point force F

Spherical impactor, radius Ri, drop height H, mass m

R

Simplification

2. Analytical model Governing equation

14

(1)

k

d12

d 22 d 26

d16  d 26   d 66 

i



 

Bending compliances of beam i

Interlaminar stresses in ply k ∂σ x ∂τ xz ∂τ xy + + =0 ∂x ∂z ∂y ∂σ y ∂τ yz ∂τ xy + + =0 ∂y ∂z ∂x ∂σ z ∂τ xz ∂τ yz + + =0 ∂z ∂x ∂y

(3)

plotted along

this line



 

plotted along

this line

Top view R*∆θ

∆θ

Cross section

h Mx    My  M   xy 



 and

Bending stresses in ply k

k σ x   Q11 Q12 Q16   d11   z   Q12 Q22 Q26  d12 σ y  = −   b( x )  τ  Q16 Q26 Q66   d16   xy 

2 1

 and

R x

   (b0 + Δθ R ) ln(b0 ) ( x − R ) + xΔθ (2b0 + Δθ (2 R + x )  2 3   Δ Δ θ 2 θ ( ) ( )    (b0 + Δθ R )(b0 + Δθ x )  i i w = − d11 Fi  −2 + ..  (2) 3 θ 2 Δ ( )     2  2 ( b0 + Δθ R ) ln(b0 + Δθ R ) − Δθ R ( 2b0 + 3Δθ R )  3   θ 2 Δ ( )  

Ply stiffnesses

3

(a)

Side view



Solution

. .

3. Results & Verification

Beam i

z

i

i

Figure 2. “Propeller” beam model, plate simplified in “slices” which are modelled as beams,  = angular width of each slice

Figure 1. Circular composite laminate under low velocity impact

d 2w M i ( x) = −d11i x 2 dx b( x )

F

z

(b)

x (mm)

b(x)

Figure 3. Schematic of laminated beam i, width b(x), height h, cross-section made of N plies. The beams are simply supported at x=R.

(4.1) (4.2)

 and 

 

plotted along

this line

 and 

 

plotted along

this line

 and 

 

plotted along

this line

(4.3)

 and 

 

(c)

x (mm)

plotted along

this line

Compatibility of centre deflection wi ( x = 0) = wi +1 ( x = 0)

i = 1.. N b

(5)

Recovery of the impact force F F1 + F2 + F3 + ... + FNb = F

(6)

Equations 2,5 and 6 lead to a closed form expression for the force acting on each beam Fi =

d11Nb F d11i  N b −1 d11N b  1 + ∑ d i  i =1  11 

(7)

Figure 4. ABAQUS shell model (top) to verify beam deflection and rotation with Nb=12 , ABAQUS solid model (bottom) to verify interlaminar stresses

Figure 5. (a) Verification of deflection with FE (b) verification of interlaminar shear stress with FE (c) top view of the plate showing delaminations obtained at each ply interface by applying a failure criterion

Thermal Buckling in Composite Aerospace Structures

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INTRODUCTION X

Y

AERODYNAMIC FLOW

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Thermal buckling is a structural instability, triggered by a heat flux or a temperature increment. This event is specially relevant in supersonic aircrafts or in spacecrafts exposed to aerodynamic heating.

SUPERSONIC WING

WING PANEL

PhD progress 2 3

Department ASM

PhD Candidate: Javier Gutiérrez Álvarez Department: Aerospace Structures and Materials Section: Aerospace Structures and Computational Mechanics Supervisor: C. Bisagni Contact: j.gutierrezalvarez@tudelft.nl

Thermal buckling can have a big impact in the structure, causing local or global changes in structural stiffness and aerodynamic effects like thermal flutter; such a distortive effect can be advantageous if a change in shape is seeked; this can be interesting for general morphing purposes, like local/global structural shape change or load alleviation.

FLEXIBLE RIBS

HEAVY SPARS

PROBLEM DEFINITION

∆x/2

MECHANICAL STRESSES

∆x/2

X WING PANEL

Νx,m

Y

THERMAL STRESSES

Under a thermal increment, light structural parts like ribs or stringers respond fast to temperature changes, allowing expansion along x axis.

Heavy spars or frames react much slower, restricting panel dilatations along Y axis.

THERMOMECHANICAL PANEL BUCKLING

Νy, t Νx,t

+

X

=

THERMAL INCREMENT

Νx

X

Structural flat panels subjected to thermomechanical loading can experiment severe reductions in their original buckling load.

However, a substantial amount of load can still carried by the panel in the post-buckling regime.

Θ

Νy,m

Y

Y

Νy

PRESENT WORK DIAGRAM FOR THERMAL INCREMENT VS. SHORTENING 200

The behavior of flat, symmetric and balanced composite laminated plates under thermal and mechanical load is currently being investigated. Plates are modelled with a combination of lamination theory and classical plate theory.

Analytical formulae were developed and subsequently verified with Abaqus.

MATERIAL: AS4/3502

150

100

BUCKLED

m=1, n=2

50

A heated plate under restrained vertical expansions and a prescribed axial ∆x will develop stresses in function to its mechanical properties and laminate expansion coefficients. Plate stretching increases buckling temperature, while shortening reduces it. For a certain value of ∆x, buckling will occur under the shape (m,n) that delivers the minimal Θ.

The locus containing all buckling temperatures is a curve that divides the (∆x ,Θ) plane into buckled and unbuckled states. Once the plate buckles, it enters a new state of equilibrium with modified mechanical properties.

m=1, n=1

0

UNBUCKLED

-50

Analytical curve

-100

F.E. calculation

m=2, n=1

-150

-200 -0.5

m=3, n=1

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1

0.4

0.5

PhD progress 2 3

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Department ASM

Ditching and Water Impact of Aerospace Structures

PhD Candidate: Yuqian Tu Department: ASM Section: ASCM Supervisor : Prof. Dr. C. Bisagni Promotor: Prof. Dr. C. Bisagni Contact: y.q.tu@tudelft.nl 1

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State of Art and Objectives Ground and Water Impact Differences

History

Differences in load transfer for impacts on rigid surfaces and water (Shah, 2010)

Ditching of a B-24 Ditching test of a 1/16-size Army Airplane (NACA, 1944) B-24 Airplane (NACA, 1940)

Ditching in the Hudson River (Paries, 2011)

Open Issues

Recent Research in Literature

Composite structures on Boeing 787 Aircraft (Hale, 2008)  Ditching and water impact of composite structures need deep investigation.  Aircraft component design and optimization of composite structures related to ditching and water impact is still missing.  The research of aircraft impact on rough water is limited.

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Guided ditching system (Iafrati, 2015)

Ditching test of a captive bullet (Maximiano, 2017)

Simulations of ditching at t=150 ms: ALE model (left) and SPH model (right) (Bisagni, 2017)

Numerical Progress – Impact of a Rigid Sphere on Water

(a)

Sphere vertical acceleration: correlation of numerical simulations with experimental data

Experimental Progress

(b)

(a)

(b)

Experimental plan: (a) Towing Tank at 3ME and (b) CATIA model

(c) Comparison of different water formulations at t=10ms and pressure distribution over sphere bottom surface: (a) ALE, (b) SPH and (c) Hybrid Lagrangian-SPH

(a)

(b)

Possible specimens: (a) Thin flat plates and (b) Subfloor structures

References [1] Bisagni, C., and Pigazzini, M. S. (2017). Modelling strategies for numerical simulation of aircraft ditching. International Journal of Crashworthiness, 1-18.

[2] Iafrati, A., Grizzi, S., Siemann, M. H., and Montañés, L. B. (2015). High-speed ditching of a flat plate: Experimental data and uncertainty assessment. Journal of Fluids and Structures, 55, 501-525.

[3] Maximiano, A., Vaz, G., and Scharnke, J. (2017, June). CFD verification and validation study for a captive bullet entry in calm water. Paper presented at the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. Retrieved from http://www.marin.nl/web/News/News-items/CFD-verification-and-validation-studyfor-a-captive-bullet-entry-in-calm-water-1.htm

Department ASM

Multi-Disciplinary Analysis and Optimization of Composite Wings

PhD Candidate: Marco Tito Bordogna Department: Aerospace Engineering Section: ASCM Daily Supervisor: Dr.ir. R. De Breuker Promotor: Prof. C. Bisagni Contact: m.t.bordogna@tudelft.nl 1

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Objectives

Develop a strategy to perform structural optimization of large scale composite structure taking into account: aeroelasticity, aerodynamic performance and composite manufacturing rules.

Blending

Bi-step approach

Locally optimized composite structure are ideal for spanwise varying loads.

From discrete to continuous design space and back.

Risks: lack of structural integrity or non-manufacturable design. Ply continuity (i.e. blending) has to be ensured.

Aeroelastic optimization strategy

Results

[2,3]

Mass

17 Stacking sequence retrieval error

CD vs weight α

Blending constraints

Composite stiffness direction

Failure indices for gust load [1]

Key: Quantify change in LPs due to ply-drops.

Strain failure index after stacking sequence retrieval

[1] T.Macquart, M.T.Bordogna, P.Lancelot, R.De Breuker (2016). “Derivation and application of blending constraints in lamination parameter space for composite optimisation”. Journal of Composite Structures, Vol. 135, p 224-235.

[2] M.T.Bordogna, P.Lancelot, D.Bettebghor, R.De Breuker (2017). “Aeroelastic Tailoring for Static and Dynamic Loads with Blending Constraints”. International Forum on Aeroelasticity and Structural Dynamics (IFASD). Italy.

[3] M.T.Bordogna, D.Bettebghor, C. Blondeau, R.De Breuker (2017). “Surrogate-based Aerodynamics for Composite Wing Box Sizing”. International Forum on Aeroelasticity and Structural Dynamics (IFASD). Italy.

Department ASM

Modelling fracture behavior of thermal barrier coatings in presence of healing particles

PhD Candidate: J Krishnasamy Department: ASM Section: ASCM Supervisor: Dr. Sergio Turteltaub Promotor: Prof. Sybrand van der Zwaag Contact: j.krishnasamy-1@tudelft.nl 1

2

3

4

Background

Approach

Results

Thermal Barrier Coating (TBC) systems are applied in gas turbine engines used for propulsion and power generation to increase their thermodynamic efficiency and to protect critical structural components. Thermal cycling, shown in Fig.1, causes high stresses in the TBC system due to a mismatch between the coefficients of thermal expansion of the substrate and the different layers in the coating system [1]. These stresses induce cracks that ultimately lead to failure of the system [2].

Due to the damage in TBC system, engines need to be repaired several times during their lifetime, leading to significant maintenance costs.

The main modelling steps include • Simulation of fracture response on periodic unit cell under thermomechanical loading of a TBC system • Coupling of a TGO growth model to the thermomechanical FEM simulations.

• Study of crack- healing particle interaction in TBC • Incorporation of a reaction-diffusion model to simulate the healing process.

The fracture behaviour will be modelled using a cohesive law. The cohesive law also accounts for simulating the healing behavior [4].

The idealized two particle set up is used to study locally the effect of CTE mismatch on crack evolution whose topology or distribution is defined by the distance between the particles and the orientation. The results from this parametric analyses is summarized in terms of crack initiation temperature in the TC layer as the function of spatial parameters as shown in Fig.4.

More realistic multiparticle simulation is carried out to study the crack evolution in the TC layer for different thermal mismatch ratios. The results are summarized as fracture pattern shown in Fig.5c. Further detailed quantification is presented based on crack initiation temperature and crack growth (Fig 5a and Fig 5b).

18

Aerospace Engineering

Finite Element Analysis

Fig.1: Typical thermal cycle of a gas turbine engine Objective

Knowledge of the damage mechanisms in TBCs in the presence of healing particle is essential in order to design a self healing TBCs. In particular, it is important to understand the effect of CTE mismatch between the particle and the TC layer.

In order to understand the failure mechanisms under thermal loading (1100o to 30o C), multiscale approach based on periodic unit cell is adopted. The substrate of the TBC whose dimension is orders of magnitude larger than the layers of the TBC, is not modelled explicitly to avoid computational complexity. Rather its effect is modelled through boundary conditions derived using thermal deformations induced by substrate during a thermal cycle. To model fracture cohesive elements are embedded through out the finite element regions of the unit cell. The healing particles are distributed in the healing layer close to the TC/TGO interface.

The goal of this project is to extend the TBC system lifetime by incorporating a self-healing mechanism in the system. The proposed mechanism relies on encapsulated healing particles dispersed in the Top Coat (TC) layer [2, 3]. In the self-healing TBC system, cracks occurring in the TC layer during the cooling process will be healed in the next cycle, as shown in Fig.2. The development of this novel TBC system will be achieved through numerical modelling combined with experimental work.

(a)

(b)

Fig.3: Finite element model of TBC system with healing particles (a) Two particle setup showing periodic boundary conditions (b) Randomly distributed multiparticle setup.

Fig.2: Schematic of crack-healing mechanism in a TBC system with encapsulated Mo-Si based particles. The TBC system comprises a nickel based superalloy substrate with a MCrAlY bond coating (BC) and the modified yttria stabilized zirconia top coat (TC). During service, a thermally grown oxide (TGO) appears between the TC and BC layers.

Fig. 4: Two particle simulations - Effect of particle distance and orientation on crack initiation temperature in TC for thermal mismatch ratio of 0.5 and 1.5 .

The microscopic periodic unit cell consists of three layers of the TBC system representing BC, TGO and TC. In this study, the interfaces between the layers (TC/TGO and TGO/BC) are modeled as a sinusoidal curve. The CTE mismatch study has been carried out for idealized two particle and realistic multiparticle simulation setups as shown in Fig.3. For multiparticle cases the particles are distributed randomly and for each study five realizations has been carried out.

Fig. 5: Multiparticle simulation setup (a)Crack initiation temperature and crack length as a function of thermal mismatch (b) Fracture pattern for different mismatch ratios.

Conclusions This study helps to identify the optimal range of CTE values essential for the healing particles to prevent cracking in the TC layer due to thermal mismatch between the particle and the top coat. From the multiparticle simulations, the optimal range of thermal mismatch ratio is from 0.75 to 1.25 where no significant cracking is observed. The goal is then set to achieve a model integration where an attempt is made to correlate the results from two simulation setups.

References 1. W.G. Sloof, (2007) “Self Healing in Coatings at High Temperatures in: Self Healing Materials an Alternative Approach to 20 Centuries of Materials Science”, S. van der Zwaag, Springer, Dordrecht, The Netherlands, pp. 309-321.

2. Jayaprakash Krishnasamy, WG Sloof, S van der Zwaag, S Turteltaub (2015), Modelling the fracture behavior of TBCs in the presence of healing particles, ICSHM 2015, Durham, NC, USA. 3. Jayaprakash Krishnasamy, WG Sloof, S van der Zwaag, S Turteltaub (2017), Life time prediction of TBC with explicitly modelled pores using fatigue life methodology, ICSHM 2017, Friedrichshafen, Germany.

4. Sathiskumar A Ponnusami, Jayaprakash Krishnasamy, Sergio Turteltaub, Sybrand van der Zwaag, A cohesive-zone crack healing model for self-healing materials, International Journal of Solids and Structures,134, 249-263.

Inclusion of gust and fatigue loads in preliminary aeroelastic design of composite wings

1

2

Department ASM

PhD Candidate: Department: Section: Supervisor Promotor Contact:

Darwin Rajpal ASM ASCM Dr. Roeland De Breuker Prof. Chiara Bisagni d.rajpal@tudelft.nl 3

4

Background CONCEPTAUL DESIGN

PRELIMINARY DESIGN

DETAIL DESIGN

100%

G

oa l

Knowledge

G l oa

Design Freedom

0% Design Timeline

Figure 1: Global market forecast (AIRBUS GMF 2012)

Figure 2: Progress of Knowledge and freedom in the design process

Objective

19

Results

Applied Load

Figure 3: Optimization framework to account for dynamic loads

Determination of distribution parameter for modified Tsai Wu

Change in R

No Change in R

First Load Cycle

Determination of Weibull parameter

Determination of probability of failure

Figure 6: Value of Constraints on the optimized wing

Determination of number of cycles to failure

Determination of degradation in residual strength

Failure Yes

Determination of modified Tsai Wu failure index

No

Determination of degradation of distribution parameter

Number of Cycles

Figure 5: Flowchart to analyze fatigue in lamination parameter domain

Figure 4: Change in critical loads during the optimization

Figure 7: Stiffness and thickness distribution for the optimized wing

LATEX Tik Zposter

Department ASM

Integration of active and passive load alleviation systems on aircraft

PhD Candidate: Paul Lancelot Department: ASM Section: ASCM Supervisor Dr. Ir. Roeland De Breuker Promotor: Prof. Chiara Bisagni Contact: p.m.g.j.lancelot@tudelft.nl 1

2

3

4

Background

An aircraft wing structure is sized for several requirements.

One of them is to sustain various types of loads that might be encountered during its service life. These loads mainly come from atmospheric conditions, neighbouring aircraft wakes as well as pilot actions on the controls [1]. Reducing the forces acting on the wing can help making it lighter and therefore improve the overall aircraft fuel efficiency.

Historically, load alleviation is achieved using conventional moveable surfaces such as the ailerons. Now it is moving towards distributed devices increasing both mechanical and logic complexity. The promise of a passive system is to be a “fail-safe” alternative that would limit the use of computers, sensors and actuators to operate the aircraft.

Objectives

Complex transonic and separated flow behaviours from specific control surfaces and gusts are evaluated using RANS CFD:

The relevant responses (force, moment) from CFD are approximated using transfer functions and look-up tables: Input: step or chirp Responses from signal simulations

This PhD aims to complete the following tasks:

Design of innovative load alleviation concepts, with the required level of maturity to be brought to the industry.

Development of the tools to combine active and passive load alleviation technologies, early in the design stage.

20

This project fits within the Clean Sky 2 “ReLOAD” consortium, which focuses on the development of a flying demonstrator embedding new load alleviation technologies.

System identification

Magnitude Phase Frequency

The approximated loads are used to increase the fidelity of a linear aero-elastic model. The aerostructural optimisation is then performed:

Aero mesh

Wing-box structure

Method

Illustration of an aircraft hit by a gust. Distributed control surfaces (in blue) can reduce the induced loads and therefore decrease structure weight.

References

The integration of active and passive load alleviation systems requires a realistic model. It must account for the free flight motion of the aircraft and its wings flexibility as gust responses are sensitive to these parameters. It also includes corrected data from unsteady RANS simulations to capture non-linearity in the flow due to the transonic regime and potential separation. The aero-elastic simulation and the structural optimisation are performed using MSC.NASTRAN [2], while the non-linear aerodynamic correction is derived with Ansys Fluent and Matlab-Simulink [3]. Finally, wind tunnel test will be performed for validation purposes.

[1] Frederic M. Hoblit, “Gust Loads on Aircraft: Concepts and Applications”, American Institute of Aeronautics and Astronautics, 1988 [2] Marco Tito Bordogna, Paul Lancelot, Dimitri Bettebghor and Roeland De Breuker, “Aeroelastic Tailoring for Static and Dynamic Loads with Blending Constraints”, International Forum on Aeroelasticity and Structural Dynamics, 2017 [3] Paul Lancelot, Roeland De Breuker, “Passively actuated spoiler for gust load alleviation” , International Conference on Adaptive Structure and Technologies, 2016

What is morphing?

Introduction

Department ASM

Morphing Blade Design for Helicopters

PhD Candidate: Yasir Zahoor Department: ASM Section: ASCM Supervisor: Roeland De Breuker Promotor: Roeland De Breuker Start date: 01-12-2017 Funding: EU, H2020 (SABRE) Contact: y.zahoor@tudelft.nl 1 2 3 4

Morphing is a term

project named “SABRE” (Shape Adaptive Blade for Rotorcraft

word Metamorphosis

Efficiency) in which blade morphing concepts have to be

form and/or shape

conceptualized, designed, materialized and tested for

comes to aviation, the

definition of morphing

instabilities and withstand structural loads. Multipronged

rotorcrafts. This project features TU Delft along with five other

principally stays the same

implying shape

challenges related to;

European partners in an effort to devise a comprehensive

changes in fixed or rotary

wings. This idea

•

morphing strategy to effectively reduce the noise and vibration

originates from the nature

of the helicopters making them more efficient and

of birds, there is “smooth”

environmentally friendly.

wings from one flight condition to

Why Morphing?

that has stemmed from the Greek

Paradox

The scope of PhD research involves working on the EU

which means to change or simply “transformation”. When it

Design of an aircraft wing possessing conflicting capabilities to be both structurally compliant to allow configuration changes and at the same time be sufficiently rigid to limit aeroelastic

Structural skeleton

•

Actuator system

transformation of

•

Skin

another adding

itself as in case

•

Sensor system

great versatility and adaptability

in terms of flight.

•

Control system

Mimicking the same capability in

aviation means

For helicopters, the task is even more daunting due to

Current helicopters are designed primarily for one flight

highly efficient aircraft that

condition giving somewhat compromised performance in other

extended flight envelop and

are able to perform

operate in truly

wing. More so, the limitations of space and rotor dynamics

flight regimes. The ability of shape changing without

different manoeuvres and

operations with the

confines the choices of morphing solutions (evident from the

conventional control surfaces improves aerodynamic

same wing just by changing its

shape during flight.

characteristics and directly influences factors like weight, cost,

“If I had to choose, I would rather have birds than airplanes”. Charles Lindbergh

performance and endurance. Morphing brings; • Flight envelop extension and drag reduction.

different loading conditions of a rotor as compared to a fixed

literature synthesis below).

Literature Synthesis The charts depicted below are an extract from literature survey depicting various aspects of research activity in

• Reduction in noise and vibration emissions for helicopters.

• Lower installation impact w.r.t traditional control surface

Active Tendons

systems.

morphing domain from 1988 till 2010 and representing data of more than 150 records.

Active Camber

• Reduced weight (after elimination of heavy actuators) • Equivalent safety level.

21

Morphing Types Shape Morphing Wing

Sweep Planform

Active Camber

Chord Airfoil Out-ofplane

Active Twist

Span

Negative Stiffness Passive Energy Balancing

Camber Thickness Twist Dihedral Spanwise bending

Research Motivation • Provide aviation industry with a practical solution for

Different morphing mechanisms conceptualized in SABRE with present work focused on Active Camber.

Envisioned Outcome The research work would focuses on design, analysis,

morphing (e.g. trailing edge translation induced camber),

manufacturing, integration and testing (both for structural

targeted at helicopters.

integrity and wind tunnel evaluation) of a morphing blade and

• Understanding the extent of increase in flight envelop without increasing weight by adopting the morphing strategy.

• Investigate the effect of similar approach for a fixed wing aircraft.

• Evaluate the limitation or elimination of one or more conventional movements of rotor blades (namely feathering or pitching, flapping and leading-lagging, all

possibly provide insight regarding; • Influence of location, size, geometry and material of trailing edge morphing flap on the performance of a helicopter

Extent of use of various actuation concepts employed for fixed wing and rotary wing aircraft.

Geometrical Parameters

keeping in view torsional behaviour of thin walled open structures.

• Development of a numerical tool to optimise the size of flap for different types and sizes of aircraft/helicopters.

• Establish trade studies involving morphing complexities,

controlled by mechanisms) after the introduction of

cost and advantage in terms of incorporating a particular

morphing capability.

morphing solution.

References

The degree of realization of a concept to either wind tunnel or full scale testing.

Number of Records

The amount of research work in a particular morphing type with respect to different types of aircraft.

• Antonio Concilio, I. D., & Leonardo Lecce, R. P. (2017). Morphing Wing Technologies: Large Commercial Aircraft and Civil Helicopters. Butterworth-Heinemann.

• Barbarino, S., Bilgen, O., Ajaj, R. M., Friswell, M. I., & Inman, D. J. (2011, June). A Review of Morphing Aircraft. Journal of Intelligent Material Systems and Structures, 22, 823-877.

• Werter, N. P., Sodja, J., Spirlet, G., & De Breuker, R. (2016). Design and Experiments of a Warp Induced Camber, and Twist Morphing Leading and Trailing Edge Device. AIAA.

Autonomous Smart Wing Aim

1

Closely coupled non-linear system

Integration of novel control laws, smart distributed sensing and actuation systems within a multi-objective optimisation framework, to enable real-time in-flight performance optimisation of flexible aircraft wings.

T. Mkhoyan ASM AS&CM Dr.ir.R. De Breuker Dr.ir.C.Visser Dr.ir.R. De Breuker T.mkhoyan@tudelft.nl

2

3

4

Towards Less model dependency Non-linear Model x0,ẋ0[δi ,.. ]

How can multidisciplinary integration of novel control laws, novel sensing methods, novel actuation mechanism be used for real-time, in-flight, multiobjective optimisation framework of actively morphing wing?

Δu[i... ]

Non-linear Control law

u[i... ]

x0,ẋ0[δi ,.. ]

-+

δi

Actuator δi

u0[i... ] ui

Sensor δi

Distributed RT flap synchronization

Possible approach: Approximation of ẋ in neighbourhood of current situation [x0, u0], with local linearisation of aircraft and actuator dynamics, A0 and B0, and increments of control input ∆u : ẋ ∼ = ẋ0 + A0(x − x0) + B0(∆u)

t

...

Department ASM

PhD Candidate: Department: Section: Supervisor 1st: Supervisor 2nd: Promotor Contact:

...

Real-Time Smart performance optimisation

Goals & Requirements Robustness Rx...

Allocation L(y)

Aeroelastic Model

Control Optimisation

Hardware Design

Domain Transfer

Realism System limits Estimation

Robustness

Rx...

Adaptation II.

Figure 1: Research work-flow part I linking questions A-F

Multi-disciplinary integration of smart system [sensors, materials, actuators, model, control] and multi-objective performance optimisation is needed for a flexible wing.

Key challenge: Accurate and distributed sensor and actuator feedback with Real-Time synchronisation of TE 1 flaps.

Smart Real-time state estimation?

Planning towards test flight: Runway analogy A

A - Sizing 0.1m

5cm

2m 0.5m

B 17

A. Parking brake: initial sizing B. Taxi runway: TE morphing discretization C. Take off runway 17: Development of discrete TE D. Full throttle: TE morphing (Sδc (y) controllable) E. Touch-and-go: Future work (full morphing wing shape (Sw (y)) and (Sδc (y)).

35

22

Limits

Optimisation I.

D

C δi

E w

c c

B - Num. studies

C - Discrete TE

D - Morphing TE

E - Full Morphing

Smart-X LATEX Tik Zposter

IMPACT OF HANDS-ON EXPERIENCE ON LEARNING STRUCTURAL MECHANICS

1

STRUCTURAL MECHANICS

2

â&#x20AC;˘ Keystone courses in every Aerospace Engineering bachelor curriculum â&#x20AC;˘ High theoretical content (maths & physics) â&#x20AC;˘ Students struggle to visualise what is â&#x20AC;&#x153;behind the formulaâ&#x20AC;? â&#x20AC;˘ This affects conceptual understanding, problem solving, motivation

1

3

4

â&#x20AC;˘ Experimental and procedural knowledge is fundamental in every engineering profession â&#x20AC;˘ Hands-on activities in engineering mechanics are often teachersâ&#x20AC;&#x2122; initiatives, with which they try to solve context specific needs â&#x20AC;˘ A systematic approach for measuring impact on learning is needed to provide long-lasting solutions

DESIRED OUTCOMES Conceive, design, implement and operate a

SO FAR Review of learning theories and concepts: active learning, learning design, conceptual understanding, spatial skills, intrinsic motivation

2

HANDS-ON EXPERIENCE

đ???đ???đ?&#x;?đ?&#x;? đ?&#x2019;&#x161;đ?&#x2019;&#x161; đ??&#x152;đ??&#x152; = đ???đ???đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x;?đ?&#x;? đ??&#x201E;đ??&#x201E;đ??&#x201E;đ??&#x201E;

5

Department ASM

PhD Candidate: Marta Gavioli Department: ASM Section: ASCM Supervisor: C Bisagni Promotor: C Bisagni Contact: mgavioli@tudelft.nl

HANDS-ON LEARNING DEVICE and measure improvements in studentsâ&#x20AC;&#x2122;

learning and motivation

Review of existing learning devices for structural mechanics

3

Strengthen the connection between mathematics and physics with engineering practice Gain insights in the learning process of structural mechanics Define measurable variables to evaluate the impact on learning of hands-on activities

RELEVANCE

Requirements Preliminary surveys and interviews to analyse environment requirements: â&#x20AC;&#x2DC;difficult topicsâ&#x20AC;? time on task lab and assignment frequency Initial device design development Development of quasiexperimental setups to field test the prototype

ENVIRONMENT

DESIGN-BASED RESEARCH METHODOLOGY â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

Students, teachers, universities, engineering education

4 Built

Continuous cycles of design, evaluation, and redesign Real-life learning settings where Grounding learning takes place normally KNOWLEDGE Aimed both at testing and refining Learning theories and also advancing practice theories,

Dolmans (2012). Building bridges between theory and practice in medical education using a design-based research approach Medical Teacher Hevner (2007). A three cycle view of design science research.

Scandinavian Journal of Information Systems.

Experience and Expertise

RIGOUR

Field testing DESIGN

A supportive learning device

Evaluate

Addition to knowledge

23

Department ASM

Structural Optimisation with Local Constraints

PhD Candidate: Zhi Hong Department: Aerospace Structures & Materials Section: Aerospace Structures & Computational Mechanics Daily supervisor: Sergio Turteltaub Promotor: Chiara Bisagni Contact: z.hong@tudelft.nl 1 2 3 4

Introduction

Optimisation of large scale structures is a challenging engineering problem. The complexity increases drastically when local constraints are considered. To tackle such a problem, the methodology involves how to formulate the problem numerically and then how to solve it effectively. In this research, we investigate the sizing optimisation with local stress constraints and gradient constraints. Since stress constraints are directly linked to the safety of a structure, we should consider them when optimising for the minimum weight. We also find that the optimal solution can be very strange in shape. Thus to make the optimal structures manufacturable, we consider the gradient constraints in this work as well.

Key techniques

Procedure

The core of the research is to design a method which minimizes the weight of a structure with local stress constrains and manufacturable shape.

Important factors: • feasibility • manufacturability • efficiency Approach: (efficient, cheap) • Convex optimisation • Conservative convex separable approximations (CCSA) Algorithm: (iterative, insensitive to size) • Interior Point Method • Finite element method (FEM) • Adjoint method

24

1. Analysis 2. Approximation 3. optimisation

Figure 1: flowchart of optimization procedure

Numerical case: Optimisation with stress constraints:

Figure 2: optimal thickness for beam Figure 3: optimal thickness for plate

Conclusion We have investigated a new method for large scale optimisation under stress constraints/gradient constraints in the research.

The conclusion we have is : Number of iterations in the interior point method is stable regardless of the problem size The gradient constraints can be used to control the regional shape of the optimal design Optimisation with both stress and gradient constraints will be attempted afterwards.

Optimisation with gradient constraints:

Figure 4: slope 1

References

Figure 5: slope 0.1

Figure 6: slope 0.01

[1] Haftka R T, Gürdal Z. Elements of structural optimization[M]. Springer Science & Business Media, 2012.

[2] Petersson, J. and Sigmund, O., 1998. Slope constrained topology optimization. International Journal for Numerical Methods in Engineering, 41(8), pp.1417-1434..

[3] Svanberg K. A class of globally convergent optimization methods based on conservative convex separable approximations[J]. SIAM journal on optimization, 2002, 12(2): 555-573.

Department ASM

25

Department ASM

Novel Aerospace Materials (NovAM)

Head of section: Prof.dr.ir. Sybrand van der Zwaag

26

Supervisors Dr. ir. Gillian Saunders Dr. Santiago Garcia Prof.dr. Theo Dingemans Dr. Wei Xu Prof. dr. Pim Groen Prof. dr. ir. Sybrand van der Zwaag

Department ASM

The NovAM group was established in 2003 in order to provide the faculty of Aerospace Engineering at the TU Delft with a group dedicated to the development of novel aerospace and space materials.

The group is truly international in composition and strives to be world leading in the fields of selected expertise; self-healing materials, smart composites and coatings, nanostructured polymers and metals by design. In our research we explore unconventional approaches, focus on fundamental concepts but also develop successful concepts to a level suitable for absorption by the industry.

We have a strong track record in preparing our students for a further career in academic or industrial research.

PhD Candidates:

-- Mariana Leandro Cruz -- Vincenzo Montano -- Wouter Vogel -- Hao Yu -- Vincent Stuber -- Nicolas Habisreutinger

27

Department ASM

PREFER: Professional Roles and Employability for Future EngineeRs Problems

% respondents who agree that new hires are adequately prepared for the labour market 80%

Skills mismatch

74%

60%

PhD Candidate: Mariana Leandro Cruz Department: AE Structures & Materials Section: NOVAM Supervisor Dr.ir. Gillian N. Saunders Promotor: Dr. W.A. Groen Contact: m.leandrocruz@tudelft.nl 1

2

3

4

PREFER consortium

40% 20%

35%

Mission

0%

Employers

Education providers

Lack of self-awareness

 Reduce the skills mismatch in the field of engineering  Help engineering students/graduates with identifying their strengths and weaknesses  Provide students with opportunities to actively explore the wide variety of engineering roles in the labour market

Goal

“What kind of an engineer am I and do I want to be?”

 Create new curriculum elements that focus on the acquisition of a particular set of transversal skills

What transversal competences are needed to reduce the gap between the labour market requirements and engineering graduates’ competences?

28

Competence selection:  Communication  Entrepreneurial  Innovation  Lifelong learning  Teamwork

Levels description per criteria (Level 0 to 3)

Define competences and criteria based on:  Siemens competence model  Literature review

Utility of rubric:  Assessment tool: what competence level do students hold pre and post-course?  Industry questionnaire: what competence level should students hold at BSc and MSc graduation?

(Submitted) Leandro Cruz, M., Saunders, G.N., and Groen, W.A. (2017) Competence Level Assessment of Communication, Innovation, Lifelong Learning and Teamwork in Engineering Education.

Which learning elements effectively support student developing or improving transversal competences?

Student survey after communication activity

Developed and currently implemented Communication activity based on Chinese whispers Role A

Role B

Role C

Access to image (10min) Can only describes verbally the image to role B (2 min)

Receives the verbal description (2 min) and cannot ask questions to role A Can only respond verbally to role C (10 min)

Can only ask questions to role B (10 min) Have to draw the image given to role A (2 min)

Lifelong learning through reflections Reflection on:  Course contribution to student’s future career  Strengths and points to improve when working in teams  Competences acquire or develop in the course

Please indicate how was your communication skills in this activity?

What do you feel you can improve?

Do you feel that this activity helped you to understand the importance of communication?

Department ASM

Self-healing brush polyurethanes: effect of dangling chain length on healing, crystallisation and mechanical properties

Vincenzo Montano Novel Aerospace Materials (NovAM) Self-Healing Materials Supervisor: S.J. Garcia Promotor: S. van der Zwaag v.montano@tudelft.nl 1

2

3

4

Introduction Brush polymers are densely grafted polymer chains in which the physical and conformation state is ruled by interactions (friction and/or steric repulsion) among dangling chains. These systems have been reported to harness stimuli responsive properties. In brush polyurethanes we address the potential of two coexistent supramolecular features (hydrogen bonding + dangling chain interactions) to exploit intrinsic self-healing property. The length of alkyl dangling chain is systematically tuned to investigate the effect of polymer architecture on the healing, crystallisation and mechanical properties.

Methodology

HDI_C8DA Selection of isocyanate and stepgrowth polymerisation

Synthesis of novel branched diols

HDI

29

IPDI

Preliminary results Tuneable thermal/mechanical properties and melt flow behaviour

High molecular weight Polymer

Mn (kDa)

Mw (kDa)

PI

HDI_C4DA

42

72.4

1.7

HDI_C8DA

24.5

32.1

1.3

HDI_C12DA

19.8

32

1.6

IPDI_C8DA

17.1

23.6

1.4

Tuneable crystallisation behaviour HDI_C8DA

No crystallisation at Î&#x201D;T = 10°C/min

HDI_C12DA

đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;*,-'( = 37 °C at Î&#x201D;T = 10°C/min

HDI_C12DA

HDI_C8DA

đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;" = 28 °C + = 13 MPa đ??şđ??ş$%&'()*

đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;" = 40 °C + = 95 MPa đ??şđ??ş$%&'()*

IPDI_C8DA

đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;" = 71 °C + = 350 MPa đ??şđ??ş$%&'()*

Verification of the healing property

Full recover of pristine tensile mechanical properties

Department ASM

PhD Candidate: Wouter Vogel Department: ASM Section: Novel Aerospace Materials Supervisor: Theo Dingemans Promotor: Theo Dingemans Contact: w.vogel@tudelft.nl

All-aromatic hyperbranched PAEK Linear vs. Hyperbranched HO

O

O

O

MW

Tg

OH n

Linear PAEK Linear Functional groups 2 Processing Hard Solubility Very low Mechanical High properties

Hyperbranched Many Easy High Very low HO

Introducing mechanical properties:

O

F O

Brittle to flexible

O

4-PEP reactive end-groups

O

O

F

F O

O O

F

F O

O

O

O

O O

O

F

O

F

O

O

F

F O

O

O

O F

O

F

O O

F

O

F

O

A. N. Keith

&

S. Sheiko

O

Membrane applications:

F

- Solution processable - Highly functional/tuneable - High Tg (250 °C) - Large free volume (9.5%) - Selectivity at high T

O

DMTA

Crosslinking

F

Hyperbranched PAEK

O

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30

4th year PhD

Tensile E. Maaskant

&

N. E. Benes

Department ASM

PhD Candidate: Hao YU

On the Cobalt – Tungsten/Chromium balance in martensitic creep resistant steels

Department: ASM Section: NoVAM Supervisor: Wei XU Promotor: Sybrand van der Zwaag Contact: H.Yu-1@tudelft.nl 1

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Introduction Novel martensitic creep resistant steels strengthened by Laves phase and M23C6 precipitates have been developed in former works. By alloying with a high level of Co, the coarsening kinetics of the conventionally-considered detrimental precipitates can be remarkably improved. In the present work, the characteristics of Laves phase and M23C6, such as volume fraction, coarsening rate and precipitation strengthening factor in the newly designed alloys are compared computationally with the existing Co-containing counterparts. The Co effects on precipitation characteristics are investigated systematically. The alloying elements which are sensitive to Co variations are identified. The binary analyses of Co-M balance show that Co-W are highly coupled in Laves strengthening system and W can partially replace Co to yield the same precipitation strengthening. For the M23C6 strengthened alloy, Cr shows a strong effect by Co and hence a high Co concentration is necessary for a high creep resistance.

Model validation

Design Methodology

Translator: Property to microstructure High strength and high stability

Martensitic matrix + Laves/M23C6 precipitate

Creator: microstructure to quantifiable criteria

Novel Aerospace Materials

Microstructure • High strength • High stability • Oxidation, corrosion resistances

1.

 p  1/ L

strength and stable at high temperature

2. Limited undesirable phases

Ms>250oC to obtain martensitic matrix

f p / 3 r03  Kt

fp / r

In which

r0  2 / Gv

Go/No-go criteria

Matrix: Martensite for high

K  8 Vm

p

.

9( x p  ximp )2 /  mpi i 1 xi Di / RT n

L : inter-particle spacing fp: volume fraction of precipitates r0: initial size of precipitates K: coarsening rate of precipitates

Cprimary carbie<0,5% no ferrite

At austenization temperature At ageing/service temperature (such as AlN, Laves phase, MU)

Precipitation Strengthening factor

Optimisation: get a best solution from the solutions satisfying all go/no-go criteria

Cundersirable phase<1% Csigma<4%

3. Cr2O3, Cr3O4 Oxide film

Ccr,matirix>11% to form Cr2O3, Cr3O4 oxide film

At ageing/service temperature

Tab. Composition (in wt.%) of newly designed alloys C

Cr

Ni

W

Co

Nb

N

V

Mo

Ti

Al Taus/oC

LavesW 0.001 10.84 3.23 10.00 10.00 0.32 0.03 0.001 0.00 0.11 0.001 1239

Cu particles

Compared with existing grades

Ni3Ti

Calculated by Thermocalc

M23C6W 0.15 16.00 0.01 1.61 10.00 0.001 0.006 1.00 0.00 0.01 0.001 1069

Results Co-W binary effect in LavesW

The effect of Co element

Co-Cr binary effect in M23C6W

Conclusion    

The newly designed alloys remarkably outperform the existing alloys.

Co effects: precipitation strengthening contributions inevitably degrade as the Co alloying decreases.

In LavesW alloy, Co can be partially replaced by W to yield the same precipitation strengthening level.

In M23C6W alloy, Co plays an irreplaceable role.

References:[1]Hao Yu, Wei Xu, Sybrand van der Zwaag. Steel Research International, 2018;89.

[2]Qi Lu, Wei Xu, Sybrand van der Zwaag. Metallurgical and Materials Transactions A. 2014;45:6067-74

31

Department ASM

PIEZOELECTRICS

PhD Candidate: Vincent Stuber Department: Aerospace Engineering Section: Novel Aerospace Materials Supervisor W.A. Groen Promotor: W.A. Groen Contact: V.L.Stuber@tudelft.nl

&

ENERGY HARVESTING

1

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OBJECTIVE More and more embedded wireless sensor systems are employed into our daily lives (e.g. medical implants, structural health sensors), normally powered by batteries.

Batteries contain limited energy, pollute the environment and are the volumetric biggest component of a sensor system. Using energy harvesting, batteries could be eliminated, which could be achieved using piezoelectrics; harvesting energy from vibrations. One of the major challenges is to move from brittle ceramics to flexible materials. In our vision, this can be achieved by moving to piezoelectric composites.

0-3 composite

Quasi 1-3 fibre composite

(top view & cross section)

(top view & cross section)

RESULTS

Piezoelectric composites

Piezoelectric ceramics

50 Vol.% 0-3 KNLN-particles in PDMS

Flexible, lightweight, environmentaly friendly

Brittle, heavy, toxic

Quasi 1-3 fibre composite

THEORY

Unable to manufacture

Ceramics

6 Vol.% quasi 1-3 KNLN-fibres in PDMS

0-3 composite

Polymer matrix Piezoelectric ceramic filler

Ceramic powder

Sintered ceramic

- Very high properties - Brittle - Heavy

ENERGY OUTPUT Dice & fill

Dielectrophoresis

Mechanical input:

F

- 10 N static load - 1, 3 & 10 N dynamic load - 1 Hz frequency 50 Vol.% 0-3 KNLN-particles in PDMS (high d33 = ‘high’ current)

32 Random (0-3)

- Easy manufacturing - Low properties at low volume fractions

Structured particles (quasi 1-3)

Structured fibres (quasi 1-3)

- Good properties at low - High properties at low volume fractions volume fractions - Anisotropic - Anisotropic - Fibre fabrication challenging

OUTPUT

Piezoelectric composite

(Voltage, Current)

6 Vol.% quasi 1-3 KNLN-fibres in PDMS (high g33 = ‘high’ voltage)

Aligned pillars (1-3)

- Very high properties - Anisotropic - Hard to manufacture - High stiffness in direction of pillars

MATERIAL COMPARISON

EXPERIMENTAL 50 Vol.% 0-3 KNLN-particles in PDMS

6 Vol.% quasi 1-3 KNLN-fibres inPDMS

CONCLUSIONS -Piezoelectric composites and ceramics can be manufactured with similair energy harvesting potential.

-Stiffness can be lowered significantly by moving to composite systems KNLN particles

KNLN fibres

-d33 relates to current, g33 relates to voltage, and so d33g33 relates to power

Polyimide-linked Covalent Organic Framework used for Lithium- and Sodium-ion batteries.

6 months of PhD Aerospace Structures and Materials Novel Aerospace Materials Prof. Atsushi Nagai Prof. Sybrand Van der Zwaag n.c.p.habisreutinger@tudelft.nl

Department ASM

Nicolas Habisreutinger

What is the common point ?

Polyimide-linked Covalent Organic Frameworks

33

Applications for Lithium and Sodium Batteries Lithium Batteries

Sodium Batteries

Many thanks to the PhD candidate Remco Van der Jagt and Alexandros Vasileiadis and Prof.

Marnix Wagemaker for their work on the electrochemistry part of this work.

Department ASM

Structural Integrity Composites (SI&C)

Head of section: Prof.dr.ir. Rinze Benedictus

34

Supervisors Prof. dr. ir. Rinze Benedictus Dr. Roger Groves Dr. Hans Poulis Dr. Irene Fernandez Villegas Dr. Sofia de Freitas Dr. ir. RenĂŠ Alderliesten Dr. ir. Otto Bergsma Dr. Calvin Rans Dr. Marcias Martinez Dr. ir. Sonell Shroff Dr. ir. Dimitrios Zarouchas

Department ASM

The mandate of the Structural Integrity & Composites group is to pursue research which will enable the aerospace products of tomorrow to meet rising performance demands by exploiting the potential synergy of Material Science, Structural Design, and Production Technologies. This goal requires the integration of various engineering disciplines and mixture of both fundamental scientific and practical engineering approaches to research.

What does this mean? It means that our group recognizes that the future performance demands of aerospace products, dictated primarily by weight, cost, structural performance, and safety, cannot be met through advances in material science, manufacturing technology, or structural design alone. In order to build the most advance and technically capable aircraft of the future, the aerospace engineer of tomorrow must be able to exploit the potential synergy between each of these areas. It is our mandate to facilitate this through our research efforts.

PhD Candidates: -- Tian Zhao

-- Pedro Ochôa -- Eirini Tsiangou -- Cornelis de Mooij -- Hongwei Quan -- Nicolas Lavalette -- Fabrício Ribeiro -- Bram Jongbloed -- Nakash Nazeer -- Jesse van Kuijk -- Ioannis Tsakoniatis -- Yuzhe Xiao -- Chirag Anand -- Ozan Çelik -- Nick Eleftheroglou -- Romina Fernandes -- Julian Kupski -- Vincentius Ewald

35

Department ASM

36

Sequential Ultrasonic Spot Welding of Thermoplastic Composites

PhD Candidate: Tian Zhao Department: ASM Section: SI & C Daily supervisor: Irene F. Villegas Promotor: Rinze Benedictus Contact: T. Zhao@tudelft.nl 1

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3

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1. Background and Motivation

2.1 Results

2.2. Multi-spot welded joints

Thermoplastic composites (TPCs) are increasingly applied in aircraft structures due to their excellent mechanical properties and rapid and cost-effective manufacturing process. For instance, they can be assembled by some fast and lowcost techniques, like fusion bonding or welding.

