Exploring the Performance and Accuracy of Digital Twin Models
1JRF , CAS DRDO, Hyderabad, Telangana, India
2JRF , CAS DRDO, Hyderabad, Telangana, India
3JRF , CAS DRDO, Hyderabad, Telangana, India
4Scientist D , CAS DRDO, Hyderabad, Telangana, India
Abstract - Digital twin technology has emerged as a promising approach to model and simulate real-world systems in various industries, including manufacturing, energy, and healthcare.However, the accuracy and performance of digital twin models are critical factors that determine their effectiveness in practical applications. This paper reviews the factors that affect the accuracy and performance of digital twin models, including data quality, model complexity, model validation, system changes, and integration with other systems. The paper highlights the challenges and opportunities associated with digital twin technology.
Key Words: Digital twins, artificial intelligence, IoT, predictive analytics, anomaly detection, optimization, productivity, cost-effectiveness, sustainability, manufacturing,healthcare,transportation.
1. INTRODUCTION
The creation of a virtual duplicate of a physical system or asset is known as a "digital twin" and is a cutting-edge technology. It can be used for a variety of applications, including monitoring, control, optimisation, and preventive maintenance. This virtual replica, or digital twin, is a highly exact and detailed digital version of the real system. Concept of digital twins has been around for severalyears,recentadvancementsinAIhaveenabledthe developmentofAI-powereddigitaltwins,whichhaveeven greatercapabilitiesthantheirtraditionalcounterparts.AIpowered digital twins can learn from data, identify patterns,andmakepredictions,enablingmanufacturersto predictandpreventissuesbeforetheyoccur.
-1:RepresentationofDigitalTwin
There are several different digital twin types, which can oftenrunsidebysidewithinthesamesystem.Whilesome digital twins replicate only single parts of an object, they're all critical in providing a virtual representation. Themostcommontypesofdigitaltwinsarethefollowing.
Component twins: A component twin, also known as a parts twin, is a digital representation of a single system component. These are necessary components for an asset'soperation,suchasawindturbine'smotor.
Asset twins: Assets are described in terms of digital twins as two or more parts that cooperate to form a larger system.Theysimulatehowthepartsinteractandgenerate performancedatayoumayusetomakewisedecisions.
System twins: These represent a greater level of abstraction than asset twins.Asystem twindemonstrates how several assets function as a unit inside a larger system. You can decide how to improve performance or increase efficiency thanks to the visibility provided by systemtwintechnology.
Process twins: The use of process twins can help to understand how an object's many parts, assets, and units interact with one another by displaying the digital environment of the entire entity. The operation of our complete manufacturing facility, for instance, might be digitallyreplicatedbycreatinga"digitalprocesstwin"that combinesallofitsparts.
2. LITERATURE SURVEY
NASA(National Aeronautics & Space Administration) was the first organization to propose the idea of utilizing a digitaltwintoexaminearealobjectinthe1960s.Inorder to emulate the systems in space for exploratory missions, NASA reproduced its spacecraft on Earth. The Apollo 13 mission served asa notable example of this technology in action. Mission Control was able to rapidly change the simulationstofittheconditionsofthedamagedspacecraft and troubleshoot methods to return the astronauts safely toEarthbecauseoftheconnectedtwins.
Tomonitormassivefacilitieslikepowerplantsintheearly 1970s, mainframe computers were utilized as systems to digital twins. In the 1980s, 2D CAD systems like AutoCAD were developed to create technical drawings, making it feasible to design anything using a computer. millions of designersandengineerssoonembracedthesesystems.
By the 2000s, parametric modeling and simulation enabled by 3D CAD allowed for the intelligent design of morecomplexassemblies,suchasadatabaseofconnected objects. Time travel to the middle of the 2010s, when all major 3D CAD suppliers introduced cloud-connected solutions, primarily for collaboration and project management, and subsequently for generative design, eventhoughCADtoolsremaineddesktop-based.
Today it is the beginning of the era of real-time 3D powered digital twins, which go beyond dashboards and 3D models to unlock data from various sources on any device or platform for better collaboration, visualization, anddecision-making.
3. APPLICATIONS
Digital twin technology has many potential applications across a wide range of industries. Some of the most notableapplicationsofdigitaltwininclude:
Manufacturing: Production process optimisation, downtime reduction, and product quality improvement areallpossiblewiththeusageofdigital twins.Businesses may predict possible issues before they arise and immediately put remedies into action by simulating the manufacturingprocess.