Ultrasonic welding is an attractive joining technique for TPCs, owing to the short welding times, absence of foreign materials at the weldline, possibility for insitu monitoring. It is a typical spot welding technique. Therefore, in order to join large structural components with this technique, the most straightforward way is using sequential ultrasonic spot welding, similar to the application of mechanically fastened joints. This research aims at developing the robust welding strategy to consistently produce ultrasonically spot welded joints and investigating the mechanical behaviour of the welded joints in comparison to that of the mechanically fastened joints.

2.1. Single-spot welded joints

• Strength of the welded joints was improved by increasing the spacing between welded spots and the number of the welded spots.

Thermoplastic composites in aircraft

•

•

Joint strength achieved by the singlewelded, double-lap joints was found comparable to that of the mechanically fastened joints with the same size.

Microscopic inspection indicated the failure of welded joints just took place in the upper-most ply but severe delamination was observed within the laminate assembled by mechanically fastened joints.

Comparison of the load-carrying capability between welded and mechanically fastened joints with double-lap configuration.

• Welded spots manufactured by sequential ultrasonic spot welding showed similar welded areas and consistent weld quality.

•

The multi-spot, single-lap welded joints achieved comparable strength compared to the mechanically fastened joints assembled with multi-fasteners.

Improvement of the joint strength by increasing the distance between spots.

(a) Spot welded joint Consistent weld quality achieved by ultrasonic spot welding

Bearing damage

Delamination

(b) Mechanically fastened joint Comparison of the damage affected zone between welded and mechanically fastened joints.

Ultrasonic spot welding

Comparable joint strength of multi-spot welded and mechanically fastened joints

P.A.ViegasOchoadeCarvalho@tudelft.nl Department: ASM Group: SI & C Supervisor: Dr. Roger Groves Promotor: Prof. Dr. Ir. Rinze Benedictus

1. Talking to the structure

6. GW measurements

Structural health monitoring (SHM) can be used to condition assessment without stopping its operation. Ultrasonic guided waves (GW) have a high potential for detailed quantitative damage diagnostic in composite structure.

Signals were acquired: a) before and after each impact; b) with and without high-amplitude, low-frequency structural vibration (LFV).

2. Becoming reality

Different boundary conditions at the moment of impact resulted in a realistic scenario with barely visible impact damage (BVID) of different severities. All different BVID accurately detected by the SHM system (Fig. 3). There was similar sensitivity to both large and small BVID.

Diagnostic must be reliable, and reliability must be proven. For that, SHM systems for composite primary structures must go through certification. It implies demonstration of mission accomplishment in different scenarios, with multiple variability factors.

This cannot be achieved without full-scale testing.

Department ASM

Ultrasonic health monitoring of full-scale composite structures

PhD Candidate (4th year): Pedro Ochôa

7. Results

3. Objectives The objectives of the test campaign on a full-scale ultrasonic GW based SHM system were to a) study diagnostic accuracy in realistic full-scale BVID scenarios, b) validate a novel procedure for consistent transducer network design, c) study structural vibration effects on GW signals, and d) study evolution of transducer network condition in a realistic succession of impacts and vibrations.

4. Test specimen Full-scale stiffened panel of a thermoplastic composite horizontal stabilizer torsion box (Fig. 1).

Fig. 3. Damage index for each state at each tested frequency.

Accumulation of successive BVID was detected and quantified using a sparse transducer network (Fig. 4), without requiring a transducer placement optimization for each different damage scenario.

37

Fig. 4. Damage index for sparse transducer network.

Effect of LFV on GW was the appearance of coherent noise in the filtered signals (Fig. 5).

Fig. 1. Ultrasonic wave guiding mechanism.

5. Impact damage Impacts of 50 J along the stringers, on the outer side of the skin, at the locations highlighted in Fig. 2.

Fig. 5. Filtered signal without LFV (top) and with LFV (bottom).

The transducer condition was barely affected by the impacts or the low-frequency vibration.

8. Conclusions 



Fig. 2. Impact locations on areas 1 and 2 (left) and area 3 (right)



The novel procedure for consistently designing the transducer network has the potential for maximizing the diagnostic effectiveness of an ultrasonic GW based SHM. It seems possible to analyse LFV effects on GW signals under the assumption of a permanently corrugated structure subjected to static stress.

These findings contribute to the reliability improvement of ultrasonic GW based SHM systems.

Background Thermoplastic composites (TPC) are increasingly being used in the aerospace market due to their excellent properties such as high impact toughness, reformability and cost-effetive manufacturing. However, as a single material for every application does not exist up-todate, the use of both thermoplastic and traditional thermoset composites (TSC) inevitable.

The co-existence of these dissimilar materials gave rise to a new issue; the efficient joining of the different parts, such as wings, spars, ribs and stiffeners. From the author’s point of view, welding is the most suitable technique as it does not require drilling holes, like in the case of mechanical fastening, or any surface pre-treatment as in adhesive bonding.

PhD Candidate: Eirini Tsiangou Department: ASM Section: SI & C Supervisor: I.F. Villegas & S.T. de Freitas Promotor: R. Benedictus Contact: e.tsiangou@tudelft.nl 1

2

3

 Interphase

4

 Mechanical Testing 45 40 35 Average LSS (MPa)

Department ASM

Ultrasonic Welding of Thermoplastic to Thermoset Composites

30 25 20 15 10 5

Welding (Fusion Bonding)

0 ED case

Fusion bonding, or welding, of composites is a technique that allows two thermoplastic materials to be joined together by exploiting the thermoplastics’ ability to be reformed after they are subjected to temperatures above Tg for amorphous polymers or Tm for semicrystalline polymers. A novel approach suggests the utilization of welding procedures in the bonding of TP to TS composite materials using a compatible, thermoplastic layer (coupling layer or CL) co- cured to the thermoset substrate.

Fig. 4: Interphase between T800S/3911 and PEI

ED-less-60

ED-less-250

Fig. 5: Lap shear strength vs welding case

 Fracture Surfaces

PEI

TP coupling layer

TSC CF/epoxy Fig. 1: Schematic of TP layer co-cured on a TSC (left) and SEM images of the

interphase between M18/1 43% G939 and PEI (right)1

Ultrasonic Welding

38

Ultrasonic welding is based in high frequency and low-amplitude mechanical vibrations.

The two parts to be welded are pressed together, vibrations are applied and concentrated heating is generated at the interface with the help of asperities that are made of the same material as the thermoplastic matrix, known as energy directors (EDs).

Fig. 6: Fracture surfaces of ED (upper), ED-less20 case (middle) and ED-less250 welding case s(bottom)

Fig. 2: Ultrasonic welding setup2

TPC

ED

TSC

TP coupling layer

ED Case

EDless50 Case

o Fully welded areas o No interphase failure o First ply failure mostly in

o Unwelded areas o Failure at the interphase o Failure in epoxy resin and

CF/PEI with partial CF/epoxy failure o No thermal degradation of epoxy or PEI

Fig. 3: Schematic of TPC to TSC ultrasonic welding apparatus

Energy director-less vs. Traditional welding  Objective For ultrasonic welding a TP ED is necessary at the interface to generate heat. For welding TSC a neat TP layer already exists, i.e. the TP coupling layer co-cured on the TS laminate.

The question lays in if an extra ED is still needed or if the neat co-cured TP layer is sufficient to guarantee a good weld. Three welding cases were explored, namely ED case, EDless50 case and EDless250 case.

 Materials TS adherent

TP adherent

Coupling layer

ED

CF/ epoxy

CF/PEI

250 μm or 50 μm PEI film

250 μm PEI film

PEI layer. No failure in the CF/PEI adherend o Thermal degradation of PEI layer

EDless250 Case o Fully welded areas o No interphase failure o First ply failure mostly in

CF/PEI with partial CF/epoxy failure o Degradation signs of PEI at the hot edges

Conclusions and Future Work Loose ED is necessary to efficiently promote preferential heat at the interface. Limited friction in the EDless cases fails to promote heating of the CL and hence overheating of the adherents occurs. Also, flow of the ED makes the heating distribution uniform and hence, thermal degradation can be prevented. s.

Next step is to weld CF/PEEK through the PEI coupling layer with the final goal being the manufacturing of a CF/epoxy skin with welded CF/PEEK stiffeners.

CF/epoxy skin

CF/PEEK stiffeners

Fig. 7: Schematic of stiffened panel

References: Van Moorleghem R. (2016). Welding of thermoplastic to thermoset composites through a thermoplastic interlayer. Master thesis Palardy G and Villegas I. F. (2016). On the effect of flat energy directors thickness on heat generation during ultrasonic welding of thermoplastic composites. Composite Interfaces, 24 (2): 203-214

Introduction An inverse finite element (iFEM) algorithm was developed for shape sensing. It estimates the full displacement field based on a limited amount of sensor data from a cantilever plate, bent by a point load. The aim is to find an accurate global strain distribution with as few sensors as possible.

Method The iFEM algorithm compares numerical displacements and/or strains to corresponding (simulated) measurements.

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4

Department ASM

Shape Sensing With The Inverse Finite Element Method

PhD Candidate: Cornelis de Mooij Department: ASM Section: SI&C Supervisor: Prof. Dr. Marcias Martinez Promotor: Prof. Dr.ir. Rinze Benedictus Contact: C.deMooij@tudelft.nl

The displacement sensors were placed semi-randomly, avoiding clustering. This results in most sensors being near the edges.

Result FEM and iFEM results are shown below. This iFEM analysis used 10 sensors. The maximum deflection has an error of 0.17%, compared to 0.4% with 28 sensors (Kefal et al.

2016). The distortion on the left is due to the optimization focussing on areas with large displacements.

39 The iFEM error functional is based on the squared differences between the numerical and the measured values: đ?&#x161;˝đ?&#x161;˝ = đ??śđ??śđ?&#x2018;&#x2019;đ?&#x2018;&#x2019; đ?&#x2019;&#x2020;đ?&#x2019;&#x2020; â&#x2C6;&#x2019; đ?&#x2019;&#x2020;đ?&#x2019;&#x2020;đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;

2

+ đ??śđ??śđ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2019;đ?&#x2019;&#x2019; â&#x2C6;&#x2019; đ?&#x2019;&#x2019;đ?&#x2019;&#x2019;đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;

2

đ??śđ??śđ?&#x2018;&#x2019;đ?&#x2018;&#x2019; and đ??śđ??śđ?&#x2018;&#x2018;đ?&#x2018;&#x2018; are weight factors, đ?&#x2019;&#x2020;đ?&#x2019;&#x2020; and đ?&#x2019;&#x2019;đ?&#x2019;&#x2019; are numerical strains and displacements and đ?&#x2019;&#x2020;đ?&#x2019;&#x2020;đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; and đ?&#x2019;&#x2019;đ?&#x2019;&#x2019;đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; are sensor data. The error functional is minimized by taking the derivatives with respect to the structural degrees of freedom. The resulting system of linear equations is solved with Gaussian elimination, similar to the classical FE approach. The strains are then calculated from the displacements.

Set-up The plate consists of a clamped aluminium cantilever plate under a point bending load as shown in the figure below.

Over-all Result The error is plotted against the number of sensors:

Sometimes a highly inaccurate result occurs, but even with few sensors, an accurate estimate can be obtained.

Conclusion Compared to literature results, this approach can achieve greater accuracy with fewer sensors. There is a lack of reliability, likely due to the random sensor placement.

Future work will apply the method to more complicated structures/loads and geometric nonlinear problems.

Department ASM

On the influence of plasticity during fatigue damage growth

PhD Candidate: H. Quan Department: ASM Section: SI&C Supervisor Dr.ir. R.C. Alderliesten Promotor: Prof.dr.ir. R. Benedictus Contact: H.Quan@tudelft.nl 1

1. Background

2

3

4

5. Work and results so far

Although the linear elastic fracture mechanics(LEFM) has been used to deal

The fatigue experiments are carried out with 2024-T3 CCT specimens.

with fatigue damage growth in engineering problems successfully for

The experiments are force controlled at various load ratios.

decades, LEFM still fails to reveal some physical nature of fatigue damage growth phenomena. Plasticity has been identified as an important contributor during the fatigue damage propagation. Hence, if we want to have a deeper understanding of fatigue damage growth, especially for the cases of variable loading of ductile materials, we have to figure out how plasticity in assorted materials influence the fatigue damage growth.

2. Basic hypothesis:

Fig 1 The fatigue experiment

Fatigue damage growth in various materials(metals, adhesive materials, composites, etc.) ought to be described based on the same energy principles: the work done by external force is equal to the sum of the new

Some extensometers are used for measuring the displacement fields of the specimens.

crack surface forming energy, the change in elastic strain energy and the plastic dissipated energy.

40

3. Research objectives: Identify and quantify the contribution of plasticity to the energy dissipation during fatigue damage propagation.

Figure out the relation between plastic dissipation and fatigue damage growth rate(da/dN)

4. Research methodology This project will contain both experimental work and numerical work. In the experimental part, fatigue experiments for adhesively bonded joints and

Extensometers in other locations to measure the displacement field

Fig 2. The extensometer locations

Fig 3. Hysteresis loops measured in different fatigue cycles

From the experimental data the strain field near crack tip could be obtained. And it is possible to make a comparison between the FEM results and experimental displacement data to validate the simulation.

Good agreement has been shown.

metals will be carried out. The values needed to be measured in the fatigue tests are as follows: â&#x20AC;˘ The fatigue damage growth rate da/dN

crack tip location

â&#x20AC;˘ The load-displacement hysteresis loops during fatigue experiments â&#x20AC;˘ The strain field especially in the plastic zone will be measured with DIC In the numerical part the corresponding fatigue tests will be simulated through FEM in order to obtain the plastic dissipation during fatigue damage growth. The simulation results will be validated by the strain or displacement fields measured during the fatigue experiments.

Fig 4. Strain field around crack tip measured from the experiment

Fig 5. Displacement values around crack tip area with 15.5mm length crack (experiment .VS. FEM)

Motivation

Department ASM

Design and Optimization of Carbon Fiber-Reinforced Composite Trusses

PhD Candidate: Nicolas P. Lavalette Department: ASM Section: SI&C Supervisor O.K. Bergsma Promotor: R. Benedictus Contact: n.p.lavalette@tudelft.nl 1

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Problematics

The goal of this research project is to develop a new kind of internal structure for applications in aerospace, by using unidirectional carbon fiber-reinforced polymer pultrusions as members of a truss.

By combining the high axial strength and low density of CFRP with the axially loaded nature of trusses, it is expected to obtain new structures that are lightweight while still being able to carry high loads.

• Current truss optimization methods do not consider the strength and weight of the joints as an optimization variable.

• Due to the small scale of the structure and the nature of the members, an efficient joint design is not straightforward.

• A model able to predict the strength of the joints for their respective configuration is needed.

Truss Optimization

?

Joint Design

Results: Preliminary Joint Design

Current work: Parameter Study

Adhesive bonding is chosen as the preferred joining method, transferring the load from the CFRP members to the aluminum joint piece through shear. Several joint designs are modelled:

FEM analyses are carried on a model of the J3r joint design, with varying dimensions (adhesive overlap length; adhesive thickness; joint thickness; member diameter), and the evolution of the joint strength is determined for each dimension's variation.

J1

41

J2s J2r J3s J3r

Joint samples are tested under tensile loading until failure, and the results obtained are used to validate those obtained through FE analysis.

3000

The performances of the joint designs are determined by comparing their respective failure load and weight. Between J3s and J3r, which have the best strength-to-weight ratio, the roundbased version comes on top due to its shape, easier to manufacture.

Fmax = 2.25 kN ± 23.0%

Force [N]

2500 2000

Sample 1

1500

Sample 2 Sample 3

1000

Sample 4

500

600%

529%

519%

500%

0

% of J1

200

400

600

800

1000

Displacement [m]

400% 286%

300% 100% 100%

303%

100%

Failure load [% of J1] Volume of the central joint piece [% of J1]

179%

173%

200% 100%

Sample 5

0

105%

0% J1

J2s

J2r

J3s

J3r

 Lavalette NP, Bergsma OK, Zarouchas D, Benedictus R (2017) Comparative study of adhesive joint designs for composite trusses based on numerical models. Appl Adhes Sci.

Once a good model is obtained, it can be used in an optimizer to determine the optimal dimensions (i.e. minimizing the total weight) required for a specific axial load.

Future work • Joint Design: manufactured vs. 3D printed joints; fatigue behavior; joint shape refinement.

• Truss Optimization Algorithm: joint efficiency and weight; physical limitations due to the size of the joints.

Department ASM

Understanding Variable Amplitude Fatigue Effects on Composite Bonded Joints

PhD (last year): Fabrício N. Ribeiro Research Group: ASM - SI&C Supervisors: Dr. Marcias Martinez Dr. Calvin Rans Promotor: Prof. Dr. Ir. Rinze Benedictus E-mail: F.Ribeiro@tudelft.nl

1. Variable Amplitude Fatigue

5. Results

Variable amplitude fatigue (VAF) loading is known to cause change on the crack growth rate within diverse materials. In metals, it is known that overloads decelerate crack growth due to crack tip plasticity effects.

In an adhesive, it is still not clear how this process occurs so VAF loading can lead to inaccurate structural life prediction.

The average disbond growth rate (da/dN) is obtained from the strain measured by the clip gauge1.

2. Objective Fig. 3 - Disbonds growing around the central cut plies.

Evaluate the presence of VAF loading effects on the damage evolution of bonded joints and improve the understanding of the disbond growth mechanism.

3. Methodology and Materials Experimental campaign using the Central Cut Plies (CCP) specimen.

The CCP specimen presents a mode II loading condition (shear), typical from bonded joints. Under constant amplitude tensile loading, the specimen presents a constant disbond growth. By changing the amplitude of the applied load, VAF effects can be observed.

Fig. 4 - Strain data from clip gauge obtained for LO-HI and HI-LO studies.

Materials comprised of:

 Unidirectional Glass Fiber Reinforced Polymer (GFRP)  Epoxy adhesive film (co-cured)

42 Fig. 5 - Results for LO-HI and HI-LO transitions with 30% load change.

Fig. 1 - Side view representation of the CCP specimen.

6. Discussion The block loading transition causes the disbond growth rate to accelerate or decelerate for a period of time. The length of disbond (Δa) observed during this period gives more information about the fracture mechanism.

The longer or shorter fracture process zone size will affect the disbond growth rate after transition.

Fig. 2 - Test setup at the MTS fatigue machine.

4. Block Loading The simplest way to study VAF effects is to evaluate two-block loading.

The effects can be observed when the load changes from a lower load block (LO) to a higher load block (HI) or vice versa.

Fig. 6 - Fracture process zone size for different loads.

7. Conclusions  The current approach allowed the observation of block loading fatigue effects on bonded joints.

 Mode II fatigue disbonding presents:  Disbond acceleration after LO-HI transitions;  Disbond deceleration after HI-LO transitions.

Baseline load: Pmax = 25.0 kN, R = 0.1, f = 4.2 Hz. Block Loading: HI = 30% load increase, LO = 30% load decrease.

All studied cases had constant load ratio and constant strain rate.

 The block loading effects are observed during a corresponding length which is related to the fracture process zone ahead of the disbond tip.

1

Ribeiro F.N., Martinez, M., Rans, C., The Journal of Adhesion (2018).

Department ASM

43

Department ASM

Structural Health Monitoring of Adaptive Aerospace Structures

PhD Candidate: Nakash Nazeer Department: ASM Section: SI&C Supervisor: Dr. R.M. Groves Promotor: Prof.dr.ir. R. Benedictus Contact: N.Nazeer@tudelft.nl 1

2

3

4

Objective To design and develop a smart sensing-system for load monitoring, shape sensing and damage detection on a morphing wing structure.

Introduction Wing-morphing technology is undoubtedly the future of aircraft design. Taking inspiration from birds, these wings are multi-role structures that can change their shape in order to fulfill different mission requirements during flight. In other words, a single flexible structure that delivers the desired motion by undergoing elastic deformations.

44

There is a growing need for active inflight structural health monitoring of such wings with the advent of these technologies. The system needs to be light, accurate, fast and reliable. This project focusses on using Fibre Optic sensing technologies to build this system.

The yellow (reference) fibre and white (sensor) fibre coming out of the interrogator box represent each arm.

Sensing Principle

The ZonaSens uses a novel interferometer sensing principle to define zones between two Fibre Bragg Gratings (FBG). The slightest change in distance within a zone due to structural displacements or acoustic signals is calculated and retrieved with high speed nanoscale precision.

Experimental Setup

Interrogator Principle Measurements are carried out by the Optics11 ZonaSens interrogator, at the heart of which is a Michelson interferometer. A laser beam passes through a beam splitter which splits it into two identical beams. One beam is transmitted through while the other gets reflected, as shown in the schematic. Each beam travels down an arm of the interferometer at the end of which are mirrors that reflect them back to the beam splitter. The incoming beams merge together and the resultant is measured at the screen.

A 3-point-bend test rig is set up for preliminary tests.

The goal is to study the effect of structural displacement on a bonded fibre optic sensor using this principle.

PhD Candidate: Jesse van Kuijk Department: DASML Section: SI&C Supervisor: René Alderliesten Promotor: Rinze Benedictus Contact: J.J.A.vanKuijk-1@tudelft.nl

Background

Objective

•

•

• • •

In 1963 Paris and Erdogan proposed that fatigue crack growth rate follows a power law relationship; many proved power law models followed.

The power law model is phenomenological, and has no physical basis.

The energy balance of the specimen throughout one or more cycles could provide insight to a physically correct fatigue crack growth model, for both CA and VA fatigue.

Small crack growth is poorly understood, likely due to crack geometry differences.

Experimental setup

Department ASM

Interaction between stress ranges and stress ratios during variable amplitude fatigue crack growth

1

2

3

4

To gain better understanding of the physics behind fatigue crack growth.

• Find a link between the current crack growth rate prediction models used in industry, and energy-based crack growth rate models.

Several fatigue tests were done to generate crack growth data of various crack geometries. It will be used to investigate the suitability of the crack surface area as base parameter instead of crack length. This might reduce the uncertainties present in small crack data from literature.

First tests results • • • •

Overload markers produced very visible results.

Irregular crack front shapes: complicated geometry analysis. da/dN vs ΔK data still has high scatter in region of interest.

Slope of small crack curve fit can be compared to data of other crack geometries. da/dN graph da/dN ASTM smoothed da/dN my a smooting

• • • • • •

Top: Schematic drawing of the specimen with two corner cracks (black) near center hole.

Middle: Overload spectrum, 1 block of 1500 cycles.

Bottom: Crack developing shear lips.

CCT specimen, Al 2024-T3, through crack.

250 kN fatigue machine.

Digital camera (crack length).

Direct current potential drop (crack length).

Digital image correlation (displacement, strain).

Optical and scanning electron microscopes (fractography).

10 -5

da/dN [mm/cycle]

Fatigue test setup with DIC, DCPD, and digital camera.

10 -6

8

10

12

14

16

18

20

22

K [MPa√m]

Yellow bands denote overload markers.

Markers are clearly visible but irregularly shaped.

da/dN vs ∆K data of through crack test. Smoothing gives a distinct change of slope in the small crack regime.

Conclusions

Future work

The combination of specimen geometry, overload spectrum, and microscopy results show that crack growth rate and area investigation of the small crack regime can succeed given the obtained data quality.

Development of the energy based cycle-by-cycle crack growth model. This will be done in steps; one single cycle, CA spectrum, and VA spectrum. Future tests will have increased measurement accuracy.

45

Department ASM

Investigation of Fracture Toughness of Ultrasonically Welded Thermoplastics Background

Carbon fibre reinforced composites offer very high specific properties.

Thermoplastic composites specifically, are becoming increasingly popular to the aerospace industry due to their inherent properties allowing them to be easily reworked and reshaped. This may lead to repairable parts as well as lower manufacturing costs. Of particular interest is their ability to be welded which can greatly facilitate joining during aircraft assembly.

PhD Candidate: Ioannis Tsakoniatis Department: ASM Section: SI&C Supervisor: I.F. Villegas Promotor: R. Benedictus Contact: I.Tsakoniatis@tudelft.nl 1

2

3

4

Ultrasonic Welding

Ultrasonic welding is a relatively new and very promising technique for joining thermoplastic composites. Through a resonating system, mechanical vibrations at low amplitude and high, ultrasonic frequencies are applied to the substrates using a sonotrode. The polymer matrix rapidly heats up and melts due to interfacial friction and viscoelastic heating, creating the weld [2].

Fig. 2: Ultrasonic welding diagram [3] (left) and ultrasonic welds of different quality (right).

Fig.1: Airbus A350 structure schematic (left) and fracture mechanics modes (right) [1], [2].

The fastest of the welding techniques is ultrasonic welding. Latest research is focusing on ultrasonic welding of composites, yet the process is not fully characterized. The fracture mechanics of welds need to be studied in order to better understand them and fracture toughness is the starting point as well as a quantity of critical importance for design applications. Therefore the aim of this project is to investigate the fracture toughness of ultrasonically welded thermoplastics focusing on the first 2 fracture modes.

Small pieces of various geometries consisting of the matrix material are called energy directors and are added to the bonding surface to direct the heat. The parameters that control the process have such as the welding time force and amplitude have a great impact on the quality of the weld Ultrasonic welding can be static, formation of welded spots, or continuous, formation of welded seams. Both techniques are relevant to this project, as welding of large areas is essential.

Research Objectives

46

To satisfy the aim of this project there are 3 main research questions that need to be answered: 1.

2.

3.

How can the weld area be increased to measure fracture toughness ?

What is and what affects the mode I fracture toughness of welds?

What is and what affects the mode II fracture toughness of welds?

These main questions involve a number of sub questions relating the welding method (static/continuous) and parameters (welding force, amplitude, energy director) to the properties of the weld, i.e. quality, strength and fracture toughness.

Thus the objectives of this research can be formulated according to a time and logical order as follows. These objectives can be formulated into corresponding publications.

Fig. 3: Static (left) and continuous (right) ultrasonic welding machines in the welding lab.

Research Method

Fig. 4: PhD objectives summary

[1] Airbus website: http://www.aircraft.airbus.com/aircraftfamilies/passengeraircraft/a350xwbfamily/ [2]Ewalds H.L., Wanhill J. H., Fracture Mechanics, (1984) http://thediagram.com/12_3/thethreemodes.html [3] I. F. Villegas, B. Valle Grande, H.E.N. Bersee, and R. Benedictus. A comparative evaluation between flat and traditional energy directors for ultrasonic welding of cf/pps thermoplastic composites, Composite Interfaces, 22(8):717–729, 2015.

[4]G. Pallardy, I. F. Villegas. Smart ultrasonic welding of thermoplastic composites, Proceedings of the American Society for Composites - 31st Technical Conference, ASC , 2016.

[5]ACCIS website,http://www.accismultifunctional.com/research/investigation-of-novel-catalysts-for-use-in-selfhealing-polymers/ [6] O’Brien K.T., Johnston W.M., Toland G.J., Mode II Interlaminar Fracture toughness and Fatigue Characterization of a Graphite Epoxy Composite Material, (2010).

The research method decided for this project can be summarized in the following way chronologically: • Initial experiments to establish reference.

• Static ultrasonic welding of large spots will be studied to investigate effects of scaling • Lap Shear Strength (LSS) tests will be carried out alongside fractography to evaluate quality of welds • When the process of welding larger is optimized, fracture toughness test coupons will be produced • Mode I fracture toughness testing will involve Double Cantilever Beam (DCB) tests.

• Mode II fracture toughness testing will be performed using End Notch Flexure (ENF) tests.

Fig. 5: DCB test (left) and ENF test (right) [4,5].

Department ASM

Structural health monitoring of additive manufactured part with embedded fibre optic sensor

PhD Candidate: Yuzhe Xiao Department: ASM Section: Structural integrity & composites Supervisor: Calvin Rans Promotor: Rinze Benedictus Contact: y.xiao-1@tudelft.nl 1

2

3

4

Introduction

Literature review

Research objectives

Additive manufacturing (AM) is a technology that enables an unprecedented amount of freedom in the shapes and designs that can be manufactured. It is leading to more organically inspired structures, more robust and damage tolerant geometries, and more weightoptimized designs.

However, the technology currently carries a risk of introducing defects that could result in premature fatigue failure which creates uncertainty that limits its application in critical primary aircraft structures. One possible way to combat the risks introduced by defects is to apply structural health monitoring (SHM) to AM structures.

In this research, we propose to embed fibre optic sensor within SLM Ti6Al4V part, and perform crack detection and localization based on strain distribution measured by embedded fibre optic sensor.

Maria Strantza from Vrije Universiteit Brussel developed an effective structural health monitoring system (eSHM) within SLM parts by checking the absolute fluid pressure changes in a network of capillaries or cavities that are integrated into the interior of a metallic part.

Development of a SHM method with embedded fibre optic sensor in order to detect, localize and characterize cracks within SLM Ti6AL4V part automatically.

SLM part with embedded capillary (Strantza, M. (2016). Additive manufacturing as a tool for structural health monitoring of metallic structures.)

Once any fatigue crack initiates and propagates to breach the capillary, pressure inside will change and approach the ambient level.

SLM part under four-point bending test (Strantza, M. (2016))

Hypothesis Cracks within the SLM Ti6Al4V can lead to redistribution of strain field around the embedded fibre optic sensor. The strain field distribution is specific to different crack location and length.

Therefore, cracks can be detected by comparing strain field of damaged state to healthy state. Crack localization and characterization can be performed based on corresponding strain field distribution.

Research plan The research will be first performed on SLA part since it is more cost-effective and easier to operate, and then corresponding method will be applied to SLM Ti6AL4V part.

In order to fulfil the objectives, this research plan is divided into following steps.

1. Development of a method to embed fibre within additive manufactured components. Investigation on factors affecting strain transfer between fibre and Ti6Al4V matrix.

2. Determining the appropriate position based on in-fibre sensitivity to cracks.

Additive manufactured blades (Image: Siemens)

fibre strain

Image: NIST.gov

Porosity Lack of Fusion

Imperfections within additive manufactured structures (Image: NIST.gov)

Pressure change inside the capillary in case of crack breaching (Strantza, M. (2016))

Reflection & criticism 1. The eSHM system can be effective only when capillary is close to the hot spots.

2. The detectable crack length is determined by fibre proximity to crack and crack propagation direction, which is hard to predict in complex stress state.

3. The eSHM cannot be used for crack localization and characterization

Embedded fibre of different proximity to hot spots of the part

3. Performing crack detection and localization with strain measured by embedded fibre optic sensor.

47

Department ASM

PhD Candidate: Chirag Anand

Imaging composite structures using ultrasonic arrays

C.Anand@tudelft.nl Department: ASM Group: SI & C Phd year: 3rd year Supervisor: Dr. Roger Groves Promotor: Prof. Dr. Ir. Rinze Benedictus

1.Introduction



Ultrasonic arrays can be used to image structures using ultrasonic waves . The usage of arrays allows the steering of the ultrasonic beam which allows for more coverage of area.

Plane wave propagation

Modelling the plane wave reflection coefficients in 8 plies CFRP as shown in figure 3. The figure shows the reflection coefficients for different frequencies for the 8 plied cfrp laminate

2. Objective The objective is to non-destructively test and image composite structures using ultrasonic arrays. This requires the development of imaging algorithms which can be used to create an image of the structure.

3.Methodology

Figure 3

Two pronged approach is used.

1. An ultrasonic measurement model is developed which would help in testing the imaging algorithm in a composite structure

48

2. Imaging algorithm will be used experimentally to image defects such as delamination and fibre waviness in composite structures.



Development of imaging algorithm

Initial development of the total focusing method imaging algorithm to be used in conjunction with anisotropic velocities. Fig 4 shows the algorithm used to identify delamination and Fig 5 shows that holes in aluminium block can be seen.

4. Results 

Beam model

Beam model was developed using multi Gaussian beams which can be made to propagate thru planar and non planar interfaces. The propagation of the ultrasonic beam was carried out through a composite structure as show in Fig 1. Beam steering was also achieved as can be seen in Fig 2.

Figure 4

Figure 5

5. Conclusions 



Figure 1



Figure 2

Ultrasonic beam propagation through composites can be modelled using multi Gaussian beams It seems possible to image defects in composite structures using total focusing method but there is lot of noise in the image Inclusion of anisotropic velocity in the imaging algorithm might solve the problem of noise in the image

â&#x20AC;˘

Introduction

Automated tape placement with in-situ consolidation is a promising manufacturing technique: â&#x20AC;˘ Possibility of fully automated production

Incoming tape

Laser heater

Substrate

F

â&#x20AC;˘ No autoclave

â&#x20AC;˘

PhD Candidate: Ozan Ă&#x2021;elik Department: Aerospace Structures and Materials Section: Structural Integrity and Composites Supervisor: Dr. Ir. Sonell Shroff Promotor: Prof. Dr. Ir. Rinze Benedictus Contact: ozan.celik@tudelft.nl 1 2 3 4

Department ASM

High Fidelity Analysis of In-Situ Consolidation of Thermoplastic Composites

Methodology

A 3-D thermo-mechanical FE simulation script including â&#x20AC;˘ Realistic heat/pressure distributions â&#x20AC;˘ Thermal/mechanical contact between layers â&#x20AC;˘ Adjustable layup scheme Has been developed.

Coupled thermo-mechanical 3-D FEM Analysis

Tool surface

â&#x20AC;˘ On-line process monitoring â&#x20AC;˘ Larger parts in a single integrated process

â&#x20AC;˘

Thermal history

Nip Point Incoming Tape-Substrate Interface

Degree of Intimate Contact

Motivation & Research Objective

â&#x20AC;˘

Selection of process parameters rely on expensive and time-consuming trial and error Accurate process models, which can predict the effects of process parameters on quality, are required to optimize the process variables Complex thermal and physical interactions require multiphysics simulations The aim of this research is to create a finite element model that can accurately and reliably predict the evolution of bonding for different process parameters (laser power, roller pressure, process velocity, fiber orientation)

â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

â&#x20AC;˘

Pressure history

Contact Interactions

Degree of Autohesion

Bond strength Correct material characterization is a key to achieve accurate bonding predictions with such a process model.

â&#x20AC;˘

Results Intimate contact development for different transverse viscosity values of CF/PEEK material available in the literature has been calculated for the same process parameters.

Theory

49

Bond formation during the process is explained with elimination of interlaminar voids and motion of polymer chains.

Intimate Contact (Elimination of Interlaminar Voids) Due to the relatively high melt viscosity of thermoplastic resins, asperities on the surface of thermoplastic layers must be mechanically deformed to provide contact. Intimate contact development expresses the squeeze flow of the surface asperities under high temperatures and pressures. Asperities on the surfaces have been approximated as rectangles (Lee & Springer, 1987) or Cantor sets (Yang & Pitchumani, 2001) .

Rectangular Representation

The results show that intimate contact development is dependent on correct characterization of viscosity. Two areas are unclear regarding the viscosity of the thermoplastic composite: â&#x20AC;˘ Whether the viscosity of neat resin or fiber reinforced resin is effective â&#x20AC;˘ Whether the viscosity value is temperature dependent only

Cantor Set Representation

đ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??ź đ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??ś = đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;(đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;, đ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Ł, đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ą, đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?)

Autohesion (Transportation of Polymer Chains) Autohesion models describe the strength development between two contacting polymer surfaces by the transfer of polymer chains. Several isothermal and non-isothermal autohesion models are proposed in the literature. Non-isothermal models are more suitable for this manufacturing process since the changes in temperature are very rapid.

đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´ = đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;(đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;)

Bonding Autohesion can only occur at surfaces which are in contact. Therefore, final bonding prediction for a set of manufacturing parameters can be made a combination of intimate contact and autohesion.

đ??ľđ??ľđ??ľđ??ľđ??ľđ??ľđ??ľđ??ľ đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; = đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;(đ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??ź đ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??śđ??ś, đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´)

â&#x20AC;˘

Future Work

â&#x20AC;˘

References

â&#x20AC;˘ Combination of intimate contact with autohesion for bonding quality â&#x20AC;˘ Development of more insight to the viscosity characteristics â&#x20AC;˘ Validation of FE model with experiments â&#x20AC;˘ Use of FE model for quality prediction of larger panels

[1] Jeyakodi, G. K. and Shroff, S., â&#x20AC;&#x153;Finite element simulation of in-situ consolidation of fibre placed thermoplastic laminate for prediction of residual stresses and laminate quality,â&#x20AC;? in Proceedings of The Third International Symposium on Automated Composites Manufacturing, 2017.

[2] Lee, W. I. and Springer, G. S., A model of the manufacturing process of thermoplastic matrix composites, J Compos Mater 1987; 21(11): 1017â&#x20AC;&#x201C;1055.

[3] Yang, F. and Pitchumani, R., A fractal Cantor set based description of interlaminar contact evolution during thermoplastic composites processing, J Mater Sci 2001; 36(19): 4661â&#x20AC;&#x201C;4671.

[4] Shuler, S. F., Advani, S.G., Transverse squeeze flow of concentrated aligned fibers in viscous fluids, J. Non-Newtonian Fluid Mech. 65 (1) (1996) 47â&#x20AC;&#x201C; 74.

Department ASM

Prognostics of Composite Structures SHM framework

PhD Candidate: Nick Eleftheroglou Department: Aerospace Structures & Materials Section: Structural Integrity Group Supervisor: Dr.ir. D. Zarouchas Promotor: Prof.dr.ir. R. Benedictus Contact: N.Eleftheroglou@tudelft.nl 1

Composite

raw data

Tra i n i n g p ro c e s s

4

Monitoring data Feature extraction

NHHSMM-RUL prediction model

Training data

Preprocessing / Feature extraction

Estimated Model’s parameters θ

Parameter's Initialization

Remaining useful life estimation (Prognostics)

3

Prognostic process

•Maintenance planning •Logistics •Part availability etc

Condition based Maintenance

2

Stochastic model (NHHSMM) Damage state identification (Diagnostics)

Damage mechanics

Prognostic measures

•Training on past degradation data •Model selection •MLE Parameter estimation

Case Study Coupon # A1 A2 A3 A4 A5 A6 A7

50

Fatigue test conditions

R=0, f=10Hz,

A= 90% * 42.66 kN

[0/±45/90]2s lay-up

Cycles to failure (x 103) 63 25 22 24.5 14 25 30

Results Parameter Estimation

Prognostics

Conclusions oNHHSMM provides an interesting framework to model damage

accumulation in composite materials under fatigue loading using SHM data. oThis framework has the crucial benefit of online utilization. oThe presented framework can accept as input any type of SHM data. oAE technique Vs DIC technique • Prognostic performance metrics: AE technique Vs DIC technique • Feature extraction process: AE technique Vs DIC technique • Implementation: AE technique Vs DIC technique • Monitor failures: AE technique Vs DIC technique

References [1] N. Eleftheroglou, T.H. Loutas: Fatigue damage diagnostics and prognostics of composites utilizing structural health monitoring data and stochastic processes. Structural Health Monitoring, 05/2016; [2] N. Eleftheroglou, D.S. Zarouchas, T.H. Loutas, R.C. Alderliesten, R. Benedictus: Online remaining fatigue life prognosis for composite materials based on strain data and stochastic modeling. Key Engineering Materials,09/2016;

Department ASM

Bi-material bonded joints – Extra Thick Bondlines

PhD Candidate: Romina Fernandes Department: ASM Section: SI&C Supervisor: Sofia Teixeira de Freitas/ Hans Poulis Promotor: Rinze Benedictus Contact: R.LopesFernandes@tudelft.nl

Mode I characterization

1

2

3

4

Motivation Maritime & Civil

Challenges

Industries

Load capacity ↑

Bonded Composites to Steel structures

Solution

Strength-weight ratios ↑

Research Goal

Demands

Hybrid structures Extra thick bondlines

Experimental Set-Up Asymmetric DCB specimen

Mode I fracture characterization of bi-material bonded joints with Extra Thick bondlines: . Criterion to obtain pure mode I on asymmetric joints . Adherends’ material and adhesive bondline thickness effects

Materials: S690, GFRP laminate, Araldite2015 Cross-head rate: 1 mm/min

Pure Mode I in Bi-Material Bonded Joints Numerical Analysis Curvature based criterion . VCCT* Efx

GFRP

Curvature

 t 3GFRP E Steel  t 3 Steel

Longitudinal strains

Strain based criterion Efx

GFRP

Curvature

 t 2GFRP E Steel  t 2 Steel

Longitudinal strains

Fractography (by SEM) Steel-Steel

Curvature based

Strain based

Pure mode I adhesive morphology

White arrows – tilted cracks (presence of mixed-mode)

Resembles the Steel-Steel fracture surface

Mode I & II fracture components at the crack tip Which one is the best criterion? *VCCT – Virtual Crack Closure Technique

Fracture Toughness under Mode I loading of (A)Symmetric DCB joints Specimen Configuration

Adhesive Thickness

Steel-Steel

0.4 mm Steel-Steel

GFRP-GFRP tadhesive = 10.1 mm

Steel-GFRP tadhesive = 10.1 mm

1.1 mm 4.1 mm

tadhesive = 4.1 mm

tadhesive = 0.4 mm

10.1 mm GFRP-GFRP

Steel-GFRP

tadhesive = 10.1 mm

0.4 mm

10.1 mm

tadhesive = 1.1 mm tadhesive = 0.4 mm

0.4 mm 10.1 mm

Fracture Toughness

tadhesive = 0.4 mm

Conclusions . Pure mode I on bi-material bonded joints is achieved by following the Strain based criterion . Fracture toughness under mode I loading tends to increase, as the adhesive bondline thickness increases . Adherends’ material effect on the fracture toughness is less pronounced for thicker adhesive bondlines . Fracture toughness on bi-material DCB joints is similar to the fracture toughness of SteelSteel symmetric DCB joints

Data reduction scheme: Compliance Calibration Method

51

Department ASM

On composite bonded joints: A look from a different angle This study presents an evaluation on the effect of different composite layups on the failure mechanisms and ultimate strength of an adhesively bonded composite joint.

The geometry of a single overlap of two adjacent adherends was subjected to quasi-static tensile loading and specimens with 4 different stacking sequences of 16 UD-laminae per adherend were tested: • [(45/90/-45/0)2]s • [(90/45/0/-45)2]s • [(0/45/90/-45)2]s • [(45/90/-45/0)2]s The experimental results were compared with numerical simulations using FEA. The strength of a single UD-layer inside a stacking sequence varies with respect to its ply thickness and position within the sequence. This in-situ effect is incorporated.

52

The study shows that an adherend with higher longitudinal bending stiffness results in a higher load corresponding to damage initiation.

However ,the load at final failure, is influenced by how the damage progresses inside the composite: A crack propagating from outer layer in contact with the adhesive further towards the inside layers of the composite adherend results in higher failure loads. Finally, the failure mode is highly influenced by the orientation of the outermost lamina in contact with the adhesive such that a fiber orientation towards 0° causes failure to occur within the bond line, while for the other extreme angle of 90° the failure occurs entirely inside the composite adherend.

PhD Candidate: Julian Kupski Department: ASM Section: SI&C Supervisor Sofia Teixeira de Freitas Promotor: Prof. Dr.ir. R. Benedictus Contact: j.a.kupski@tudelft.nl 1

2

3

4

Department ASM

Structural Health Monitoring for Automated Aircraft Maintenance TU Delft PhD Poster Day, March 16th 2018 Vincentius Ewald ( V.Ewald@tudelft.nl ), Roger M. Groves ( R.M.Groves@tudelft.nl )

Material

Fatigue & Damage Tolerance

Non-Destructive Testing & Structural Health Monitoring

Manufacturing, Joining & Production

Structure

Problem Statement To design an aircraft health monitoring system powered by artificial intelligence that can automatically detect faulty or damaged parts in the structure to increase maintenance operational efficiency and airline revenue.

Structural Health Monitoring

SHM Technical Approach

Structural Health Monitoring (SHM) is an integrated real-time damage detection system in engineering structures such as aircraft, bridge, wind turbine, etc.

One technical approach for SHM implementation is using ultrasonic wave called Lamb wave. The signal is actuated and recorded by piezoelectric sensors.

Philosophically, SHM is comparable with human body which has many sensory nerves

53 Propagation of Lamb wave in aluminum plate

Sensor Network Signal Processing Information about damage: location, size, severity, …

The advantage of using SHM over existing NonDestructive Testing (NDT) method:

Generally, a more complex structure leads to a more complicated signal and thus aggravates the physical correlation between the signal and the damaged state. We are developing a new signal processing algorithm by using multilayer neural network (also known as “deep learning”) that can predict this correlation to increase the damage detection rate enabling faster maintenance decisions to be made.

1. Automated damage detection, thus less prone to human error 2. No need to access difficult-to-access inspection areas such as aft pressure bulkhead

Benefit 1. Increasing Mean Time Between Failure (MTBF) 2. Reducing the number of maintenance man-hours and Mean Time To Repair (MTTR) 3. Possibility for On-Demand Aircraft Maintenance Summary: Higher aircraft operational efficiency and increasing airline operating revenue Structural Integrity & Composites www.aerondt.tudelft.nl

www.aerondt.tudelft.nl

Structural Integrity & Composites

Depar Aerodynamics, Wi Performance (AW

tment ind Energy, Flight e Propulsion WEP)

Department AWEP

Aerodynamics (AERO)

Heads of section: Prof. dr. ir. Fulvio Scarano Prof. dr. Stefan Hickel

56

Supervisors Dr. Mario Kotsonis Dr. Steven Hulshoff Dr. Marc Gerritsma Prof. dr. Stefan Hickel Dr. Richard Dwight Dr. Andrea Sciacchitano Dr. Ferdinand Schrijer

Department AWEP

The Aerodynamics group is engaged in both fundamental and applied research related to the understanding and control of aerodynamic flows. The groupâ&#x20AC;&#x2122;s strengths lie in the development of new experimental and computational techniques and their application to the design of transport systems.

PhD Candidates:

The Aerodynamics group is a member of the Burgerscentrum Research School for Fluid Mechanics and the Delft Centre for Computational Science and Engineering. It is also an active partner in collaborative projects with industry and other research institutes.

-- Alexander Spoelstra

-- Haohua Zong

-- Liesbeth Florentie -- Varun Jain -- Yi Zhang -- Weibo Hu -- Martin Schmelzer -- Tiago Pestana -- Zeno Belligoli -- Constantin Jux -- Xiaodong Li -- Luis Laguarda Sanchez -- Wouter Terra -- David Faleiros

57

Flow control with plasma synthetic jet actuators (PSJA)

PhD Candidate: Haohua Zong Department: AWEP Section: Aerodynamics Supervisor Marios Kotsonis Promotor: Prof. Dr.ir. F. Scarano Contact: h.zong-1@tudelft.nl

1

2

3

4

Separation control with PSJA at Re=1.8Ă&#x2014;105

Why control flow?

Department AWEP

When confronted with severe adverse pressure gradient or sharp turns in wall geometry, boundary layers tend to separate naturally, creating significant pressure drag that lowers the efficiency of vehicles. Active Flow control technique employs actuators to impose local controllable disturbances on the boundary layer thus can alter the flow scenario globally, as shown in the following two figures.

Plasma actuator off

Plasma actuator on

Why use PSJA to control flow?

To achieve satisfying control effect in high-Reynolds number high-speed flow, disturbances with sufficient amplitudes should be delivered at the susceptible frequency range of the flow by actuators. Among all the fluidic and plasma actuators, plasma synthetic jet actuator (PSJA, right figure) is the only one that can fulfill the abovementioned task. Using pulsed arc discharge to rapidly pressurize the cavity air, high-velocity (>300 m/s) pulsed jets can be produced at high frequency (>5 kHz). The schlieren image of this powerful plasma synthetic jets (PSJ, middle figure) looks like an atomic explosion (left figure), doesnâ&#x20AC;&#x2122;t it?

58

Control of flow separation over a NACA-0015 airfoil at 10 m/s with PSJs issued at 8% chord length from the leading edge. 26 actuators are embedded in the airfoil model. As shown, 25% increase in maximum lift coefficient and 9 degrees postpone in stall angle are obtained

PSJs in Mach 2 cross flow Precursor shock wave

PSJA structure

Issuing jet

How PSJA controls flow?

Jet plume

Jet terminates

PJSs with a peak jet velocity of 500 m/s are ejected from x/D=0 to impinge on a Mach 2 cross flow. Shock waves and jet plume are visualized by high-speed schlieren imaging.

Related Publications Zong, H. and Kotsonis, M., 2018. Formation, evolution and scaling of plasma synthetic jets.

Journal of Fluid Mechanics, 837, p.147-181.

Zong, H. and Kotsonis, M., 2017. Experimental investigation on frequency characteristics of plasma synthetic jets. Physics of Fluids, 29(11), p.115107.

Zong, H. and Kotsonis, M., 2017. Interaction between plasma synthetic jet and subsonic turbulent boundary layer. Physics of Fluids, 29(4), p.045104.

Pesudo-3D Vortex structures produced by the interaction between PSJs and crossflow.

FVR (front vortex ring) and CVP (counter rotating vortex pair) enhance the mixing in boundary layers by transporting high-momentum flow to the near-wall region.

Zong, H. and Kotsonis, M., 2017. Effect of slotted exit orifice on performance of plasma synthetic jet actuator. Experiments in Fluids, 58(3), p.17.

Zong, H. and Kotsonis, M., 2016. Characterisation of plasma synthetic jet actuators in quiescent flow. Journal of Physics D: Applied Physics, 49(33), p.335202.

Department ASM

59

Department ASM

Use of algebraic dual space in discretization schemes for Finite Element Methods We present a discretization method that explicitly makes use of the algebraic

Next, we apply the scheme to a constraint minimization problem (see (1) ). We solve the system on the two mesh of Figure 1. The resulting system is extremely sparse, and the discretization of the constraint and the Lagrange multiplier is independent of the grid size and the grid shape.

1

1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

2

3

10 8

hi(Ξ)

dual hi(Ξ)

0.6 0.4

0.4

0.3

0.3

0.2

0.2

0.1

2

0

0

â&#x2C6;&#x2019;0.8

â&#x2C6;&#x2019;0.6

â&#x2C6;&#x2019;0.4

â&#x2C6;&#x2019;0.2

0 Ξ

0.2

0.4

0.6

0.8

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

â&#x2C6;&#x2019;2 â&#x2C6;&#x2019;1

1

Edge polynomials

5

â&#x2C6;&#x2019;0.8

â&#x2C6;&#x2019;0.6

â&#x2C6;&#x2019;0.4

â&#x2C6;&#x2019;0.2

0 Ξ

0.2

0.4

0.6

0.8

1

Dual edge polynomials

1.4 1.2

4

1 0.8

3

dual ei(Ξ)

ei(Ξ)

1

0.6 0.4 0.2 0

0

â&#x2C6;&#x2019;0.2 â&#x2C6;&#x2019;1 â&#x2C6;&#x2019;2 â&#x2C6;&#x2019;1

â&#x2C6;&#x2019;0.4 â&#x2C6;&#x2019;0.8

â&#x2C6;&#x2019;0.6

â&#x2C6;&#x2019;0.4

â&#x2C6;&#x2019;0.2

0 Ξ

0.1

0

6 4

0.2

2

0.4

Dual Lagrange polynomials

12

0.8

â&#x2C6;&#x2019;0.2 â&#x2C6;&#x2019;1

4

14

1

dual space. We propose topological construction of (algebraic) dual basis functions.

The discretization method is applied to the pair of Dirichlet-Neumann problem in [1]. For this problem we prove that the equivalence of the solution that holds true at the continuous level also holds true at the discrete level. We show this equivalence on the two meshes shown in Figure 1.

1

Lagrange polynomials

1.2

Abstract:

PhD Candidate: Varun Jain Department: Aerospace Engineering Section: Aerodynamics Supervisor: Dr. M. I. Gerritsma Promotor: Dr. S. Hickel Contact: v.jain@tudelft.nl

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.4

0.6

0.8

â&#x2C6;&#x2019;0.6 â&#x2C6;&#x2019;1

1

â&#x2C6;&#x2019;0.8

â&#x2C6;&#x2019;0.6

â&#x2C6;&#x2019;0.4

â&#x2C6;&#x2019;0.2

0 Ξ

0.2

0.4

0.6

0.8

1

Figure 2. Basis functions in 1-D. Top : Nodal Lagrange polynomial basis functions and the associated dual polynomials. Bottom :The edge polynomial basis functions and the associated dual polynomials.

0 0

0.2

1

Figure 1. Left : Orthogonal mesh (đ?&#x2018;?đ?&#x2018;? = 0.0). Right : Curved mesh (đ?&#x2018;?đ?&#x2018;? = 0.3).

Application I

Consider the Dirichlet problem and the Neumann problem given by,

Dirichlet problem

Q

R đ?&#x2018;&#x153;đ?&#x2018;&#x153;đ?&#x2018;&#x153;đ?&#x2018;&#x153; đ?&#x153;&#x2022;đ?&#x153;&#x2022;đ?&#x153;&#x2022;đ?&#x153;&#x2022; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; = đ?&#x153;&#x2122;đ?&#x153;&#x2122; â&#x2C6;&#x2019; đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; + đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; = 0 đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; đ??žđ??ž

If đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; solves the Neumann problem, then đ?&#x153;&#x2122;đ?&#x153;&#x2122; solves the Dirichlet problem, if and only if đ?&#x153;&#x2122;đ?&#x153;&#x2122; = đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;.

Figure 3. The difference between đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; & and the solution đ?&#x153;&#x2122;đ?&#x153;&#x2122;& for i) Left : Orthogonal mesh (đ?&#x2018;?đ?&#x2018;? = 0.0), and ii) Right : Curved mesh (đ?&#x2018;?đ?&#x2018;? = 0.3).

Application II

Convergence of error in flux, đ?&#x2019;&#x2019;đ?&#x2019;&#x2019;đ?&#x2019;&#x2030;đ?&#x2019;&#x2030;

100

Variational form A

+ đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;E , đ?&#x153;&#x2122;đ?&#x153;&#x2122;

đ?&#x153;&#x2122;đ?&#x153;&#x2122;L, đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;

A

Sparsity plots

A

=>

HA

đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;E , đ?&#x2018;&#x203A;đ?&#x2018;&#x203A; đ?&#x153;&#x2122;đ?&#x153;&#x2122;8đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;Î&#x201C; â&#x2C6;&#x20AC;đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;E â&#x2C6;&#x2C6; đ??ťđ??ť(đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;; đ??žđ??ž)

= đ?&#x153;&#x2122;đ?&#x153;&#x2122;L, đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;

A

(2)

â&#x2C6;&#x20AC;đ?&#x153;&#x2122;đ?&#x153;&#x2122;L â&#x2C6;&#x2C6; đ??żđ??ż2 (đ??žđ??ž)

Primal - primal

10â&#x2C6;&#x2019;10

10â&#x2C6;&#x2019;15

0

10

10â&#x2C6;&#x2019;12

Primal - dual

10â&#x2C6;&#x2019;13

5

đ?&#x;?đ?&#x;?

5

10

10

đ?&#x201D;źđ?&#x201D;źđ?&#x;?đ?&#x;?,đ?&#x;?đ?&#x;?

đ?&#x2018;ťđ?&#x2018;ť 15

15

đ?&#x2018;ťđ?&#x2018;ť

30

40

50

Primal-Dual, c=0.0 Primal-Primal, c=0.0 Primal-Dual, c=0.3 Primal-Primal, c=0.3

10â&#x2C6;&#x2019;5

10â&#x2C6;&#x2019;10

10â&#x2C6;&#x2019;15

0

10

20

30

40

N

N

Continuity constraint (đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2019;đ?&#x2019;&#x2019;đ?&#x2019;&#x2030;đ?&#x2019;&#x2030; â&#x2C6;&#x2019; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;đ?&#x2019;&#x2030;đ?&#x2019;&#x2030;)

Growth in condition number

50

108

10â&#x2C6;&#x2019;14

10â&#x2C6;&#x2019;15

20

20

20

Primal Dual, c=0.0 Primal-Primal, c=0.0 Primal-Dual, c=0.3 Primal-Primal, c=0.3

0

0

106

Primal-Dual, c=0.0 Primal-Primal, c=0.0 Primal-Dual, c=0.3 Primal-Primal, c=0.3

4 4

104

3

3

102

25

25

30

0

5

đ?&#x2022;&#x201E;đ?&#x2022;&#x201E;

đ?&#x;?đ?&#x;?

10

đ?&#x201D;źđ?&#x201D;źđ?&#x;?đ?&#x;?,đ?&#x;?đ?&#x;?

30

15

20

25

30

0

5

10

đ?&#x2022;&#x201E;đ?&#x2022;&#x201E;(2) đ?&#x201D;źđ?&#x201D;ź2,/

4 đ?&#x201D;źđ?&#x201D;ź2,/ đ?&#x2022;&#x201E;đ?&#x2022;&#x201E;(2)

0

đ?&#x2019;Šđ?&#x2019;Š / (đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;& ) đ?&#x2019;Šđ?&#x2019;Š67 (đ?&#x153;&#x2122;đ?&#x153;&#x2122;8) = đ?&#x2019;Šđ?&#x2019;Š 2 (đ?&#x153;&#x2122;đ?&#x153;&#x2122;& ) đ?&#x2019;Šđ?&#x2019;Š 2 (đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;)

đ?&#x201D;źđ?&#x201D;źđ?&#x;?đ?&#x;?,đ?&#x;?đ?&#x;?

10â&#x2C6;&#x2019;16

4

đ?&#x2022;&#x201E;đ?&#x2022;&#x201E;(/) đ?&#x201D;źđ?&#x201D;ź2,/ đ?&#x201D;źđ?&#x201D;ź2,/ 0

0

10

20

30

N 15

20

25

30

nz = 144

nz = 504

đ?&#x2022;&#x201E;đ?&#x2022;&#x201E;(/)

10â&#x2C6;&#x2019;5

â&#x2C6;Ľdqh â&#x2C6;&#x2019; fh â&#x2C6;ĽL2 (â&#x201E;Ś)

đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;E ,đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;

(1)

A

â&#x2C6;ĽĎ&#x2020;h â&#x2C6;&#x2019; Ď&#x2020;ex â&#x2C6;ĽL2 (â&#x201E;Ś)

đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; 2 đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; + > đ?&#x153;&#x2122;đ?&#x153;&#x2122; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; â&#x2C6;&#x2019; đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;

Condition N umber

1

A2

â&#x2C6;Ľqh â&#x2C6;&#x2019; qex â&#x2C6;ĽL2 (â&#x201E;Ś)

â&#x201E;&#x2019; đ?&#x153;&#x2122;đ?&#x153;&#x2122;, đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;; đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; â&#x2030;&#x201D; >

Convergence of error in potential, đ??&#x201C;đ??&#x201C;đ?&#x2019;&#x2030;đ?&#x2019;&#x2030;

100

Primal-Dual, c=0.0 Primal-Primal, c=0.0 Primal-Dual, c=0.3 Primal-Primal, c=0.3

Constraint minimization problem

đ?&#x201D;źđ?&#x201D;źđ?&#x;?đ?&#x;?,đ?&#x;?đ?&#x;? đ?&#x2022;&#x201E;đ?&#x2022;&#x201E;

60

Neumann problem

R đ?&#x2018;&#x153;đ?&#x2018;&#x153;đ?&#x2018;&#x153;đ?&#x2018;&#x153; đ?&#x153;&#x2022;đ?&#x153;&#x2022;đ?&#x153;&#x2022;đ?&#x153;&#x2022; đ?&#x153;&#x2122;đ?&#x153;&#x2122; = đ?&#x153;&#x2122;đ?&#x153;&#x2122; Q â&#x2C6;&#x2019; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;đ?&#x2018;&#x201D; đ?&#x153;&#x2122;đ?&#x153;&#x2122; + đ?&#x153;&#x2122;đ?&#x153;&#x2122; = 0 đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; đ??žđ??ž

đ?&#x2019;Šđ?&#x2019;Š / (đ?&#x2018;&#x17E;đ?&#x2018;&#x17E;& ) đ?&#x2019;Šđ?&#x2019;Š67 (đ?&#x153;&#x2122;đ?&#x153;&#x2122;8) = : 7 (đ?&#x153;&#x2122;đ?&#x153;&#x2122;& ) đ?&#x2019;Šđ?&#x2019;Š đ?&#x2019;Šđ?&#x2019;Š 2 (đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;)

40

50

100 100

101

102

N

Figure 4. Top left : đ??żđ??ż2 â&#x2C6;&#x2019; đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019; in flux đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; & . Top right : đ??żđ??ż2 â&#x2C6;&#x2019; đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019; in potential đ?&#x153;&#x2122;đ?&#x153;&#x2122;& . Bottom left : : đ??żđ??ż2 â&#x2C6;&#x2019; đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2019; in constraint (đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x17E;đ?&#x2018;&#x17E; & â&#x2C6;&#x2019;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; & ). Bottom right : Condition number for the meshes đ?&#x2018;?đ?&#x2018;? = 0.0 and đ?&#x2018;?đ?&#x2018;? = 0.3.

References : [1] C. Carstensen, L. Demkowicz, J. Gopalakrishnan, Breaking spaces and forms for the DPG method and applications including Maxwell equations, Computers and Mathematics with Applications 72 (2016) 494â&#x20AC;&#x201C;522.

[2] V. Jain, Y. Zhang, A. Palha and M. Gerritsma, Construction and application of algebraic dual polynomial representations for finite element methods, arXiv1712.09472, 2017.

Department ASM

61

Department ASM

Stability Analysis of a Backward-facing Step in a Laminar Supersonic Flow

PhD Candidate: Weibo Hu Department: AWEP Section: Aerodynamics Supervisor: Stefan Hickel Bas van Oudheusden Promotor: Stefan Hickel Contact: W.Hu-2@tudelft.nl 1

2

3

Motivation

4

After scaled for LES:

Backward-facing step (BFS) is one of the canonical geometries in aerospace applications. This case is featured with various flow phenomena, like separation with a fixed separation point, flow reattachment, shock waves and their interactions. These flow dynamics may result in accelerating transition over the airfoils, extra flight drags and thermal loads of the aircraft body due to its sharp geometry. Reducing these adverse effects first requires the wellunderstanding of the physical mechanism for flow over the BFS.

đ?&#x153;śđ?&#x153;śđ?&#x153;šđ?&#x153;šđ?&#x;&#x17D;đ?&#x;&#x17D; = đ?&#x;&#x17D;đ?&#x;&#x17D;. đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;, đ?&#x153;ˇđ?&#x153;ˇđ?&#x153;šđ?&#x153;šđ?&#x;&#x17D;đ?&#x;&#x17D; = đ?&#x;?đ?&#x;?. đ?&#x;?đ?&#x;?đ?&#x;?đ?&#x;?đ?&#x;?đ?&#x;?đ?&#x;?đ?&#x;?đ?&#x;?đ?&#x;?, đ??&#x201C;đ??&#x201C; = đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;đ?&#x;&#x201D;. đ?&#x;&#x17D;đ?&#x;&#x17D;â&#x2C6;&#x2DC; đ?&#x153;šđ?&#x153;š

đ??&#x17D;đ??&#x17D;đ?&#x2019;&#x201C;đ?&#x2019;&#x201C; đ?&#x;&#x17D;đ?&#x;&#x17D; = đ?&#x;&#x17D;đ?&#x;&#x17D;. đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D; đ?&#x153;šđ?&#x153;š

đ??&#x17D;đ??&#x17D;đ?&#x2019;&#x160;đ?&#x2019;&#x160; đ?&#x;&#x17D;đ?&#x;&#x17D; = đ?&#x;&#x17D;đ?&#x;&#x17D;. đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;đ?&#x;&#x17D;

Contour of wave growth rate đ?&#x153;&#x201D;đ?&#x153;&#x201D;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; in the coordinates of streamwise wave number đ?&#x203A;źđ?&#x203A;ź and wave angle đ?&#x153;&#x2122;đ?&#x153;&#x2122;. The unstable wave range is indicated by the blue plate with dashed lines.

Spatial Evolution of TS modes

Schematic of a supersonic flow over a BFS.

Numerical Setup

62

Instantaneous streamwise boundary layer profile of velocity perturbations (đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6;â&#x2C6;&#x17E; /đ?&#x203A;żđ?&#x203A;ż0 = 1600)

Schematic of the test case. The computation domain is represented by a dashed box, including an instantaneous numerical schlieren image. â&#x201E;&#x17D;

đ??żđ??żđ?&#x2018;Śđ?&#x2018;Ś

đ??żđ??żđ?&#x2018;Ľđ?&#x2018;Ľ

đ??żđ??żđ?&#x2018;§đ?&#x2018;§

đ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6;â&#x2C6;&#x17E;

3mm

160h/3

33h/3

5.48h/3

648 m/s

đ?&#x2018;?đ?&#x2018;?â&#x2C6;&#x17E;

đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;â&#x2C6;&#x17E;

đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;0

đ?&#x2018;?đ?&#x2018;?0

đ?&#x203A;żđ?&#x203A;żđ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;

1512 Pa

6.11 Ă&#x2014; 106 mâ&#x2C6;&#x2019;1

300 K

1 Ă&#x2014; 105 Pa

0.6314 mm

Main geometry and flow parameters of the test case.

đ?&#x2018;&#x20AC;đ?&#x2018;&#x20AC;đ?&#x2018;&#x20AC;đ?&#x2018;&#x20AC;â&#x2C6;&#x17E; 3.4

đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x203A;żđ?&#x203A;żđ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;

3858

Contour of the time-averaged density.

â&#x20AC;˘

TS modes slowly grow upstream the separation point.

â&#x20AC;˘

Perturbations suddenly become much larger downstream the separation point.

â&#x20AC;˘

The shape of the boundary layer perturbations profile shows significant different features with the TS modes, which suggests other modes dominate the separation region.

â&#x20AC;˘

Growth rate of perturbations from LES and LST cannot match very well, thus this case need to be further corrected.

References

Linear Stability Theory qď&#x201A;˘( x, y, z ,ď&#x20AC;˝ t ) eď ˇit (q r cos ď ą ď&#x20AC;­ q i sin ď ą ) ď&#x20AC;Ť ieď ˇit (q r sin ď ą ď&#x20AC;Ť q i cos ď ą ) where ď ą ď&#x20AC;˝ ď Ą x ď&#x20AC;Ť ď ˘ z ď&#x20AC;­ ď ˇr t

Imposed TS modes:

Growth rate of streamwise perturbations at đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6;â&#x2C6;&#x17E; /đ?&#x203A;żđ?&#x203A;ż0 = 1600

A(q r cos ď ą ď&#x20AC;­ q i sin ď ą ), A ď&#x20AC;˝ 0.01

Zhu Yangzhu, Yi Shihe, Gang Dundian & He Lin. Visualisation on supersonic flow over backward-facing step with or without roughness. Journal of Turbulence, 2015, 16 (7), 633-649.

Jordi, Bastien E., Colin J. Cotter & Spencer J. Sherwin. Encapsulated formulation of the selective frequency damping method. Physics of Fluids. 2014, 26(3): 034101.

Groot, K. J. Derivation of and Simulations with BiGlobal stability equations. Master Thesis, 2013.

Aerodynamics Group, Faculty of Aerospace Engineering

Department ASM

Symbolic Regression to learn RANS Turbulence Closure from Data

PhD Candidate: Martin Schmelzer Department: AWEP Section: Aerodynamics Supervisor: Richard P. Dwight Promotor: Stefan Hickel Contact: m.schmelzer@tudelft.nl 1

2

3

4

Turbulence Modelling is a key-challenge for computational fluid dynamics (CFD) especially in industry. Explicit Algebraic Reynolds-stress Models (EARSM) offer higher predictive fidelity compared to Linear-Eddy Viscosity Models (LEV) and are numerically more robust than Reynolds-stress Models (RSM) at similar computational costs as LEV [1]. Commonly, EARSM are derived from a projection of (simplified) RSM onto a set of tensorial polynomials [2]. To overcome the influence of model assumptions we utilise deterministic symbolic-regression, a data-driven machine learning method, to infer an EARSM-like nonlinear model-form correction for a LEV directly from high-fidelity data.

1. Define model-form error term

Introduce an additive term to the linear constitutive relation for the Reynolds-stress. Variables come from high-fidelity data (LES, DNS) and are identified by passively solving turbulent transport equations (frozen approach):

aij = ⌧ij + ⌫t 2Sij

2 kδij 3

2. Concept of Nonlinear Eddy Viscosity

aij (Sij , ⌦ij ) =

In , In0.5 , In2 , In3 , exp(In ), tanh(In ), ..., 2 3 In I m , exp(In0.5 Im ), ...

Pope [2] derived an integrity basis of ten base tensors and five invariants as a nonlinear extension of the linear stress-strain relation.

We use this basis to regress the model-form error from step 1 onto it:

N X

4. Build library of nonlinear functions

The raw input features are subject to different mathematical operations and the resulting new features are stored as column vectors in a matrix Bmk called library-matrix [6,7]:

(n)

↵n (Im )Tij

n

1 ij Smn Snm , 3 1 4 5 Sik Skl ⌦lj Tij = ⌦ik ⌦kj ij ⌦mn ⌦nm , Tij = ⌦ik Skl Slj 3 I1 = Smn Snm , I2 = ⌦mn ⌦nm , I3 = Skm Smn Snk , I4 = ⌦km ⌦mn Snk , I5 = ⌦km ⌦mn Snp Spk

5. Symbolic-regression

The goal is to find a parsimonious combination of features from the library-matrix to build a model-correction term. In order to do so, we formulate another minimisation problem, in which the l1-norm regularisation promotes sparsity, i.e. !m contains only a few nonzero coefficients [6,7]. The corresponding columns of the library-matrix Bmk build the resulting model for each #n. The sparsity is controlled by λϴ, i.e. the larger, the more sparse the model is:

Tij1 = Sij , Tij2 = Sik ⌦kj = ⌦ik Skj , Tij3 = Sik Skj

ˆ ⇥(n) m = arg min Bmk ⇥m ˆm ⇥

↵n

2 2

ˆm + λ⇥ ⇥

1

Example: Square-duct flow Re=3,500

A new turbulence closure M1 is identified from DNS data [4], which can be implemented into existing RANS-codes. Fig. 2 shows a good match of the velocity for the flow in a square duct at Re=3,500 compared to DNS. N N X X (n)

M1 := aij =

Figure 1: Coefficient #1 for flow over 2D periodic hills at Re=11,595 (l.) and in a square duct at Re=3,500 (r.). The regularisation parameter is 0.01 for both. Data from [3,4].

3. Identify optimal coefficients

(n)

Bmk ⇥(n) m Tij

↵n Tij =

n

n

� (1) � = 3.1 · I10.5 + 0.048 · I1 1.0 · I3 Tij � (2) � 1.0 + I1 · ( 10375.59 · I32.0 + 35.81 · I3 ) + 10.88 · I10.5 Tij � (3) � + 2.06 · I10.5 + 3.29 · I1 + 2.44 · I1 1.0 · I3 1.0 Tij � � (4) + 2.02 · I10.5 3.38 · I1 2.09 · I1 1.0 · I3 Tij � � (5) 0.5 1.0 + I3 · ( 1.95 · I1 0.82 · I1 ) Tij

Solve minimisation problem using l2-norm regularised least-squares regression. Regularisation lowers the condition number of the corresponding matrix, but also introduces bias. An optimum between regularisation and bias needs to be identified:

� �2 N �X � � � (n) 2 ↵ = arg min � Tij ↵ ˆ n − aij � + λ↵ kˆ ↵ n k2 � ↵ ˆn � n

2

Figure 2: Velocity field in a square duct at Re=3,500 for DNS (l.), model M1 (m.) and baseline LEV (r.). Data from [4].

[1] Wallin, S. and Johansson, A.V.: An explicit algebraic Reynolds stress model for incompressible and compressible turbulent flows. Journal of Fluid Mechanics, 2000.

[2] Pope, S.B.: A more general effective-viscosity hypothesis. Journal of Fluid Mechanics, 1975.

[3] Breuer et al.: Flow over periodic hills - Numerical and experimental study in a wide range of Reynolds numbers. Computers & Fluids, 2009.

[4] Pinelli, A., Uhlmann, M., Sekimoto, A. and Kawahara, G.: Reynolds number dependence of mean flow structure in square duct turbulence. J. Fluid Mech., 2010.

[5] Weatheritt, J. and Sandberg, R. D.: The development of algebraic stress models using a novel evolutionary algorithm. Flow, Turbulence and Combustion, 2017.

[6] Brunton, S. L., Proctor, J. L., and Kutz, J. N.: Discovering governing equations from data: Sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 2015.

[7] Mcconaghy, T.: FFX: Fast, Scalable, Deterministic Symbolic Regression Technology. Genetic Programming Theory and Practice IX, 2011.

Uncertainty Management for Robust Industrial Design in Aeronautics

Umrida

63

Tiago Pestana

1

3

4

INSTANTANEOUS FLOW FIELDS

MOTIVATION AND OBJECTIVES Homogeneous background rotation affects the energy transfer and isotropy within the turbulent kinetic energy cascade. For strong rotation, i.e., sufficiently small Rossby numbers, backward transfer from small to large scales becomes significant. Ultimately, this inverse cascade leads to the formation of columnar eddies elongated along the axis of rotation. In numerical simulations, the growth of such columnar eddies is artificially constrained by the finite domain size and periodic boundary conditions, which results in an over accumulation of energy at the large scales. Through Direct Numerical Simulation (DNS) we show that the strength of the inverse energy cascade (Áinv ) and, therefore, the amount of energy that is accumulated at the large scales, depends on the ratio of integral length scales (LÎ) to domain size (L).

Isotropic

Rotating

�� ��

�� �� �� �� ��

� ��

�� �� �� �� � � �� �� ��

� �� ��

METHODOLOGY DNS, in rectangular periodic domains, of incompressible NavierStokes equations in a rotating frame of reference

I

ˆu 1 1 + (Ê ◊ u) + (ê ◊ u) = ≠Òp̃ + Ò2u + f. ˆt Ro Re Stochastic Gaussian force f with zero-velocity correlation.

I

Pseudo-spectral discretization with massively parallelized 3D FFT

I

I

� �� �� �� �� � �� �� �� 10 4 ��

E (Ÿ)/(ÈÁÍ2/3÷ 5/3)

I

Parameter Lz /LÎ is controlled by: i) varying forced scales kf or ii) domain aspect ratio Az .

� �

1

�

� � �

0.9

�

� �

� �

0.7

� �

� �

0.6

� �

I

I

I

�

0.5

�

�

0

50

100

t /t

150

200

250

10 -2

10 -1

Ÿ÷

10 0

I

300

Inverse energy cascade in homogeneous rotating turbulence strongly depends on the relation between domain size and initial characteristic size of flow structures.

Confinement enhances the inverse energy cascade and the transition towards 2D flow.

Varying Lz by increasing the domain size and varying LÎ by reducing the forcing wave-number might not be interchangeable.

Long time integration, however, leads to steady-state solutions, which are influenced by the domain size.

Isotropic

10 -4

CONCLUSIONS

Inverse cascade reduces with increasing Lz /LÎ!

� �

0.8

0.4

Áinv = PI ≠ Á‹

Lz /LÎ

10 -2

Front-view of computational domain Rotation is directed upwards Iso-surfaces of Q colored by Êz / ÎÊÎ

Energy dissipation rate after rotation has been imposed and starting from an isotropic flow field.

1.1

10 0

Resolution: 768 ◊ 768 ◊ 12288 ≥ 7 · 109 Domain Size: [0, 2fi] ◊ [0, 2fi] ◊ [0, 32fi]

BOX-SIZE EFFECTS I

Ÿ

10 2

10 -6

Exact time-integration for all forces; 3rd order Runge-Kutta for advection

Á‹ /PI

64

2

Aerodynamics Group, Faculty of Aerospace Engineering, t.pestana@tudelft.nl

b

100

Rotating Is there a domain size for which the inverse cascade vanishes?

Áinv

Department ASM

INVERSE ENERGY CASCADE IN FORCED Inverse Energy Cascade in HOMOGENEOUS ROTATING TURBULENCE Forced Rotating Turbulence PhD Candidate: Tiago Pestana Department: AWEP Section: Aerodynamics Promotor: Prof. Stefan Hickel Contact: t.pestana@tudelft.nl

10-1

6

10-2 100

Iso-surfaces of Q colored by Êz

101

Lz /LÎ

102

103

DELFT UNIVERSITY OF TECHNOLOGY FACULTY OF AEROSPACE ENGINEERING INSTITUTE OF AERODYNAMICS

Ring of Fire

1

2

3

4

Department ASM

PhD Candidate: Alexander Spoelstra Department: AWEP Section: Aerodynamics Supervisor: Dr. Andrea Sciacchitano Promotor: Prof. Dr. Fulvio Scarano Contact: a.m.c.m.g.spoelstra@tudelft.nl

Background A body moving through a fluid experiences aerodynamic forces such as lift and drag. In speed-sports such as cycling or speed skating, the contribution of the aerodynamic drag to the total drag can go up to 90%.

The last few decades many studies have discussed the improved performance obtained by aerodynamic drag reduction. Conventionally, these studies use force balances or pressure taps to evaluate changes in aerodynamic drag due to geometry variations. These measurements, however, may be qualified as ‘blind’ as they give no information about the flow field. In more recent years the amount of aerodynamic studies that make use of local flow measurements increased, either by scanning through the measurement area with pressure probes, conducting whole-field measurements using particle based velocimetry or by CFD simulations, leading to increased understanding of the human body aerodynamics. Presently, however, no measurement system exists that is able to capture the detailed aerodynamics of athletes in action.

On-site Measurements

Figure 1 - Schematic view of the experimental setup.

Working principle The aerodynamic drag acting on the cyclist can be expressed using a control volume approach invoking the conservation of momentum in a frame of reference moving with the model assuming stationary upstream conditions. The force can be defined as the difference between momentum before and after passage of the model. When a wake station sufficiently far from the object is considered, the pressure term can be neglected and the aerodynamic drag is expressed as:

The Ring of Fire makes it possible to perform full-scale,  D t    uwake  uenv  uC  uwake  dS (1) on-site aerodynamic measurements on athletes in action Swake and visualize the flow around him. By using large-scale stereoscopic particle image velocimetry (stereo-PIV) with where uC is the velocity of the cyclist, uwake and uenv are Helium Filled Soap Bubbles (HFSB) as tracer particles, a respectively the velocity fields after and before passage of the cyclist. Finally, a statistical analysis on the measurement area of 1.5×1.8m2 was achieved. instantaneous drag through equation 1 yields the ensemble-averaged drag and its uncertainty.



Further readings Spoelstra, A.; Terra, W.; Sciacchitano, A. The Ring of Fire for in-Field Sport Aerodynamic Investigation. Proceedings 2018, 2, 221.

Figure 2 – Ensemble-averaged spatial development of non-dimensional streamwise velocity in the wake of the cyclist.

65

Department ASM

PhD Candidate: Zeno Belligoli Department: AWEP Section: Aerodynamics Supervisor: Dr. R.P. Dwight Promotor: Prof.Dr.Ir. G. Eitelberg Contact: z.belligoli@tudelft.nl

The Anti­Fairing

Reducing drag in junction flows

1

2

3

4

Usually, corner separation does not contribute much to the interference drag.

1. Problem Junction flows occur when a boundary layer separates due to the presence of an obstacle on its path they are responsible for increasing the drag with an → additional component called “interference drag”.

The most common solution to the problem relies on the modifiication of the wing leading­edge by applying a fairing/fillet to prevent the boundary layer from separating.

The horseshoe vortex is the main contribution to the drag at junction configurations.

11% drag reduction compared to baseline geometry.

T: wing thickness

2. Methodology What is the effect of, instead of modifying the wing shape, modifying the geometry of the body? A RANS gradient­based optimization with adjoint techniques was carried out with this constraint.

The optimized geometry presents itself as a shallow dent wrapped around the junction area, and of a thickness comparable to the boundary layer thickness.

66 3. Experimental and Numerical Validation Results from two PIV experimental campaigns and a RANS simulation with a finer mesh than the one used for the optimization were analyzed. The drag difference between the optimized geometry and the baseline was estimated using (variations of ) the integral form of the momentum equations:

D=∬S (−ρ ⃗v (⃗v⋅⃗ n )− p ⃗ n + ⃗τ⋅⃗ n )ds

CFD

EXP 1

EXP 2

3% 12% 17% Estimated drag reduction compared to baseline geometry

where S is the surface of a closed control volume surrounding the junction area.

4. Working Mechanism The experimental and numerical results confirm that the optimized geometry reduces the momentum deficit in the wake of the junction area. Usually, the momentum component is the largest contribution to the drag, hence the large drag reduction. The influence of the pressure component has not been completely studied yet, but premliminary results suggest it can be non­negligible

Stream­wise velocity at x/T = 4.4 downstream the wing leading­edge. Left: baseline; Right: optimized. U: free­stream velocity.

Furthermore, a definitive explanation of the funtamental mechanism reducing the momentum losses is still object of investigation.

Robotic PIV

1

2

3

4

Department ASM

PhD Candidate: Constantin Jux Department: AWEP Section: Aerodynamics Supervisor: Andrea Sciacchitano Promotor: Fulvio Scarano Contact: C.Jux@tudelft.nl

Volumetric PIV of a full-scale cyclist What is new?

Robotic PIV presents a novel approach to quantify large-scale aerodynamic flows of complex geometries, based on the introduction of coaxial volumetric velocimetry (CVV) which lends itself to robotic manipulations.

A compact velocimetry probe is realized by alignment of imaging and illumination axes. The probe can easily be manipulated, without effecting the relative position of the imagers and the illuminated volume. Thus, the probe can be repositioned, while maintaining its original calibration.

A collaborative robotic arm is installed to accurately control the position and orientation of the velocimetry probe. The resulting robotic PIV system allows for fast repetition times of subsequent measurements.

Its potential

The first major experiment with the introduced system featured the flow analysis of a full-scale cyclist in the Open Jet Facility at TU Delft. Data is gathered in a 2mÂł volume, composed of 450 individual acquisitions. This presents a major leap forward in the measurable volume size.

Further readings

Schneiders JFG, Jux C, Sciacchitano A & Scarano F. (2018). Co-axial volumetric velocimetry. Measurement Science and Technology Jux C, Sciacchitano A, Schneiders JFG & Scarano F. (to appear). Robotic volumetric PIV of a full-scale cyclist. Experiments in Fluids

Coaxial volumetric velocimetry

Alignment of imaging and illumination direction offers several advantages along with a number of challenges.

The compactness of the system yields a robust velocimetry probe. The small aperture angle with near parallel illumination greatly enhances optical access as compared to classical tomographic PIV. Yet, the low tomographic aperture impacts reconstruction uncertainty in the in-depth direction of the deep measurement volume. Accuracy is enhanced by using temporal information of the tracer particles.

Further, the light intensity reduces with fourth order of the measurement depth, requiring scattering efficient tracer particles such as Helium filled soap bubbles.

DOF

x y

z

β zmin

zf

zmax

2D illustration of coaxial volumetric velocimetry principle

67

Department ASM

Efficient Adjoint Approach to Automatic Mesh Optimization for Predictive Large Eddy Simulation

PhD Candidate: Xiaodong Li Department: AWEP Section: Aerodynamics Supervisor: Steven Hulshoff Promotor: Stefan Hickel Contact: X.Li-12@tudelft.nl

Motivation and Objectives

1

2

3

4

Project Route

Large Eddy Simulation(LES) is needed for more reliable flow predictions in industrial application[1] which involves in high Re turbulent flows. LES can resolve

Literature Research

an appropriate range large scales of turbulence when the computational mesh is fine enough. However, constructing an appropriate mesh requires close supervision and strongly depends on the

Perform Preliminary Study Using Flow Solver

users’ experience[2].

Therefore, our research aims to develop a novel

Determine Methods for

technique that can

Solving Adjoint Problems

 improve the accuracy and efficiency of LES.  devise an intervention-free high-fidelity simulation method.

Adaptive meshes on symmetry plane for Launch Abort Vehicle, Ma = 1.1, Alpha= −25°[3]

Implement the Dual Solution Procedure

Research Methods This approach is based on INCA (a flow solver) , which completes the Cartesian

68

Study Grid

Adaptive Mesh Refinement (AMR) techniques

Adaptation Strategy

in Cut-cell immersed boundary method. AMR will be utilized in current project to fulfill the automatic mesh adaptation, which will yield optimized meshes for global functions.

Cartesian AMR grid, from Stefan Hickel 2017.

Evaluate Test Cases

The Adjoint-based approach, which connects the local mesh density to the accuracy of the chosen outputs (e.g. Cl, Cd), will be used. We will develop efficient

Apply Proposed Technique

approaches to solve adjoint equations and use the primal solution of adjoint

in Applications

problems to estimate the error of global outputs in unsteady flow problems.

Verifications and Applications  Analyze the proposed technique in Shock

Turbulent Boundary Layer Interaction.  Employ this approach for the evaluation of flow-control method in flow separation.  Apply this method in practical aerodynamic problems, e.g. a transonic nozzle cascade.

Shock Boundary-Layer Interaction[4] Iso-surfaces of lambda 2 criterion, from Stefan Hickel 2017.

References

1.

2.

3.

4.

Witherden, F. D., & Jameson, A. (2017). Future Directions in Computational Fluid Dynamics. In 23rd AIAA Computational Fluid Dynamics Conference (AIAA paper No. 3791).

Nemec, M., & Aftosmis, M. (2007, June). Adjoint error estimation and adaptive refinement for embedded-boundary Cartesian meshes. In 18th AIAA CFD Conference (AIAA paper No. 4187).

Nemec, M., Aftosmis, M., & Wintzer, M. (2008). Adjoint-based adaptive mesh refinement for complex geometries. In 46th AIAA Aerospace Sciences Meeting and Exhibit (AIAA paper No. 725).

Pasquariello, V., Hickel, S., & Adams, N. A. (2017). Unsteady effects of strong shock-wave/boundary-layer interaction at high Reynolds number. Journal of Fluid Mechanics, 823, 617-657.

AEROSAPCE ENGINEERING - AERODYNAMICS

Department ASM

69

Department ASM

Aerodynamic drag determination of a cyclist mannequin by large-scale PTV Motivation

The reduction of the aerodynamic drag of elite cyclists leads to improved performance. Hence, measurement of this resistive force is essential.

Understanding of the flow structures generating the drag force allows a systematic drag reduction. This work aims to assess the feasibility of a largescale PIV wake rake for the investigation of the wake flow field and determination of the aerodynamic drag on a full-scale cyclist model.

HFSB seeding injection

Results

A distinct asymmetric flow topology is observed, partly due to the flow separation on the left leg producing two strong separating shear layers.

Regions of high velocity deficit are located behind the saddle and drivetrain areas, where 50% of the aerodynamic drag stems from the lower legs and bike. The pressure largely recovered to freestream conditions.

Wouter Terra AWEP Aerodynamics May 2015 Prof. Dr. F. Scarano Dr. A. Sciacchitano w.terra@tudelft.nl

Reconstructed particle tracks

U∞

Laser light

y

z x

Experiments in the OJF wind tunnel

cameras

70

PhD Candidate Department Section Start PhD Promotor Co-promotor Contact

       

Full-scale cyclist mannequin at time-trial pro bike Measurement between 13 and 15 m/s 3 x Photron High-Speed camera Helium-filled soap bubbles (HFSB) tracers Field of view of 1x1.6 m2 Flow velocity from Lagrangian Particle Tracking Drag force from conservation of momentum Balance measurements for validation

Theoretical background

The time-average drag of an object in relative motion to an incompressible fluid can be expressed invoking the conservation of momentum in a control volume: D 

 (U



 u )udS  

S wake

momentum term

 u ' dS   ( p 2

S wake

Re stress term



 p )dS

S wake

Pressure term

The pressure term is evaluated solving the Poisson equation for pressure.

The obtained drag from PIV is compared to force balance data. The drag accuracy is estimated to be within 2.4% of CD: The drag resolution of this PIV wake rake is 24 drag counts.

European Research Council Proof of Concept Grant “Flow Visualization Based Pressure”

Helium-filled soap bubbles for large-scale PIV Why Helium-filled soap bubbles?

Helium-filled soap bubbles (HFSB) of 0.5 mm diameter are used for large-scale Particle Image Velocimetry (PIV) because they are 10,000 times brighter than conventional PIV seeding. This allows for the illumination of measurement volumes larger than 10 liters or field of views that can capture the full wake an airplane model (>1 m²).

1

2

3

4

Department ASM

PhD Candidate: David Faleiros Department: AWEP Section: Aerodynamics Supervisors: Andrea Sciacchitano and Marthijn Tuinstra Promotor: Fulvio Scarano Contact: d.englerfaleiros@tudelft.nl

Bubble properties The combination of flow rates of air đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D; , helium đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ??ťđ??ťđ??ťđ??ť and soap đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; yields the bubble size and density, the polydispersity of the size distribution and the bubble production rate.

Bubbling

Jetting

How are the bubbles generated?

HFSB are produced in a bubble generator that coaxially supplies air, helium and soap. The soap film is extruded from the edge of the internal duct by the air and helium flows. The soap film accelerates and reduces its thickness until it reaches the orifice, where it breaks into soap bubbles filled with helium.

Transition from bubbling to jetting occurs by increasing đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D; , đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ??ťđ??ťđ??ťđ??ť or by decreasing đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; . Bubbling is required for a monodisperse distribution. The bubble volume increases linearly with đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ??ťđ??ťđ??ťđ??ť /đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D; . The bubble production rate increases linearly with đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D; .

71

Do they follow the flow?

Particle time response indicates how well a particle responds to flow acceleration. It scales with the density difference of particle and fluid (đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x2018;?đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x153;&#x152;đ?&#x153;&#x152;) and with the particle diameter squared đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;?đ?&#x2018;?2 . A neutrally-buoyant bubble (đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x2018;?đ?&#x2018;? = đ?&#x153;&#x152;đ?&#x153;&#x152;) is obtained through the perfect balance of helium and soap flow rates (đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ??ťđ??ťđ??ťđ??ť /đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; ~ 1,000). Per definition, a neutrally-buoyant bubble has zero time response, following the flow accurately.

Flow upstream of a cylinder

Can they measure turbulence?

A study in a turbulent boundary layer at 50 m/s has shown that the bubbles follow the velocity fluctuations with comparable accuracy to conventional tracers (DEHS). In the case of turbulent flows, the unsteady added-mass force act to reduce the difference in acceleration between the particle and the fluid. Even heavy bubbles (AFSB, đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x2018;?đ?&#x2018;? ~ 4đ?&#x153;&#x152;đ?&#x153;&#x152;) were able to capture the fluctuations accurately.

DEHS is an established PIV tracer and it is used as reference.

HFSB follows closely the DEHS streamlines. Air-filled soap bubbles (AFSB) are four times heavier than air and cannot follow the flow.

Department AWEP

Flight Performance & Propulsion (FPP)

Heads of section: Prof. dr .ir. Leo Veldhuis Prof. dr. ir. Piero Colonna

72

Supervisors Dr. ir. Roelof Vos Dr. Daniele Ragni Prof.dr.ir Piero Colonna Dr.ir. Matteo Pini Dr. Arvind Gangoli Rao Dr.ir. Mark Voskuijl

Department AWEP

The Flight Performance group focuses on advanced and innovative aircraft configurations, novel propulsion concepts, and aircraft engine integration.