Aerospace and defense: Digital twins can be used to simulatetheperformanceofaircraft,spacecraft,andother vehicles. This technology can help improve safety, reduce maintenancecosts,andoptimizeperformance.
Energy and utilities: Digital twins can be used to monitor and optimize energy usage, reduce waste, and improve efficiency.Thistechnologycanbeusedinpowerplants,oil andgasrefineries,andotherenergy-intensiveindustries.
Healthcare: Digital twins can be used to simulate the human body, allowing doctors to diagnose and treat patientsmoreeffectively.Thistechnologycanalsobeused to monitor patient health remotely and predict potential healthissuesbeforetheyoccur.
Smart cities: Digital twins can be used to simulate and optimize city infrastructure, including transportation systems,utilities,andpublicservices.Thistechnologycan help cities become more sustainable, efficient, and responsivetotheneedsofresidents.
Overall, digital twin technology has the potential to revolutionizemanyindustriesbyprovidingaccurate,real-
time simulations that can be used to optimize performance, reduce costs, and improve safety. New conceptcalledAIpoweredDigitaltwincanalsobeusedin above mentioned application where AI powered digital twin can analyze the available data, identify the patterns, make predictions, enabling manufacturers to predict and preventissuesbeforetheyoccur.
4. ADVANTAGES
Userscan profitgreatlyfromdigital twins. We'll discussa fewofthesenow.
a) Acustomizedresponsetoeachneed:
Thebeautyofdigitaltwinningisthatitenablesbusinesses to create virtual, tailored versions of their web infrastructuretodopredictiveanalysisthatisappropriate for their purposes. It is a customized method of adjusting securitytocertainbusinesses.
b) Higherperformance:
We can improve the performance of an equipment, plant, or facilities using the real-time data and insights supplied by digital twins. As problems arise, they can be resolved quickly to maintain system performance and minimize downtime.
c) Predictivecapabilities:
Evenifourmanufacturingplant,officebuilding,orfacility containshundredsofdifferentpiecesofequipment,digital twins can nevertheless provide us with a comprehensive visual anddigital imageofeverything.Everycomponent's output is monitored by sophisticated sensors, which immediately alert the user to any problems or errors. As opposedtowaitinguntilapieceofequipmenttotallyfails, wecaninterveneassoonasissuesarise.
d) Remotemonitoring:
Digital twins virtual nature allows for remote facility monitoring and management. Additionally, remote monitoring eliminates the need for personnel to inspect industrialmachinerythatmightbehazardous.
e) Acceleratedproductiontime:
By creating digital duplicates, we can hasten the manufacturingofgoodsandinfrastructurebeforetheyare actually created. By executing scenarios, we may examine how our facility or product responds to errors and make the necessary adjustments prior to beginning actual production.
AI-powereddigitaltwinswillcontinuetoplayakeyrolein thisnewphaseofmanufacturing.Herearesomepotential benefits of AI-powered digital twins in manufacturing industry:
i.Greater collaboration between humans and machines: In the manufacturing industry, humans and machines will work together in a highly collaborative and integrated manner. AI-powered digital twins can enable humans to interact with machines in a more natural and intuitive way,improvingcollaborationandproductivity.
ii.Improved customization and personalization: In the manufacturing industry, there will be a greater focus on customization and personalization of products. AIpowered digital twins can be used to simulate and optimizetheproductionofcustomizedproducts,resulting inhigherqualityandgreatercustomersatisfaction.
iii.Greater flexibility and agility: manufacturing industry willrequiregreaterflexibilityandagilityinmanufacturing processes, enabling rapid response to changing customer demandsandmarketconditions.AI-powereddigitaltwins can be used to simulate and optimize manufacturing processes in real-time, enabling greater flexibility and agility.
iv.Enhanced sustainability: manufacturing industry will require a greater focus on sustainability and environmental responsibility. AI-powered digital twins can be used to optimize resource utilization and reduce waste and energy consumption, resulting in greater sustainabilityandenvironmentalbenefits.
v.Improved safety: manufacturing industry will require a greater focus on safety, as humans and machines work more closely together. AI-powered digital twins can be used to simulate and test safety scenarios, enabling manufacturers to identify and mitigate potential safety hazardsbeforetheyoccur.