PhD Candidates:

Our mission is to advance the design of innovative aircraft configurations and propulsion concepts, by exploration of new technologies to obtain novel or improved solutions, advances in flight physics to improve the prediction and simulation of airvehicle performance, and new methods and tools to improve the quality and effectiveness of the design process. The knowledge and competences within the Flight Performance group are clustered in three key areas: aircraft design & design methodologies, flight mechanics, and propulsion integration.

-- Lucia Azzini

-- Nando van Arnhem -- Tomas Sinnige -- Reynard de Vries -- Tom Stokkermans -- Carmine Varriale -- Biagio Della Corte -- Sumit Tambe

73

Aerodynamic installation effects of propellers integrated with the aircraft tail

PhD Candidate: Nando van Arnhem Department: AWEP Section: FPP Supervisor: Dr. Roelof Vos Promotor: Prof. dr. ir. Leo Veldhuis Contact: n.vanarnhem@tudelft.nl 1

3

4

Main challenges of integration

Opportunities of tail-mounted propellers

• Determining implication on control and stability • Weight and balance • Non-uniformity of inflow to propellers • Structural integration • Safety

• Reduction in cabin noise • Lower wing-interaction at high Mach number • Beneficial contribution propeller to stability

Examples of tail-mounted propeller configurations

Methods

interaction

On the interaction

• Experimental: flow fields, surface pressure and integral forces • Numerical: (un)steady RANS CFD • Configurations: from propeller-tailplane combination, to full aircraft geometries

• Propellers interact with stabilizing surfaces • Wing influences inflow field to propeller • Interaction highly depends on flight conditions

Installation effects on vertical tail

Installation effects on horizontal tail

Loading distribution • Loading increased by propeller • Direction of elevator deflection has large effect • At angle of attack, significant increased loading on nacelle

Normal force coefficient cn

0.6

• Co-rotating propeller create a sidewash on vertical tail • Trim drag penalty ~ 2 deg sideslip angle • Vertical tail loses ~ 4% effectiveness at no side slip angle (destabilizing effect)

Exp. prop-on Exp. prop-off CFD (FB) prop-on CFD prop-off

0.4 0.2

= +10 deg e 0.0

= −10 deg e

−0.2

−1.2

−1.0

−0.8 −0.6 −0.4 Spanwise coordinate

74

−0.2

Side force on vertical tail

1.0

vertical coordinate -z/b vertical tail

Department AWEP

2

0.0

Prop off One engine inoperative, OU One engine inoperative, IU Both propellers

0.8 0.6

IU

0.4

z 0.2

- high thrust condition - no wing present

0.0 -0.15

−1.0

p 1.0 Exp. J = 0.8

1.5

CFD (FB)

800 -400 0 400 800 isosurface of axial vorticity [s-1] vorticity at 800 s-1

1.0

= 0 deg

1.15

e

= −10 deg

e

= +10 deg

3.2

e

> 0 deg

0.9

Advance ratio J

1.0

2.4 1.6 e

e

= −10 deg

0.8

< 0 deg

e

0.7

e

Γb

= 0 deg

5.6

= +10 deg

= +10 deg

Ω

1.00

evaluated at

6.4

Ω

4.0

Ω

1.05

Ω

4.8

1.10

e

CN / CN

e

,prop-off

1.20

Tailplane

Upstream effect: Non-uniform thrust distribution, normal and side forces

0.8 0.0 − 0.8

[1/m]

0.2 0.4 0.6 0.8 Chordwise coordinate x' / c

/ Tiso

0.0

Sectional thrust dT

Pressure coefficient C

• In-plane and out-of-plane forces directly contribute to trim (drag) and aircraft stability • Propeller forces depend on the local inflow field • Wing, fuselage and tail surfaces influence inflow field • Non-uniform inflow result in noise and vibrations

0.5

Ω

Integral loading: elevator effectiveness

-0.05 0.00 0.05 0.10 sectional side force coefficient c y

Installation effects on propeller forces

0.0

2.0

-0.10

tip vortices (CFD)

−0.5

Pressure distribution

OU

y

normal force = f(α, Γ, ...) varying qlocal and αlocal

Γb wake can impinge on propeller at high α

Downstream effect: Change of inflow field to propeller

Future work 6.4 5.6 4.8 4.0 3.2 2.4 1.6 0.8 0.0 −0.8

Fundamental study of propeller behind a wing: impact on propeller forces

Experimental campaign [1/m] on full aircraft configuration Sectional thrust dT / T iso

The project leading to these results has received funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under Grant Agreement number 699715

Aerodynamic Benefits of Wingtip-Mounted Propellers

PhD Candidate: Tomas Sinnige Department: AWEP Section: FPP Supervisor dr. D Ragni Promotor: prof.dr.ir. L.L.M. Veldhuis Contact: T.Sinnige@tudelft.nl 1

2

3

4

Opportunities Department AWEP

• Wingtip-mounted propellers feature aerodynamic benefits due to attenuation of the tip vortex by the propeller slipstream • Distributed electric propulsion can overcome challenges that have prevented application on aircraft until now • Research of key interaction effects is required to maximize performance benefits

Methods • Experiments at TU Delft’s Low-Turbulence Tunnel • Modular wing to compare wingtip-mounted and conventional configurations • Force, flowfield, and surface-flow measurements 75

Performance Benefits • Decrease in drag by wingtip-mounting due to reduction of induced drag (wingtip vortex attenuation) • Drag reduction of 80 counts at loading conditions typical of cruise, 170 counts for climb • Different slipstream deformation and flow topology in wingnacelle junction compared to conventional configuration

Conclusions • Wingtip-mounted propellers offer integration benefits due to tip-vortex attenuation • Significant drag reductions compared to conventional propeller-wing configuration • Multidisciplinary analyses required to evaluate impact on performance benefits at aircraft level

Department AWEP

Aerodynamic Performance of Over-the-Wing DistributedPropulsion Concepts Background Hybridâ&#x20AC;&#x201C;electric propulsion (HEP) is an enabling technology for distributed propulsion (DP), i.e. the distribution of propulsive elements along the airframe in order to improve the aero-propulsive efficiency of the vehicle. Placing the propulsors in an over-the-wing (OTW) configuration is a synergistic solution which can lead to increased effective bypass ratio, improved lift-to-drag ratio, and reduced flyover noise.

Type of research

PhD Candidate: Reynard de Vries Department: AWEP Section: Flight Performance & Propulsion Supervisor Dr. ir. Roelof Vos Promotor: Prof. dr. ir. Leo Veldhuis Contact: R.deVries@tudelft.nl 1

2

3

4

Problem statement No systematic design procedure exists for HEP aircraft. Thus, the aero-propulsive interaction effects are not taken into account in the design process, despite their impact on wing and powertrain sizing. Furthermore, the aerodynamic interaction effects between the airframe and OTW propulsors are not readily understood, making it difficult to predict the aerodynamic performance of these systems.

Fundamental/Descriptive

Means-end/Engineering

L

Objectives

T D

Identify flow phenomena caused by interaction effects

Determine influence of governing parameters on interaction effects

Relate interaction effects to system performance

Assess impact on aircraft performance

76

Activities

â&#x20AC;˘ Detailed investigations of the unsteady interaction effects of OTW propeller systems: â&#x20AC;˘ Propellerâ&#x20AC;&#x201D;boundary-layer interaction in high-lift conditions â&#x20AC;˘ Non-circular ducts â&#x20AC;˘ Propellerâ&#x20AC;&#x201D;propeller interaction

â&#x20AC;˘ Experimental investigation of steady OTW system performance â&#x20AC;˘ Develop numerical tool for aerodynamic performance prediction

â&#x20AC;˘ Develop conceptual sizing method for hybrid-electric distributed-propulsion aircraft â&#x20AC;˘ Assess impact of aero-propulsive interaction effects at aircraft level

F

đ?&#x2018;&#x192;đ?&#x2018;&#x192;f

GT

BAT

đ?&#x2018;&#x192;đ?&#x2018;&#x192;bat

PM

đ?&#x2018;&#x192;đ?&#x2018;&#x192;gt

đ?&#x2018;&#x192;đ?&#x2018;&#x192;e1 đ?&#x2018;&#x192;đ?&#x2018;&#x192;e2

GB đ?&#x2018;&#x192;đ?&#x2018;&#x192;gb

đ?&#x2018;&#x192;đ?&#x2018;&#x192;s1

P1

đ?&#x2018;&#x192;đ?&#x2018;&#x192;p1

EM1 EM2

đ?&#x2018;&#x192;đ?&#x2018;&#x192;s2

P2

đ?&#x2018;&#x192;đ?&#x2018;&#x192;p2

Characterization of Metastable Condensation in High-Speed Flows for arbitrary fluids

PhD Candidate: Lucia Azzini Department: AWEP Section: FPP Supervisor: M. Pini Promotor: P. Colonna Contact: l.azzini@tudelft.nl 1

2

3

4

Background & Motivation: Non-equilibrium condensation occurs in a wide range of industrial processes (steam turbines, ORC power plants, carbon capture, etc.).

The formation of droplets lead to significant fluid-dynamic losses and component damage.

Department AWEP

â&#x20AC;˘ Components must be designed such that metastable condensation is mitigated. â&#x20AC;˘ Need of reliable models to predict the onset of condensation and the droplet characteristics.

Aerospace Engineering

Research objectives

ď&#x192;ź

Semi-analytical approach to predict the Wilson point for arbitrary fluids

ď&#x192;ź

Robust and computationally efficient numerical schemes for design purposes

Physical Understanding

Numerical methods

â&#x20AC;˘ Investigation on steam supersonic expansions â&#x20AC;˘ Wilson point redefinition: new time-dependent approach

â&#x20AC;˘ Transport equations according to the method of moments â&#x20AC;˘ Calibration on available experimental data

ď&#x192;ź ď&#x192;ź

ď&#x192;ź ď&#x192;ź

No two-phase simulation required Applicable to arbitrary fluids

đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤ = đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;(đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ą)

More robust and efficient Simulation time < 2x single phase

nimphea Non-ideal multi hase eulerian analysis

Deliverables: 1. Science

Final goal

â&#x20AC;˘ Validated semi-analytical method for steam and CO2 data â&#x20AC;˘ Investigation on organic fluids (R11, R21) â&#x20AC;˘ Evaluation of the Wilson line for arbitrary flows

â&#x20AC;˘ Design of turbomachinery components

2. Numerics

investigation of metastable condensing flows with an implicit upwind method.

â&#x20AC;˘ Azzini, L. and Pini, M. , Numerical investigation of high pressure condensing flows in supersonic nozzles. â&#x20AC;˘ Azzini, L. and Pini, M. , Semi-analytical approach for Wilson point prediction in homogeneously condensing steam flow.

Sponsor / project logo

â&#x20AC;˘ Development of a multi-D numerical model for metastable condensation in the open-source CFD code SU2

Propulsion & Power

References: â&#x20AC;˘ Azzini, L., der Stelt, T. V., and Pini, M. , Numerical

â&#x20AC;˘ Gyarmathy, G., 2005. Nucleation of steam in highpressure nozzle experiments. Proceedings of the Institution of Mechanical Engineers, Part A, 219(6), Jan, pp. 511-521, SAGE Publications

nimphea

77

PhD Candidate: Tom Stokkermans Department: AWEP Section: FPP Supervisor: Dr. ir. Mark Voskuijl Promotor: Prof. dr. ir. Leo Veldhuis Contact: t.c.a.stokkermans@tudelft.nl

Aerodynamic Installation Effects of Lateral Rotors on a Novel Compound Helicopter Configuration

1

right upper wing

right nacelle

Expand the flight envelope, especially improve the high speed capability of the helicopter while maintaining a helicopters efficient hover advantage over fixed wing aircraft.

right spinner

V∞ right lower wing

left upper wing

Department AWEP

4

Compounding, how?

Additional lift in the cruise condition provided by a box-wing design; additional thrust and counter-torque provided by wingtip-mounted lateral rotors in pusher configuration.

left lower wing

left nacelle

left spinner

left lateral rotor

Hover condition

Cruise condition • •

3

right lateral rotor

fuselage

Compounding, why?

2

• •

Interaction of lateral rotors with box-wings.

Left and right lateral rotor delivering forward thrust.

Interaction of lateral rotors with main rotor and box-wings.

Left lateral rotor delivering forward thrust and right reverse thrust.

Tmr

Tmr

Method:

V∞

Tlr

unsteady RANS CFD

Tlr

Vd

+0.05

0.18Rlr

2.0

0.5

0.09 left right f = -3º f = 0º f = 5º

0.07 0.06 0.05

2.5

1.5 1.0 0.5 0.0 -0.5

+0.1 Wing-half lift coefficient [-]

+0.02

Drag distribution Cd c [m]

Lift distribution Cl c [m]

0.5 (b) Right lateral rotor flowfield.

0.0

(b)

(a)

Installation effects on lateral rotor performance: • •

+0.1

Significant sinusoidal variation in blade loading.

Reduction in lateral rotor efficiency for non-optimised design: - Blade sections operate partially in non-linear part of the lift curve; - and experience a stall-unstall phenomenon for high thrust.

thrust power 0

Low pressure on suction side of blade extends to upstream wings.

Small variation in wing lift and drag of 1.4% and 6.3% of mean resp.

+0.1

1.5 1.0

φl = 230º

Upstream effect on wing loading: • •

2.5 2.0

Blade section efficiency [-]

2.5

1.5

0.10

Blade section thrust w.r.t mean [-]

0.11

1.5

Propulsive efficiency gain [-]

Strong positive correlation between wing lift and lateral rotor efficiency.

Largest increase in efficiency near root of rotor blade.

Large variation in blade loading of 70% of mean.

0.08

3.0

(a) Left lateral rotor flowfield.

Installation effects on lateral rotor performance: • • •

Near perpendicular inflow to lateral rotors due to main rotor slipstream.

Disturbance from wings to inflow due to deflection of main rotor flow.

Blade thrust, power [-]

78

+0.05

• •

Lateral rotor power w.r.t nom. case [-]

Va Vt

Time-averaged flowfield:

Tangtential velocity Vt / V∞ [-]

Wing wake dominant effect in axial velocity inflow to lateral rotors.

Wing induced flow around nacelle dominant in tangential velocity inflow.

Axial velocity (Va-V∞) / V∞ [-]

• •

Velocity magnitude |V| / Vd [-]

Time-averaged flowfield:

φl = 230º

90 180 270 360 Lateral rotor rotation angle [º]

Validation of hover interaction: • •

3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.0 0.5 1.0 1.5 2.0 Lateral rotor thrust w.r.t. nom. case [-]

lateral rotor

Lateral rotor−open jet−wing interaction in OJF. Future comparison with numerical model.

Vd wing

upper wing

left installed right installed left isolated right isolated

flap

blade

time variation lower wing

upper wing: Cl c Cd c

0.75Rlr

(b) Cp plane

lower wing: Cl c Cd c

Cp [-]:

+

Instantaneous pressure distribution on Cp plane.

Future work: lateral rotor design, improving cruise benefits and reducing hover drawbacks The project leading to this application has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 685569 — PROPTER — H2020-CS2-CFP01-2014-01/H2020-CS2-CFP01-2014-01.

Flight Control Methods exploiting Control Surface Redundancy and Interactions

PhD Cand.: Carmine Varriale Department: AWEP Section: FPP Supervisor: Mark Voskuijl Promotor: Leo Veldhuis Contact: C.Varriale@tudelft.nl 1

2

3

4

Background and motivation

+ Innovative configurations

?

= Redundant controls

Improved Performance and Comfort

New Maneuvering Techniques

 How to allocate pilot commands among redundant control surfaces?  How to exploit interaction effects to improve performance and comfort?

79

 Aerodynamic models focused on control surfaces  Estimation of interaction effects / unsteady effects  Non-linear algorithms for optimal Control Allocation

Primary Goals

 Generation of aerodynamic models through CFD / 3D Panel Methods / Wind Tunnel Experiments  Control Architecture Design  Flight Simulation with Multi-Body Dynamics

Approach

References

Department AWEP

Conventional aircraft configurations are nowadays optimized to the limit of their possibilities.

But air traffic keeps growing and the air transport system is saturating quickly.

To face the future market evolution, new unconventional aircraft are being investigated, with multiple surfaces and high part integration

The quantification of the benefits that multiple redundant control devices and their integration have on the performance and safety of future airplanes

Outcome

Johansen, Fossen - 2013 - Control allocation - A survey Harkegard, Glad - 2005 - Resolving actuator redundancy - Optimal control vs. control allocation Oppenheimer, Doman - 2007 - A Method for Including Control Effector Interactions in the Control Allocation Problem Durham, Bolling, Bordignon - 1997 - Minimum Drag Control Allocation

Knowledge

for better future aviation

Investigation of Boundary Layer Ingestion for Civil Transport Aircraft

PhD Candidate: Biagio Della Corte Department: Aerospace Engineering Section: Flight Performance and Propulsion Supervisor: Arvind Gangoli Rao Promotor: Leo Veldhuis Contact: B.DellaCorte@tudelft.nl

1

2

3

4

Department AWEP

Boundary Layer Ingestion Innovative aircraft designs are being investigated in the last decades in the effort of making transport aviation greener and more environment-friendly. These unconventional aircraft concepts exploit a better integration of the propulsive systems with the airframe. In this panorama, Boundary Layer Ingestion (BLI) is one of the most promising propulsion integration technologies.

In a BLI configuration, the propulsor is integrated onto the airframe such that thrust is produced by accelerating the low-momentum flow in the boundary layer. In this way, the wake of the airframe is re-energized and less kinetic energy is wasted. Therefore, the required thrust can be obtained with a lower power consumption. A schematic of the physical principle of BLI is shown below.

Numerical simulations of a Propulsive Fuselage configuration. Adapted from Kenway et al., 2018.

Ongoing analysis A wind-tunnel experiment on a representative Propulsive Fuselage was carried out at the Open-Jet Facility at TU Delft. The setup and its main features are shown in the photograph below.

80 Schematic of the physical working principle of Boundary Layer Ingestion.

Reproduced from Hall et al., 2017.

The most promising BLI configuration is the so-called Propulsive Fuselage Concept. It features a BLI engine placed at the back of the fuselage body, and it maximizes the portion of the airframe’s boundary layer re-energized by the propulsor.

Objectives of the research The PhD programme is focussed on the numerical and experimental investigation of the application of BLI in commercial aviation. The Research Question is formulated as follow:

Experimental setup in the OJF wind tunnel at TU Delft.

Outcome of the experiments: 1. BLI reduces power consumption of approximately 18% in simulated cruise conditions.

2. The shear and pressure forces on the fuselage are increased due to the suction produced by the propeller.

3. The propeller blade loading is shifted inboard, where the inflow velocity is lower.

Numerical simulations were also carried out. The aft-fuselage fan has been simulated through an actuator disk model. An example flow field is shown in the picture here below.

What are the effects of propulsion—airframe integration on system performance in aircraft featuring boundary layer ingestion?

The Research Question is articulated in these sub-tasks: 1. Identify the key steady and unsteady aerodynamic phenomena.

2. Identify the key design parameters driving these interactions.

3. Assess the effects of these parameters on system performance and noise emissions in on- and off-design conditions.

References

[1] Hall, D. K., Huang, A. C., Uranga, A., Greitzer, E. M., Drela, M., and Sato, S., “Boundary Layer Ingestion Propulsion Benefit for Transport Aircraft”, Journal of Propulsion and Power, Vol. 33, No. 5, 2017, pp. 1118-1129.

[2] Kenway, G. K. W., and Kiris, C., “Aerodynamic Shape Optimization of the STARC-ABL Concept for Minimal Inlet Distortion”, AIAA SciTech Forum, 2018

CFD simulations at V = 40 m/s and T/D = 1,5.

Outcome of the simulations: 1. The fuselage’s drag is increased by the interaction with the propulsor.

2. The drag penalty is mostly due to an increase in pressure drag due to the suction exerted by the propulsor.

Aerodynamic Interaction of Non-Uniform Inflows and Transonic Fans

PhD Candidate: ir. Sumit Tambe Department: AWEP Section: FPP Supervisor: Dr. A. Gangoli Rao Promotor: Prof.dr.ir. L.L.M. Veldhuis Contact: s.s.tambe@tudelft.nl 1

2

3

4

Silent Aircraft (Cambridge - MIT)

T

Background

AHEAD (TU Delft) Courtesy: : TU Delft

NOVA (ONERA)

D8 (NASA)

Courtesy: : NASA

Courtesy: ONERA

Problem â&#x20AC;˘ The efficiency and stability margin of a transonic fan is reduced due to the non-uniform inflow. â&#x20AC;˘ The reduced stability margin may lead to unsteady stall on the fan blades and can be a show-stopper for certain aircraft configurations. â&#x20AC;˘ The flow physics involved is complex and still not completely understood.

Department AWEP

â&#x20AC;˘ Aero-propulsive benefits of Boundary Layer Ingestion (BLI) have been proven in theory and are supported by simplified experiments in the recent literature. â&#x20AC;˘ Potential of up to 15% fuel burn reduction (Gunn and Hall, 2014)1. â&#x20AC;˘ Futuristic aircraft configurations from all over the world feature BLI.

Courtesy: : University of Cambridge

Comparison of benefits vs penalties involved in BLI (Source: Arend et al, 2017) 2.

Causes:

â&#x20AC;˘ Non-uniform mass flow deficit at the fan face. â&#x20AC;˘ Fan induced attenuation of the upstream non-uniform inflow (Induced swirl).

81

Breakdown of loss sources:

â&#x20AC;˘ Near hub: â&#x20AC;˘ Effect of spinner shape and rotation â&#x20AC;˘ Effect of local swirl angle and radial angle â&#x20AC;˘ Boundary layer losses â&#x20AC;˘ Mid-span: â&#x20AC;˘ Induced swirl â&#x20AC;˘ Induced vortical structures â&#x20AC;˘ Near tip: â&#x20AC;˘ Increased incidence angle â&#x20AC;˘ Off-design shock position â&#x20AC;˘ Shock Wave Boundary Layer Interaction (SWBLI) GE90 transonic fan (Courtesy: : GE)

Research Aim: To identify and characterize the flow phenomena leading to losses in the interaction regions.

Current Work: â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

Effect of non-axial inflow on the boundary layer instabilities on the spinner is studied at low Reynolds numbers.

Fundamental shapes are considered: Cones, Ellipsoid.

Infrared Thermography is used to measure the helical vortex structures in the boundary layer.

It is found that the angle of attack has influence on the wavelength and location of turning of the vortices. đ?&#x153;&#x201D;đ?&#x153;&#x201D;

AoA=0đ?&#x2018;&#x153;đ?&#x2018;&#x153;

AoA=4đ?&#x2018;&#x153;đ?&#x2018;&#x153;

AoA=20đ?&#x2018;&#x153;đ?&#x2018;&#x153;

Results of IR thermography measurements. 14th POD mode is shown here for different angle of attacks, for comparison. đ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6; â&#x2C6;&#x17E; = 2.5 đ?&#x2018;&#x161;đ?&#x2018;&#x161;/đ?&#x2018; đ?&#x2018; ; đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026; = 5000 ; D= 47mm (Flow from left to right).

1. Gunn, E. J. and Hall, C. a. (2014). Aerodynamics of Boundary Layer Ingesting Fans. In ASME 2014 Turbine Expo, pages 1â&#x20AC;&#x201C;13.

2. Arend, D. J., Wolter, J. D., Hirt, S. M., Provenza, A., Gazzaniga, J. A., Cousins, W. T., Hardin, L. W., and Sharma, O., 2017. â&#x20AC;&#x153;Experimental Evaluation of an Embedded Boundary Layer Ingesting Propulsor for Highly Efficient Subsonic Cruise Aircraftâ&#x20AC;?. In 53rd AIAA/SAE/ASEE Joint Propulsion Conference, no. July, pp. 1â&#x20AC;&#x201C;34.

Department AWEP

Wind Energy (WE)

Heads of section: Prof. dr. Gerard van Bussel Prof. dr. Damiano Casalino Prof. dr. Simon Watson

82

Supervisors Dr. ir. Carlos Simão Ferreira Dr. ir. Axelle Viré Dr. Sergio Turteltaub Dr. Daniele Ragni Dr. Francesco Avallone Prof. dr. Gerard van Bussel Dr. ir. Alexander van Zuijlen Dr. Gerard Schepers Koen Boorsma Dr. ir. Benjamin Sanderse Dr. ir. Wim Bierbooms Dr. ir. Roland Schmehl

In its research activities there is a focus on large electricity generating wind turbines on the multi megawatt scale. Both technology development aspects as well as fundamental aspects of wind energy conversion are part of the research program. A small but challenging part of the research activities addresses the urban deployment of wind power.

Regarding education several dedicated courses on the BSc and the MSc level are developed for students of the faculty and of the TU Delft. Apart from offering an MSc focus on Wind Energy for TU Delft Aerospace students, the section participates in the new 3TU MSc curriculum Sustainable Energy Technology and in the European Master course in Renewable Energy offered by EUREC.

PhD Candidates: -- Bruce LeBlanc

-- GaĂŤl de Oliveira

Department AWEP

The scope of the activities of the Section Wind Energy is to facilitate the development of wind energy technology and the expansion of the use of wind power all over the world through research and education.

-- Jaco Brandsen -- Christopher Teruna -- Delphine De Tavernier -- Sebastian Sanchez PerezMoreno -- Navi Rajan -- Carlos Baptista -- Chihoon Hur -- Vinit Dighe -- Laurent van den Bos -- Sebastian Rapp -- Pranav Manjunath

83

Department AWEP

Overview and Design of PitchVAWT Vertical Axis Wind Turbine With Active Variable Pitch Due to advances in numerical modeling and hardware scaling, aspects of Vertical Axis Wind Turbines (VAWTs) can now be studied in greater detail than ever before. Turbine blade pitch has been proposed as a method to control overall turbine loading.

PhD Candidate: Bruce LeBlanc Department: L&R Section: Wind Energy Supervisor: Carlos Ferreira Promoter: Gerard Van Bussel Start date: 15-3-2016 Funding: S4 VAWT Contact: b.p.leblanc@tudelft.nl

A 1.5 meter diameter, 1.5 meter height 2-bladed H-Darrieus VAWT with individual blade pitch control has been designed, built, and tested at the wind tunnel facilities of Delft University of Technology.

Predicted Cp vs Tip Speed Ratio Varying fixed pitch positions

84

Model of the PitchVAWT turbine with installed sensors.

Computational modeling is perfomed in 2D and 3D systems in order to study the aerodynamic capabilities of individual pitch control of a VAWT

Measured normal force from strain gage response vs rotor azimuth for TSR 2.9

Thrust loading in X and Y directions during normal operation

the issue with turbulence modelling on wind turbine Airfoils Airfoils

«Big whirls have little whirls

Large Wind Tunnels

Errors in Load Prediction

needed to replicate airfoil field

come

conditions mean experimental

that feed on their velocity, and little whirls have lesser whirls and so on to viscosity.

play essential role in

»

with

with insufficient data [2, 3]

wind turbines

Weather Prediction by Numerical Process Richardson LF, CUP

semi-empirical models

incomplete physics callibrated

number grows

performance of large

from

turbulence

costs escalate as Reynolds

Exaggerated boundary layer thickness estimation at Re

CFD simulation

Turbulence is a complex flow

For high Reynolds numbers,

process

by

turbulent processes are too

Mach < 0.3 Reynolds >10e6

eddy

complex to be fully resolved

Wind turbine airfoil flows are

scarce validation and

motions of multiple scales.

(DNS) in Computational Fluid

incompressible and have very high

large uncertainties

Large eddies decompose into

Dynamics (CFD) simulations.

Reynolds

smaller

Engineers use approximate

dominated

seemingly

chaotic

of

appearance,

nearly but

equations

(VI/RANS/LES)

handle turbulent phenomena

larger coherent structures [1].

with closure models [2, 3].

« Turbulence remains the last unsolved

with

»

problem of classical mechanics.

Deterministic Chaos, Kumar N, U. Press

stays

constant while Reynolds grows

to

small eddies reorganize into

Mach

used

« Perhaps the single, most critical area in CFD simulation capability that will remain

as turbines increase in size to reduce costs

a pacing item by 2030 (...) is the ability to adequately predict viscous turbulent flows

»

CFD Vision 2030 Study, NASA CR 2014-218178

Gazing at Clouds to understand turbulence on Wind TURBINE Airfoils S o l u t i o n

Department AWEP

eddies

random

number.

codes

Gather 1 Adopt data rich approach to tune flow turbulence models measurements of very High Reynolds FlowS Flow

We propose to rethink the procedure for calibrating turbulence models used in Flat plate, Aerofoil

critical for successful data driven turbulence modelling,

recognize that current turbulence models were calibrated

Backward Facing Step

motivating

with a single handful of reference cases, and therefore attempt to create a large unified calibration dataset.

The large calibration dataset will be used to learn optimal

PREDICT

Power Production Loads Flow Law of the wall, e^N theory, Isotropic Turb.

Start with data assimilation and grow into (Deep)Learning

« Every flow is an observation of

»

by Durasaimy at Michigan University [4,5], Turbgate (http://turbgate.engin.umich.edu/) ?

Or should we develop a European alternative with broader flow data ?

High Altitude Winds, Clouds, Oceanic Currents

measurements of very high Reynolds turbulent flows, quantify data quality

Flow

and reduce redundant points

EO flow

Cannonical Flows

Instrument Pressure Tap, Wake Rake, Load Balance, Particle Image Velocimetry (PIV) Instrument Ultrasonic Anemometry, Wind LIDAR, SCADA and Turbine Controller Data Instrument N.A., Direct Numerical Simulation (to provide asymptotic behaviour)

Should we rely on the pioneer database hosted

Flow

Gather

Wind Tunnel

measurements

Water Pipe Experiments

hold unique information on high Reynolds turbulent

Taylor and Couette Flow

phenomena, essential to identify asymptotic tendencies.

Instrument [6,7] ADM Aeolus Instrument, Lagrangian Tracers on Optical/IR Measurements Instrument Hot wire, PIV, Pressure Tap and Stanton Tube

Learn 2 turbulence model calibration Curves

3 predict turbulent flows

4 infer new closure terms

There are many ways to learn from data. Our first experiment consisted in reproducing the way aerodynamicists work [2] with a genetic optimizer.

The data pool was too narrow and asymptotic tendencies were unreliable. Our 2nd experiment, a simple version of [4], had a virtually unlimited data pool and used neural networks. Results were better, but computationally expensive. Data assimilation approaches used in EO [ 7] could yield better results..

Learn dissipation coefficient (CD) closure to match a VI code (RFOIL)

Once established, the methodology will be

Model calibration curves can hint towards the

applicable to any type of turbulent closure

most problematic simplifications behind current

to RANS model (Spalart Almaras)

relation, thereby higlighting the common

turbulence models [5], and neural networks can

features of seemingly diverse models:

even learn improved closure terms [4]. But learning

Compare

VI

code

Optimize (NSGAII) G-Beta

results with trustable

closure

reference data

match results

2014 Tune the G-Beta constants of a viscous-inviscid (VI) solver (RFOIL) with genetic algorithms (NSGA2).

constants

to

Long term idea open to partners 2015 Process CFD Fields to learn neural VI closure relations.

Generate velocity fields in OpenFoam and process into CD , H and Retheta Run Neural Network to learn the CD in terms of H and Retheta

Navier Stokes

Large Eddy Filtered LES Navier Stokes Viscous-Inviscid

Obtain CD closure for VI Codes

2016 Gather partners to share data and write proposals.

Summer schools: JMBC Turb., LxMLS16 and 8th ESA EO.

algorithms do not aim to replace researchers: like

Reynolds Averaged

RANS Navier Stokes Vi Asymptotic

closure

1st simple experiment

2nd simple experiment

Designed by the Authors using: Simple Infographic Set by BNIMIT Bebas Font by Ryoichi Tsunekawa Open Sans Font by Steve Matteson

the phenomenon of turbulence.

Industrial Wind Cases

fill data gaps.

Semi Analytical Solutions

Turbulent Flow Database

models

LEARN

LES subgrid scale (SGS) closure models

multidisciplinary Earth Observation (EO)

Wind Turbine Wake

calibration for

models: Integral Boundary Layer (IBL) closures, RANS models like Spalart-Almaras (SA) and

bridges to

Flow

turbulence

conditional calibration rules for popular turbulence

View publication stats

Early experiments show that size of calibration dataset is

popular Computational Fluid Dynamics (CFD) codes. Like Duravaisamy [4,5] , we

genetic airfoil optimizers enhance the work of airfoil designers, neural networks can empower turbulence modellers. course in Turbulence, Tennekes & Lumley [1] AMITfirstPress of the boundary layer calculation in Rfoil for improved airfoil stall prediction [2] Modification van Rooij R, TU-Delft Report IW-96087R

Even when good calibration is achieved,

evaluation of RANS turbulence modelling for aerodynamic applications, Catalano P & [3] An Amato M, Aerospace Science and Technology, 7 493-509

turbulence models will still rely on many coarse

[4]

assumptions: most popular RANS and LES

A paradigm for data-driven predictive modeling using field inversion and machine learning, [5] Parish EJ & Duraisamy K, J. Comp. Phys. 305 758-774

closures rely on the Boussinesq hypothesis

turbulence par l'image, Heas P Heitz D and Memin E [6] LaLa Recherche: L’actualité des Sciences 2010-444

and rule some (if not all) anisotropy out.

Parameterization Of Turbulence Models Using 3DVAR Data Assimilation, Olbert AI, Nash S, [7] Ragnoli E and Harnett M, 11th International Conference on Hydroinformatics

Group D8 of the AE-2223 course

developped

neural

network

the code:

Koopman, Henger, Lebesque, Mekic, Mollinga, Vijverberg, Reutelingsperger

Gael de Oliveira1 Ricardo Pereira1 Nando Timmer1 Danielle Ragni1 Fernando Lau2 Gerard van Bussel1

2CCTAE

, IDMEC

Inst. Superior Técnico Universidade de Lisboa

Av. Rovisco Pais 1 1049-001 Lisbon Portugal

Machine Learning Methods for Data-Driven Turbulence Modeling, Zhang ZJ & Duraisamy K, AIAA 2015-2460

1AWEP

Department

Aerospace Faculty, Delft University of Technology

Kluyverweg 1 2629HS Delft The Netherlands

85

Shape Optimisation of Dynamic FluidStructure Interaction Problems

Department AWEP

Introduction Wind turbines and airborne wind energy systems both have to deal with fluid-structure interactions (FSI). The aim of this PhD project is to use transient shape optimisation (Fig. 1) to improve the aerodynamic performance of these and other devices. This is equivalent to employing a passive control strategy [1] and may be beneficial for applications where it can be used in combination with, or instead of, an active control system.

1

Reference

2

3

4

IB method

DLM method

đ?&#x2018;Ąđ?&#x2018;Ą = â&#x2C6;&#x2020;đ?&#x2018;Ąđ?&#x2018;Ą

đ?&#x2018;Ąđ?&#x2018;Ą = 50â&#x2C6;&#x2020;đ?&#x2018;Ąđ?&#x2018;Ą

Figure 1: An example of transient shape optimisation for a wind turbine blade. The shape of the aerofoil is specified before operation so that its average performance is optimal for the duration of the transient load.

Development of Lagrange Multiplier Method

86

PhD Candidate: Jaco Brandsen Departments: AWEP, ASM Sections: Wind Energy, ASCM Co-promotors: A. VirĂŠ, S. R. Turteltaub Promotor: G. J. W. van Bussel Contact: j.d.brandsen@tudelft.nl

đ?&#x2018;Ąđ?&#x2018;Ą = 300â&#x2C6;&#x2020;đ?&#x2018;Ąđ?&#x2018;Ą

The FSI simulation tool utilised (Fig. 2) was created by coupling Fluidity [2], a computational fluid dynamics (CFD) code, to a đ?&#x2018;Śđ?&#x2018;Ś rigid-body dynamics solver that describes geometry using nonuniform rational basis splines (NURBS). The tool used the đ?&#x2018;Ľđ?&#x2018;Ľ Vertical velocity (m/s) immersed-body (IB) method [2] in which the CFD mesh also fills the space occupied by the structure, which is represented by a Figure 3: Velocity fields from the defined body (reference), IB and DLM penalty body force. A distributed Lagrange multiplier (DLM) body approaches. For the IB and DLM methods, the vertical white lines indicate the force has been added to the tool as an alternative. Both locations of the walls. đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026; = đ?&#x2018;&#x160;đ?&#x2018;&#x160;đ?&#x2018;Łđ?&#x2018;Łavg đ?&#x153;?đ?&#x153;? = 33.3, đ?&#x153;?đ?&#x153;? = 1 m2/s, â&#x2C6;&#x2020;đ?&#x2018;Ąđ?&#x2018;Ą = 0.001 s. formulations were evaluated through a comparative analysis.

Conclusions and Future Work

A DLM formulation has been incorporated into the FSI simulation tool. For the channel flow problem, this formulation was more accurate than the IB method. Each method is now being tested on a FSI problem containing a moving body.

Eventually, the tool will be combined with a transient shape optimisation algorithm, which will be used to improve the shapes of aerodynamic bodies for unsteady operating conditions.

Acknowledgements This PhD project is supported by the Cross Fertilisation Research Incentive Scheme of the Faculty of Aerospace Engineering, TU Delft, and the TKI Hernieuwbare Energie project ABIBA.

Figure 2: The FSI simulation tool developed during the PhD project.

Comparative Analysis: Flow in a Channel The IB and DLM formulations were compared by solving the laminar flow between two walls that are separated by a distance đ?&#x2018;&#x160;đ?&#x2018;&#x160; = 2 m. The defined body approach, in which the CFD mesh only covers the fluid domain, was used as a reference, together with the analytical steady-state solution. The DLM formulation produces a solution that is almost identical to the reference (Fig. 3). In contrast, the velocity field computed by the IB formulation lags behind the other two solutions, but still reaches a similar steady-state. The IB formulation also struggles to accurately impose the no-slip boundary condition at the walls and underpredicts the pressure drop (Fig. 4).

Figure 4: Vertical velocity at the outlet of the channel, and pressure along its centerline from outlet ( đ?&#x2018;Śđ?&#x2018;Ś = â&#x2C6;&#x2019;2 m) to inlet ( đ?&#x2018;Śđ?&#x2018;Ś = 2 m). đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026; = đ?&#x2018;&#x160;đ?&#x2018;&#x160;đ?&#x2018;Łđ?&#x2018;Łavg đ?&#x153;?đ?&#x153;? = 33.3 , đ?&#x153;?đ?&#x153;? = 1 m2/s, â&#x2C6;&#x2020;đ?&#x2018;Ąđ?&#x2018;Ą = 0.001 s.

References

[1] Z.-P. Wang and S. Turteltaub. (2015). Isogeometric shape optimization for quasi-static processes. International Journal for Numerical Methods in Engineering. 104(5). pp. 347â&#x20AC;&#x201C;371. http://dx.doi.org/10.1002/nme.4940 [2] A. VirĂŠ, J. Spinneken, M. D. Piggott, C. C. Pain. and S. C. Kramer (2016).

Application of the immersed-body method to simulate wave-structure interactions. European Journal of Mechanics - B/Fluids. 55(2). pp. 330â&#x20AC;&#x201C;339. http://dx.doi.org/10.1016/j.euromechflu.2015.10.001

PhD Candidate: Christopher Teruna Department : AWEP Section : Wind Energy Supervisor : Dr. D. Ragni, Dr. F. Avallone Promotor : Prof. Dr. D. Casalino Contact: c.teruna@tudelft.nl 1

3

4

Department AWEP

Test section

2

Wind tunnel contraction

87

1. http://www.dlr.de/at/desktopdefault.aspx/tabid-1579/2088_read-3607/ 2. Woodward, R.P., et al. “Fan Noise Source Diagnostic Test-Farfield Acoustic Results”. 8th AIAA/CEAS Aeroacoustics Conference & Exhibit. June 2002.

3. Hubbard, H. H. “Aeroacoustics of Flight Vehicles: Theory and Practice: Volume 1 Noise Sources”, NASA-L-16926-VOL-1. NASA Langley Research Center Hampton, VA, 1990.

4. M.C. Jacob, J Boudet, D. Casalino, and M. Michard., “A Rod-Airfoil Experiment as Benchmark for Broadband Noise Modeling”. Theoret. Comput. Fluid Dynamics , 19:171–196, 2005.

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 722401.

VERTICAL-AXIS

DELPHINE DE TAVERNIER

AWEP – Wind Energy Daily supervisor: Carlos Ferreira Promotor: Gerard van Bussel Contact: d.a.m.detavernier@tudelft.nl

WIND TURBINES

1

2

3

4

in double-rotor configuration EFFECT ON POWER COEFFICIENT

Department AWEP

Vertical-axis wind turbines might take advantage from being in double-rotor configuration, i.e two rotors in close proximity. This configuration has the potential to enhance the power performance.

Besides the power increment, other advantages of the double-rotor configuration are fast wake recovery and lower costs with respect to offshore floaters and operations and maintenance.

The double-rotor configuration shows enhanced performance: -

Power coefficient: The increase in power varies from 1 to 7% and is larger at high tip speed ratios or rotor solidities.

CT|CU/SI

CP|CU/SI

FIGURE 3: EFFECT OF COUNTER-UP (CU) VAWTS ON POWER AND THRUST COEFFICIENT NORMALISED WITH PERFORMANCE OF SINGLE-ROTOR (SI).

CONFIGURATION

EFFECT OF ROTOR SPACING

For the double-rotor VAWT different layouts can be set up:

VAWTs are benefiting from the aerodynamic interaction with neighbouring turbines:

-

-

-

-

88

Co-rotating: Two turbines rotate in same direction.

Counter-up: Two turbines rotate in opposite direction; wind velocity in between rotors in same direction as rotational velocity.

Counter-down: Two turbines rotate in opposite direction; wind velocity in between rotors in opposite direction as rotational velocity.

Rotor spacing: The highest power coefficients can be obtained when the two rotors are in the closest arrangement.

FIGURE 4: EFFECT OF ROTOR SPACING ON POWER COEFFICIENT OF COUNTER-UP (CU) VAWTS NORMALISED WITH PERFORMANCE OF SINGLE-ROTOR (SI).

FIGURE 1: VAWT IN DOUBLE-ROTOR CONFIGURATION

EFFECT OF WIND DIRECTION

EFFECT ON FLOW FIELD The velocity field reveals two phenomena for the double-rotor configuration:

Double-rotor VAWTs are sensitive to wind direction:

‐

-

‐

Flow acceleration: The flow in between the rotors is accelerated caused by the blockage effect or flow restriction of the turbines itself.

Wake contraction: The wake is restricted to expand causing an increased mass flow through the downstream part of the rotor.

-

Positive wind window: If wind direction deviates within ±40 degrees, double-rotor outperforms single-rotor.

Negative wind window: If wind direction deviates further, one turbine comes in the wake of other and the double-rotor loses power compared to single-rotor.

These two flow phenomena make VAWTs to operate at more favourable flow conditions in double-rotor configuration.

FIGURE 2: VELOCITY FIELD AND STREAMLINES OF COUNTER-DOWN VAWTS.

FIGURE 5: EFFECT OF WIND DIRECTION ON POWER COEFFICIENT OF

DOUBLE-ROTOR NORMALISED WITH PERFORMANCE OF SINGLE-ROTOR.

WINDOW

Wind farm INtegrated Design and Optimisation W

Sebastian Sanchez Perez-Moreno Michiel B. Zaaijer Gerard van Bussel Wind Energy / AWEP s.sanchezperezmoreno@tudelft.nl Fourth year

...and its optimal configuration

MDAO workflow

INTRODUCTION We present a Multidisciplinary Design Analysis and Optimisation (MDAO) tool for the domain of offshore wind farms (OWF): WINDOW. On the right hand side we show the concept of an MDAO workflow.

Below is the design structure matrix of WINDOW, showing its building blocks and the number of model fidelities available for each.

At the bottom, a diagram of our methodology to find the most useful combination of model fidelities within WINDOW. Our use case: OWF layout optimisation with respect to LCOE.

Wind farm INtegrated Design and Optimisation Workflow

Case study

Offshore Wind Farm Design

Levelised Cost of Energy Driver algorithm (e.g. optimisation)

Department AWEP

User-required output (e.g. optimal wind farm layout)

Layout Inflow x 4

Windrose

Wind farm INtegrated Design and Optimisation Workflow Windrose

Ordered layout

Order layout Wind turbine thrust x 6

Thrust

Thrust

Downstream

Wake

Wake merging x 3

Ui

Wind speeds

Wind speeds

Wind turbine power x 6

Power

Wake added turbulence x 6

Turbulence intensity

Seabed depth x 4

Water depths

Support structure design x 2

Cost support

O&M

Availability

topology x 3

Cable topology

Cost O&M

89

Cost

AEP

Total costs

AEP

Total costs

LCOE

LCOE

HOW TO SELECT THE BEST MDAO WORKFLOW? Set of site conditions and fixed design parameters.

Case study

A particular set of model fidelities.

1. To select the optimal configuration of WINDOW, multiple performance criteria are defined. A multiobjective categorical optimisation provides the Pareto front of configurations w.r.t. the criteria. A configuration is a set of model fidelities.

Evaluate WINDOW performance

WINDOW instantiation

2. To select the most useful MDAO workflow, different driver (e.g. optimisation) algorithms are scored against multiple performance criteria using the configurations in the Pareto front found in step 1.

A multicriteria decision analysis follows.

Multi-criteria WINDOW instantiation performance

Multiobjective optimiser of WINDOW instantiation

MDAO workflow

User data requirements, MDAO activity specification.

Use case

Most useful WINDOW instatiation

A driver can do optimisation, uncertainty quantification, sensitivity analysis, certification, etc.

+

Driver algorithm (e.g. optimisation)

Multi-criteria MDAO workflow performance

Selection of driver algorithm

Performance of MDAO workflow Most useful MDAO workflow

An Immersed Boundary Method Based on Domain Decomposition

PhD Candidate: Navi Rajan Department: Wind Energy Section: AWEP Supervisor: Dr. A. Viré Promotor: Dr. G. J. W. van Bussel Contact: n.k.rajan@tudelft.nl 1

2

3

4

Forcing Immersed Boundary Methods

Department AWEP

· Forcing term computed from the projected field

90

Domain Decomposed Immersed Boundary Method · Test functions computed from the projected field

· Operators act in the corresponding domains.

No forcing term.

· Extended domain for fields with natural boundary conditions

Domain of the solid operators

Domain of the fluid operators

Domain of the interface operators

Extended domain of the feilds with natural BCs

Comparison of the Imposed Velocity Boundary Condition

Forcing IBM

Solid mesh is projected onto the fluid mesh, to compute a solid concentration field

Domain decomposed IBM

Hybrid Eulerian-Lagrangian Flow Solver

PhD Candidate: Carlos Baptista Department: AWEP Section: Wind Energy Supervisor: C.J. Simao Ferreira A.H. van Zuijlen Promotor: G.J.W. van Bussel Contact: c.f.baptista@tudelft.nl 1

2

3

4

Introduction Simulating flows around wind turbines is very costly due to multi-disciplinary complexity. An efficient approach is to decompose the flow domain into sub-domains (on basis of local flow features) and to apply the most effective method for each sub-domain. pHyFlow [1] is a hybrid Eulerian-Lagrangian framework combining a meshbased Navier-Stokes solver (for near-body flows) and a mesh-free Vortex-Particle Method [2] (for wakes).

Department AWEP

Governing equations

Coupling procedure

Eulerian flow solver The Eulerian solver follows a mesh-based approach using either a Finite-Element method (based on FEniCS) or a Finite-Volume method (based on OpenFOAM) to solve the incompressible Navier-Stokes equations in the (u, p) formulation:

1 ∂u +(u⋅∇ )u=− ρ ∇ p+ ν ∇ 2 u ∂t Lagrangian flow solver The Lagrangian solver follows a mesh-free approach using a Vortex-Particle method with a mollified kernel to solve the incompressible Navier-Stokes equations in the (u, ω) formulation:

Coupling procedure flow diagram

Hybrid Eulerian-Lagrangian domain

Advance Lagrangian Evolve the vortex particles by one time step, while neglecting vortex generation at solid boundaries.

∂ω +(u⋅∇ ) ω=ν ∇ 2 ω ∂t Convection and diffusion of the particles are treated separately by applying Chorin's Viscous-Splitting method:

Determine Eulerian boundary conditions Apply the Biot-Savart law on the vortex particles to determine the Dirichlet boundary conditions for the velocity field in the Eulerian domain.

Advance Eulerian Evolve the Eulerian solution by one time step using any mesh-based solver.

Convection sub-step

Diffusion sub-step

Correct Lagrangian Use the Eulerian solution to correct the Lagrangian solution in the near-wall region.

Application: stalled elliptic airfoil

Application: multi-body interaction

The purpose of studying the flow around an airfoil in stall is to demonstrate the capability of pHyFlow to simulate complex flow problems, similar to the flow condition for Vertical-Axis Wind Turbines. The flow around a thin elliptic airfoil with a maximum thickness of 12% chord is simulated at AoA = 20 and Re = 5000. Results indicate that pHyFlow is capable of simulating unsteady flow separation. A turbulence model is, however, required to run simulations at higher Reynolds numbers in a computationally feasible manner.

pHyFlow allows the use of multiple Eulerian domains. This facilitates mesh generation for multi-body simulations as each body can be meshed independently. The purpose of studying the flow around multiple cylinders is to demonstrate the capability of simulating multi-body interactions and to explore the application of segregated meshes. The feasibility of simulating the flow around two cylinders using segregated meshes leads to the next step of simulating the flow around multiple moving bodies. This will be explored in the future.

Flow past stalled elliptic airfoil

Flow past two cylinders using segregated meshes

[1] Artur Palha, Lento Manickathan, Carlos Simao Ferreira, and Gerard van Bussel. A hybrid Eulerian-Lagrangian flow solver, 2015. arXiv:1505.03368v2.

[2] Georges-Henri Cottet and Petros D. Koumoutsakos. Vortex methods - Theory and Practice. Cambridge University Press, 2000.

[3] FP7 AVATAR (AdVanced Aerodynamics Tools for lArge Rotors) project. http://www.eera-avatar.eu, 2013.

91

Comparison of momentum models and a high-fidelity vortex model for an actuator disk in yaw

Progress

Background ď&#x201A;§

Department AWEP

ď&#x201A;§ ď&#x201A;§

PhD Candidate: Chihoon Hur Department: AWEP Section: Wind Energy Supervisor Carlos SimĂŁo Ferreira Gerard Schepers (ECN) Koen Boorsma (ECN) Promotor: Gerard van Bussel Contact: c.hur@tudelft.nl 1 2 3 4

Blade Element Momentum (BEM) approach is the most widely used method for Horizontal Axis Wind Turbines (HAWT) due to its lower computational resource.

This approach relies on momentum balance which is the relation of loads and inductions at the disk.

However, the validity of BEM decrease with yawed flow.

ď&#x201A;§ ď&#x201A;§

Induction field has been compared, using different momentum corrections and free wake vortex methods (See figure 2).

Database on the comparison between momentum with corrections and vortex methods are being updated to validate models at different CT (Ct 0.1 ~ 0.9) and yaw angle (0 ~ 90 deg).

Goals of PhD project ď&#x201A;§ ď&#x201A;§

To evaluate the error by current BEM models in yawed conditions at rotor scale.

To develop an improved BEM model for yawed flow.

Methods 1. As a first step of improving BEM, the error of induction field in yaw when load is uniform at the actuator disk has been carried out.

2. Modification of momentum theory for yawed inflow ď&#x201A;§ A yaw model has been suggested based on the Glauertâ&#x20AC;&#x2122;s autogiro theory (Glauert, 1926) to handle skewed wake effects. cosÎŚđ?&#x2018;Śđ?&#x2018;Ś đ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6;â&#x2C6;&#x17E;

đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;1

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ÎŚđ?&#x2018;Śđ?&#x2018;Ś

92

đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;4

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Conclusions and future perspectives

đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2DC; đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤đ?&#x2018;¤

đ??&#x2018;đ??&#x2018;đ??&#x2018;đ??&#x2018;đ??&#x2018;đ??&#x2018;đ??&#x2018;đ??&#x2018;đ??&#x2018;đ??&#x2018; đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC;đ??&#x20AC; đ???đ???đ???đ???đ???đ???đ???đ???

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Figure 1. The concept of Glauertâ&#x20AC;&#x2122;s yaw model

ď&#x201A;§

In yawed flow the relation between loads and induction changes, depending on yaw angles. đ??śđ??śđ?&#x2018;&#x2021;đ?&#x2018;&#x2021; = 4đ?&#x2018;&#x17D;đ?&#x2018;&#x17D; 1 â&#x2C6;&#x2019; đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;(2 cos ÎŚđ?&#x2018;Śđ?&#x2018;Ś â&#x2C6;&#x2019; đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;))

The advantage of Glauertâ&#x20AC;&#x2122;s yaw model is that the non-uniformity of inflow can be applied to momentum theory. This inclined induced velocity differ from yaw angle ÎŚy , azimuthal angle ÎŚz and radial position r. đ?&#x2018;&#x;đ?&#x2018;&#x; đ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;,đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ą = đ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;,đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x17D; (1 + đ??žđ??ž sin ÎŚđ?&#x2018;§đ?&#x2018;§ ) đ?&#x2018;&#x2026;đ?&#x2018;&#x2026; ď&#x201A;§ Various research have been performed regarding to K factor by (Coleman, Feingold, & Stempin, 1945), (White & Blake, 1979), (Pitt & Peters, 1980), (Howlett, 1981), (Ă&#x2DC;ye, 1992) to deal with skewed wake effects. ď&#x201A;§ These different corrections will be compared with a high-fidelity free wake vortex methods to analyze the relation between induction field and different uniform loads in yaw.

3. Free wake vortex model of actuator disk in yaw ď&#x201A;§ The 3D free wake vortex model will be used to validate the momentum corrections. This model has been validated by comparing it with wind tunnel and numerical results from literature. (Berdowski, 2018) ď&#x201A;§

Figure 2. The comparison of induction field from different models (note that the right below figure has different scale to show the peak of induction at edge of disk, conditions: CT = 0.67, yaw angle = 30 deg, wind speed = 4.88 m/s, rotor radius = 0.075 m)

Conclusions ď&#x201A;§ Results of momentum approach fluctuate depends on different corrections. ď&#x201A;§ Skewed wake effects increase with higher CT (0.1~0.9) and higher yaw angle (0 ~ 90deg) in momentum methods. ď&#x201A;§ The trend that downwind part has higher induction field is same on all models. ď&#x201A;§ Results from momentum models shows linear induction distribution, while there is the peak of induction in vortex approach. ď&#x201A;§ Due to the kidney shape of wake, results from free wake vortex model shows higher induction also upper and down part of rotor. ď&#x201A;§ Detailed validation of momentum models with free wake vortex method is necessary.

Future perspectives ď&#x201A;§ Research on Induction filed for a heavily actuator disk(CT > 0.9) in yaw will be performed by comparison of momentum, vortex and CFD approach. ď&#x201A;§ Research on the induction field in yaw in the case of non-uniform loads, dynamic loads will be carried out. ď&#x201A;§ A experiment is essential to validate an improved BEM.

References - Glauert, H. (1926). A general theory of the autogyro (Vol. 1111): HM Stationery Office.

- Coleman, R. P., Feingold, A. M., & Stempin, C. W. (1945). Evaluation of the induced-velocity field of an idealized helicoptor rotor. Retrieved from - White, F., & Blake, B. B. (1979). Improved Method Predicting Helicopter Control Response and Gust Sensitivity.

- Pitt, D. M., & Peters, D. A. (1980). Theoretical prediction of dynamic-inflow derivatives.

- Howlett, J. J. (1981). UH-60A Black Hawk engineering simulation program. Volume 1: Mathematical model.

- Ă&#x2DC;ye, S. (1992). Induced velocities for rotors in yaw-an extension of the blade element momentum method.

Paper presented at the Sixth IEA Symposium on the Aerodynamics of Wind Turbines, ECN, Petten.

- Berdowski, T. (2018). Three-Dimensional Free-Wake Vortex Simulations of an Actuator Disc in Yaw and Tilt. In 2018 Wind Energy Symposium: American Institute of Aeronautics and Astronautics.

,

Increase drag for improved performance?

A Ducted Wind Turbine study

PhD Candidate: Vinit V. Dighe Department: Aerospace Engineering Section: Wind Energy Supervisor: G.J.W. van Bussel Promotor: G.J.W. van Bussel Contact: V.V.Dighe@tudelft.nl 1

2

3

4

Performance using reference DWT

Department AWEP

The commercial DonQi® DWT model has been used as the reference case. A numerical study is carried out by means of a vortex panel code and Reynolds Averaged Navier Stokes (RANS) solver. [1] In two-dimensional numerical model, it consists of airfoil symmetrically placed about the center axis; the flow is parallel to the axis of symmetry.

The rotor is represented using the actuator disc (AD) model. Previous validation studies carried out by the author indicate good agreement with the experiments.[2]

Ducted wind turbines

Over the past several years, the concept of ducted wind turbines (DWT) has managed to create some curiosity. It is an interesting concept that attempts to enclose the turbine blades in a cylindrical shaped casing, the duct, shroud or diffuser as it is denoted, designed to accelerate the moving airflow before passing through the blades. The project aims towards improvement of the aerodynamics and energy performance of the DWT for urban applications: The Energy Wall. The Energy Wall is a large scale conceptual framework to combine noise barriers, solar panels and urban wind turbines for energy applications.

The correlation between the duct force coefficient Cduct and the AD force coefficient Crotor is depicted in Figure (left). The relation is nearly linear at lower AD loadings. Departures from linearity is noticeable at higher AD loadings, peaking appears at Crotor ≈ 0.7. Figure (right) represents the power coefficient CP of the bare and ducted AD. The results clearly show that the apparently universal AD loading of 8/9 seems to be invalid for the DWT case, where the maximum CP ≈ 0.75

Aerodynamics of DWT

The overall thrust of the DWT can be regarded as the algebraic sum of the rotor and the duct forces exerted in the free-stream direction, so that the following equation holds:

ൌ ൅ Ǥ

The rate of energy exchange leading to a compact expression for dimensionless power coefficient of DWT using rotor area as the reference area leads to: 1 ⨜Ux dA Arotor

ൌ

U∞

Flow analysis

ǡ

where U∞ is the free-stream velocity and Ux is the velocity calculated using radial integral across the rotor surface.

[1] Dighe.

The flow phenomenon, occurring due to the displacement of stagnation point along the surface of the duct, explains the reduction of Cduct as in Figures, obtained using RANS CFD. The reduction of Cduct characterized by reduced AD velocity, leads to reduced CP at higher AD loadings.

V.V., et al. ``On the effects of the shape of the duct for ducted wind turbines” 36th Wind Energy Symposium, AIAA, 2018.

[2] Dighe. V.V., et al. ``Effects of gurney flaps on the performance of diffuser augmented wind turbine.” 35th Wind Energy Symposium, AIAA 2017.

93

Bayesian calibration of model uncertainty Laurent van den Bos (wind energy) l.m.m.van.den.bos@cwi.nl Supervised by: • Benjamin Sanderse • Wim Bierbooms • Gerard van Bussel 3rd year

Bayesian model calibration

Problem setting (in general)

Determine likelihood (function of #), e.g.:  � 1 p(z | #) / exp - 2 kz - u(#)k2 2

Department AWEP

Law of Bayes:

p(# | z) / p(z | #)p(#)

Here: 1. p(#) describes prior knowledge; 2. p(z | #) describes likelihood of observing data; 3. p(# | z) describes posterior distribution of the parameter #

Interpolation with Leja nodes Weighted Leja nodes: let x0, . . . , xk be given, then xk +1 = arg max |x - x0||x - x1| · · · |x - xk | x

Input parameters

·10-3 5 4 3 2 1 0 -1 -2 -3 -4 -5 -1

Quantity of Interest Model u(#)

Model parameters #

Data z

Goal Quantify uncertainty of # using relation between model and data

94

-0.5

0

0.5

1

The approach Choose statistical model

Calculate inital node #0

Construct interpolant

Convergence?

Yes: Done

Calculate new node #k +1

Posterior

Propagation σ 0.6 0.4 0.6 0.8 1

Cb1

Cb2

0.1 0.15 0.2 0.4 0.6 0.8

Cv1 5

Cw3

Cw2 10

0.2

0.4

1

2

3

0.4

0.6

0.4

0.4

0.2

0.2

0.15

0.15

0.1

0.1

0.8

0.8

0.6

0.6

0.4

0.4

10

10

5

5

-Cp

σ

0.8

0.6

0

Cb2

-1

0

Cv1

Cv1

σ Cb1

1

0.8

Cb2

1

Cb1

1

µ±2 µ± Data

0.6 

 0.4

0.2

0.2

Cw2

0.4

Cw2

0.4

Cw3

3

1

Want to know more?

L. M. M. van den Bos, B. Sanderse, W. A. A. M. Bierbooms, and G. J. W. van Bussel. Bayesian model calibration with interpolating polynomials based on adaptively weighted Leja nodes. ArXiv 1802.02035v1, 2018.

2 1

0.5 x

0.4 

0.6 0.4 0.6 0.8 1 σ

0.1 0.15 0.2 0.4 0.6 0.8 Cb2 Cb1

5

10 Cv1

0.2

0.4 Cw2

1

2 Cw3

3

This research is part of the Dutch EUROS program, which is supported by NWO domain Applied and Engineering Sciences and partly funded by the Ministry of Economic Affairs.

Robust Autonomous Operation of Airborne Wind Energy Systems 1. What are Airborne Wind Energy (AWE) Systems? â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

â&#x20AC;˘

â&#x20AC;˘

1

2

3

4

4. Control Strategy Radial motion and airspeed is controlled with the winch.

Path following problem is solved on the tangential plane at the projected kite position on the unit sphere.

Department AWEP

Drones that make high altitude wind energy accessible The tower of conventional wind turbines is replaced by a tether, which connects a glider airplane (wind drone) to the ground On-board vs. ground-based power generation AWE power plants are much more flexible in terms of location, and cheaper in construction but much more challenging to control compared to conventional wind turbines In this work control methods for AWE systems with ground-based power generation are investigated: Crosswind flying drone pulls on the tether, which turns a generator (pumping cycle) To leverage the advantages of AWE sophisticated control systems are requiredâ&#x20AC;Ś

PhD Candidate: Sebastian Rapp Department: AWEP Section: Wind Energy Supervisor: Dr.-Ing. Roland Schmehl Promotor: Prof. dr. Gerard v. Bussel Contact: s.rapp@tudelft.nl

Flight path is defined on the unit sphere.

2. Research overview â&#x20AC;˘

Assessment and development of control systems that enable reliable autonomous operation throughout every phase of flight including: takeoff, crosswind flight, and landing Flight envelope characterization and protection for wind drones Adaptive and robust control as well as path planning in uncertain and dynamic environments

â&#x20AC;˘ â&#x20AC;˘

High-Level State Machine

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3. Simulation Models â&#x20AC;˘ The overall control architecture is first tested in a simulation environment consisting of models for drone, tether and ground station Tether model đ?&#x2018;­đ?&#x2018;­đ??ˇđ??ˇ,đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;+1 đ?&#x2018;­đ?&#x2018;­đ??ˇđ??ˇ,đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;

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â&#x20AC;˘ Cascaded structure â&#x20AC;˘ Requires wind vector and tether force estimation

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Kite-Mode Flight Controller

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Passive wall treatments for improved aeroacoustic measurements in a closed test section wind tunnel

Department AWEP

noise

2

3

4

Acoustic liners â&#x20AC;&#x201C; Noise suppression add-on. Figure 2 illustrates one such, single degree of freedom liner.

Microphone array Boundary layer

đ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6;â&#x2C6;&#x17E;

Figure 2: Illustration of a single degree of freedom liner [3].

Wind tunnel walls

Model

96

1

â&#x20AC;˘ Possible Solution

â&#x20AC;˘ Background and Motivation In a closed test section tunnel, background originates from: â&#x20AC;˘ Wind tunnel fan. â&#x20AC;˘ Boundary layer and its reflection. â&#x20AC;˘ Reflection of the noise from model being tested.

PhD Candidate: Pranav Manjunath Department: AWEP Section: Wind Energy Supervisor Dr. F. Avallone Promotor: Prof. dr. D. Simons Contact: p.manjunath@tudelft.nl

Figure 1: Illustration of the noise inside a closed test section wind tunnel

Performance of the liner to reduce the intensity of incident sound wave is characterised by its impedance (Z), a complex number. Initial results indicate quantitative consistency in predicting the impedance, using a LatticeBoltzmann solver (PowerFLOW).

â&#x20AC;˘ Research Objective Reducing the background noise in the frequency range between 250 Hz and 2500 Hz using suitable wall treatment.

â&#x20AC;˘ Methodology Numerical Method Lattice-Boltzmann approach

Figure 3: Comparison of normalised impedance prediction with reference cases Experiments

Optimised wall treatment

Looking ahead, study will focus on the development of computational process to predict the absorption properties for various liners. Further, use of devices such as Large eddy breakup devices will also be considered.

References:

1. Jones, M., Watson, W., Parrott, T., & Smith, C. (2004, May). Design and evaluation of modifications to the NASA Langley flow impedance tube. In 10th AIAA/CEAS Aeroacoustics Conference (p. 2837).

2. Zhang, Q., & Bodony, D. J. (2012). Numerical investigation and modelling of acoustically excited flow through a circular orifice backed by a hexagonal cavity. Journal of Fluid Mechanics, 693, 367-401.

3. Singh, D. K. (2016). Interaction of sound with vorticity.

Department AWEP

97

Depar Control and (C&

tment d Operations &O)

Air Transport & Operations (ATO)

Heads of section: Prof. dr. Ricky Curran Prof. dr. ir. Henk Blom

Department C&O

100

Supervisors Prof.dr. Ricky Curran Prof.dr.ir. Henk Blom Dr. ir. Mirjam Snellen Dr. ir. Wim Verhagen Dr. ir. Bruno Santos Dr. Alexei Sharpanskykh

The Air Transport And Operations Section is a vibrant and youthful group of 15 enthusiastic staff and PhDs with 40 MSc students per year. ATO has three research aims: 1) To develop radical new ways to optimise aircraft operations for efficiency, safety, cost and environmental impact; 2) To extend the analysis to an airline fleet and network level to include capacity and resilience; 3) To synthesize these to include operational safety at an airline and ATM level.

-- Timo Constantin Gaida -- Rui Li -- Roberto Merino-MartĂ­nez -- Yalin Li -- Qichen Deng -- Matt Vert

101

Department C&O

The generic competencies being used to facilitate the above research goals include Value Operations Methodology (VOM), multidisciplinary operations optimization research, multi-agent systems modelling, knowledgebased systems, stochastic modelling and analysis, and acoustic modelling, measurement and analysis. Central to ATOâ&#x20AC;&#x2122;s research is the VOM as a theoretical construct for establishing effective objective value statements and simulation goals (and underlying models), in conjunction with developing much more efficient modelling and optimisation methods to implement VOM.

PhD Candidates:

Seabed sediment classification for monitoring underwater nourishments using time series of multi-beam echo-soundings Background

PhD Candidate: Timo Constantin Gaida Department: Control and Operations Section: Aircraft Noise and Climate Effects Supervisor: Dr. Mirjam Snellen Promotor: Prof. Dick G. Simons Contact: t.c.gaida@tudelft.nl 1 2

3

4

Data •

• • • •

Beach and shore nourishments to protect the coastline against coastal erosion

4 Multi‐beam echo‐sounder (MBES) bathymetry measurements (May – Nov. 2017) 2 MBES bathymetry and backscatter measurements (April and October 2017) 24 Box core samples in survey area (May 2017) First nourishment in June 2017; nourished material is sand (d50 is 200 ‐ 400 µm)

State-of-the-art nourishment monitoring Temporal and spatial development of nourishments •

High natural dynamic of the seabed (Fig. 3a) Migrating ripples and megaripples

•

Two main dumping locations in the E and NW (red areas)

•

Continuous channel wall erosion (blue stripe in NW‐SE direction)

Fig. 2 Applying the nourishments.

•

To increase the efficiency, long‐term monitoring of the nourishments is carried out at Ameland (Netherlands)

Can we use multi‐beam backscatter for monitoring beach nourishments and seabed dynamics?

Fig. 3 Difference of MBES bathymetry measurements in time series. Red indicates decrease of water depth over time.

Fig. 1 Study area: a) North sea, b) Dutch and German Wadden Sea, c) Tidal inlet and delta, d) nourished area: bathymetry (April 2017) and box core samples (May 2017) are shown.

Theory

Method Bayesian technique for seabed sediment classification

Sonar equation

General Objective and unsupervised classification technique Idea The backscatter strength can be assumed to follow a Gaussian distribution if a sufficient number of independent scatter pixel are considered. Thus, the backscatter histogram (per beam) can be represented by a sum of Gaussians being representative for the different seafloor Fig. 5 Model fit (red dotted) of Gaussians (black ) to the measured backscatter histogram (blue) per incident angle. types

‫ߠ ܵܤ‬ǡ ݂ ൌ ‫ ܮܧ‬െ ܵ‫ ܮ‬൅ ʹܶ‫ ܮ‬െ ͳͲ ݈‫ܣ ݃݋‬

• BS Backscatter Strength, EL Echo Level , SL Source Level, TL Transmission Loss, A ensonified area

Backscatter strength is a function of frequency, incident angle and seabed properties

102

Fig. 4 Principle of multi-beam echosounding and seabed scattering.

Gaussians represent acoustic class (AC).

Results and conclusions Acoustic seabed classification

Department C&O

 Acoustic seabed classification (ASC) is able to distinguish between sand nourishments (AC 1 to 2) and original shell layer (AC 4) (Area 1)

Fig. 6 a) ASC results, April 2017 b) ASC results, October 2017 c) Difference between ASC results d) Difference in bathymetry between April and October.

 Part of the nourishments might be covered by harder/rougher material due to eroded material from the channel wall (Area 2). Preliminary investigations of seismics indicate channel wall consists of shell or clay layer

Correlation of MBES backscatter and box core samples

Signatures of grain size sorting in MBES backscatter a)

b)

c)

Fig. 7 a) Backscatter mosaic and b) ASC results (April, 2017), including Box core location and photos. Area is marked as Z1 in Fig. 6a.

Fig. 8 Relationship between backscatter and percentage of grains > 2 mm covering the seabed surface. Reliable (red) and unreliable (blue) samples. Samples are analysed by semi-automated image analysis.

 Positive correlation of backscatter/acoustic  Correlation hampered by time difference between class with amount of large grains (shells and MBES and sampling campaign (2 weeks), positioning gravel) error (~10 m) and sample analysis technique

d)

Profile 1

Profile 2

Distance [m]

Fig. 9 a) Bathymetry (October 2017), b) Profile 1 and c) Profile 2 compare bathymetry and backscatter. d) ASC results. Area is marked as Z2 in Fig. 6b.

 Rapid development of very high (~2 m) and steep (~25°) megaripples on nourishments (~3 months)  Contrasting grain size distribution along bedforms for nourished and natural areas Nourished area: coarse material in trough; finer material on crest (Profile 1) Natural area: finer material in trough; coarser material on crest (Profile 2)  ASC is able to classify grain size sorting along megaripples

Data acquisition • In cooperation with Rijkswaterstaat

Systematic Design Methodology for Integrated Prognostic and Health Management Systems OBJECTIVES

PhD Candidate: Rui Li Department: Control and Operations Section: Air Transport and Operations Supervisor: Dr. ir. Wim J.C. Verhagen Promotor: Prof. dr. Richard Curran Contact: r.li-4@tudelft.nl 1

2

3

4

RESULTS

• Define a systematic design methodology for engineering a Prognostic and Health Management (PHM) system for predictive maintenance.

• Formulate a decision framework to select an appropriate prognostic approach.

METHODOLOGY

103

CONCLUSION

FUTURE WORK • The Future research will discuss more prognostic approaches while bridging the gap between prognostic techniques and their uptake in PHM system design.

• The future research will complete the systematic design methodology.

In processing outcomes: • The draft full paper has been submitted (A comparative study of Data-driven Prognostic Approaches: Stochastic and Statistical Models) to 2018 IEEE International Conference on Prognostics and Health Management (ICPHM).

• Abstract has been submitted (Systematic Design Methodology for Integrated Prognostic and Health Management Systems) to 2018 AIAA AVIATION Forum conference

Department C&O

• Wiener process is more effective in formulating the degradation trends.

• The linear regression technique improves the performance when compared to PCA for HI construction.

Microphone arrays for imaging of aerospace noise sources

PhD Candidate: Roberto Merino-MartĂ­nez Department: Control and Operations Section: Aircraft Noise and Climate Effects Supervisor: Dr. Mirjam Snellen Promotor: Prof. Dr. Dick G. Simons Contact: r.merinomartinez@tudelft.nl 1

2

3

4

How can we see noise?

Microphone array

Scan grid

Sound source

Main challenges in aeroacoustic measurements

Souce map Phase delays of each microphone Beamforming algorithm Minimize the difference between experimental recordings and propagation model

Loud

â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

Limited spatial resolution Sidelobes (spurious sources) Background noise Moving sources USE OF ADVANCED BEAMFORMING ALGORITHMS

Quiet

APPLICATIONS Aircraft flyovers Noise emissions are one of the current main limitations for aircraft operations

104

Assess aircraft noise variability Aircraft noise prediction models fail to account for the variability in the emitted noise levels.

Assess noise reduction measures The turbulent boundary layer noise is the main source of noise for modern wind turbines. This causes a loss of revenue due to noise restrictions.

[**]

Where current models provide a single noise level value, variations of about 7 dB are measured in practice for the same aircraft type.

Department C&O

Trailing edge serrations are passive noise reduction devices inspired in the silent owl flight.

[*]

The nose landing gear system is one of the main airframe noise sources for commercial aircraft.

[***]

Aircraft size 6 dB reduction (-75% power)

Open cavities Open cavities cause intense tonal noise, which is perceived as highly annoying. Closing these cavities would considerably reduce the noise levels.

Microphone-array measurements showed strong and significant correlations between: â&#x20AC;˘ Engine noise levels and the fan rotational speed (N1%) â&#x20AC;˘ Airframe noise and the aircraft true air speed.

These findings can be used to improve current noise prediction models used for route planning and noise contour calculations.

đ?&#x2018;&#x2030;đ?&#x2018;&#x2030;

đ?&#x2018;&#x2030;đ?&#x2018;&#x2030;

đ?&#x2018;&#x2030;đ?&#x2018;&#x2030; = 40 m/s đ?&#x203A;źđ?&#x203A;ź = 0â&#x2C6;&#x2DC; f = 1 to 5 kHz

The serrations alleviate the acoustic impedance discontinuity at the trailing edge.

A 1 dB noise reduction allows for approximately 3% higher power production and, hence, revenue.

Sources for images: [*] Airbus A320 - Airplane Characteristics Airport and Maintenance Planning. [**] Sijtsma, P., Oerlemans, S., and Holthusen, H., â&#x20AC;&#x153;Location of Rotating Sources by Phased Array Measurementsâ&#x20AC;?, AIAA Paper 2001-2167. [***] https://scienceblog.com

PhD Candidate: Yalin LI Department: C&O Section: ATO Supervisor: Bruno F. Santos Promotor: Richard Curran Contact: Y.LI-12@tudelft.nl

A Measure of Dynamic Resilience of ATM Network Introduction

1

2

3

4

Results

Resilience is the strength, or the ability of elements, systems, and other units of analysis to withstand a given level of stress or demand without suffering degradation or loss of function.

The simulation period is one day, which is 1440 minutes. Denote t0 and tend as the start and the end of the simulation and ta and tb as the disruption occurs in the network.

The loss of resilience is: 30

Q(t) is the quality of infrastructure by percentage.

25

Delay

20

15

10

5

0

tb

1 29 57 85 113 141 169 197 225 253 281 309 337 365 393 421 449 477 505 533 561 589 617 645 673 701 729 757 785 813 841 869 897 925 953 981 1009 1037 1065 1093 1121 1149 1177 1205 1233 1261 1289 1317 1345 1373 1401 1429

ta

t0 Figure.1 Measure of resilience-conceptual definition

Method Generate Schedule

tend

Time

Figure.3 Delay according to scheduled time of departure t1

1,05

1

Generate a daily schedule of flights Performance

0,95

t3

t0

tend h

Area

0,9

Free Flow

Run the ATM simulation freely and calculate free flow performance

105

t2

0,85

0,8

Run the ATM simulation with disruption and calculate performance

Forward Flights No

No Arrive D? Yes Relay for New Flights Calculate Delay

End of Day? Yes Stop and Output

Figure.2 Simulation process of the method

Covert the simulation result in Figure.3 into the form of Figure.1, we can obtain a similar shape in Figure.4. Denote t1 and t3 as the start and end of impact in the network and t2 as the time of the lowest performance, and h is the largest performance drop.

The blue area is the losing performance over all time.

We can calculate the performance by calculating the area.

1 Performance  1  Area / (tend  t0 )  1   h  (t3  t1 ) / (tend  t0 ) 2

Next step This work is a block of the PhD project. It is followed with optimization of the resilience performance of ATM network.

Department C&O

No

tb

Figure.4 Measurement of performance of resilience

Depart New Flights

Relay New Destination

ta

Time

Set Disruption

Block D? Yes Retour New Destination

0,75 1 30 59 88 117 146 175 204 233 262 291 320 349 378 407 436 465 494 523 552 581 610 639 668 697 726 755 784 813 842 871 900 929 958 987 1016 1045 1074 1103 1132 1161 1190 1219 1248 1277 1306 1335 1364 1393 1422

Resilience Measure

Aircraft Maintenance Scheduling Optimization

PhD Candidate: Qichen Deng Department: Control and Operations Section: Air Transport and Operations Supervisor Dr. Bruno Santos Promotor: Prof. Dr. Richard Curran Contact: q.deng@tudelft.nl 1

2

3

4

Abstract Aircraft maintenance is the overhaul, repair, inspection or modification of an aircraft or aircraft systems, components and structures in an airworthy condition. It takes place when an aircraft undergoes certain flight hours, flight cycles or calendar months. There are three major types of maintenance: A-check, C-check and D-check. A-check is performed about every 3 to 4 months; C-checks are planned within 18 to 24 months interval; D-check occurs every 6 to 8 years. In practice, the aircraft maintenance schedule is usually prepared according to the experience of maintenance operators. They need to spend several days or weeks planning the A-checks, C-checks and D-checks one after another according to individual inspection interval and maintenance resource of airline. Limited by such planning approach, the goal is to find a feasible maintenance schedule instead of an optimal one. Since the cost of aircraft maintenance plays an important role on the balance sheet of airlines, an efficient maintenance planning algorithm can reduce the cost, increase aircraft availability while keeping safety regulations.

Challenges •

• • • • •

Each aircraft has several independent counter for counting calendar days (DY), flight hours (FH) and flight cycles (FC) with respect to different check types. Maintenance checks have to be scheduled before any counter reaches its limit.

There is limited capacity for each maintenance check type.

C-/D-check is not allowed to schedule in commercial peak season, A-check is not allowed to plan on public holidays.

D-check has a different counter other than C-check, but D-check is incorporated in C-check as a heavy C-check.

Each maintenance check type has different elapsed time.

No previous work optimizes the 3 check types as a whole.

Methodology (Approximate Dynamic Programming)

106

• • •

Dynamic Programming + Machine Learning Dynamic programming is the main solution framework Machine learning is applied to estimate the consequence of each A-/C-/D-check decision.

Case Study Check Type

Department C&O

D-/C-Check

A-Check

KPI 2013-2016

Airline Schedule

ADP Schedule

Average FH

6426.5

6949.8

Standard Deviation

857.3

425.8

Total D-/CChecks

89

73

Average FH

690.8

692.8

Standard Deviation

60.6

49.0

Total A-checks

818

799

Resilience in complex air transport systems Research objectives

• • • •

•

Runway – Taxiway use

Small

Aircraft + pilot

•

Terminals

•

Airline

•

Air transport network

3

4

Adverse events Any modifications of planning, human behaviour, tasks, goals as a response of adversity, especially when resources are exhausted:

Disturbances, disruptions, perturbations, hazards, threats, surprise events, alterations, shocks, pitfalls, degradations, accidents…

Expected or unexpected Coming from the system itself or from the environment

Before adverse events: Anticipation Monitoring the system and the environment to avoid disturbances or disruptions

Large

Traffic control tower

2

Adaptation

adjusting, changing, controlling, coordinating, deploying, mobilizing, regulating, re-organizing, responding

•

1

Provide a conceptual framework of resilience Model resilience of sociotechnical systems Operationalise & enhance resilience Study relations with safety, security & efficiency

Air transport systems where resilience occurs •

PhD Candidate: Matt Vert Department: Control & Operations Section: Air transport & Operations Supervisor A. Sharpanskykh Promotor: R. Curran Contact: m.p.j.vert@tudelft.nl

Organisational characteristic

107

Resilience • Intrinsic ability of a system to adapt its functioning prior to, during, or after expected or unexpected adverse events so that it can sustain required operations

Decentralized control

• Emergent phenomenon due to the combination of different mechanisms operating at the individual, social, and organizational levels

After adverse events: Recovery Individual mechanisms Cognitive processes:

Social mechanisms •

Coordination

•

Perception of the environment

•

Interpretation of the environment

•

Learning

Emergent phenomenon •

Self-organisation

•

Local

•

Non-linear interactions

•

Feedback loops

•

Unpredictability

Global

Department C&O

Capacity of a system to quickly reach the normal performance level of functioning after being impacted

Control & Simulation (C&S)

Heads of section: Prof. dr. ir. Max Mulder Prof. dr. ir. Jacco Hoekstra

108

Department C&O

Supervisors Dr. ir. Erik-Jan van Kampen Dr. ir. Daan Pool Dr. ir. Coen de Visser Dr. ir. Joost Ellerbroek Dr. ir. Guido de Croon Dr. Jerry Guo Dr. Marilena Pavel Dr. Marios Kotsonis Dr.ir. Clark Borst Dr. ir. RenĂŠ van Paassen Prof. Pierangelo Masarati (PoliMi) Dr. Giuseppe Quaranta (PoliMi)

Reaching for ever-increasing levels of safety, efficiency and capacity of aerospace will require developing more capable automatic control systems in terms of adaptability and autonomy, and more advanced human-machine systems to interact with them. The section Control and Simulation (C&S) aims to advance the development of such systems, building on a solid theoretical basis and physical insights while exploiting theoretical progress in adjacent fields, and to validate these systems experimentally in worldclass facilities, closing the loop between theory and practice.

PhD Candidates:

C&S aims to be a leading research group in the integration, development and testing of new theories on control, autonomous and cognitive systems (with and without human elements), while addressing industrial and societal needs. We tackle those problems in our domain that best fit our mission and objectives while at the same time are the most challenging from a scientific point of view. We set high standards for our team, our experimental facilities and the academic and industrial networks in which we cooperate.

-- Julia Rudnyk

-- Xuerui Wang -- Ye Zhou

-- Kasper van der El -- Sihao Sun -- Emmanuel Sunil -- Ewoud Smeur -- Kirk Scheper -- Mario Coppola -- Yu Ying

109

-- Henry Tol -- Daniel Friesen -- Isabel Metz

-- Shushuai Li -- Shuo Li -- Ezgi Akel -- Yke Bauke Eisma -- Kimberly McGuire -- Annemarie Landman -- Dirk Van Baelen -- Malik Doole

Department C&O

-- Tom van Dijk

Incremental Control Methods and Applications Project Description Initially, this project aims at alleviating the gust load for flexible aircraft, using the advanced control method, Incremental Nonlinear Dynamic Inversion (INDI).

PhD Candidate: Xuerui Wang Department: Control and Operation Section: Control and Simulation Supervisor: Erik-Jan van Kampan Promotor: Max Mulder, Qiping Chu Contact: x.wang-6@tudelft.nl 1

2

3

4

Applications on GLA The applications of INDI on rigid and flexible aircraft Gust Load Alleviation (GLA) problem will be presented here.

Until now, this project has been extended to the method improvements of INDI, including reformulation, stability and robustness analyses.

Besides, applications are not restricted to flexible aircraft gust load alleviation problem. Other sources of perturbations, that can test the robustness of the improved control method, are also applicable.

Examples can be given as: external disturbances, model uncertainties, actuator faults, and structural damages.

Theoretical Development The theoretical development of INDI contains three parts: reformulation, stability and robustness analyses, and combinations of INDI with other robust control methods.

110

The main motivation of reformulation is to generalize INDI to generic multi-input/multi-output (MIMO) nonlinear system control problems. In this new formulation, there is no constrain on the relative degree of the outputs with respect to the inputs. Besides, the internal dynamics are included, and will be considered in the stability analyses. Moreover, the control input is derived without using the time scale separation principle, which increases the task applicability of this control method. Main methods can be found in Ref. [1].

The above two figures show the spatial realizations of a two-dimensional von Karman turbulence field, and a two-dimensional ‘1-cos’ gust field. A typical INDI controller design for GLA problem is illustrated below: INDI loop Actuator dynamics

Velocity keeping loop

Aircraft configurations used for research are shown below:

Department C&O

The stability and robustness of this control method are proved using Lyapunov methods and nonlinear system perturbation theory. Details can be found in Ref. [1].

The combinations of INDI with other robust control methods are still under research.

Details about the effectiveness of INDI, and GLA problems can be found in Ref. [2-5]. Other applications about fault-tolerant control are still under research.

References 1. Wang, X., Van Kampen, E.-J., Chu, Q. P., and Lu, P., “Stability Analysis for Incremental Nonlinear Dynamic Inversion Control,” 2018 AIAA Guidance, Navigation, and Control Conference, No. January, American Institute of Aeronautics and Astronautics, Kissimmee, Florida, Jan 2018, pp. 1–16.

2. Wang, X., Van Kampen, E., and Chu, Q. P., “Gust Load Alleviation and Ride Quality Improvement with Incremental Nonlinear Dynamic Inversion,” AIAA Atmospheric Flight Mechanics Conference, American Institute of Aeronautics and Astronautics, Grapevine, Texas, Jan 2017, pp. 1–21.

3. Wang, X., Van Kampen, E., De Breuker, R., and Chu, Q. P., “Flexible Aircraft Gust Load Alleviation with Incremental Nonlinear Dynamic Inversion,” 2018 AIAA Atmospheric Flight Mechanics Conference, No. January, American Institute of Aeronautics and Astronautics, Kissimmee, Florida, Jan 2018, pp. 1–21.

4. Ferrier, Y., Nguyen, N. T., Ting, E., Chaparro, D., Wang, X., de Visser, C. C., and Chu, Q. P., “Active Gust Load Alleviation of High-Aspect Ratio Flexible Wing Aircraft,” 2018 AIAA Guidance, Navigation, and Control Conference, No. January, American Institute of Aeronautics and Astronautics, Kissimmee, Florida, Jan 2018, pp. 1–36.

5. Natella, M., Wang, X., and De Breuker, R., “The Effects of Aeroelastic Tailoring on Flight Dynamic Stability,” 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, No. January, Kissimmee, Florida, 2018.

Online Reinforcement Learning Control for Aerospace Systems

PhD Candidate: Ye ZHOU Department: Control & Operation Section: Control & Simulation Supervisor: E. van Kampen Promotor: M. Mulder, Q. Chu Contact: Y.Zhou-6@tudelft.nl

Background Reinforcement Learning (RL) is a framework of intelligent, self-learning methods that can be applied to different levels of autonomous operations and applications. It links bio-inspired artificial intelligence techniques to the field of control and decision-making. RL methods, in the low-level control field, can be used to improve the control efficiency and adaptability when the dynamical models are unknown or uncertain. In the high-level decision-making field, RL methods can be applied to enhance the intelligence of planning and to ensure the coordination with the low-level control. RL methods are relatively new in the field of aerospace guidance, navigation, and control. They have many benefits, but also some limitations, when applied to aerospace systems.

1

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3

4

System description

Figure 2. Architecture of the iADP (full state feedback) using an online identified incremental model, where solid lines represent the feedforward flow of signals, and dashed lines represent the adaptation pathways.

Figure 4. An example of the agent, sensing a relative state, đ?&#x2019;&#x201D;đ?&#x2019;&#x201D; = [đ?&#x2018; đ?&#x2018; đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; , đ?&#x2018; đ?&#x2018; đ?&#x2018;&#x2122;đ?&#x2018;&#x2122; , đ?&#x2018; đ?&#x2018; đ?&#x2018;&#x;đ?&#x2018;&#x;, đ?&#x2018; đ?&#x2018; â&#x201E;&#x17D;], in a discrete environment.

Partial Observability

II. Online Adaptive Critic Designs (Actor-Critic)

Figure 1. An example of the system-environment interaction with Reinforcement Learning: the system represents an air vehicle; the environment encompasses everything that surrounds and may change the system states.

Research Goal

Adaptive Critic Designs (ACDs) can utilize online identified incremental models to prevent off-line learning of the global model, to speed up the convergence rate, and to improve the control performance. An Incremental model based Heuristic Dynamic Programming (IHDP) method is proposed to deal with reference signal tracking problems. It generates a nearoptimal controller for nonlinear systems, without a priori knowledge of the system dynamics. The IHDP method utilizes an online identified incremental model, instead of a neural network plant approximator, to simplify the updating of the actor network. This method can avoid off-line learning of the global system model, so as to improve the control performance and to accelerate the online learning efficiently.

This idea is applied to another form of ACDs, which generailizes another online self-learning adaptive controller, namely Incremental model based Dual Heuristic Programming (IDHP). This method accelerates the online learning compared to traditional DHP methods, and increases the convergence rate and control performance compared to the IHDP method. In addition, this method is validated in an fault-tolerant control task and in the presence of measurement noise.

Figure 5. An example of an agent in two possible situations, sensing the same relative state, đ?&#x2019;&#x201D;đ?&#x2019;&#x201D; = [1, 0, 0, 180°], which, however, are in two different absolute states (positions) and should take different actions.

111

This research aims to exploit RL methods to improve the

autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical uncertainties, and partial observability?

This main research question is addressed in three specific methods and/or applications: (i) Approximate Dynamic Programming (ADP) with a quadratic cost function, (ii) Adaptive Critic Designs (ACDs), and (iii) high-level guidance and navigation.

I. Incremental Approximate Dynamic Programming (Critic-only)

Publications

Future Works Figure 3. Architecture of the IHDP using a time-varying incremental model, where solid lines represent the feedforward flow of signals, and dashed lines represent the adaptation pathways.

III. Hybrid Hierarchical Reinforcement Learning A hybrid Hierarchical Reinforcement Learning (hHRL) method consisting of several levels is developed for online guidance and navigation problems. Each level uses different methods to optimize the learning with different state information and objectives. This method can help to accelerate learning, address the â&#x20AC;&#x2DC;curse of dimensionalityâ&#x20AC;&#x2122; in complex guidance and navigation tasks, reduce the uncertainty or ambiguity at higher levels, and efficiently transfer the learning results within and across tasks. The formulated rules of establishing the hierarchies make this method more flexible, transferable and closer to human behavior. It is a systematic design for guidance and navigation tasks with multiple objectives, partial observability, and even non-stationary environments.

â&#x20AC;˘ The current research focuses on the method development, theoretical analysis, and simulation experiments. Further research should, therefore, concentrate on the validation of the proposed methods on more complex and higher degree-of-freedom aerospace models and then on real systems. â&#x20AC;˘ The proposed online ACD can be used not only in low-level control tasks but also in high-level guidance and navigation tasks with highly nonlinear value functions. Further experimental investigations into different tasks are recommended to validate the generalization of online ACDs. â&#x20AC;˘ The hHRL method is applied to a simplified discrete systemin a discrete guidance and navigation environment in this dissertation. Further research is recommended in more realistic environments and aerospace systems in continuous state spaces, by expanding downwards with a control level or by directly replacing the discrete microstate level RL method with the proposed ADP methods.