Overall, the use of AI-powered digital twins in the manufacturingindustryhasthepotentialtoenablegreater collaboration,customization,flexibility,sustainability,and safetyinManufacturingprocesses.
5. DIGITAL TWIN CREATION
Togeneratedigitaltwins,conceptualmodelsareimported (through Building Information Modeling, Computer aided design, or Geographic information system) or physical objectsarescannedintherealworldforvisualizationand analysiswithenterpriseandInternetofThings(IoT)data. Real-time3D,acomputergraphicstechnologythatcreates interactive content more quickly than human perception,
canbeusedtopoweradigitaltwinthatcanfilter,arrange, and present various data sources (both information and models)asrealistic,interactivevisualizations.
Digital twins are representations in the virtual realm of the forces, interactions, and movements that assets may experience in the real world. Users can interact with dynamiccontentthatisthree-dimensionalandresponsive to their actions in real-time. They may efficiently mimic real-world situations, what-if scenarios, and any situation imaginable in this virtual environment, and instantaneously visualize the results on any platform, includingmobiledevices,PCs,andaugmented,mixed,and virtualreality(AR/MR/VR)devices.
The deployment of each digital twin is distinct. The complexity and business effect of deployments often increase with each stage that is added. With each of its components dynamically linked to engineering, construction,operationaldata,andvirtualdesign,adigital twincanrangefromaproductconfiguratorofa3Dmodel toanexactrepresentationofanetworkorsystemaswide asametropolis.
Digital twins guide decision-making at every stage of the life cycle as teams from several disciplines and locations plan, engineer, build, sell, and eventually run and manage complexbuilds.
Although both digital twins and simulations are virtual model-based simulations, they have some significant differences.Typically,simulationsareusedfordesignand, in some circumstances, offline optimisation. On the other hand, digital twins are intricate virtual worlds that can engagewithandchangeinrealtime.
Let's take an automobile simulation as an illustration. A novicedrivercanengageinanimmersivetrainingsession, understand how various automotive components work, andexperiencenumerous real-worldcircumstanceswhile drivingvirtually.Thecircumstancesaren'trelatedtoareal car,though.Adigital replicaofthecarisconnectedtothe real thing and is fully informed about it, including important performance data, previously replaced parts, prospective problems as detected by the sensors, prior servicehistory,andmore.
6. PERFORMANCE, ACCURACY AND CHALLENGES OF DIGITAL TWIN
Thefidelityandaccuracyofdigitaltwinmodelsarecritical factors in determining the effectiveness of digital twin technology in representing and simulating physical systems. Fidelity refers to the level of detail and accuracy
with whichthedigital twin model represents the physical system. Accuracy refers to the degree to which the digital twin model can predict the behavior and performance of the physical system under different conditions. A simulation-basedstudycanbeaneffectivewaytoevaluate the fidelity and accuracy of digital twin models. By simulating the behavior and performance of the digital twinmodelunderdifferentconditions,theaccuracyofthe model can be verified and any discrepancies can be addressed.
The accuracy and performance of digital twin models can beimpactedbyseveralfactors,someofwhichinclude:
Data quality: Digital twins rely on accurate and highquality data to model and simulate the real-world system they represent. If the data used to build the model is incomplete, outdated, or unreliable, it can lead to inaccurate predictions and poor performance. Obtaining real world data of every system is not feasible hence digitaltwinsforallrealworldsystemsischallenging
Model complexity: Digital twins can be complex models that incorporate a large number of variables and require significant computational resources to run. As the complexity of the model increases, the accuracy and performance can be impacted, especially if the computational resources are insufficient. hence digital twinforcomplexrealworldsystems is achallenge.
Model validation: Digital twin models must be validated against real-world data to ensure they are accurate and reliable. If the model validation process is inadequate or the validation data is insufficient, it can lead to inaccuracies and poor performance. Getting Accurate validationdataforrealworldsystems ischallenging.
System changes: Digital twins are designed to simulate real-world systems, but these systems are constantly changing. If the digital twin model does not account for these changes or is not updated regularly, it can lead to inaccuraciesandpoorperformance.
Integration with other systems: Digital twins are often integrated with other systems such as sensors, data analyticsplatforms,andcontrolsystems.Iftheintegration isnotseamlessorthedata exchangedbetweensystemsis not accurate, it can impact the accuracy and performance ofthedigitaltwinmodel.