- Y. Zhou, E. van Kampen, and Q. P. Chu, Incremental model based online dual heuristic programming for nonlinear adaptive control, Control Engineering Practice, Vol. 73, p. 13-25, 2018.

- Y. Zhou, E. van Kampen, and Q. P. Chu, Nonlinear adaptive flight control using incremental approximate dynamic programming and output feedback, Journal of Guidance, Navigation, and Dynamics, Vol. 40, No. 2, p. 493-500, 2017.

- Y. Zhou, E. van Kampen, and Q. P. Chu, Adaptive spacecraft attitude control with incremental approximate dynamic programming, in 68th International Astronautical Congress (IAC), Adelaide, Australia, 2017.

- Y. Zhou, E. van Kampen, and Q. P. Chu, Launch vehicle adaptive flight control with incremental model based heuristic dynamic programming, in 68th International Astronautical Congress (IAC), Adelaide, Australia, 2017.

- Y. Zhou, E. van Kampen, and Q. P. Chu, Autonomous navigation in partially observable environments using hierarchical Q-Learning, in IMAV 2016, Beijing, China, 2016.

- Y. Zhou, E. van Kampen, and Q. P. Chu, Incremental model based heuristic dynamic programming for nonlinear adaptive flight control, in IMAV 2016, Beijing, China, 2016.

- Y. Zhou, E. van Kampen, and Q. P. Chu, An incremental approximate dynamic programming flight controller based on output feedback, AIAA Guidance, Navigation, and Control Conference, San Diego, California, USA, 2016.

Department C&O

An effective and systematic adaptive control method, incremental Approximate Dynamic Programming (iADP), is proposed to deal with system nonlinearity. This method combines the advantages of linear ADP methods and the incremental nonlinear control techniques to generate two model-free flight control algorithms to solve optimal control problems with direct availability of full states and with only the availability of the system outputs. This idea is expanded to optimal tracking control problems for MIMO nonlinear systems for two different observability conditions: full state measurement and partial observability. Because of the incremental model, the cost function only need to be a rough approximation of the true cost-to-go. This approximation is a quadratic function only of the current tracking error, without expanding the dimension of the state space for the cost function to an augmented one.

Figure 6. The online performance after 30 iterations. (a) An example of the agentâ&#x20AC;&#x2122;s internal memory, which takes 272 primitive actions, 15 behaviors. The blue line shows the trace of the agent in this episode. (b) The greedy behaviors in the external memory with hHRL.

Understanding Manual Control in Preview Tasks Motivation Todayâ&#x20AC;&#x2122;s pilots are supported by a wide variety of technological systems to control their aircraft. Examples include computer-generated (head-up) displays, fly-by-wire and control augmentation systems, and even autopilots that temporarily alleviate the pilot from any manual control duties. Similar technologies are currently also making their way into road vehicles. Nonetheless, in most vehicles, manual control is expected to remain a key safety-backup for failing automation for the near foreseeable future.

Driver or pilot support systems that are developed in the meantime are often based on rules-of-thumb, trial-and-error, or extensive experiments, leading to suboptimal, one-sizefits-all designs. To obtain optimal, individualized systems, knowledge and quantitative models of (individual) humansâ&#x20AC;&#x2122; manual control capabilities and limitations are essential. It is safe to say that our current knowledge and models are not sufficiently advanced to exploit the full potential of tomorrows advanced support systems.

Research Objective This research project aims to improve our understanding and models of human manual control behavior, in particular in tasks with preview of the future trajectory to follow.

Examples of preview information are the road that is visible through the front windshield while driving, and the runway or a displayed tunnel-in-the-sky while flying (see below).

While preview clearly allows humans to anticipate upcoming corners, it is currently poorly understood exactly how available preview is used for control.

112

PhD Candidate: Kasper van der El Department: Control & Operations Section: Control & Simulation Supervisor: dr. ir. Daan M. Pool Promotor: prof. dr. ir. Max Mulder Contact: k.vanderel@tudelft.nl 1

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3

4

Cybernetic Approach The scientific field of cybernetics aims to understand human behavior, often using models. Manual control cybernetics quantifies how humans control vehicles and devices in control-theoretic models, obtained using system identification techniques.

Step 1: Data acquisition Human-in-the-loop experiments in the SIMONA simulator and the HMI-lab Measured time traces of human control behavior Step 2: System identification Multiloop, black-box, instrumental variable techniques Frequency-response function estimates of human feedforward and feedback control dynamics Step 3: Modelling Proposal of models whose dynamics capture the measured frequency-response functions Control-theoretic parametric model, for example: đ??ťđ??ťđ?&#x2018;&#x153;đ?&#x2018;&#x153;đ?&#x2018;Ľđ?&#x2018;Ľ (đ?&#x2018; đ?&#x2018; ) = đ?&#x2018;˛đ?&#x2018;˛đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; 1 + đ?&#x2018;ťđ?&#x2018;ťđ?&#x2018;łđ?&#x2018;ł,đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2018;&#x2019;đ?&#x2018;&#x2019; â&#x2C6;&#x2019;đ??&#x2030;đ??&#x2030;đ?&#x2018; đ?&#x2018;

Step 4: Parameter estimation Fitting the model to the measurement data

Physically interpretable parameters that explain how humans use preview for control (e.g., đ?&#x2018;˛đ?&#x2018;˛đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; , đ?&#x2018;ťđ?&#x2018;ťđ?&#x2018;łđ?&#x2018;ł,đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; , đ??&#x2030;đ??&#x2030;)

From Compensatory Tracking to Curve Driving

Main Results and Implications The proposed models provide a variety of valuable novel opportunities: 1. Model parameters estimated from experimental data provide a quantitative, physical explanation of human control and adaptation. The figure show that the amount of preview used by humans depends on the available preview and the vehicle dynamics.

2. The proposed models facilitate predicting human control and adaptation offline in computer simulations (thick gray line), alleviating the need for elaborate human-in-the-loop experiments.

3. The models allow for optimizing and individualizing the design of human-machine interfaces. For example, an indicator on the tunnel-in-the-sky display at a pilotâ&#x20AC;&#x2122;s estimated, preferred look-ahead time (đ?&#x153;?đ?&#x153;?đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; ), could help to quickly guide that pilotâ&#x20AC;&#x2122;s visual attention to the most critical information.

4.

Due to their resemblance with the humanâ&#x20AC;&#x2122;s control strategy, using the models as controller in shared or fully automated vehicle control systems will likely yield high levels of user-acceptance. Additionally, online identification of the humanâ&#x20AC;&#x2122;s control parameters may lead to adaptive, intelligent support systems.

Publications -

rate control preview used đ?&#x153;?đ?&#x153;?đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; , s

Aerospace Engineering

Department C&O

The point of departure for this research project is compensatory tracking, the only control task for which widely accepted models of manual control behavior are available. A series of human-in-the-loop experiments is performed, stepwise introducing elements of â&#x20AC;&#x153;realisticâ&#x20AC;? preview control tasks (here, driving).

Step 1: introduce preview information in a plan-view laboratory display tracking task Step 2: investigate the effects of linear perspective on human use of preview Step 3: introduce additional feedbacks, such as inertial motion and visual rotations Step 4: move from tracking (error-minimization) to boundary avoidance In each step, new models of manual control and adaptation are proposed using the cybernetic approach.

acceleration control

available preview đ?&#x153;?đ?&#x153;?đ?&#x2018;?đ?&#x2018;? , s

Mulder, Pool, Abbink, Boer, Zaal, Drop, Van der El, and Van Paassen, â&#x20AC;&#x153;Manual Control Cybernetics: State-of-the-Art and Current Trendsâ&#x20AC;?, IEEE Trans. Human-Mach. Syst., 2018.

Van der El, Pool, Damveld, Van Paassen, and Mulder, â&#x20AC;&#x153;An Empirical Human Controller Model for Preview Tracking Tasksâ&#x20AC;?, IEEE Trans. on Cybern., 2016.

Van der El, Pool, and Mulder, â&#x20AC;&#x153;Measuring and Modeling Driver Steering Behavior: From Compensatory Tracking to Curve Drivingâ&#x20AC;?, Transp. Res. Part F: Traffic Psychol. Behav., 2017.

Damaged Drone Model Identification and Flight Envelope Prediction

PhD Candidate: Sihao Sun Department: Control and Operation Section: Control and Simulation Supervisor Dr. ir. C.C. de Visser Promotor: Prof. Dr. ir. M. Mulder Contact: S.Sun-4@tudelft.nl 1

2

3

4

Currently, Currently, Loss Loss of of Control Control (LOC) (LOC) isis the the primary primary cause cause of of aviation aviation.accidents.

One of theOne remaining of the remaining technical challenges technical challenges to prevent to LOC prevent is to beLOC able is to predict be able the to predict flight envelope the flightofenvelope a damaged of aaircraft damaged or drone aircraftinorreal-time. drone in The realtime. main The goalmain of this goal research of thisproject research is project to create is accurate to createmodels accurateofmodels damaged of damaged drone based drone on based flight test on flight data test and data use them and use to construct them to construct a database a database containing containing flight envelopes. flight envelopes.

A data-based Subsequently, real-time flight a data-based envelope real-time predictionflight thusenvelope can be validated prediction on approach the real drone. can be applied to drones in real flight.

STEP1: Controllable damaged drone development A controllable damaged drone should be developed for real flight tests, by using state-of-art nonlinear and robust control method.

Fig1. A severe damage case â&#x20AC;&#x201C; complete loss of one rotor.

STEP2: System identification of damaged drone

Flight envelopes can be predicted from identified models using reachability analysis. Level-Set method is chosen as the algorithm for this calculation.

Shrinkage of safe flight envelope in damage cases should be analyzed.

113 Fig4. Flight envelope shrinkage in damage case.

STEP4: Data-based flight envelope real-time prediction fault

Fault diagnosis & detection

+

Fig2. Flight test of damaged quadrotor in the wind tunnel.

Modern system identification approaches such as neural networks or multivariate splines can be applied to establish accurate models.

Fig3. Illustration of damaged drone mathematical model.

Safe Flight Envelope

The expected output of this project is a data-based realtime flight envelope prediction approach. A lookup technique of the database of damaged drone flight envelope should be developed.

Department C&O

Flight test should be conducted to establish aerodynamic model of damaged drone using system identification approach.

STEP3: Flight envelope prediction

Analyzing and Modeling Capacity for Decentralized ATC Background The current centralized en-route airspace design is congested. To increase airspace capacity, future plans for the modernization of Air Traffic Management (ATM) systems propose a transfer of the separation responsibility from the ground to the cockpit. Although most researchers agree that some form of â&#x20AC;&#x2DC;airborne separationâ&#x20AC;&#x2122; will increase capacity, there is no consensus yet on the level of traffic organization, or structuring, that is required to maximize capacity for such decentralized ATM concepts.

Contradicting evidence in literature on the benefits of structure suggests that the relationship between structure and capacity is not well understood, i.e., does more or less structuring lead to higher capacity? Or, is there a transition point, where a further increase in capacity will require a switch from one approach to the other?

2

3

4

Modelling Airspace Capacity

â&#x20AC;˘ A procedural mechanism for a priori separation and organization of traffic.

For decentralized separation, conflict chain reactions can destabilize the airspace, which can be measured using the Domino Effect Parameter (DEP).

â&#x20AC;˘

Example: the hemispheric rule separates east and west bound traffic at alternating flight levels.

â&#x20AC;˘ Generally, any a priori structuring of traffic implies posing constraints on degrees of freedom to reduce the conflict probability.

For identical scenarios: â&#x20AC;˘ S1: The set of all conflicts without resolutions â&#x20AC;˘ S2: The set of all conflicts with resolutions The DEP is defined as:

The BlueSky open-source ATM simulator, developed at Control & Simulation, is used to simulate extreme traffic densities and validate capacity models

Four Airspace Concepts of Increasing Structure Layers

1

What is Airspace Structure?

Empirical Analysis of the Airspace Structure - Capacity Relationship Full Mix

PhD Candidate: Ir. Emmanuel Sunil Faculty: Aerospace Engineering Department: Control and Simulation Supervisor: Dr. ir. Joost Ellerbroek Promotor: Prof. dr. ir. Jacco Hoekstra Funding: EU 7th Framework (Metropolis) Cooperations: NLR, ENAC & DLR

Zones

Tubes

đ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ =

đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026; â&#x2C6;&#x2019; đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = â&#x2C6;&#x2019; đ?&#x2018;&#x2026;đ?&#x2018;&#x2026; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;

Assuming that the conflict rate per unit time (or distance) with and without conflict resolution is equal for decentralized traffic demand, then the DEP can be re-written as: đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´ đ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ â&#x2030;&#x2C6; đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161; â&#x2C6;&#x2019; đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´ â&#x20AC;˘ đ??&#x2020;đ??&#x2020;đ??&#x2020;đ??&#x2020;đ?&#x2018;¨đ?&#x2018;¨đ?&#x2018;¨đ?&#x2018;¨đ?&#x2018;¨đ?&#x2018;¨đ?&#x2018;¨đ?&#x2018;¨ : Airspace density

â&#x20AC;˘ đ??&#x2020;đ??&#x2020;đ??&#x2020;đ??&#x2020;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x2019;&#x17D; : Maximum airspace density

114 0 Constraints

Density

1 Constraint â&#x20AC;˘Altitude Safety

2 Constraints â&#x20AC;˘X Position â&#x20AC;˘Y Position

Goal:

4 Constraints â&#x20AC;˘X Position â&#x20AC;˘Y Position â&#x20AC;˘Altitude â&#x20AC;˘Speed

When đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ??´đ??´đ??´đ??´đ??´đ??´đ??´đ??´ = đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161; , đ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ â&#x2020;&#x2019; â&#x2C6;&#x17E; and the airspace is completely unstable with every resolution triggering at least one new conflict, i.e., an uncontrollable conflict chain reaction. It is possible to define đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161; , the theoretical maximum airspace capacity, as:

đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x161; =

Empirical comparison of four airspace concepts of increasing structure

Method:

Department C&O

Large scale simulations up to 30,000 aircraft per 10,000 square nautical miles Efficiency

Stability

Results:

â&#x20AC;˘ The vertical structuring of the Layers concept resulted in the highest safety and stability as it reduced relative velocities between cruising aircraft â&#x20AC;˘ Strict structuring and prescribed routing only reduced performance without any gains in safety, or any other metric as it led to artificial bottlenecks

đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;? =

2đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;? (đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;+đ??ˇđ??ˇđ??ˇđ??ˇđ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A; ) đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ??ˇđ??ˇđ??ˇđ??ˇđ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A; đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ??´đ??´đ??´đ??´

4đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;â&#x201E;&#x17D; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Ł đ??¸đ??¸đ??¸đ??¸ đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;,â&#x201E;&#x17D; đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122; +đ?&#x153;&#x2039;đ?&#x153;&#x2039;đ?&#x153;&#x2039;đ?&#x153;&#x2039;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;â&#x201E;&#x17D;2 đ??¸đ??¸đ??¸đ??¸ đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;,đ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Ł đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;

â&#x20AC;˘ đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;? : Conflict duration

â&#x20AC;˘ T:

đ??ľđ??ľđ??ľđ??ľđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;

Analysis time interval

â&#x20AC;˘ đ??ˇđ??ˇđ??ˇđ??ˇđ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A; : Flight distance CR OFF â&#x20AC;˘ p:

Conflict probability

â&#x20AC;˘ đ??ˇđ??ˇđ??ˇđ??ˇđ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A; : Extra distance flown due to CD&R (empirical) â&#x20AC;˘ đ??´đ??´đ??´đ??´:

Analysis area

â&#x20AC;˘ đ??ľđ??ľđ??ľđ??ľ:

Airspace volume

â&#x20AC;˘ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;â&#x201E;&#x17D;/đ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Ł : Horizontal/vertical separation requirement â&#x20AC;˘ đ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;Ąđ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122; :

Look-ahead time

â&#x20AC;˘ đ??ˇđ??ˇđ??ˇđ??ˇ đ?&#x2018;&#x2030;đ?&#x2018;&#x2030;đ?&#x2018;&#x2030;đ?&#x2018;&#x2030;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x203A;,â&#x201E;&#x17D;/đ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Łđ?&#x2018;Ł

:

Horizontal/vertical velocity

expected

relative

Selected Publications: 1.

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E. Sunil, J. Ellerbroek, J. M. Hoekstra , A. Vidosavljevic, M. Arntzen, F. Bussink, and D. Nieuwenhuisen, â&#x20AC;&#x153;Analysis of Airspace Structure and Capacity for Decentralized Separation Using Fast-Time Simulationsâ&#x20AC;?, published online in AIAA Journal of Guidance, Dynamics and Control, 2016 E. Sunil, J. M. Hoekstra, J. Ellerbroek, F. Bussink, A. Vidosavljevic, D. Delahaye, and D. Nieuwenhuisen, â&#x20AC;&#x153;The influence of Traffic Structure on Airspace Capacityâ&#x20AC;?, presented at the 7th International Conference on Research in Air Transportation, 2016 J.M. Hoekstra, J. Maas, M. Tra and E. Sunil, â&#x20AC;&#x153;How Do Layered Airspace Design Parameters Affect Airspace Capacity and Safetyâ&#x20AC;?, presented at the 7th International Conference on Research in Air Transportation, 2016 E. Sunil, J. M. Hoekstra, J. Ellerbroek, F. Bussink, D. Nieuwenhuisen, A. Vidosavljevic, and S. Kern, â&#x20AC;&#x153;Metropolis: Relating Airspace Structure and Capacity for Extreme Traffic Densitiesâ&#x20AC;?, in proceedings of the 11th USA/Europe Air Traffic Management R&D Seminar, 2015.

I nc r e me nt alc ont r olofhy br i d c r oai rv e hi c l e s mi

PhD Candidate: Ewoud Smeur Department: C&S Section: MAVLab Supervisor: Guido de Croon Promotor: Jacco Hoekstra Funding: Delphi Consortium Contact: e.j.j.smeur@tudelft.nl 1

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Tai l s i t t e r Hy b r i dmi c r oa i rv e h i c l e sc o mb i neh o v e r i ngwi t h f a s ta n de ffic i e ntf o r wa r dfli g ht .The yp r o v i d et he v e r s a t i l i t yo faq u a dr o t o r ,wi t ht h ee ffic i e n c yo fa fix e dwi n g .Thet a i l s i t t e ri sv e r ye ffic i e nt ,a si t us e st hes a mea c t u a t o r si nh o v e ra si tdoe si n f o r wa r dfli g ht .

Chal l e ngi ngPl at f or m At a i l s i t t e rt r a n s i t i o nsf r o mh o v e rt of o r wa r d fli g h tb yp i t c hi n gd o wn9 0d e g r e e s .Wh e n fly i ngs l o wl y ,t hea n g l eo fa t t a c ki sv e r y l a r g e ,a n dt heflo wo v e rt hewi n gi ss t a l l e d .

Th efla p so f t e ns a t ur a t ei nt he i re ffo r tt o p i t c hu p,ma k i ngs a t ur a t i o nh a n dl i ng e s s e n t i a l .

I nc r e me nt alCont r ol Th ea e r ody na mi c so fhi g ha ng l eo fa t t a c k fli g hta r ec o mp l e xa ndr e q ui r ee x pe ns i v e wi ndt u nn e le x pe r i me n t st omode l a c c ur a t e l y .I nmyr e s e a r c h,Ih a v ea pp l i e d I nc r e me n t a lNo n l i n e a rDy na mi cI nv e r s i o n ( I NDI )t ohy b r i dv e h i c l e st oo v e r c o met hi s p r o bl e m.

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I ns t e a do fc a l c u l a t i n gi npu t st omo t o r sa nd s e r v o sf r o ms c r a t c h ,wec o mpu t e i nc r e me n t st ot hep r e v i o usi n pu t s ,b a s e do n d e s i r e dc h a ng e si na c c e l e r a t i o no ra n g ul a r a c c e l e r a t i o n.Th i si sal o tl e s sc o mpl e x , d r a s t i c a l l yr e du c i ngmode l i n gn e e d s .

Di s t ur banc eRe j e c t i on

Thi sfle x i b i l i t ya l l o wsu st oma k eas i n g l e c o n t r o l l e rt h a tg o v e r n st hee nt i r efli g h t e n v e l o pe .Th et r a ns i t i o nma ne u v e ri sno t p r o g r a mme d,b u tf o l l o wsi mpl i c i t l yf r o mt he t r a c k i n go fa c c e l e r a t i o nc o mma n ds .

Ot he rHy br i ds Th eI NDIc o nt r o l l e rdoe sno tr e l yo na c o mp l e xmode lo ft h ev e h i c l e ,s oi ti se a s yt o a pp l yo ndi ffe r e ntv e hi c l e s .Fo re x a mpl et he Qua ds ho t( l e f t )o ro urv e r yo wnDe l f t a c o p t e r ! ( r i g h t ) Mo r ei nf o :ma v l a b. t u d e l f t . n l / c o n t r o l c y c l o ne h y b r i d u a v Pa pe r s :r e s e a r c hg a t e . ne t / p r o fil e / Ewo ud_Sme ur

Department C&O

Be c a us eI NDIc a l c u l a t e st hed e s i r e di n c r e me n t o fa n( a ng ul a r )a c c e l e r a t i o nme a s ur e me n t ,i t k no wsa bo u ta l lf o r c e sa ndmo me nt so nt he v e hi c l e .Th i sma k e si tg r e a tf o rdi s t ur ba nc e r e j e c t i o n,wh i c hwede mo n s t r a t e db yu s i nga qu a d r o t o rt oflyi na n do u to fawi n dt un n e l bl o wi n ga t1 0m/ s .

PhD Candidate: Kirk Scheper Department: Control & Operations Section: Control & Simulation Supervisor: Dr. Guido de Croon Promotor: Prof. Dr.ir. M. Mulder Contact: k.y.w.scheper@tudelft.nl

Abstraction as a Tool to Bridge the Reality Gap

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Research Problem Moving from the Virtual to the Real As the technology to design and manufacture robots improves, the ability to automatically develop robotic behavior is more important than ever. In practice, this development is typically performed in simulation to ensure that the robot doesn’t damage itself or its environment. Additionally, this allows the development to run at faster than real-time speeds accelerating the overall process.

Although effective in simulation, when this behavior is moved to the real world, it generally does not work. No matter how detailed a simulation is, it can never fully describe the real world. These differences are referred to as the reality gap and cause the robotic behavior to fail in reality, limiting the use of this approach.

The reality gap is particularly problematic for Micro Air Vehicles (MAVs) where a high degree of fidelity is required to accurately simulate their dynamics. To date, there has not been a demonstration of an effective transfer of MAV behavior from simulation to reality.

Bridging this gap is essential to facilitate the development of useful robotic behavior moving forward.

Additional Work I

Additional Work II

Bio-Inspired Systems: Neuromorphic sensing and computing

Directed MAV Exploration: Autonomous door detection and fly-through

As MAVs continue to get smaller, we will need novel systems to enable them to effectively perform useful autonomous tasks. Inspired by the way the brain works, one such promising system is neuromorphic sensing and computing. We have performed some initial developments to use these systems with MAVs.

Due to limited memory and computation capabilities of MAVs, it is can be difficult to perform useful tasks. A task which is required to perform autonomous exploration of an office environment is to find and traverse doorways. To achieve this, we developed a very efficient method to identify doors.

Aim Bridge the Reality Gap

1. Changing the robotic behavior representation.

2. Abstracting away from raw actuator commands.

3. Abstracting away from raw sensor data.

4. Utilizing learning to automatically adapt to changing or uncertain environments.

Snake-gate door detector Rino 2 neuromorphic camera

Below are some trajectories of high speed, constant divergence landings of a MAV. These landings are characterized by exponentially decreasing vertical speed. Literature shows that the fastest landings were performed at constant divergence of 0.3. We were able to perform landings at divergence of 1.

Vertical Speed [m/s]

Preliminary Results To date, we have demonstrated the effect of abstracting the behavioral representation and the actuator systems (1 & 2 above) with significant results.

Divergence [1/s]

Department C&O

The Sausage

Behavior to find and fly through a window was optimized using Evolutionary Optimization using a novel representation structure called the Behavior Tree. Used extensively in the computer gaming industry to represent the AI characters, it has seen limited use in robotics. The optimization resulted in the simulated DelFly Explorer, 27g flapping wing MAV, successfully fly through the window 88% of the attempted trials. Due to the reality gap, when transferred to reality this initially dropped to 0%. Given the comprehensible structure of the behavior tree, the user was able to understand the reality gap and actively adjust the behavior to improve performance to 56% in the real world. This would not be possible with a typical Neural Network representation.

– Ground truth - - Estimate

We have recently begun investigating new neuromorphic computing methods to process the data from this novel sensor. This research should allow us to make very powerful yet power efficient robotic perception systems.

Publications 

For the action abstraction, we optimized two controllers to perform coordinated formation flight of 3 quadrotor MAVs. One controller was tasked with commanding the vehicle rotor speeds and the other commanded the vehicle velocity, which was implemented with 2 nested control loops. This abstracted controller transferred well to the real world with almost identical flight performance as in simulation while the low level controller did not.

4 gram stereo camera

With the door detector and a collision avoidance system using the on-board stereo camera system on the DelFly, together with some developments on the control systems, we were able to routinely perform door traversals.

Height [m]

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In control theory, abstraction is a commonly used tool to achieve robust performance in the presence of uncertain or changing environments. Inspired by this approach, the goal of this project is to investigate how abstraction of robotic behavior can be used to bridge the reality gap for MAVs. We investigate the effect of abstraction on many aspects of the reality gap namely:

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K.Y.W. Scheper, M. Karásek, C. De Wagter, B.D.W. Remes and G.C.H.E. de Croon, First autonomous multi-room exploration with an insectinspired flapping wing vehicle. IEEE International Conference on Robotics and Automation (ICRA2018). May 2018, Brisbane, Australia.

B.J. Pijnacker Hordijk, K.Y.W. Scheper, G.C.H.E. de Croon, Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow, Journal of Field Robotics 35(1):69- 90, 2018 K.Y.W. Scheper and G.C.H.E. de Croon, Abstraction, Sensory-Motor Coordination, and the Reality Gap in Evolutionary Robotics, Artificial Life, 23(2), 2017.

K.Y.W. Scheper and G.C.H.E. de Croon, Abstraction as a Mechanism to Cross the Reality Gap in Evolutionary Robotics, in From Animals to Animats 14: Proceedings of the 14th International Conference on Simulation of Adaptive Behavior (SAB2016), Aberystwyth University, Wales, UK, 2016, pp. 280–292.

K.Y.W. Scheper, S. Tijmons, C.C. de Visser, and G.C.H.E. de Croon, Behavior trees for evolutionary robotics, Artificial Life, 22(1):23–48, 2016.

PhD Candidate: Mario Coppola

Designing Provable Robotic Swarms

Departments: Control & Simulation Department: Space Systems Engineering Supervisors: Dr. G. de Croon Supervisors: Dr. J. Guo Promotors: Prof. Dr.ir. M. Mulder Promotors: Prof. Dr.ir. E. Gill Contact: m.coppola@tudelft.nl 1

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The paradigm of swarm robotics is based on the idea that large teams of simple robots can accomplish complex tasks, far beyond the capabilities of any such robot by itself.

The challenge lies in developing artificial intelligence algorithms to enable the robots to behave such that, despite having very little knowledge of what all other robots are up to, or what is actually going on, they can make the choices that are in the best interest of the entire swarm's common goal. Moreover, we also need to make sure that the algorithms are such that it is guaranteed that our swarm will succeed without faults.

Because it's fun Imagine a swarm of satellites that can selfreorganize while in deep space, or a swarm of drones that can coordinate in surveillance and inspection applications, or a swarm of robotic ants that crawls in the tiniest of places and then self-organizes into a larger robot. These teams exceed the capabilities of any individual robot - all at low cost, high system robustness, and full-blown scalability. However, to really begin using them, we have to make sure that the robots can work together reliably!

How can we create patterns using robots with all these limitations below?

• Homogeneous (all exactly the same!)

• Asynchronous • Memory-less • Anonymous (no IDs!) • Cannot communicate • Have limited sensing range • Don't know where they are (no GPS!)

To design local agent controllers with a mathematical proof of reaching a desired swarm behavior.

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In this work, we are focusing on developing an agent centered behavior and understanding the repercussions on the total structure.

Phase 1 : Homogeneous Swarms A Pattern Formation

How can very limited robots generate complex patterns?

B Behavior Coordination

How can very limited robots coordinate their actions?

Phase 2 : Heterogeneous Swarms A Heterogeneous coordination

How can different types of robots collaborate?

B Swarms in complex tasks

How can swarms collaborate in complex multi-step tasks?

The first step to collaboration is to sense each other and not get in each other's way. We developed a method with which MAVs can localize each other using only using their wireless transceiver. The MAVs exchange onboard states (velocity, height, orientation) while the signal strength indicates range. We tested with miniature drones (≈ 50 g) flying in tight areas, the MAVs could localize each other and fly several minutes without collisions.

Watch the video

Relative Localization Estimates

Department C&O

How should an agent move?

When should it move? Should it move it all?

Even then, can we create an arbitrary pattern starting from any initial condition?

Revealing Adverse Rotorcraft Pilot Couplings Induced by Flight Control System

PhD Candidate: Yu Ying Department: C&O Section: C&S Supervisor: M. D. Pavel & Erik-Jan van Kampen Promoter: Max Mulder Email: Y.Yu-2@tudelft.nl 1

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Project Description Rotorcraft-Pilot Couplings (RPCs) are annoying vehicle oscillations that arise from the effort of controlling rotorcraft. Known in the past under the name pilot induced/pilot assisted oscillations PIO/PAO, helicopter RPCs are still a subject of concern for helicopter’s safety. The current practice of revealing adverse rotorcraft pilot couplings (RPC) shows significant gaps in predicting the effects of the vehicle automatic flight control system (FCS) on human pilot.

The main objective of this project is to better predict and alleviate those RPCs related to malfunctioning of automatic flight control system that result in loss of vehicle. The project will focus especially on the Category III RPC, i.e., those RPCs associated with the non-linear elements in the FCS (such as breakout and hysteresis in the command shaping, effects of gain scheduling, mode switching, and aerodynamic nonlinearities). Currently there are only a few tools for predicting Cat III RPC, this in the context of increased use of FCS in the helicopter and these tools need to be urgently developed.

Classification of Rotorcraft Pilot Coupling Problems The classification of RPCs is listed in Fig 1.

Fig 1. Classification of RPCs

The peak input power versus time is

Pmax (t )  P (max (t ), t )

And the phase lag versus time is

max (t )  phase(Yc (max (t )))

Fig 3. Example of wavelet Scalogram

Ingredients for RPCs to Develop

Department C&O

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There are three crucial ingredients that have to exist for an A/RPC to develop. The first is unfavourable vehicle dynamics. This means that the vehicle system as a whole, including the FCS, displays, actuators, etc., should be prone to A/RPC. The second necessary element for an A/RPC situation to occur is the pilot dynamic behaviour. However, the essential element for an A/RPC to appear is commonly accepted to be a trigger event. The trigger causes the pilot to quickly alter his/her control strategy. Without a triggering event (or a chain of triggering events) A/RPCs would not exist. Therefore, it can be concluded that only with the appropriate combination of the unfavourable vehicle dynamics, pilot dynamic behavior and trigger events can the A/RPC occur.

Fig 4. Example of the inceptor peak power-phase (IPPP) PIO metric

Fig 2. Ingredients for RPCs

Wavelet Based Technique for A/RPCs Prediction-The Inceptor Peak Power-Phase Metric Wavelet transforms are a relatively new way of characterizing time-varying systems. Rather than just a power versus frequency relationship averaged over an entire time history, the use of wavelet transforms can produce plots of power or auto-spectrum versus both time and frequency.

The power frequency was developed to utilize this time-varying auto-spectrum to illustrate the differences in the frequency and intensity of signals as a function of time. This intensity has been shown to differentiate run-to-run and pilot-to-pilot characteristics in pilot-vehicle system behavior.

A wavelet scalagram-based PIO metric that features a time-varying measure of peak input power at a given time versus weighted phase lag. Assume that the input signal is c (t ) , and the controlled element is Yc ( ) . Then, the scalogram P ( , t ) is the input power versus both frequency and time.

Results for one example case are shown in Fig 3 and Fig 4. It shows that the IPPP PIO metric can detect the PIO start and end time by using the boundary of this method, which may be a potential method for detecting and predicting Cat. III RPCs.

Research Goals The aim of the project is to extend and improve existing procedures used to predict category III rotorcraft pilot couplings (RPC) and give guidelines to the designer how the automatic flight control system (AFCS) can be adjusted to provide RPC-free design. The project results will help:

- understanding the mechanisms through which cat. III RPC can be triggered; - unmasking Cat III RPC in existing new designs before the actual prototype has been developed and tested; - understanding the effects of nonlinear control systems and their role in triggering Cat. III RPC. The mentioned results will take into consideration the necessary accuracy needed for rigid-body and aeroelastic RPC prediction.

Finite-dimensional approximation and control of shear flows

PhD Candidate: Henry Tol Department: AWEP / C&O Section: Aerodynamics / C&S Supervisors: M. Kotsonis & C.C. de Visser Promotor: Prof.dr. F. Scarano Contact: h.j.tol@tudelft.nl 1

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Dynamical systems theory can contribute a great deal to the understanding and control of fluid flows. Systems and control theory is an established field dealing with the analysis and control of continuous-time dynamical systems. However, fluid dynamical systems are continuous in both time and space, resulting in a state space of infinite dimension. A new methodology is proposed for the derivation of finite-dimensional approximations and controllers for fluid dynamical systems. The controllers have been successfully tested in the newly build anechoic vertical wind tunnel at TU Delft.

Experiments have been conducted on a flat plate geometry under the influence of an externally imposed adverse pressure gradient. The objective is to supress natural flow perturbations to delay transition, extend laminar flow and reduce skin friction drag. A Kalman filter is used to estimate the effect of upstream disturbances based on pressure information from a wall embedded microphone.

This information is used within a state feedback control law to cancel the perturbations using a surface DBD plasma actuator. A downstream sensor monitors the control performance.

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Multivariate splines defined on triangulations are used to obtain the state-space discretisation of the governing equations that can be used for control design. The state-space model is coupled with balanced truncation to design experimentally feasible low-order controllers.

Department C&O

The estimates obtained from the Kalman filter (top) are compared with the experimental data obtained using particle image velocimetry (bottom). The results show that the Kalman filter is able to estimate the spatio-temporal behavior of naturally occurring perturbations in the presence of unknown external disturbances.

The controller is evaluated in comparison with open-loop continuous forcing. An additional 60% reduction in the rms of the downstream pressure signal is measured.

[1] H.J. Tol, C.C. de Visser, and M. Kotsonis, Model reduction of parabolic PDEs using multivariate splines, International Journal of Control, 2016 [2] H.J. Tol, M. Kotsonis, C.C. de Visser and B. Bamieh, Localised estimation and control of linear instabilities in 2-D wall-bounded shear flows, Journal of Fluid Mechanics, 824 pp. 818-865, 2017 [3] H.J. Tol, M. Kotsonis, C.C. de Visser, Estimation and control of TS waves in Falkner-Skan boundary layers, under review in AIAA journal, 2018 [4] H.J. Tol, C.C. de Visser and M. Kotonis, Experimental model-based estimation and control of natural TS waves, under review in AIAA journal, 2018

Understanding the use of automation in helicopters

PhD Candidate: Daniel Friesen Department: Control & Operations Section: Control & Simulation Supervisor: Dr. M.D. Pavel, Dr.ir. C. Borst, Prof. P. Masarati (PoliMi) Promotor: Prof. Dr.ir. M. Mulder Contact: d.friesen@tudelft.nl

Background

Automation

The goal of this project is to better understand the relationship between the human pilot and different implementations of automation, for example stability and control augmentations, “classic” autopilot modes (comparable to fixed-wing applications),or decision support systems. Part of this goal is to investigate the influence of different interface approaches (for example ecological interface design) on the control and management of the helicopter.

Automation can be classified according to different criteria. The level of automation [2] describes the intensity of its involvement. A high level of automation might mean that the human mainly performs supervisory tasks, while a low level of automation requires the pilot in the loop. The stage of automation [3] determines the processing stage in which the automation is active. Each stage can possess a different level of automation, see the figure below.

Automation often seems to work best in routine tasks, but can fail to be of much help in unanticipated situations. Depending on the situation, it might even have a negative impact on system performance, pilot situational awareness, or system safety.

Several “ironies of automation” [1] have to be considered while designing automated systems. Opaque or brittle system architectures, as well as causing automation complacency, vigilance problems or a degradation of piloting skills should be avoided.

Expected results consist of • guidelines related to the requirements on automation in different helicopter operating conditions, • novel automation and interface applications in the helicopter domain, • a methodology to predict how to achieve the best coordination between helicopter automated systems and the pilot in support of emergencies and unanticipated situations.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 721920.

Matrix of possible solutions (A, B) in the level (high, low, manual) and stage (information acquisition (IAc), information analysis (IAn), action selection (AS), action implementation (AI)) of automation space. [3]

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Approach

During this project, approximately four human-in-the-loop experiments will be conducted, for example in TU Delft’s SIMONA research simulator. Original experiments or experiment reproductions might take place at the facilities of NITROS project partners. Each experiment is embedded in one complete iteration of the following research steps: 1. Analyse existing automation and interface applications in the helicopter domain, and identify specific tasks, situations or missions that afford further analysis; 2. Develop different automation and interface applications, exploring the solution space spanned by the automation classification methods; 3. Employ and test the developed applications in different helicopter operating conditions, including unanticipated and emergency situations via human-in-the-loop experiments; 4. Evaluate the influence of the developed system on performance, situational awareness, safety, and handling qualities, depending on the considered task and situation.

Lastly, the level of control sophistication [4] describes the complexity of the goal of the automation (or the human in the same situation). These can reach from low-level stability augmentation functions, typical autopilot modes like “heading hold” or the execution of a specific attitude change, to complex mission objective functions like “search and rescue”. The level of control sophistication correlates with the position in the flying task hierarchy, as elaborated in the offshore supply mission description below.

SIMONA Research Simulator, TU Delft. [http://cs.lr.tudelft.nl/simona/wp-content/uploads/sites/3/2016/05/img_0085.jpg]

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AgustaWestland 109 helicopter of the Swiss Air-Ambulance. [http://www.leonardocompany.com/en/news-media/media/galleria-fotografica/elicotteri]

“Cockpit of the AgustaWestland 139 helicopter”, by Jet Request, 06-08-2012. [https://commons.wikimedia.org/wiki/File:AW_139_helicopter_cockpit.jpg]

Department C&O

Offshore supply: A typical civil helicopter mission

Helicopter operations can be divided into a number of different possible missions. A mission may be divided into multiple mission phases, each of which consists of multiple mission-task-elements (MTE). “An MTE is a collection of individual manoeuvres and will have a definite start and finish (…).” [5] An MTE is made up of several manoeuvre samples. Manoeuvre samples represent the smallest flying element.

They are often related to a change in only one particular flying axis.

Typical manoeuvre samples include a change in pitch attitude or roll attitude. [5] A typical mission, offshore supply, is depicted in the figure to the right. The goal of the mission is to transport goods and/or people from a land-based heliport to an offshore platform. The mission “offshore supply” is divided into the mission phases “takeoff”, “climbout”, “cruise”, “descent” and “approach/land”. In the figure to the right, the mission phase of “approach/land” is depicted in greater detail, revealing the necessary MTEs (“decelerating approach”, “final approach”, and “land”). Finally, the MTE “land” is depicted in even greater detail. To perform a landing, several manoeuvre samples like “align yaw angle with required approach angle”, “sidestep with constant velocity” and “lower height with constant velocity” are required.

Literature

“Elements of a civil mission – offshore supply: (a) offshore supply mission; (b) mission phase: approach and land; (c) mission task element: landing.” [5]

“The hover display,” (employed by Abildgaard and Binet), “showing velocity vector as red line and estimated velocity in 5 seconds as blue circle.” [7]

Current state & next steps The analysis and identification work package for the first experiment is almost completed. The first experiment will most likely involve low-speed tasks and hovering tasks, and will utilize performance metrics and task descriptions defined in the ADS-33 handling qualities requirements [6].

The development work package for the first experiment is underway. Possible interface solutions might be comparable to the hover display depicted in the figure above. In this example, the current and predicted horizontal and vertical velocity components are shown to the pilot. This design could be expanded by incorporating relative velocity or position information with respect to the environment. Additional environmental influence factors like wind or system-related operational boundaries like undesirable areas of flight states could be integrated, as well.

[1] Lisanne Bainbridge. Ironies of automation. Automatica, 19(6):775–779, 1983.

[2] R. Parasuraman, T.B. Sheridan, and C.D. Wickens. A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(3):286–297, 2000.

[3] Linda Onnasch, Christopher D. Wickens, Huiyang Li, and Dietrich Manzey. Human Performance Consequences of Stages and Levels of Automation: An Integrated Meta-Analysis. Human Factors: The Journal of the Human Factors and Ergonomics Society, 56(3):476–488, 2014.

[4] Matthijs H. J. Amelink. Ecological Automation Design: Extending Work Domain Analysis. PhD thesis, Delft University of Technology, 2010.

[5] Gareth D. Padfield. Helicopter Flight Dynamics: The Theory and Application of Flying Qualities and Simulation Modeling. American Institute of Aeronautics and Astronautics, 2nd, illustrated edition, 2007.

[6] US Army AVSCOM. AVSCOM, Aeronautical Design Standard (ADS) 33E - Handling Qualities Requirements for Military Helicopters, 2000.

[7] Max Abildgaard and Laurent Binet. Active Sidesticks Used for Vortex Ring State Cueing System. In 35th European Rotorcraft Forum, 2009.

PhD Candidate: Isabel C. Metz Department: Control & Simulation Section: CNS / ATM Supervisor: Dr.ir. J. Ellerbroek Promotor: Prof. Dr.ir. J.M. Hoekstra Contact: i.c.metz@tudelft.nl

ATC Advisory System for the Prevention of Bird Strikes

1

Air Traffic Control

4

Delaying departing traffic in case of potential conflicts

Four Main Research Questions

NOTAM 1

ď&#x192; ATC receives general information on high bird densities

3

Concept

Current Information Flow at Airports Wildlife Control

2

Increased Safety?

Initial Trajectory

2

3

deterministic?

Decreased Capacity?

Intended Information Flow Airports Revised Trajectory

Wildlife Control

4

Air Traffic Control

Requirements for a Bird Strike Advisory System

Advisory System ď&#x192; ATC receives detailed information on current and predicted bird movements

Protected Zones

Simulation Environment

Verification Simulation Environment

defining a conflict

BlueSky Open Air Traffic Simulator enhanced with bird movement information

including four traffic intensities and bird movement information from one year

đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?

121

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engines

fuselage

Bird Movement Information including flock and bird sizes based on modelling radar information

Department C&O

đ?&#x153;&#x201A;đ?&#x153;&#x201A;đ?&#x153;&#x201A;đ?&#x153;&#x201A; = đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x153;&#x152;đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;? đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;? [ đ?&#x2018;&#x;đ?&#x2018;&#x;đ?&#x2018;&#x;đ?&#x2018;&#x; =

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2

based on: Graham, Ronald L., et al. "Dense packings of congruent circles in a circle." Discrete Mathematics 181.1-3 (1998): 139-154.

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reflectivity density radar cross section

based on: Dokter, Adriaan M., et al. "Bird migration flight altitudes studied by a network of operational weather radars."

Journal of the Royal Society Interface (2010): 30-43. van Gasteren, Hans, et al. "Extracting bird migration information from Câ&#x20AC;?band Doppler weather radars." Ibis 150.4 (2008): 674 â&#x20AC;&#x201C; 686.

Used by permission of Gary Clark, www.swamp.com.au

PhD Candidate: Julia Rudnyk Department: Control & Operations Section: Control & Simulations Supervisor Dr.ir. J. Ellerbroek Promotor: Prof.dr.ir. J.M. Hoekstra Contact: i.rudnyk@tudelft.nl

Trajectory Prediction for MTCD Background

1

2

3

4

Route Schiphol (Amsterdam) – Munich 110 filed flight plans

Decision support tools are widely used to assist air traffic controllers in their duties and reduce congestion of and delays in air traffic. All decision support tools rely on the information about a current and/or future aircraft position.

110 flown flights

Top view

Medium Term Conflict Detection

Aircraft position in 5 – 20 min Problems in prediction: • Large positional/time errors

Side view

Trajectory prediction errors and uncertainty TP inputs influence

Prediction uncertainty

Altitude error

Altitude error

5 to 20 min

10 min

Predicted trajectory (green) vs real trajectory (blue)

122

Trajectory Predictor

Altitude

Speed 5 min

Altitude error

Longitude

Vertical speed

Latitude

A typical structure of a Trajectory Predictor

Wind Probabilistic predictions Altitude

Department C&O

Probabilistic prediction

Typical structure of a trajectory predictor

Sources of errors: • Weather • Aircraft performance data • Route shortcuts • Military zones • Procedures • Other traffic

Project Outline For the research on Trajectory Prediction for MTCD the following work packages are proposed: 1. Analysis (investigate sources of uncertainty and their propagation through a trajectory prediction) 2. Design (improvement(s) to currently used trajectory predictors) 3. Implementation (develop and incorporate a prototype into a trajectory predictor) 4. Validation (using historical data)

Input A Input B …

Input I

TP

t2 t3

t1

Trajectory 1 Trajectory 2 …

Trajectory N

Longitude Latitude

Sensitivity analysis: Monte Carlo simulations Parameters distributions • Surveillance data • Weather forecasts Estimated parameters: • Speed settings • Vertical speed • Bank angle setting • Temporary level-offs • Air temperature • Wind vector

Monte Carlo simulations x1i, x2i, x3i, x4i, x5i, x6i, x7i … x1n, x2n, x3n, x4n, x5n, x6n, x7n

x1 x2 x3 x4 x5 x6

Additional parameters: x7 • ATC intent

Trajectory predictor yi … yn

Obstacle avoidance for UAVs using Self-Supervised Learning

PhD Candidate: Tom van Dijk Department: Control & Operations Section: Control & Simulation Supervisor: Dr. G.C.H.E. de Croon Promotor: Prof.dr.ir. M. Mulder Contact: j.c.vandijk-1@tudelft.nl 1

2

3

4

Background The private and commercial use of drones will grow rapidly in the coming years. Example applications include infrastructure inspection, agriculture and package delivery. A larger number of drones, however, means a larger risk of collisions. At a typical height of 0 – 150 m, the most likely obstacles UAVs will encounter are buildings, low-flying helicopters and other UAVs (see figure). Collisions must be avoided to keep the operation of UAVs safe. The goal of the `PercEvite’ project is therefore: Development of a Collision Avoidance Package for UAVs, which will be used to avoid ground-based obstacles and other aircraft.

The Collision Avoidance Package should be lightweight ( ∼200 g for drones > 1 kg, ∼50 g for smaller ones) so that the impact on payload capacity is small. This means only a few lightweight sensors can be used to detect obstacles: cameras, microphones and radio.

Research goal Unlike aircraft, ground-based obstacles do not tend to transmit radio signals nor to produce sound. Therefore, these obstacles have to be detected visually. Existing methods are limited in range and accuracy, but machine learning may improve the performance of visual depth perception. This leads to the following research question:

Typical heights for drones and obstacles. Obstacles in the highlighted area pose the most likely threats for collision. Adapted from the PercEvite project description.

Motion Instead of taking two side-by-side images – which requires two cameras – it is also possible to use the motion of a single camera to estimate depth.

If images are taken close to each other, optical flow can provide a measure of depth. A larger change in bearing θ is expected for nearby obstacles (small z):

How can Self-Supervised Learning improve the range and/or accuracy of visual depth perception?

Visual depth perception Cameras provide a large amount of information while at the same time their weight and power consumption are small.

This makes them ideal for lightweight applications. The images, however, require significant processing before a usable distance estimate is obtained.

For obstacle avoidance, cameras are used to estimate the distance to nearby obstacles. Where does this distance information come from?

Stereo vision When two images are taken side-by-side, objects close to the camera show a larger shift in position (disparity, d) than those further away. By measuring this disparity, the distance to the object can be calculated:

A disadvantage of optical flow, however, is that very little flow is observed near the Focus-of-Expansion where θ ≈ 0, i.e. directly in front of the UAV. This makes optical flow less suitable for obstacle avoidance.

If the distance between camera positions is large, Structurefrom-Motion (SfM) and Simultaneous Localization and Mapping (SLAM) can be used to reconstruct the environment.

Appearance cues

Learning to see depth from images requires large datasets, where each image is typically labeled by its true depth map.

Building such a dataset takes a considerable amount of time and effort. Additionally, it may be difficult to transfer the results learned on this dataset to a different environment.

Self-Supervised Learning (SSL) provides a solution to both these problems. It uses the same techniques as Supervised Learning, but instead of requiring a manually labeled dataset, the UAV collects its own labeled training data during operation. These labels can, for instance, be produced using `classical’ stereo algorithms such as SGBM (Hirschmüller, 2008) or ELAS (Geiger et al., 2011).

Self-Supervised Learning would not be useful if the learned depth estimator is limited by the performance of the algorithm used to create the labels. However, for simpler problems it has already been shown that the learned estimator can exceed the performance of the labelgenerating algorithm (de Croon, 2017).

Recent approaches eliminate the need for an initial stereo algorithm altogether. Instead, the estimator is trained to predict the right image while looking at the left one and vice versa.

123

Other topics

Even a single image already contains a large amount of depth information, as demonstrated by the above example.

The car on the left is clearly closer than the car in the center of the image. Cues that indicate this are its larger apparent size and lower position in the image. Perspective, occlusion and texture gradients (tree leaves) provide additional cues to the depth of this scene.

Depth estimation from a single image comes naturally to humans, but is difficult to program on a computer. Instead, Machine Learning allows a computer to learn depth estimation from examples. The use of appearance cues in addition to depth from stereo vision or motion will likely improve the range and accuracy of depth perception for UAVs.

Avoidance maneuvers and odometry When an obstacle is detected, the UAV will need to plan an avoidance maneuver. To perform it, the UAV also needs an estimate of its velocity. Visual Odometry ensures that such an estimate is available even in GPS-denied environments.

Radio communication By communicating their positions and velocities, UAVs and other aircraft can collaboratively avoid each other. Noncollaborative aircraft can be avoided by measuring the bearing at which their RF signals arrive (for instance: telemetry, video streams or WiFi).

Research into RF communication and measurements within the PercEvite project is performed by KU Leuven.

References de Croon, G. C. H. E. (2017). Self-supervised learning: When is fusion of the primary and secondary sensor cue useful? arXiv Preprint, (arXiv:1709.08126).

Eigen, D., Puhrsch, C., & Fergus, R. (2014). Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. In Advances in neural information processing systems (pp.

2366–2374).

Geiger, A., Roser, M., & Urtasun, R. (2011). Efficient Large-Scale Stereo Matching. In Computer Vision – ACCV 2010 (pp. 25–38). https://doi.org/10.1007/978-3-642-19315-6_3 Hirschmüller, H. (2008). Stereo Processing by Semiglobal Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 328–341. https://doi.org/10.1109/TPAMI.2007.1166

Pe r c Ev i t e

Department C&O

Hear-and-avoid With a negligible mass, microphones can easily be added to the Collision Avoidance Package. Microphones could provide additional information in low-visibility conditions. They can be used to detect the presence of other aircraft, and also their bearing when placed in an array. Range and velocity measurements might be possible by examining the spectrogram.

Depth cues by Brazzit at English Wikipedia / CC-BY-SA

Estimating the disparity is the main challenge of stereo vision; it is not always straightforward to find which pixel in the right image corresponds to a specific pixel in the left image (this is, for instance, very difficult on untextured surfaces such as blank walls). Reflections and other nondiffuse surfaces can also cause incorrect matches between the two images.

The derivative of the disparity is larger for nearby objects than for far-away ones (see figure). Since the uncertainty in the disparity remains constant, this causes the accuracy of the depth estimate to decrease. As a result, the range of stereo vision is limited to ∼30 – 100 m in practice.

Self-Supervised Learning

Design and Control for a Swarm of Autonomous Drones Background

Over the last few years, swarm flight of small unmanned aerial vehicles (UAVs) has attracted increasing attention as UAVs can execute tasks in parallel with more efficiency and flexibility, and the reduced size allows them to perform tasks in narrow space. In general, the pose information of multiple drones relies on external positioning systems such as GPS or pre-installed systems such as camera capturing system which, however, causes that an exploratory task of a swarm of drones cannot be achieved in unknown environments.

Relative localization between drones can be utilized to solve this issue. This research will use onboard sensors to estimate the position states of a swarm of drones. Then design novel and robust swarm control methods for multiple drones based on aforementioned pose estimation. Overall, the proposed research will pave the way for swarms of small autonomous drones, able to perform complex missions even in difficult, cluttered, GPS-denied environments.

PhD Candidate: Shushuai Li Department: Control & Operations Section: Control & Simulation Supervisor: Guido de Croon Promotor: Max Mulder Contact: s.li-6@tudelft.nl

Altitude filter

1

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4

Thrust model

Since accelerator usually has large noise due to the vibration, it is natural to find an alternative variable such as thrust for prediction in the altitude filter.

Assume the thrust model as: Altitude filter structure

The first step towards the swarm UAVs is the low-level robust flight of a single drone. And the first step is to achieve the height control of one drone using filter methods to process the data from height related sensors such as sonar, barometer and accelerator, based on the Paparazzi autopilot software. The three sensors contain different advantages and drawbacks as follows.  accelerator has bias and noise  sonar has a limited range  barometer has larger noise and is easily influenced, but obtains an unlimited range

accz  g kT * T 2  nT  k f * f vz where T denotes the vertical control command and f_{vz}=sign(v_z)*v_z^2 is the square of velocity. The other parameters are constants. All of them can be estimated by using least square method.

Right figure shows the thrust model smoothly estimates the vertical acceleration with high precision.

The next figure shows results of the altitude filters which use different acceleration.

The characterization of two altitude sensors

Future swarm control

124

Covariance matrix R The scheme of a swarm autonomous UAVs

Department C&O

Problem

The proposed altitude filter is based on the basic linear Kalman filter, where the reliable values R of sonar and barometer vary according to the sensor characterization given as follows.

Small UAVs have limited onboard processing ability and sensor performance due to the limited payload.

From the left figure, it shows that if baro_alt > 0, the real altitude will be zero. Hence, the first criterion is: if baro_alt > 0, then baro_alt = 0 and decrease R_baro.

Challenges also range from low-level navigation in unknown environments of the individual robot, to the avoidance of other drones in the swarm, and coordination between swarm members to achieve the central mission goals.

From the right figure, it is obvious that if the subtraction of barometer and sonar output is above 1.5, the sonar output will be wrong. Hence, the second criterion is: if subtraction > 1.5, then decrease R_baro, else decrease R_sonar.

The practical application of swarm theory into real multiple UAVs in GPS-denied situations is still rare. For this purpose, this research will deal with swarm coordination techniques and intelligent onboard filter for estimating the state information.

Preliminary results

After incorporating adaptive R into the Kalman filter, the real experiment result of the proposed filter is shown in the following figure.

Approach

The proposed filter has a better approximation of real altitude than that of Paparazzi, and is more robust.

Develop a new method to perform detection and localization of other drone members. Combine sensor inputs from cameras, IMU, and wireless strength measurements, and fuse them with state information available on board the drones.

This heavily relies on state estimation and filtering. Overall, appropriate sensor hardware will be introduced to achieve swarm flight.

Design reliable control algorithms to avoid other swarm members, only using the onboard sensors, communication means and processing. Multi-agent based coordinated control methods will be presented to investigate the stability and synchronization of swarm drones’ dynamic networks.

Furthermore, approaches for other problems arising from the swarm drones will also be considered, such as Lyapunov stability, disturbance attenuation, robustness to uncertain dynamics, actuator saturation, fault tolerant control, etc.

For swarm control strategy, the first step is to model the motion dynamics of multiple MAVs by using kinematics equations.

Then, use methods such as  algebraic graph theory  artificial potential fields  switching topology  leader-following flocking to generate the coordinated commands for swarm operations. Therefore, a collection of vehicles can perform a shared task using inter-vehicle communication to coordinate their actions.

In addition, multiple Lyapunov functions theory will be introduced to investigate the stability and synchronization of a swarm of drones’ systems in Matlab Simulink. This coordinated methodology will include both theoretical work and experimental work that can be achieved on the aforementioned platforms.

Swarm flight of multiple UAVs Onboard image sensory information can be used to avoid obstacles by estimating the potential dangers. Also, deep learning method can be explored and implemented into swarm drones.

Altitude filter based on sensor characterization

In-flight Model Parameter and State Estimation using Gradient Descent for High-speed Flight

PhD Candidate: Shuo Li Department: C&O Section: Control and simulation Supervisor: Coen de Visser Guido de Croon Promotor: Max Mulder Contact: s.li-4@tudelft.nl

Background:

Research steps:

Result:

High-speed flight in GPS-denied environments is currently an important frontier in the research on autonomous flight of Micro Air Vehicles (MAVs). Autonomous drone races stimulate the advances in this area by representing a very challenging case with tight turns, texture-less floors, and dynamic spectators around the track. For example, the first autonomous drone race held in 2016 by IROS.

Step 1: Fly a Bebop 1 quadrotor in Cyberzoo to gather onboard sensor data and position measurement. Since onboard accelerometers and gyros suffer from bias and high frequency noise, the estimation of attitude is always biased.

1.

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With more data to be used by gradient descent method, AHRS error estimation and trajectory prediction are more accurate.

After a 180 degree turn, the final point error can be kept with 20 cm.

0.8 0.7 0.6 0.5 0.4 0.3

These autonomous drone races put out challenges to drone‘s navigation, control and guidance capacity outside the laboratory experiment. Thus, they are an overall evaluation of drone’s autonomous fast flight capacity. This research mainly focuses on navigation technique of quadrotors in the indoor environment without any external navigation equipment.

Aim of research: In GPS-denied environment, use only on-board resources, for example, camera, IMU and sonar to provide relatively accurate state estimation for quadrotor to make it fly autonomously and aggressively.

0.2

experiment

0.1 0

Step 2: Generate noisy vision measurements based on ground-truth trajectory.

1

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4

5

30

25

number of iterations

Figure 1.

In this drone race, the UAVs have to fly through orange gates in a pre-specified order as fast as possible.

The map of the IROS 2016 drone race.1

Figure 2.The quadrotor we use in the

Figure 3.The top view of the experiment track.

The noisy vision measurements are generated.

Step 3: Estimate AHRS error using on-board AHRS measurements, aerodynamic model of quadrotor and vision measurements by gradient descent method. Find optimal AHRS error by minimizing the error between predicted trajectory and vision measured trajectory.

The result of this research could be used to provide relatively accurate position estimation when drones lost vision for a short time, which finally helps drones fly faster in in-door environment.

20

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10

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2. Compared to Extended Kalman filter (EKF), which is widely used in the navigation of drones, our method converges faster and more stable.

7 VIEKF VGD

6 5

1.

No GPS signal is available for navigation.

2.

On-board IMU is affected by time-varying bias and high frequency noise, which introduce bias to attitude estimation.

4 3 2

3.

Vision navigation requires a large amount of computational resource which is scarce on-board.

4.

During the flight, known objects are not always in the vision of filed, which may lead to the failure of the flight.

5.

The quadrotor is a non-linear system especially when velocity is high.

Figure 4. A gradient descent method optimizes a set of parameters Θ to best fit a predicted trajectory (grey) through a measured trajectory (blue). During the fitting phase, gradient descent method converges to groundtruth trajectory.

Step 4: Use AHRS error estimated in previous step and aerodynamic model to predict trajectory of the arc. To evaluate the performance of the navigation method, we compare the error between the end point of predicted trajectory and corresponding point of ground truth trajectory.

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arc number

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Future works: 1.

Improve the efficiency of gradient descent method further more.

2.

Run the algorithm on-board to help quadrotor fly faster.

3.

Improve the efficiency and accuracy of the detection algorithm.

Reference: 1. http://ris.skku.edu/home/iros_downloads.html 2. Moon H, Sun Y, Baltes J et al. The IROS 2016 Competitions [Competitions]. IEEE Robotics & Automation Magazine 2017; 24(1): 20–29.

Department C&O

Challenges:

ENHANCED HELICOPTER HANDLING QUALITIES THROUGH VIBRATORY LOADS EXPLORATION

1

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DEFINITION OF THE RESEARCH

PLAN OF THE RESEARCH

Bad handling qualities (HQs) of helicopters have been a concern for safety of piloted flights. “Handling qualities” represent the integrated value of helicopter stability in flight, helicopter controllability and pilot’s ability to maneuver the helicopter. The current standart for helicopters is ADS 33 [1] in terms of handling qualities.

1.

Definition of Scope : Literature study, studies of Pavel & Padfield [2], [3] are taken as guideline and base of the study. In this study attitude quickness of ADS 33 enhanced into two parameters called agility quickness and load quickness which includes vibratory loads as well. Basic model will be generated and first the attitude quickness and then the agility and load quickness developed in baseline study will be applied and model will be verified. There will be a model available both in Flightlab [4] and an in house code.

2.

Developing and Validating New Handling Quality Criteria : New approach will be developed or a current criterion will be extended in terms of handling qualities of helicopters for a defined maneuver and will be verified.

3.

Multi Disciplinary Structural Allevation : There will be a multi disciplinary approach during the study which will consider the helicopter from the structural point of view as well as the performance

4.

Predicting the Vibratory Loads: With the new model and approach, vibratory load calculation and load prediction will be also a branch of the studies.

Pilot Workload

https://twitter.com/helicopassion/status/726164367813808129

•

Heavy vibration in demanding maneuvers increases both pilot and structure workload. Although handling qualities are a measure of helicopter stability and controllability which affect pilot ease to perform a maneuver, in some cases developments are needed to fill the gaps in the criteria or enhance it further.

•

Up to the present no criteria have been ever elaborated for either helicopters to assess simultaneously the helicopter performance and the vibratory activity on the structure.

•

The aim of this project is to develop new inter-disciplinary handling qualities criteria are needed capable of predicting the complex dynamic behaviour of rotorcraft, which combines pilot workload to the work of the structure and try to decrease vibrational loads and enhance performance.

126

Department C&O

PhD Candidate: Ezgi Akel Department: Control & Operations Section: Control & Simulation Supervisor: Dr. M.D. Pavel, Dr.ir. Rene van Paassen, Dr. Giuseppe Quaranta Promotor: Prof. Dr.ir. Max Mulder Contact: e.akel@tudelft.nl

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Action.

ADS 33 Quickness Criteria where levels indicates how good the helicopter meet the given requirements by ADS33.

Because of the complex nature of helicopter design, effects of couplings, different design parameters, flexibility and stall models will be investigated throughout the study both in terms of handling qualities and accurate load prediction. It is known that adding second flap harmonics results in more accurate loads [5]. For this project simulation models, missions and environments will be developed and assessed in a multi-disciplinary manner in the flight simulators available to the NITROS project.

LITERATURE

Helicopter in demanding weather conditions

http://www.rotorandwing.com/2011/07/01/safe-flying-in-unsafe-weatheraesaes/

[1] anon., Aeronautical Design Standard-33E-PRF, Performance Specification, Handling Qualities Requirements for Military Rotorcraft”, US Army AMCOM, Redstone, Alabama, March 21, 2000 [2] Pavel, Marilena D., Padfield, Gareth. D., “Defining Consistent ADS-33-Metrics for Agility Enhancement and Structural Loads Alleviation”, 58th American Helicopter Society Conference, 11-14 June 2002, Montreal, Canada [3] Pavel, Marilena D., Padfield, Gareth. D., “Progress in the Development of Complementary Handling and Loading Metrics for ADS-33 Manoeuvres”, 59th American Helicopter Society Conference, 6-8 May 2003, Phoenix, Arizona [4] https://www.flightlab.com/flightlab.html [5] Abhishek A., Ananthan Shreyas, Datta Anubhav, Chopra Inderjit, “Prediction and Analysis of Main Rotor Loads in a Prescribed Pull-Up Maneuver”, 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, May 2009

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Department C&O

PhD Candidate: K.N. McGuire Faculty: AeroSpace Engineering Department: Control & Operations Section: Control & Simulation Supervisor: G.C.H.E. de Croon Promotors: K. Tuyls H.J. Kappen Contact: k.n.mcguire@tudelft.nl

Swarm Exploration with Pocket Drones

Hardware Bluetooth

Application Pocket drones are small MAVs that fit in the palm of your hand, extremely lightweight (< 50 g) and are therefore inherently safe for humans (and plants). Additionally, their tiny size makes them ideal to maneuver through tight spaces, which is practical for indoor navigation.

Therefore they could be used for: o structure-integrity inspection o greenhouse surveillance o parcel scanning in warehouses.

o AutoPilot: We work exclusively with an open-source autopilot program called Paparazzi UAV [1]. It can run completely on-board the Lisa MXs autopilot (2 g) which is lightweight enough to be carried a Lisa MXs Autopilot pocket drone.

o Stereo Camera: The pocket drone carries a 4 g stereocamera to sense essential information about its Hardware specifics of the Pocket Drone, adapted from [4] surroundings. It consist of an additional STM32F4 microprocessor to run computer vision algorithms in parallel with the processes on the autopilot.

Stereoboard

*Representation of a pocket drone in a green house. *photoshopped

If multiple pocket drones are operational at these locations, they can act as a swarm of movable sensors, deployable anywhere the user wants them to.

o Communication: Close-range communication between the multiple drones is possible through BlueTooth. Moreover, they can sense their inter-MAV range by the measured signal intensity.

Single Autonomous Pocket Drone

Challenges

Department C&O

128

Since wireless communication are known for being unreliable in indoor environments due to wall interference, the pocket drones should be fully autonomous for indoor navigation. There are 3 sub-challenges to consider: o Low-Level Navigation: onClose up of the pocket drone's hardware board ego-motion estimation, stable flight and obstacle avoidance. o High-Level Navigation: designing a computationally light-weight navigation strategy suitable to fit on a pocket drone. o Swarm coordination: achieve inter-drone communication, localization and avoidance.

We like to take inspiration of nature to develop efficient navigation strategies. Bees, for instance, do not use gigabytes of memory to explore a field of flowers and remember where they have been. We believe our pocket drone does not need that either.

References [1] http://wiki.paparazziuav.org [2] K. McGuire, G. de Croon, C. De Wagter, B. Remes, K. Tuyls, and H. Kappen, “Local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones,” in Robotics and Automation (ICRA), 2016 IEEE International Conference on, pp. 3255–3260, IEEE, 2016.

[3] K. McGuire, G. de Croon, C. De Wagter, K. Tuyls, and H. Kappen, “Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone,” IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 1070–1076, 2017.

[4] K. McGuire, M. Coppola, C. de Wagter, and G. de Croon, “Towards autonomous navigation of multiple pocketdrones in real-world environments,” International Conference on Intelligent Robots and Systems (IROS), 2017.

[5] M. Coppola, K. McGuire, K. Y. Scheper, and G. C. de Croon, “On-board communication-based relative localization for collision avoidance in micro air vehicle teams,” Conditionally accepted in Autonomous Robots, 2018.

In recent experiments, we were able to show an autonomously flying 40 g pocket drone inside an indoor environment, using nothing but a 4 g stereo-camera[3]. With a new computer vision method, called Edge-FS, the tiny MAV can detect obstacles with stereovision and calculate its own velocity in realSingle autonomous pocket drone flying in a room, taken from [3] time.

Edge-FS uses edge-distributions and on the microprocessor, it only needs 17.5 ms of computation time, allowing the velocity estimation to run 25Hz, the full frame rate of our cameras.

Multiple Autonomous Pocket Drones A single pocket drone does not make a swarm, so we added another pocket drone [3] to get closer to our final goal. A BlueTooth module has been added for inter-MAV ranging and communication (as in our paper [5]).

By communicating their velocity (estimated by the stereo camera) and fusing this information with the signal intensity, the pocket Multiple autonomous pocket drones flying in a room, drones are able to estimate their relative position to Taken from [4] one another.

With these components, autonomous flight was achieved in an office environment, avoiding the walls and each other without help of a global localization system.

Startle and surprise in flight crew

PhD Candidate: Annemarie Landman Department: Aerospace Engineering Section: Control and Operations Supervisor RenĂŠ van Paassen Promotor:Max Mulder Contact: h.m.landman@tudelft.nl 1

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3

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Conceptual model of startle and surprise Information

Information

â&#x153;&#x201D; Frame

Reframing

Stress disrupts reframing

New frame

Landman et al. (2017) Dealing with unexpected events on the flight deck: A conceptual model of startle and surprise

Interven&on studies

1. Stall recovery performance decreases when pilots are surprised

1. Stress management / reframing aid: breathe, call out observa:ons (in development)

2. Variable and unpredictable training: prac:ce reframing, improve frames Landman et al. (2017) The effect of surprise on upset recovery performance in airline pilots

2. Disorienta:on aďŹ&#x20AC;ects display interpreta:on (in development)

Landman et al. (in review) Training against startle and surprise: Using unpredictability and variability in simulator-based pilot training

Department C&O

Valida&on studies

129

PhD Candidate: Department: Section : Supervisor :

Design of Haptic Feedback for Flight Envelope Protection

Promotor : Contact:

1

2

Dirk Van Baelen Control & Operations Control & Simulation Joost Ellerbroek, Rene van Paassen Max Mulder d.vanbaelen@tudelft.nl

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4

Introduction Most information in nowadays cockpits is visual or aural, even though humans have more senses. One underused sense is touch: conveying information using force feedback through the control device or haptics. With the introduction of fly-by-wire, the physical connection between the control device and surfaces was replaced by electrical signals and hence even more force feedback was lost.

Now current control devices have the availability of low-weight reliable force feedback, it is time to re-introduce haptic cues in the cockpit.

Below, our design for haptic feedback in existing Airbus type aircrafts is presented using an emergency scenario encountered by a pilot.

The longitudinal haptic feedback provides the pilot with information on the limitations of the aircraft, the flight envelope, as to improve the situation awareness.

1

2

3

4

5

Summary and Outlook To increase the situation awareness, the haptic feedback informs the pilot of: - Protection boundary: tick - Proximity to limits: stiffness - Critical low velocity: stick shaker - Autopilot commands: center shift Our next step evaluates the design in the SIMONA Research Simulator.

6

When a pilot encounters a windshear, a meteorological phenomenon where a cylinder of air is dropping downwards, he/she has to use all the performance of the airplane to regain altitude.

Numbers in the following combine the state indications on the flight envelope and the graphs with feel on the controls.

Airbus A330 control device (side stick) in use [https://www.youtube.com/watch?v=8YaQch_y9DA]

n [g]

6) At this point, the recovery is completed: the aircraft is flying within normal operation limits and normal control device feel is resumed.

2

130

3

F [N]

5

4

F [N]

1 1

6

Department C&O

δ [deg]

1) Initially, all systems are nominal and no warnings are active: the control device has the nominal feeling. This is a proportional relationship between deflection and force required, just like turning a steering wheel in a car.

F [N]

δ [deg] V [ms−1]

Flight Envelope The allowable velocity (V ) and load factor (n) within the limitations of the aircraft Protection boundary Stick shaker limit State trajectory in scenario

5) At the end of the recovery, full throttle is set which can result in an overspeed.

To alert the pilot, the previous haptic cues are present (tick and heavy stick).

The automatic pitch-up command by Airbus philosophy is also translated in a control device center shift, so the pilot feels the command.

F [N] F [N]

t[s]

F [N] F [N]

F [N]

t[s] δ [deg]

t[s] δ [deg]

δ [deg] 2) When the windshear warning is activated, the pilot has to pitch up, and not be blown down by the dropping wind. If the pilot crosses a protection boundary, he will receive a tick on the stick: the entire graphs shifts up for a short time, depicted by the inset-graph.

3) If the pilot keeps pitching, keeps going closer to the limit, the stiffness in that direction increases. This means that it becomes harder, yet still possible, to keep pitching to the limit. This gives the pilot a feeling on how close he is to the limit.

4) Hopefully the pilot is now able to climb again. Nevertheless, if required, the pilot needs to pitch more. Ultimately, there is one condition you do not want to be in: stall. In order to give a clear cue, a stick shaker is implemented, as can be seen on the added forcing function in time.

Maximizing Urban Airspace Capacity for Drones

PhD Candidate: Malik Doole Department: Control & Simulation Section: Control & Operation Supervisor Dr. ir. Joost Ellerbroek Promotor: Prof. dr. ir Jacco Hoekstra Contact: m.m.doole@tudelft.nl 1

2

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4

Introduction

DREAMS

It is expected that a high density of drones will operate in urban environments by 2030. These drones will need to perform commercial operations such as package delivery and security surveillance in a safe and efficient manner. As result, the need to maximize the urban airspace capacity for drones is imperative.

The project work performed for the DREAMS project will add clarity and will form the basis of knowledge required for the PhD research.

This PhD research is a part of an European project which aims to add definition to Europeâ&#x20AC;&#x2122;s Unmanned Traffic Management system (USpace).

Drone European AIM Study is an on-going European research project with the goal of adding definition to information management in U-Space.

The DREAMS project will be concluded by fall 2019. www.u-spacedreams.eu

Scenario selection validation

Research Questions

131 What is the best way to maximize capacity of an urban airspace?

What is the effect of space-time constraints (e.g. geocaging or geofencing) on the urban airspace capacity?

What is the effect of geovectoring on the urban airspace?

How do we design the airspace for a drone delivery distribution centre?

To what extent can we segment traffic before complexity of the traffic increases?

How do we predict drone traffic congestion in an urban airspace?

Department C&O

Can we maximise the urban airspace when we apply dynamic spacetime constraints?

(Hoekstra J. M. et al. 2018)

Aircraft Noise and Climate Effects (ANCE) Head of section: Prof.dr.ir. Dick Simmons

Department C&O

132

Supervisors Prof.dr.ir Dick Simmons Dr. ir. Mirjam Snellen Dr. ir. Daniele Ragni Harry Brouwer (NLR) Sander Hebly (NLR)

PhD Candidates:

-- Tannaz Mohammadloo -- Leo Koop

133

-- Afrizal Tengku Ali -- Anwar Malgoezar -- Salil Luesutthiviboon -- Colin van Dercreek -- Thijs Bouwhuis -- Ana Vieira

Department C&O

Aircraft noise continues to be a very serious source of disturbance to the public. Also, the current contribution of aircraft emissions to global warming is estimated to lie in between 1.5 and 5.5%, but is predicted to increase significantly. In addition, the level of scientific understanding of the climate effects of aviation is low.

The vision of ANCE is that for the growth of aviation (5 % per year) to be sustainable with a decreasing impact on the environment, more accurate modelling of the impact due to noise and emissions is required.

Correcting the Bathymetric Errors Induced by the Erroneous Water Column Sound Speed 1. General Background • Multi-Beam Echo-Sounder (MBES) are used for producing bathymetric maps and seafloor sediment classification.

• If the local sound speed profile (SSP) in the water column is known, water depths along the swathe is derived. a) b)

Tannaz H. Mohammadloo Control and Operations Aircraft Noise and Climate Effects Supervisor: Dr. Ir. Mirjam Snellen Promotor: Prof. Dr. Dick G. Simons Contact: t.hajimohammadloo@tudelft.nl 1

2

3

4

3. Method 1. MBES surveys are performed with at least a small overlap between adjacent swathes.

2. Bottom features are not expected to vary during the survey time.

The depths along two overlapping swathes should be the same at equal points on the bottom.

Assumption: The main cause for difference in water depths at overlapping parts is erroneous SSP b)

a) Fig. 1. a) Transmission and b) reception for the MBES. • To perform SSP measurement, the ship needs to remain stationary, making the SSP measurement a time-consuming process.

Fig. 3. a) survey configuration with sailing directions, b) Estimated bathymetry at overlapping swathes

Limited amount of SSPs are measured.

2. Problem

Idea

• Insufficient knowledge about SSP induces errors in 1) Sound propagation 2) beam steering

Incorrect estimate of the water depth in dynamic environment by inducing an omni-present artificial wave pattern perpendicular to the sailing direction.

Fig.2. Difference Sound speed profile corresponding to different conditions

134

Question Can we compensate MBES bathymetric measurements for errors due to erroneous or unknown SSP without additional SSP measurements?

 SSPs are searched for which maximize the agreement between water depth along the adjacent parts.  Energy function is minimized using 1) Differential Evolution (DE)

2) Gauss Newton (GN)

5. Results (erroneous SSP) Wrong SSP is assumed by shifting the measured SSP by 50 m/s Fig. 5. a) Cross section and b) dynamic surface from three overlapping swathes based on the wrong SSP. a) b)

4. Data Description

Fig. 4. Measured and Erroneous Sound speed profiles.

Department C&O

 Location: Maasgeul of the Dutch coast at the entrance to the port of Rotterdam, known to be a highly dynamic environment.  The data acquired using EM2040c Dual-head with One SSP measurement.

6. Result and Conclusion (DE and GN application) a)

b)

 Notice that the droopy artefact is no longer observed.  Notice the non-overlapping areas cannot not corrected

To what extend DE and GN can reproduce the original data?

STD (m)

Mean (m)

Median (m)

DE

0.13

-0.06

-0.07

GN

0.56

-0.03

-0.05

Fig. 6. a) Cross section and b) dynamic surface from three overlapping swathes after applying DE. GN is faster than DE by a factor of 30

However

1) Complex representation of SSP cannot be considered.

2) GN can converge to a local minima due to its nature.

3) GN is more affected by bottom relief.

7. Future Work

 Maasgeul area is monitored extensively and is surveyed 12 times a year using the same ship and MBES Applying inversion (GN and DE) to monitor the temporal behavior of the bottom  Testing the algorithm with different datasets to assess its performance in different 1) oceanographic condition, 2) survey geometries, 3) overlap percentage between and 4) seafloor morphologies to identify potential weaknesses and find an optimal solution.

Project DISCLOSE

Distribution, structure, and functioning of low-resilience benthic communities and habitats of the Dutch North Sea.

PhD Candidate: Leo Koop Department: Control and Operations Section: Aircraft Noise and Climate Effects Supervisor: Mirjam Snellen Promotor: Dick Simons Contact: L.Koop@tudelft.nl 1

2

3

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DENMARK

ENGLAND

THE NETHERLANDS GERMANY

Preliminary Interactive web application available at: http://arcg.is/20KUiBj

Large scale

Intermediate scale

Small scale

1

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3

Leo Koop

Mirjam Snellen & Dick Simons

Karin van der Reijden

Laura Govers, Adriaan Rijnsdorp & Han Olff

Sarah O’Flynn

Tom Ysebaert & Peter Herman Ground-truthing using grab sampling techniques

Video observations

Videos

Photos

Biogeochemical analysis

Integrated & detailed habitat maps Sand ripples Biogenic reef 1 Organic sand Biogenic reef 2 Rocks http://www.vliz.be/projects/westbanks/background.ph p

l.koop@tudelft.nl

k.j.van.der.reijden@rug.nl

Assess distribution, structure and functioning of macrobenthic communities

sarah.oflynn@nioz.nl

PROJECT AIMS: • Increased interpolation potential of remote sensing techniques for habitat mapping of the sea floor • Integration of remote sensing techniques with biological knowledge of prevailing species and their characteristics • High-resolution maps of the various habitats and benthic communities in the Dutch North Sea which can be used in marine spatial planning and management

Logo’s

Department C&O

Use advanced acoustic signal processing on multiple sensor data to make detailed sediment maps.

135

Assessing The Depth Residual Features For Sediment Classification

Afrizal Tengku Ali Control & Operations Aircraft Noise & Climate Effects, Group Acoustics Supervisor: Dr. Ir. M. (Mirjam) Snellen Promotor: Prof.dr. D.G. (Dick) Simons Contact: ttengkuali@tudelft.nl 1

Introduction

2

3

4

Problem

• Different types of sediment can be discriminated based on the • Features derived from bathymetry • Depth residual is so called depth residuals which deviations between the interaction of sound with the sediment – backscatter strength. The assume to be the roughness also measured depth and backscatter strength can be measured by the Multibeam Echo‐ used for sediment classification fitted surface sounder System (MBES) • The bathymetry as determined by the MBES also could discriminate • Angular variations in depth residual exist due to bathymetric between different sediments uncertainty of the sonar systems Different types of acoustic data that can be recorded by multibeam systems (taken from Lurton,2002)

Results

AMUST vs Measurements • There is a good agreement between measurement and AMUST • The agreement is an indication of smooth seafloor • This discrepancy can be due to the seafloor morphology or presence of stones

Motivation

136

Backscatter working group (BSWG) vision: "Backscatter data acquired from different sonar systems, or processed using different software, are scientifically meaningful and usable by end users from all application domains if they generate consistent values over the same area under the same conditions ”

Data

• Two MBES dataset from Kongsberg EM2040 and Reson7125 over the same area • Location: Plymouth sound, United Kingdom

AMUST

Department C&O

• AMUST calculates the vertical and horizontal uncertainties of operational circumstances • AMUST calculate all contributions separately before calculate uncertainty using error propagation

d 

2  d21   d22   d23   d24   H2   GPS

 d Depth error

 d21 Contribution of the echo sounder

 d22 Contribution of sound speed  d23 Contribution of attitude sensor measurements  d24 Contribution of correction accuracy for misalignment

Classification results • Using Principal Component Analysis and K‐means • Good agreement except in certain area • The difference could be the uncertainties as both have been classify using uncorrected depth residual Reson7125

EM2040

 H2 Contribution of heave 2 GPS Contribution of GPS, water level and dynamic draught

Conclusions

• Standard deviations from real measurement higher than AMUST prediction • Expected to have an identical classification map after the depth residual being corrected • Necessary step has to be taken to account before used this features for classification

Future works

• Used AMUST to correct standard deviation for depth and angular effect

References: Hare, R. (1995). Depth and Position Error Budgets for Multibeam Echosounding. International Hydrographic Review, (March), 37–69 Eleftherakis, D., Amiri‐Simkooei, A., Snellen, M., & Simons, D. G. (2012). Improving riverbed sediment classification using backscatter and depth residual features of multi‐beam echo‐sounder systems. Journal of the Acoustical Society of America, 131(5), 3710–3725

Anwar Malgoezar Control and Operations Aircraft Noise and Climate Effects Supervisor: Dr. ir. Mirjam Snellen Promotor: Prof. dr. Dick Simons Contact: A.M.N.Malgoezar@tudelft.nl

Optimization techniques for imaging aircraft noise

1

• •

Use of optimization techniques to improve source localization • •

3

4

Optimization problem

Source localization using microphone arrays

•

2

• •

By hardware: Optimizing microphone positions By software: Optimizing beamforming algorithm

Define cost function which captures the minimization goal Minimize function for parameter-set Use of genetic algorithms Allow fit candidates to move on to the next generation

137

Microphones:

Beamforming:

Optimize positions

Optimize 4 source positions

• • •

Single source x : microphone positions goal : minimize side-lobes in region around source

• • • •

Multiple sources x : source coordinates goal : minimize difference between modelled and measured signals extra: allows estimation of other quantities

Department C&O

Also allows for estimation of the speed of sound

High-Reynolds Application of Airfoils with Porous Open-Cells for Noise Reduction* •

Background

PhD Candidate: Salil Luesutthiviboon Department: Control & Operations Section: Aircraft Noise & Climate Effects Supervisors: Dr.ir. Mirjam Snellen, Dr.ir. Daniele Ragni Promotors: Prof.dr. Dick Simons, Prof.dr. Damiano Casalino Contact: S.Luesutthiviboon@tudelft.nl 1 2 3 4

NACA0018 airfoil with porous TE, Rec = 2,6x105 Results and pictures from A. Rubio Carpio and [3]

Solid airfoil

 Deteriorates airfoil’s lift [3]

• •

Objective Approach

“

0,4

0,7

1,0

1,3 1,6 1,9 2,2 Frequency [kHz]

450 μm

2,5

2,8

3,1

To reduce airfoil self noise emissions at high Reynolds number by using porous inserts with customizable small- and large-scale geometry

”

Material manufacturing

Acoustics study

3D printing porous open-cell inserts with customizable…

Small-scale geometry

Using acoustic beamforming

Large-scale geometry Lp [dB]

3D

Airfoils with porous TE, cell diameter (dc):

800 μm

Lp [dB]

40  Noise emission is limiting operations of modern wind turbines and aircraft.

35  Dominant noise sources: 30 - Wind turbines: Turbulent Boundary Layer – Trailing Edge (TBL-TE) noise [1] 25 - Landing aircraft: Flap side edge noise [2] 20 15  Embedding porous open-cells on airfoil’s trailing edge:  Shows promising trailing edge noise reduction in a certain frequency range [3,4] 10 5  Does not require additional structures on the airfoil 0  Increases noise emission at some frequencies [4]

138

Pore size

Pore direction

Outer shape

Wind tunnel test

Department C&O

Performed at high Reynolds number

Frequency [kHz] Solid airfoil Airfoil with porous open-cell insert

(~106)

Flow & aerodynamics study -

Numerical models

Flow topology TBL interactions with porous open-cell inserts Change in airfoil’s lift

- Design of experiments - Understanding influences of porous open-cell insert on air flow and the subsequent noise emission

•

Expected Benefits

•

References

 Embed on wind turbine blades for noise reduction  Concepts for future aircraft wing-flap design with embedded porous open-cell structure for noise reduction

Wind turbine

Aircraft wing & flap

[1] Brooks, T.F., Pope, D.S. and Marcolini, M.A., 1989. Airfoil self-noise and prediction.

[2] Michel, U., Helbig, J., Barsikow, B., Hellmig, M. and Schüttpelz, M., 1998, June. Flyover noise measurements on landing aircraft with a microphone array. In 4th AIAA/CEAS aeroacoustics conference, (p. 2336).Vancouver [3] Rubio Carpio, A., Merino Martinez, R., Avallone, F., Ragni, D., Snellen, M. and van der Zwaag, S., 2017. Broadband Trailing-Edge Noise Reduction Using Permeable Metal Foams. Inter-Noise 2017.

[4] Geyer, T., Sarradj, E. and Fritzsche, C., 2010. Porous airfoils: noise reduction and boundary layer effects. International journal of aeroacoustics, 9(6), pp.787-820.

Wind turbine; Oerlemans, S., Sijtsma, P. and López, B.M., 2007. Location and quantification of noise sources on a wind turbine. Journal of sound and vibration, 299(4-5), pp.869-883.

Aircraft wing & flap; www.decodedscience.org *This project is a part of the Innovative PERmeable Materials for Airfoil Noise reduction (IPER-MAN) project, sponsored by:

PhD Candidate: Colin van Dercreek Department: Control & Operations Section: Aircraft Noise & Climate Effects Supervisor: Dr. Snellen & Dr. Ragni Promotor: Dr. Simons & Dr. Casalino Contact: c.p.vandercreek-1@tudelft.nl

Design of Low Noise Acoustic Array for Closed Wind tunnels

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Project Objective:

Develop microphone array with optimized microphone array geometries to improve array signal to noise ratio (SNR) by reducing turbulent boundary layer noise

Experimental Design

Approach:

• Optimize cavities using experimental data and Lattice-Boltzman CFD simulation • Develop scalable design methodology for commercial wind-tunnels • Demonstrate improved array in DLR Windguard Tunnel

• Systematic approach to quantify the relationship between input factors (geometric parameters) and measured turbulent boundary layer noise Factor

Levels

Power

Diameter

5, 10 mm

99%

Mesh

Yes, No

99%

Depth

5, 10 mm

99%

Gap

100%, 50%

99%

Chamfer

0, 4 mm

99%

Geometrical Factors Chamfer

Acoustic Source: Omnidirectional, producing Gaussian white noise

Depth

Diameter

Microphone Mount

Plate with 7 of 14 microphone geometries

Gap

Factor Correlation Matrix

Wind tunnel Run Plan: •

Microphone Mount

36 runs to achieve statistical significance

Factor

Levels

Power

Runs per Case

Wind Speed

30, 50, 70 (m/s)

99%

3

Acoustic Source

Yes, No

99%

3

Wind Tunnel Test Set-Up

139

Stainless Steel Mesh

Preliminary Results:

12B: Chamfer, Partial Gap

11B: Mesh, Chamfered, Partial Gap

Flush B

GRAS 40LS

10B: Mesh, Chamfer

9B: Partial Gap

8B: Mesh

7B: Mesh, Partial Gap

Future Work: • • • • •

Complete detailed analysis of how cavity geometries influence microphone measurements in a turbulent boundary layer Simulate best performing cavities with Lattice-Boltzman CFD solver Improve cavity designs based on simulations Develop design tools to developo a full acoustic measurement array capable of improved measurements over a wide range of wind speeds Work with Deutche Windguard Tunnel to implement and test improved array

1. Fleury, V., et al. (2010). Optimization of Microphone Array Wall-Mountings in Closed-Section Wind Tunnels. 16th AIAA/CEAS Aeroacoustics Conference, American Institute of Aeronautics and Astronautics.

Department C&O

• Specific cavities show ~10 dB improvement over flush mounted microphones • Matches previous experimental work (Fleury, Coste et al. 2010)1 • Completed analysis will examine factor interactions, statistical significance, correlation between input audio and measurements and influence of boundary layer characteristics

PhD Candidate: Thijs Bouwhuis

Aircraft noise auralization for the VCNS

Department: Control and Operations Section: Aircraft Noise and Climate Effects Supervisor: H.H. Brouwer (NLR) & S.J. Heblij (NLR) Promotor: D.G. Simons Contact: thijs.bouwhuis@nlr.nl 1

2

Background  Virtual Community Noise Simulator (VCNS)

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4

Method  Set up:

 Fully-immersive virtual reality simulator  Artificial generation of (aircraft) noise  New aircraft designs  New operational procedures  Inform communities on future situations

Auralization Starting point

Noise Source

 Obtain lossless condition at the source, account for:  Spherical spreading  Atmospheric attenuation  Sound transmission through shear layer  Subtract tare wind tunnel data  Convert SPLwindtunnel to SPLstatic, account for:  Convective amplification  Pressure correction to ISA  Convert SPLstatic to SPLflight,  Model scale to full scale:

 Split spectrum

140 Propagation Moving median filter

Audible result

Department C&O

Sound source model  Current sound source in VCNS:  Semi-empirical relations (ANOPP)  Fly-over recordings  Aimed input for sound source:  Scaled wind tunnel tests  Entire aircraft  Aircraft components

Future work

 Input for sound sources:  CFD-CAA computations

 Implementation of urban environment:  Reflections  Shielding  Refractions

Engine Noise shielding in current aircraft

Ana Vieira Department: Control and Operations Section: ANCE (Aircraft Noise and Climate Effects) Supervisor: Prof. Dr. Dick G. Simons Daily supervisor: Dr. Mirjam Snellen Contact: A.E.Alvesvieira@tudelft.nl Beginning the 3rd year of the PhD

Measurements Landing flyovers recorded using a 32 microphone array arranged in a spiral distribution.

Normalized curves of measured OSPL as a function of time for flyovers show significant differences for aircraft with the engines mounted above the wings (= noise shielding).

Predictions

Limit shadow-light of the Fokker 70.

141

Frequency spectrum of the F70 engines and approximation used for the predictions.

Predictions and measured normalized OSPL curves indicate noise shielding.

•

Shielding values were observed at ~ 4 sec before overhead

•

Predictions overestimate noise shielding by a few dB.