Cost: Developing and implementing digital twin technology can be costly, especially for complex systems. The cost of obtaining and managing data, building and validatingthemodel,andintegratingthemodelwithother systemscanalladdup.
Security: Digitaltwinscancontainsensitivedataaboutthe real-worldsystemtheyrepresent,makingthemapotential target for cyber-attacks. Ensuring the security of the digitaltwinsystemcanbeasignificantchallenge.
Overall, ensuring the accuracy and performance of digital twin models requires careful consideration of these and otherfactors,challengesaswellasongoingvalidationand updatingofthemodelasthereal-worldsystemevolves.
7. RESULT
The accuracy and performance of digital twin models depend on several factors such as the quality of the data used, the complexity of the model, the validation process, system changes, and integration with other systems. To ensure the accuracy and performance of digital twin models,itisessentialtocarefullymanagethedatausedin the model and to ensure that the model is validated againstreal-worlddata.Thecomplexityofthemodelmust alsobecarefullymanagedtoensurethatitisaccurateand reliablewhilebeingcomputationallyfeasible.
The digital twin model must also be updated regularly to account for system changes and to integrate seamlessly with other systems. Addressing these challenges requires careful consideration of the specific system being modeled, the data and computational resources available, andtheorganizational contextinwhichthedigital twin is being developed and implemented. Ultimately, ensuring the accuracy and performance of digital twin models is crucial to their successful adoption and use in various industriesandapplications.
8. CONCLUSION
In conclusion, Digital twin uses several advanced technologies to provide a digital model of an asset. These technologies are Internet of Things (IoT), big data analytics, and artificial intelligence (AI) to enhance productivity,quality,andefficiencyacrossvarioussectors, including manufacturing, healthcare, and transportation. To ensure the accuracy and performance of digital twin models,itisessentialtocarefullymanagethedatausedin the model and to ensure that the model is validated against real-world data. The digital twin model must also beupdatedregularlytoaccountforsystemchangesandto integrateseamlesslywithothersystems.
9. FUTURE WORK
The future scope of "Exploring the Performance and Accuracy of Digital Twin Models" is vast, as the use of digital twinmodelsis expectedtogrow rapidlyinvarious
industries. Future work will include exploration of following:
a) Developmentoftechniquesfordatacollectionand analysis, such as the use of Internet of Things (IoT) sensorsandedgecomputing.
b) Exploration of open-source digital twin platforms for developing digital twin model and application and validationofit.
Overall, the future of "Exploring the Performance and Accuracy of Digital Twin Models" is promising, with continued research and development expected to lead to significantadvancementsinthefield.
ACKNOWLEDGEMENT
For his continued cooperation with the research for drafting this study at CAS, Sri BV Papa Rao, Outstanding Scientist and Director, is gratefully acknowledged by the authors.TheauthorsthankAssociateDirectorSriPraveen Tandon Sc G for his unwavering support. The authors are extremely grateful to all CAS staff members for their selflesscontributionsoftimeandknowledgetothisstudy inavarietyofways.
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BIOGRAPHIES
[1] Shri. Vidit Kumar, ispresentlyworkingasaScientistD at CAS DRDO Hyderabad. he has more than 10 years experience in the field of communication, computer networking&softwaredevelopment.
[2] Sri Timmapuram Srinivas is presently working as JRFinDRDO,CAS,Hyderabad.Hehasmorethan6yearsof experience in the field of embedded systems, IT networking. He has published a research paper in International Journal ofResearchon IoT based embedded smartlockcontrolsystems.
[3] Smt G.Suchitra is presently working at DRDO, CAS, Hyderabad as a JRF. She completed her M.Tech in Computer Science & Engineering from Osmania University.Shehas2yearsofworkexperienceinthefield of computer science. She is certified as CISSP-Certified InformationSystemsSecurityProfessional.
[4] Smt Shahela is presently working as a JRF at DRDO,CAS,Hyderabad.She completed her M.Tech in VLSI.Shehas1.5yearsofworkexperienceinthefieldofIT & Cybersecurity. She has published a paper on Quantum Circuitry in International Journal & Magazine of Engineering, Technology, Management and Research. She iscertifiedasaISO27001leadauditor.