Department C&O

•

Depar Space System (Sp

tment ms Engineering pE)

Astrodynamics and Space Missions (A&SM) Head of section: Prof.dr.ir. Pieter Visser

144

Department SpE

Supervisors Prof.dr.ir Pieter Visser Dr. ir. Erwin Mooij Dr. ir. Jose van der IJssel Dr. ir. Eelco Doornbos Ir. Ron Noomen Dr. ir. Coen de Visser Dr. Angelo Cervone Dr. ir. Matteo Pini Dr. Jian Gao

The section Astrodynamics and Space Missions is one of the two sections comprising the Space Engineering department. Our section focuses on satellite orbits, mission analysis and applications, space propulsion, ascent and reentry systems and solar system exploration.

PhD Candidates:

In the Master of Science (MSc) track Spaceflight we are responsible for the profile Space Exploration. We also offer a number of MSc courses.

-- Fiona Leverone

-- Kartik Kumar

-- Svenja Woicke

145

-- Xinyuan Mao -- GĂźnther March -- Yuxin Liu -- Tim Visser -- Johan Carvajal-Godinez -- Jacco Geul

Department SpE

Dynamics of Uranus’ outer rings: Mab/μ-ring system

PhD Candidate: Kartik Kumar Department: Space Engineering Section: Astrodynamics & Space missions Promotor: Prof. P.N.A.M. Visser Co-Promoter: Prof. I. de Pater Contact: K.Kumar@tudelft.nl

1

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(de Pater, et al.; 2006)

Introduction  Discovery of moon Mab announced in IAU Circular by Showalter & Lissauer (2003)  Showalter & Lissauer (2006) subsequently reported discovery of η-ring & μ-ring  Rings and moon detected in Hubble Space Telescope (HST) images with follow-up

Keck telescope observations  Peak radial brightness of μ-ring coincides with orbit of Mab  Observations of the Mab/μ-ring system indicate a highly dynamic environment  Motion of Mab determined to be anomalous over short time scales  μ-ring observed to be peculiar: steep size distribution, azimuthal brightness

asymmetry, “disappearing” dust

Newly discovered outer ring system (NASA, ESA, M, Showalter; 2005)

Uranian ring system (Ruslik0; 2014)

Problem statement & methodology “Mab is the single most severely perturbed moon in the Solar System, showing orbital deviations of ~ 100 km over intervals as brief as 6 days.” – Showalter, et al. (2008) Orbit fit residuals for Uranian moons (Kumar, et al.; 2015)

“[μ-ring] is probably produced by impacts into the embedded moon Mab, which apparently orbits at a location where nongravitational perturbations favor the survival and spreading of submicronsized dust.” – de Pater, et al. (2006)  Mab’s anomalous motion studied through test particle integrations in 3-body (Uranus+Mab) system

and random walk simulations to determine effect of N nearby moonlets (Kumar, et al.; 2015)  Steep size distribution and disappearing dust in μ-ring investigated by running test particle

146

integrations subject to gravitational (central+J2) and non-gravitational forces (SRP, PR drag, tilted & offset magnetic dipole) Steep size distribution & azimuthal brightness variations in μ-ring (de Pater, et al.; 2006 & Showalter, et al.; 2008)

Results & Conclusions “The orbit of tiny Mab is well described by a precessing ellipse; previous reports of unusually large residuals were the result of a 0.13% plate scale error in HRC images taken with the CLEAR filter.” – French, et al. (2017)

Simulation setup for mu-ring dust particle integrations (Kumar, et al.; in prep)

 Mab random walk simulations generate deviations that indicate that nearby

moonlets could generate observed anomalous motion (Kumar, et al., 2015)  French, et al (2017) however announced that HST plate scale error resulted in

incorrect residuals; hence Mab’s orbit is fully explained  Strange dynamics of μ-ring however persist in follow-up observations and strong

relation to Mab remains highly likely  Presence of Mab and nearby moonlets in μ-ring “core” could play a role in

“disappearing dust”, along with size sorting due to effect of non-gravitational forces on sub-micron sized dust particles.

Random walk of Mab’s semi-major axis (Kumar, et al.; 2015)

Real reason for Mab’s strange motion (French, et al.; 2017)

Selected references Showalter, M.R., Lissauer, J.J. (2003). IAU Circular, 8209.

Showalter, M.R., Lissauer, J.J. (2006). Science, 311(5763):973-7. de Pater, I., et al. (2006). Science, 312(5770):92-94.

Showalter, M.R., et al. (2008). Vol. 3, EPSC2008-A-00254.

Kumar, K., et al. (2015). Icarus, 254:102-121

Department SpE

Acknowledgements Thanks to the Mosaic Grant 2009 from the Netherlands Organisation for Scientific Research (NWO) for enabling this research project. Part of this research was funded by NASA grant NNX07AK70G to UC Berkeley.

Future work  Current research is focussed on establishing the effects of gravitational and non-gravitational forces

on wide range of dust particle sizes (sub-micron to ~100s of microns) in the μ-ring  Implementation of force models and numerical integration schemes for ~1000-year test particle

simulations on-going  Expected results include lifetime of dust particles, as a function of size and orbital parameters  Results will be benchmarked against published literature, e.g., Juhász & Horányi (2002), Sfair, et al.

(2009), Sfair & Giuliatti (2012), Hsu, et al. (2014)  Simulation software being developed within open-source framework:

dustsim: https://github.com/kartikkumar/dustsim  Publication expected to be submitted to Icarus by the end of Q3 2018

A Stereo-Vision Based Hazard-Relative Navigation Current navigation systems, based on IMUs and altimeters only, are not accurate enough to make use of the data provided by a Hazard Detection and Avoidance system (HDA), to perform a safe and precise landing. The knowledge of hazard locations will only increase the safety of a landing, if the full GNC system is actually able to avoid this hazard. One of the necessities to this end is more accurate navigation capabilities. In our research we are addressing this need by proposing a Hazard Relative Navigation (HRN) algorithm, which can improve the localisation accuracy.

SOFTWARE-IN-THE-LOOP SIMULATION SET-UP Features Extracted

Resulting DEM

Trajectory: Near-vertical final descent, simulated from 200 m till touch down (0 m).

Sampling Frequency IMU

500 Hz

Image-based measurements

2 Hz

Single Run of HRN Algorithm

Stereo Pair

Final touchdown error: Error X [m] Error Y [m] Error Z [m]

8.89 2.06 11.97 1.64 0.52 6.04 ► Algorithm improves localisation compared to conventional method. ► Relative error reduced and error growth limited in x- and y-direction. ► Absolute error reduced in z-direction.

IMU only HRN

Monte Carlo Simulation of HRN Algorithm - 200 Runs Mean touchdown error:

Update

IMU

Measurement

Lander

Landmarks

Error Y [m]

Error z [m]

147

HARDWARE-IN-THE-LOOP TEST

Stereo-vison Map

Camera 1

Error X [m]

8.12 7.31 13.11 2.23 0.99 7.57 73 % 86% 42% ► Algorithm significantly improves localisation compared to conventional method. ► Few outliers ( < 3 %) ► HRN is more accurate and more precise than the conventional approach IMU only HRN Error reduction

Augmented State

Propagation

512 x 512 pixel, field of view 30 deg PANGU generated [4]

RESULTS

An Error-State Kalman Filer (ESKF) [1] is used as navigation filter. Here, the conventional IMU measurements are updated with camera-based measurements, obtained from feature tracking in combination with stereo surface-maps generated with a Hazard Detection Algorithm [2].

Thus it is possible to assign full 3-D coordinates to each of the tracked features.

Since a Simultaneous Localisation and Mapping (SLAM) approach is followed, the detected features are added to the state and will therefore be updated in the next step; non-reobserved features are deleted. This method improves the overall navigation accuracy, as well as improving the mapping accuracy.

inertial Initial Lander State

Model scale, bias, random walk [3]

One descent data set was acquired at the TRON facility at DLR Bremen [5].

Camera 2

Flowchart of the SLAM HRN Algorithm Stereo Pair

TRON Facility (Source: DLR) Features Tracked

TRON Generated Image

Resulting DEM

Measurement Set-up

► Algorithm works as expected based on software test presented above ► Errors greatly reduced as compared to the conventional method ► Supports findings from software test that the method is a feasible candidate for future missions Error X [m] Error Y [m] Error Z [m] IMU only TRON HRN Error reduction

Hazards:

3.23 0.56 83%

4.40 1.85 58%

20.65 2.17 89%

CONCLUSION Slopes

Craters

Stereo vision based HRN is a suitable method for future precise landing mission, as it can reduce the final touch down error.

References

[1] Woicke, S. and Mooij, E. (2017) Passive Hazard Detection for Planetary Landing, CEAS Guidance, Navigation and Control Conference, Warsaw.

[2] Woicke, S. and Mooij, E. (2016) A Stereo-Vision Hazard-Detection Algorithm to Increase Planetary Lander Autonomy, Acta Astronautica, Volume 122, pp. 42-62, ISSN 0094-5765.

[3] Geller, D.K., Christensen, D.(2009) Linear covariance analysis for powered lunar descent and landing. Journal of Spacecraft and Rockets 46, pp. 1231–1248.

[4] Parkes, S., Martin, I., Dunstan, M., Matthews, D. (2004) Planet surface simulation with pangu.

In: Eighth International Conference on Space Operations, pp. 1–10.

[5] Krüger, H., Theil, S., Sagliano, M., and Hartkopf, S. (2014) On-Ground Testing Optical Navigation System for Exploration Missions, 9th International ESA Conference on Guidance, Navigation & Control Systems

Department SpE

Boulders

Absolute and relative orbit determination for satellite constellations

PhD Candidate: Xinyuan Mao Department: Space Engineering Section: Astrodynamics and Space Missions Supervisor: Dr. Jose van den IJssel Promotor: Prof. Dr. Pieter Visser Contact: x.mao@tudelft.nl 3

4

5

No

Background An increasing number of space missions use spacecraft formations or constellations in Low Earth Orbit (LEO) to meet certain scientific objectives.

Precise absolute and relative orbit determination is a prerequisite to take full advantage of the information provided by them.

In addition, more LEO missions such like CHAMP, GRACE, GOCE and the newly launched Swarm are equipped with high-quality, dual-frequency Global Positioning System (GPS) receivers. Based on the precise collected GPS observations, absolute orbit determination nowadays achieves two cm level precision, while for relative orbit determination even sub mm level precision is reasonable as it further takes advantage of double differenced observation combination.

CHAMP(2000,GFZ)

GRACE(2002,DLR&NASA)

In this research we make use of one important orbit determination software called GHOST, which has been developed by DLR (German Space Center) and TUDelft.

GOCE(2009,ESA)

Swarm(2013,ESA)

148

Methodologies Left: Absolute orbit determination strategy

Top: Relative orbit determination strategy using double differenced observation combination based on the absolute orbit determination

Results 1. The use of GPS receiver antenna patterns improves orbit determination for GRACE formation.

2. GPS receiver modifications, half-cycle ambiguities, data converter and ionospheric activity impact on orbit determination for Swarm two satellites pendulum formation.

Department SpE

3. Relative dynamic constraint, iterative Kalman filtering, full-cycle ambiguities correction improve orbit determination for Swarm three satellites constellation.

4. Ambiguity fixing and baseline determination with preference improves orbit determination for the GRACE-CHAMP three satellites constellation.

5. Swarm kinematic baselines can be used for gravity field recovery.

Improved aerodynamic models for atmospheric density determination from Low Earth Orbit satellites

PhD

Department

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Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands.

Graduate School Poster Day, 16th March, 2018, Delft, The Netherlands.

Introduction During the last two decades, accelerometers on board of the Low Earth Orbit (LEO) satellites have provided high-resolution thermosphere density data. This data has improved our knowledge on atmospheric dynamics and coupling processes in the thermosphere-ionosphere region. So far, several differences between datasets and models have been detected and neglected. These variations arises from errors in the aerodynamic modelling, specifically in the modelling of the satellite outer surface geometry and of the gas-surface interactions. The first step to remove these differences is to enhance the geometry modelling.

Once accurate geometric models of the satellites are available, more reliable data can be obtained. This poster offers an accurate approach for determining aerodynamic forces and improved density data for four interesting satellites in Low Earth Orbits: CHAMP, GRACE, GOCE and Swarm. An overview of achieved improvements and dataset comparisons are provided through a statistical approach and a shorttime domain analysis.

Methodology

Attitude Manoeuvre Analysis

Through detailed high fidelity 3-D CAD models and Direct Simulation Monte Carlo (DSMC) computations, flow shadowing and complex concave geometries can be investigated. This was not possible with previous panels method, especially because of the low fidelity geometries and the inability to model shadowing effects. The panel method consists of the application of Sentmanâ&#x20AC;&#x2122;s equations to a simplified geometry model. A limited number of flat panels describe the entire structure of the satellite. Normal vectors and areas of each panel give the fundamental information needed to retrieve aerodynamic coefficients. This geometry and aerodynamic modelling turned out to have a large influence on derived densities, particularly for satellites with complex elongated shapes and protruding instruments and beams.

Accelerometer data have been reprocessed leading to higher fidelity density estimates. In particular, the Stochastic PArallel Rarefied-gas Time-accurate Analyzer (SPARTA) simulator from SANDIA Laboratories has been used for the aerodynamic modelling. The collisions between atmospheric particles and satellite outer surfaces are simulated within a fixed domain. Pressures and shear stresses associated to each surface element are computed and processed to retrieve force coefficients and thermospheric densities. In order to improve previous panel geometries for CHAMP, GRACE, GOCE and Swarm, new geometries have been designed.

In order to understand if the current modelling is satisfactory, it is possible to investigate satellite attitude changes. If the geometry and aerodynamic modelling is reliable the density trend should be countinuous, otherwise discontinuities can be detected. Below there are two different manoeuvres for CHAMP and Swarm satellites. Panels results are characterized by clear discontinuities. SPARTA densities are continuous between outside (dashed lines) and inside (solid lines) the manoeuvres.

Forward

Forward

Sideways

ACCELERATIONS

X (10â&#x2C6;&#x2019;7 m/s2)

09:00

12:00

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NRLMSISEâ&#x2C6;&#x2019;00

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06:00

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JBâ&#x2C6;&#x2019;2008

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8 7 6 5 4 3 03:00

06:00

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DTMâ&#x2C6;&#x2019;2013

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8 7 6 5 4 3 03:00

06:00

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12:00

15:00

18:00

November 06 2002

Y (10â&#x2C6;&#x2019;7 m/s2)

06:00

0 â&#x2C6;&#x2019;10

Z (10â&#x2C6;&#x2019;7 m/s2)

X (10â&#x2C6;&#x2019;7 m/s2)

03:00

Y (10â&#x2C6;&#x2019;7 m/s2)

IN

OUT

21:00

00:00

Density (10â&#x2C6;&#x2019;12 kg/m3) Density (10â&#x2C6;&#x2019;12 kg/m3) Density (10â&#x2C6;&#x2019;12 kg/m3)

GRACE

Sideways

ACCELERATIONS 8 4 0 â&#x2C6;&#x2019;4 â&#x2C6;&#x2019;8 â&#x2C6;&#x2019;12

Density (10â&#x2C6;&#x2019;12 kg/m3) Density (10â&#x2C6;&#x2019;12 kg/m3) Density (10â&#x2C6;&#x2019;12 kg/m3)

CHAMP

Forward

IN

OUT

0 â&#x2C6;&#x2019;2 â&#x2C6;&#x2019;4 18:00

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8 6 4 2 0 â&#x2C6;&#x2019;2 18:00 2

0

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21:00

06:00

NRLMSISEâ&#x2C6;&#x2019;00

SPARTA Panels

2.0 1.5 1.0 0.5 18:00

21:00

00:00

03:00

JBâ&#x2C6;&#x2019;2008

06:00 SPARTA Panels

2.0 1.5 1.0 0.5 18:00

21:00

00:00

03:00

DTMâ&#x2C6;&#x2019;2013

06:00 SPARTA Panels

2.0 1.5

149

1.0 0.5 18:00

21:00

00:00

May 12 2014

03:00

06:00

May 13 2014

2.0 8

Swarm

1.6

Density (10â&#x2C6;&#x2019;12 kg/m3)

Density (10â&#x2C6;&#x2019;12 kg/m3)

GOCE

1.8

DTMâ&#x2C6;&#x2019;2013 (OUT) DTMâ&#x2C6;&#x2019;2103 (IN) Panels (OUT) Panels (IN) SPARTA (OUT) SPARTA (IN)

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1.4 DTMâ&#x2C6;&#x2019;2013 (OUT) DTMâ&#x2C6;&#x2019;2103 (IN) Panels (OUT) Panels (IN) SPARTA (OUT) SPARTA (IN)

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Satellite geometry models designed with CATIA V5 R21.

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Statistical Analysis Within this study, the densities obtained with Panels and SPARTA aerodynamic models have been compared with three atmospheric models.

Below, the results for SWARM-C between 19/07/2014 - 30/09/2016 are presented.

Panels

SPARTA

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Argument of latitude (deg)

Argument of latitude (deg)

Results & Future Work A general improvement can be found comparing the mean ratio (Âľ*) between Panels and SPARTA models with the atmospheric models.

New densities turned out to be higher, reaching a mean +11% for CHAMP, +5% for GRACE, +9% for GOCE and +32% for Swarm.

Further work is going to be performed on Gas-Surface Interactions estimation.

New models about it are currently under investigation.

References â&#x20AC;˘ E. N. Doornbos, Thermospheric Density and Wind Determination from Satellite Dynamics, Springer-Verlag Berlin Heidelberg, doi:10.1007/978-3-642-25129-0, 2012. â&#x20AC;˘ M. A. Gallis, J. R. Torczynski, S. J. Plimpton, D. J. Rader and T. Koehler, Direct Simulation Monte Carlo: The Quest for Speed, Proceedings of the 29th Rarefied Gas Dynamics Symposium, Xiâ&#x20AC;&#x2122;an, China, July 2014.

Funding for this study is provided by the Netherlands Organisation for Scientific Research (NWO)

Department SpE

A better fitting with atmospheric models is reached with SPARTA. This example confirms the general improvements that is achieved for all the satellites, which is shown in Results & Future Work section.

Investigating Orbital Dynamics in CR3BP based on Machine Learning

PhD Candidate: Yuxin Liu Department: Space Engineering Section: Astrodynamics & Space Missions Daily supervisor: Ron Noomen Promotor: P.N.A.M Visser Contact: yuxin.liu@tudelft.nl 1

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Introduction The flyby is a useful technique that saves propellant in deep space missions [1]. The classical method of predicting a post-flyby effect is numerical integration. It provides accurate result but is time-consuming when trajectory optimization involved. The patched conics model is an efficient analytical method which assumes that the spacecraft is affected by the flyby body only within the sphere of influence of it [2]. However, it is error-prone for some high-altitude flybys. A semi-analytical method was developed by Alessi and SĂĄnchez for high-altitude cases but the spacecraft should be outside the Hill sphere to guarantee accuracy [3]. In this work, a new Keplerian map is developed based on Gaussian Process Regression (GPR) to predict flyby effect accurately and efficiently.

Methodology Z

Sun Earth

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ď &#x2014;

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Schematic of CR3BP in the Sun-Earth-Spacecraft system. Both the before-flyby and post-flyby state of the spacecraft are denoted in five Kepler orbital elements except for the true anomaly. The true anomaly of initial state corresponds to apogee and that of the final state is 2đ?&#x153;&#x2039;đ?&#x153;&#x2039;.

Step 1. Selection of the covariance function. The covariance function is a basic module of GPR model which expresses the relevance between two samples.

Step 2. Generation of training samples. Each sample consists of an input and an output. They are before-flyby and post-flyby states, respectively.

The training samples provides empirical information for GPR model.

Step 3. Training the GPR model. The training samples are used to optimize the hyperparameters of the covariance function.

Step 4. Predicting the post-flyby state for any before-flyby state using the trained GPR model.

Results and Conclusion The variation of semi-major axis after flybys for 100 random test samples. The largest relative error is 29%. The relative error of 87% of the cases is lower than 1%. The average computation time for prediction using GPR-based Keplerian map is half that of numerical integration. The construction of covariance function and the generation of training samples will be modified to improve accuracy in the future.

Department SpE

References

[1]Campagnola, S. and Kawakatsu, Y. Three-Dimensional Resonant Hopping Strategies and the Jupiter Magnetospheric Orbiter. Journal of Guidance, Control, and Dynamics. 2012, 35(1): 340-344.

[2]Shang, H. and Liu, Y. Assessing Accessibility of Main-Belt Asteroids Based on Gaussian Process Regression. Journal of Guidance, Control, and Dynamics. 2017, 40(5):1-11.

[3] Alessi, E.M. and SĂĄnchez, J.P. Semi-Analytical Approach for Distant Encounters in the Spatial Circular Restricted Three-Body Problem. Journal of Guidance, Control and Dynamics. 2016, 39(2): 351-359.

Horizontal and vertical wind measurements from GOCE angular accelerations In the past the linear accelerations measured by GOCE have been used to derive the neutral density and cross-wind in the thermosphere [1]. On this poster the result of a similar effort is presented, in which the angular accelerations were used for the same purpose. Although modeling the disturbance torque requires a greater effort than modeling the force (compare the left and right wing), a similar level of detail can be obtained from both sources. Combining the forces and torques will in the future allow for estimating more aerodynamic parameters.

All time series are taken on May 28, 2011; the results section uses data from the whole month of May, 2011.

Force

Torque

The 'measured' force is derived from the measured linear acceleration.

_ T

T̂ A

=

The resultant force is assumed to be aerodynamic.

X

∂µu eu ∂u

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The residual force is reduced to zero if the winds are estimated based on the forces.

With winds from the torque the solution is more erratic than with the reference wind.

If the wind is derived from forces and torques combined, we find an intermediate solution after a fixed number of iterations that favors force in the cross-wind estimation.

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Time of day [HH:MM] Using only the torque to derive the wind and density (vertical axis) we find reasonable consistency with the force-derived cross-wind.

Because the roll torque hardly depends on wind, the density cannot be estimated in this case, and remains at the NRLMSISE-00 value used in the initialization.

µ

3

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2

∂u

The same process in a second direction yields another tangent derivative (product of the unit vector and the angle). Together they form the Jacobian used to approximate the required change in a least squares sense.

The least squares a∂µ e + b∂µ e ∂u ∂w solution found above is T̂ A applied to find the new velocity. Both the new velocity and the new a∂u + b∂w aerodynamic torque are normalized, and the V V algorithm is repeated. u u

T̂ A

4

It was found that although this algorithm works for forces, the Jacobian in step 3 is not full rank using only torques. The roll torque is very small and hardly depends on the wind direction. Because the aerodynamic torque is almost purely yaw, the two derivatives in step three are parallel.

To circumvent this problem, a large constant offset is added in roll to both the residual and the aerodynamic torque in all above steps.

Results

y = 0.83x+30 (R2=0.77)

0.04 0.02 0 −0.02 −0.04

T̂ R

w w

Finally, when the error angle is below 10-4 degrees, the aerodynamic torque is scaled to best fit the residual by estimating the neutral density.

Torque-only

0.04 0.02 0 −0.02 −0.04

Torque and Force

y = 0.98x+15 (R2=0.98)

0.04 0.02 0 −0.02 −0.04 0.04 0.02 0 −0.02 −0.04 0.04 0.02 0 −0.02 −0.04 0.04 0.02 0 −0.02 −0.04

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Below the individual model outputs are compared to the measured torque.

The magnetic control torque is dominant in the tightly controlled roll and pitch axes.

Estimated scale factors are included.

The magnetic torque due to residual dipoles is the primary torque to counteract for the torquers. Estimated dipoles are included.

The ion propulsion system uses a magnet, which causes a significant torque in pitch.

Solar radiation pressure causes a minor offset in the yaw torque, but is most significant in the roll torque, which is small overall.

The gravity gradient over the satellite body is largest during maneuvers, that generally occur over the equator. J2 effects are observed in the same region, in the pitch torque.

151

Correcting the thrust direction based on jumps in torque at orbit lowering maneuvers, the yaw torque is affected strongly by the thrust level.

The aerodynamic model is used in residual dipole estimations. It uses ANGARA* coefficients and GOCE derived wind and density data [1].

The total modeled torque (including reference aerodynamics) closely approximates the measured torque.

Time of day [HH:MM]

0.01 0 −0.01

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0.04 0.05 0.02 0 0 0 −0.02 −0.05 −0.01 −0.04 15:30 16:00 16:30 15:30 16:00 16:30 15:30 16:00 16:30 0.01

The residual torque is reduced to zero when torque is used to derive the wind. With wind from forces the residual is clearly different from that obtained using the reference wind data.

The solution from forces and torques especially improves the pitch residual, implying that the torques significantly influence the vertical wind.

Time of day [HH:MM]

Cross-wind (force-only) [m/s] y = 0.66x+45 (R2=0.38)

y = 0.85x+11 (R2=0.17)

Vertical wind (force-only) [m/s]

Density [kg/m3]

y = 1.00x-4e-16 (R2=1.00)

Using both the torque and force to derive the wind and density (vertical axis) we observe that the crosswind closely follows the forcederived wind, whereas the vertical wind is more driven by the pitch torque.

Density can now be derived from the Xforce, as in the forces-only case.

Future work The results show that the wind and density from forces are inconsistent with those derived from the torques. As these discrepancies also stem from errors in the yaw torque and side force where aerodynamic effects are dominant, this implies errors still remain in the aerodynamic model. In our future work we will attempt to correct these inconsistencies by estimating more parameters, such as the accommodation coefficient.

Density (force-only) [kg/m3]

Department SpE

*ANGARA is a Monte-Carlo simulator developed by HTG, Göttingen.

The resultant, unmodeled torque is assumed to be aerodynamic.

Vwi nd

Roll

y = 0.80x+8e-14 (R2=0.87)

References: [1] E.N. Doornbos. GOCE+ Theme 3: Air density and wind retrieval using GOCE data: Data Set User Manual, July 2016

The thrust does not point through the center of mass, causing a misalignment torque.

eu T̂ A ∂µu [T̂ A ]V +∂u

Cross-wind [m/s]

Y

∂u

e

T̂ R

=

T̂ R

Vertical wind [m/s]

X

T̂ A

∂w V

The Earth radiation is a combination of albedo and Earth infrared. It has a significant influence in the vertical direction.

The total modeled force (including the reference aerodynamic model) closely approximates the measured force.

Roll

Yaw

Solar radiation pressure causes a small, constant force in the cross-track direction due to the sun-synchronous nature of the orbit.

This aerodynamic model is used as a reference. It uses ANGARA* force coefficients and cross-wind and density derived from GOCE linear accelerations in the past [1].

Pitch

Gravity gradient torque effects the pitch attitude when the pitch angle is large.

g

0.04 0.02 0 −0.02 −0.04

Magnetic torque is caused by control from magnetic torquers, the ion thruster magnet, and residual magnetic dipoles. The residual dipole of the payload and scale factors for the control dipoles were estimated, reducing the residual torque in a least-squares sense.

Solar radiation pressure causes a small torque when not in eclipse.

g

S

The aerodynamic velocity is initialized as the sum of the orbital velocity and the corotation of the atmosphere. Both the aerodynamic and residual torque are normalized to unit vectors.

The velocity is changed perpendicular to its original direction, leading to a new aerodynamic torque. The angle between the two normalized torques and the direction along the arc connecting them are stored. ∂µw ew ∂w

Below the individual model outputs are compared to the measured force.

The thrust counteracts the drag, and is therefore dominant in the in-flight direction.

Other components are due to misalignment.

1

V

Vwi nd

N N

Torque [mNm]

T̂ R

Y

T

An iterative algorithm was implemented to obtain the wind and neutral density at the satellite's location from the force and/or torque. The algorithm is initialized by finding the residual force and torque as indicated on the left and right respectively.

Radiation pressure is modeled for sunlight, Earth infrared and Earth albedo.

All axes used on this poster are defined in the body frame displayed here. The x axis points in the direction of flight, z points down and y completes the right hand frame. Roll, pitch, and yaw are defined poisitive as shown, around the x, y, and z axis respectively.

_

Algorithm

The thruster in controlled to counteract the drag, primarily in the in-flight direction.

The 'measured' torque is derived from the measured angular rate and acceleration.

Integrated Solar Thermal Propulsion and Power System for Small Spacecraft

PhD Candidate: Fiona Leverone Department: SpE & AWEP Section: SSE & FPP Supervisor: A. Cervone & M. Pini Promotor: E. Gill & P. Colonna Contact: F.K.Leverone@tudelft.nl 1

Background

How does the system work?

Replacing large satellites with smaller ones has become an increasing trend in recent years with the intention of reducing mass and associated launch costs. However, for small satellites to be highly competitive, integrated sub-systems are required that extend capabilities while adhering to the very strict mass, volume, and power restrictions. Key capabilities for satellites are power and propulsion generation, to provide functions such as payload operation, orbit manoeuvring, and attitude control.

This has lead to the investigation of a highly efficient integrated Power and Propulsion sub-system based on Solar Thermal Propulsion (STP) and Organic Rankine Cycle (ORC) thermal-to-electric energy conversion systems.

STP is a propulsion mechanism that uses concentrating devices, such as a lens or mirror, to focus the sunlight either directly or via fibre-optic cables onto a receiver. The thermal energy is used to heat up the propellant up to temperatures of 2500 K to improve performance and achieve thrust levels on the order of 1 N. The receiver is also coupled to the 100W micro-ORC to heat up a working fluid to undergo a phase change and drive a micro-turbogenerator. Including a regenerator improves the efficiency of the ORC system and reduces the heat transfer surface area of the receiver and condenser, at the expense of increased complexity and mass and reduction in reliability.

2

Incoming solar radiation

Solar thermal propulsion (STP) bridges the gap (Fig. 1) between electric and chemical propulsion systems. It offers greater performance (specific impulse > 320 s for storable propellants) than chemical propulsion systems and shorter transfer times than electric propulsion systems while using “free” energy from the sun (Etheridge, 1979).

4

Fig 3: Generic T-s diagram of an ORC with superheating and regeneration.

Propellant tank

Why use STP and ORC systems?

3

Turbine

Electrical energy output

Receiver Boiler

Generator Regenerator

Concentrator & fibre-optic transmission

Pump

Thruster

Condenser

Propulsion output

Fig 2: Schematic of the proposed integrated solar thermal propulsion and power system.

14 - STP (Water)

13 - STP (Ammonia)

11 - PPT (PTFE)

12 - Hall (Xenon)

10 - Ion engine: BIT-3 (Xenon)

8 - Resistojet (Butane)

9 - Ion engine: BIT-1 (Xenon)

6 - Solid (HTPB + AP)

7 - Resistojet (Ammonia)

5 - Bipropellant (MMH/NTO)

0

3 - Monoprellant (LMP-103S)

25

Propulsion Technology Options

Angelino, G. et al., 1991. Organic working fluid optimization for space power cycles, In Modern research topics in aerospace propulsion, Springer, New York, USA. Baker, A., et al., 2005. "you can get there from here": Advanced low cost propulsion concepts for small satellites beyond LEO, Acta Astronautica, (2-8), 288-301.

Etheridge, F., 1979, Solar rocket system concept analysis, Technical report, Rockwell International Downey, Satellite Systems, Div., CA, USA.

50

Fig 5: Total cost figure of merit for various propulsion systems and mission profiles.

References

Gerrish, H., 2003. Solar thermal propulsion, NASA Marshall Space Flight Center, [online] available at: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20030061199.pdf Leverone, F. et al., 2017. Feasibility of an integrated solar thermal power and propulsion system for small satellites, In 68th International Astronautical Congress, Australia.

75

4 - Monoprellant (AF-M315E)

System Challenges:  Two-phase flow management  Micro-turbine efficiency and design  Transient inertial effects  Compact lightweight and efficient components (eg: heat exchangers, turbine, and concentrator)  Thermal cycling issues and large temperature gradients  Leakages

100

1 - Cold Gas (Nitrogen)

An Organic Rankine Cycle (ORC) has the potential to offer high overall energy conversion efficiencies (20 to 30%) and has a greater resistance to degradation in the space environment compared to conventional photovoltaic systems. An additional advantage is the smaller concentrator area required than photovoltaic panels (80% efficiency versus 30%) assuming the same power requirement (Angelino et al., 1991).

Investigations have been conducted to identify low-cost applications of STP systems on-board a 120 kg satellite compared to state-of-the-art propulsion systems. The results show STP offer potential low-cost solutions for missions with velocity increment requirements between 600 and 2500 m/s.

Figure 5 illustrates the STP-water system achieves the lowest total cost figure of merit for a GTO to Lunar mission, based on short transfer time (< 1 year), low power (< 200 W) and propellant mass and volume fractions < 50% requirements.

Further reduction in cost is achieved by coupling the STP to an ORC, as expensive photovoltaic panels and batteries can be excluded.

Total Cost Figure of Merit

Fig 1: Performance comparison of propulsion systems regarding thrust and specific impulse (Baker, et al., 2005).

Department SpE

Small on-orbit mission LEO station-keeping mission (Traditional approach) LEO station-keeping mission (Non-traditional approach) GTO to Lunar mission

Mission Application

2 - Monoprellant (Hydrazine)

152

Fig 4: Propulsion system mass advantage of STP systems versus state-of-theart monopropellant propulsion systems as a function of the total satellite mass.

Fig 6: Conceptual representation of a GTO to lunar mission using STP. The satellite image is adapted from Gerrish (2003).

Agent-based Software for Small Satellites

PhD Candidate: Johan Carvajal-Godinez Department: Space Engineering Section: Space Systems Engineering Supervisor Dr. Jian Guo Promotor: Prof. Dr. Eberhard Gill Contact: j.carvajalgodinez@tudelft.nl 1

2

3

4

AOCS= Attitude and Orbit Control System FDIR= Fault Detection, Isolation and Recovery CDHS = Command and Data Handling

153 • • •

Precise Earth observation features Laser communication capabilities Swarming control

AR APP

Department SpE

• A new computational model based on software agents is proposed to deal with increased software complexity in satellites.

• Intra-communication protocols are key enablers for agent-based software applications.

• AI-based optimization algorithms are proposed for onboard resource mapping and allocation.

Department SpE

154

155

Department SpE

Space System Engineering (SSE)

Head of section: Prof.dr. Eberhard Gill

156

Department SpE

Supervisors Prof.dr. Eberhard Gill Dr. Angelo Cervone Dr. ir. Hans Kuiper Dr. Jian Guo

The section of Space Systems Engineering provides unique and recognized education, research, and engineering of end-to-end space systems. Focus areas within Space Systems Engineering are Miniaturization and Distributed Space Systems.

PhD Candidates: -- DaduĂ­ Guerrieri

-- Victor Villalba Corbacho

157

-- Marsil Silva -- Dennis Dolkens -- Linyu Zhu

Department SpE

THE LOW-PRESSURE MICRO-RESISTOJET MODELLING AND OPTIMIZATION FOR FUTURE NANO- AND PICO-SATELLITES

PhD Candidate: Daduí C. Guerrieri Department: SpE Section: SSE Daily Supervisor: Dr. A. Cervone Promotor: Prof. dr. E.K.A. Gill Contact: d.cordeiroguerrieri@tudelft.nl 1

2

3

4

Introduction

Propellant Selection

The standardization of nano- and pico-satellites has created a niche market which has been more and more explored in the last years. CubeSat and PocketQubes are the most popular standard for this class of very small satellites. They use commercial-off-the-shelf (COTS) products, widely available on the market for several subsystems and components. However, there is still a lack of commercially available options regarding the propulsion system. Among the most promising micro-propulsion systems for this class of satellites are the ones which have been developed by Space Systems Engineering (SSE) chair at Delft University of Technology (TU Delft). They are known as Vaporizing Liquid MicroResistojet (VLM) and Low-Pressure Micro-Resistojet (LPM).

This poster is focussed on the LPM development.

In total 95 fluids have been investigated including conventional and unconventional propellants in order to select the most promising “green” propellant to be used in this propulsion concept. A feasibility assessment step is carried out following a trade-off using a combination of the Analytical Hierarchy Process (AHP) and the Pugh matrix. A final list of nine best-scoring candidates have been analysed in depth with respect to the thermal characteristics involved in the process, performance parameters and safety issues.

Concept

158

The LPM is a relatively new concept that works with rarefied gas dynamics. It is divided into three main parts: tank, feed system, and thruster. The tank stores the propellant in solid or liquid state, and a heater is used to sublimate or evaporate the propellant. The feed system is composed of a valve which receives the opening or closing command allowing the passage of the Propellant vapour. The thruster is composed of a plenum and a heater chip where the propellant gas is expelled to the outer space.

The heater chip is usually made of silica wafer and presents a grid of microchannels which heats up the propellant increasing its velocity, and providing thrust.

MEMS manufacture and tests Space

Plenum

Heater Chip Plenum Valve Gas

Department SpE

Propellant Solid/Liquid

Tank

References

The devices were manufactured using silicon-based micro electro mechanical systems (MEMS) technology including a heater made of molybdenum for better operations at high temperature. The resistance of the heaters is used to estimate the chip temperature giving them a double function as heater and sensor simultaneously. A special interface was manufactured to hold the MEMS device considering the mechanical and electrical aspects. The MEMS devices are characterized for three different aspects: mechanical, electrical and propulsion. The three designed devices were tested mechanically and electrically, and one design was tested in terms of propulsion performance in a near-operational condition. The tests are promising and open the path to design a flight demonstration model.

[1] Guerrieri, D. C.; Cervone, A. & Gill, E. Analysis Of Nonisothermal Rarefied Gas Flow In Diverging Microchannels For Low-pressure Micro-resistojets, ASME Journal of Heat Transfer, 2016, 138, 11 [2] Guerrieri, D. C.; Silva, M. A. C.; Cervone, A. & Gill, E. Selection And Characterization Of Green Propellants For Micro-resistojets, ASME Journal of Heat Transfer, 2017, 139, 9 [3] Guerrieri, D. C.; Silva, M. A. C.; van Zeijl, H.; Cervone, A. & Gill, E. Fabrication And Characterization Of Low-pressure Micro-resistojets With Integrated Heater And Temperature Measurement, Journal of Micromechanics and Microengineering, 2017, 27, 125005 [4] Guerrieri, D. C.; Silva, M. A. C.; Cervone, A. & Gill, E. An Analytical Model To Characterize Thrust Performance Of Low-Pressure MicroResistojet , Acta Astronautica - Under Review

Thermo-mechanical design for a Deployable Space Telescope Introduction

Design of optical space instrumentation is usually constrained by limitations in available mass and volume, as well as the need to meet stringent precision requirements . Deployable optics open new possibilities to design large aperture optical systems without increasing the volume needed in the launcher.

However, the structural impact of the launch event and the dynamic space environment make it difficult to meet the required tolerances in practice.

Victor Villalba Corbacho Department: Space Engineering Section: Space Systems Engineering Supervisor Dr. Ir. J. M. Kuiper Promotor: Prof. E. K. A. Gill Contact: v.m.villalbacorbacho@tudelft.nl 1

2

3

4

Methodologies

Phase 1 – Trade-off studies based on semi-analytical models and Airbus’ engineering experience.

Phase 2 – Numerical simulation of relevant phenomena.

Phase 3 – Breadboarding and testing.

In parallel, engineering efforts will be needed to design and build a technology demonstrator for launch, possibly in 2019.

The Deployable Space Telescope project seeks to propose a complete optical and mechanical design for a deployable, high resolution, Earth Observation instrument.

Aim

The objective of this PhD project is to analyse the relevant disturbances to the operation of the aforementioned system, and design strategies to bring them to acceptable levels.

159

In early stages, the thermal modelling problem is the first issue to address. The convergence between top down engineering requirements, presented by Dolkens, and bottoms up component budgets needs to be established. This will lead to a list of ‘critical components’, which will then be assessed experimentally.

Microdynamics On-deployment

Structural failure Manufacturing tolerances

Disturbance type

High frequency vibration (>1Hz) Thermal flutter Operational

Coupled phenomena

Creaking Thermo-elastic deformation Department SpE

THRUST CONTROL OF MICROPROPULSION SYSTEMS

PhD Candidate: Marsil. A. C. Silva Department: Space Engineering Section: Space Systems Engineering Supervisor Angelo Cervone Promotor: Eberhard Gill Contact: m.deAthaydeCostaeSilva@tudelft.nl 1

2

3

4

7 mm Area for cutting Nozzle Inlet hole

Heater Micropropulsion systems may significantly increase the capabilities of a micro- or nanosatellite. It gives the satellite the ability to perform position and attitude maneuvers for applications such as reaction wheel desaturation, attitude control, or compensation of small perturbations. Also, the propulsion system is very helpful for providing the functionality of changing the original orbit in which the satellite was inserted. This can be used in a wide range of applications that include station keeping, orbit transfers, deep space exploration, removal of space debris, de-orbiting, and formation flying.

Vaporizing chamber pillars or channels Nozzle

Si

Mo

160

H2O

Pressure sensor Inlet section

LPCVD SiN

Glass

Inlet section Teflon Pins

Heaters Inlet

N2

Chamber

Flow

Temperature Vaporization sensors chamber

Nozzle

Inlet hole

Nozzle Glass

Fluid input VLM: The Vaporizing Liquid Microthruster is a chip of 7mm x 15mm manufactured with MEMS and silicon technologies. Liquid water enters the chip through the inlet section and goes to a heating chamber used to increase the enthalpy of the propellant from storage levels to the boiling point. A nozzle is then used to accelerate the vapor generated in the boiling to the space generating thrust. It operates with pressures up to 10 bar and contains integrated heaters and temperature sensors used to vaporize the water. More details can be found in [4].

Gas Liquid Phase change

Department SpE

Vapor

1. MAC Silva, M Shan, A Cervone, E Gill, Hybrid fuzzy control allocation of microthrusters for space debris removal using CubeSats, submitted to Engineering Applications of Artificial Intelligence in Feb-2018.

2. MAC Silva, S Silvestrini, DC Guerrieri, A Cervone, E Gill, A Comprehensive Model for Control of Vaporizing Liquid Microthrusters, submitted to IEEE Transactions on Control Systems Technology in Oct-2017.

3. MAC Silva, DC Guerrieri, A Cervone, E Gill, A review of MEMS micropropulsion technologies for CubeSats and PocketQubes, Acta Astronautica 143 (February 2018), 234-243, 2017.

4. MAC Silva, DC Guerrieri, H van Zeijl, A Cervone, E Gill, Vaporizing Liquid Microthrusters with integrated heaters and temperature measurement, Sensors and Actuators A: Physical 265, Pages 261-27, 2017.

161

Department SpE

Reaction Sphere for Microsat Attitude Control

PhD Candidate: Linyu Zhu Department: Space Engineering Section: Space Systems Engineering Supervisor: Jian Guo Promotor: Eberhard Gill Contact: l.zhu@tudelft.nl 1

2

3

4

Introduction

Methodology

Reaction spheres are innovative momentum exchange devices which performs 4Ď&#x20AC; rotations. Compared to conventional reaction wheels, reaction spheres provide control torques about three principle axes. Therefore, a single reaction sphere is sufficient for three-axis stabilizations.

In this project, a reaction sphere suitable for Microsatellite attitude control is designed.

â&#x20AC;˘ Magnetic flux density distribution B is modelled analytically by solving magnetic potentials of each region; â&#x20AC;˘ Based on the obtained B distribution, electromagnetic forces and torques are calculated through Maxwell Stress tensor; â&#x20AC;˘ Validation through comparisons with numerical simulations.

Expected Performance

Results

â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

â&#x20AC;˘ Distribution of magnetic flux density when one set of AC windings is energized

Mass â&#x2030;¤ 1 kg Output torque â&#x2030;Ľ 15 mNm Rotational speed: 2 ~ 8,000 rpm Nominal continuous power consumption â&#x2030;¤ 3 W Maximum continuous power consumption â&#x2030;¤ 15 W

Design components

162

rotor

A solid steel core, 2 permanent magnets and the copper layer stator 3 sets AC windings and 3 sets DC windings sensor To be determined

Operation â&#x20AC;˘ Rotations about the symmetry axis (the PM axis) are based on electromagnetic induction â&#x20AC;˘ Orientation of the symmetry axis is adjusted through interactions between PMs and energized DC windings â&#x20AC;˘ Translational displacements of the rotor are also controlled through interactions between PMs and energized DC windings

â&#x20AC;˘ Force/torque modelling Pđ?&#x2018;&#x2014;đ?&#x2018;&#x2014; = đ??&#x2C6;đ??&#x2C6;T ¡ đ??&#x152;đ??&#x152;đ?&#x2019;&#x2019;đ?&#x2019;&#x2019;đ?&#x2019;&#x2039;đ?&#x2019;&#x2039; ¡ đ??&#x2C6;đ??&#x2C6; + đ??&#x152;đ??&#x152;đ?&#x2019;?đ?&#x2019;?đ?&#x2019;&#x2039;đ?&#x2019;&#x2039; ¡ đ??&#x2C6;đ??&#x2C6; + Mđ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x2014;đ?&#x2018;&#x2014;ÇĄ for j=x, y, z where P is the output force F or torque T, I is the input vector and đ??&#x152;đ??&#x152;đ??Şđ??Şđ?&#x2019;&#x2039;đ?&#x2019;&#x2039;, đ??&#x152;đ??&#x152;đ?&#x2019;?đ?&#x2019;?đ?&#x2019;&#x2039;đ?&#x2019;&#x2039;, Mđ?&#x2018;&#x203A;đ?&#x2018;&#x203A;đ?&#x2018;&#x2014;đ?&#x2018;&#x2014; are corresponding coefficients for quadratic, linear and non-dependence items.

Table 1 Generated torques for an orientation control case

Torques Numerical results Analytical results Error đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;Ľđ?&#x2018;Ľ

đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;Śđ?&#x2018;Ś đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;§đ?&#x2018;§

4.83 mNm

4.99 mNm

3.3%

8.90 mNm

9.41 mNm

5.7%

4.44 mNm

4.75 mNm

7.0%

Conclusions â&#x20AC;˘ Developed models allow to calculate the flux density distributions and generated forces/torques precisely. â&#x20AC;˘ The force/torque model of the reaction sphere is nonlinear.

Department SpE

Future Work â&#x20AC;˘ Validation of rotations about any desired axis â&#x20AC;˘ Validation of simultaneous rotations and levitations â&#x20AC;˘ Controller design

163

Department SpE