Potentials of game engines for wind power digital twin development: an investigation of the Unreal Engine

Springer Science and Business Media LLC - Tập 5 Số S4
Jonas Vedsted Sørensen1, Zheng Ma1, Bo Nørregaard Jôrgensen1
1SDU Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230, Odense, Denmark

Tóm tắt

AbstractDigital twin technologies have become popular in wind energy for monitoring and what-if scenario investigation. However, developing a digital representation of the wind is challenging, especially due to the digital twin platform constraints. Game engines might be possible to solve this issue, especially since game engines have been used for product design, testing, prototyping, and also digital twins. Therefore, this study investigates the potential of developing a digital twin of wind power in the Unreal game engine. A case study of two types of wind turbines (Vestas V164-8 and Enercon E-126 7.580) and one location (Esbjerg, Denmark) is chosen for this study. The digital twin includes the environment with historical wind data and the visual representation of the wind turbine with a wind power production model and the estimated production in the given wind conditions of the area. The results show that game engines are viable for building entire digital twins where a realistic graphical user interface is required. Unreal Engine 5 provides the tools for modelling the landscape, surrounding water, and lighting. In addition, the Unreal Engine ecosystem provides vast amounts of content, such as 3D assets and game logic plugins, easing the digital twin development. The results prove that digital twins built in Unreal Engine 5 have great potential development of digital twins and user interfaces for communicating with a digital twin. The developed digital twin allows for further extension to benefit future digital twins utilizing wind turbines.

Từ khóa


Tài liệu tham khảo

Abdallah A, Primas M, Turcin I, Traussnigg U (2020) The Potential of Game Development Platforms for Digital Twins and Virtual Labs: Case Study of an Energy Analytics and Solution Lab. CAMPUS 02, University of Applied Sciences, Graz, 8010, Austria, annote = Export Date 3 January 2022 Methods & Technologies Unity3D Purpose Integration of DT and lab environment Application domain? Contributions? Other abstract to vague to be further analysed. p. 117–21

Andryushkevich SK, Kovalyov SP, Nefedov E (2019) Composition and application of power system digital twins based on ontological modeling. 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). p. 1536–42

Bradac Z, Marcon P, Zezulka F, Arm J, Benesl T (2019) Digital Twin and AAS in the Industry 4.0 Framework. IOP Conference Series: Materials Science and Engineering

Chang-won L: Hyundai Motor works with Unity to build digital-twin of factory supported by metaverse platform. https://www.ajudaily.com/view/20220107083928529 (2022). Accessed 13/07 2022

Christensen K, Ma Z, Værbak M, Demazeau Y, Jørgensen BN (2019) Agent-based decision making for adoption of smart energy solutions. 2019 IEEE Sciences and Humanities International Research Conference (SHIRCON). p. 1–4

Commission E: photovoltaic geographical information system. https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html Accessed 05/07 2022

Engine U: Digital twins—For architecture, real estate, and the built environment. https://www.unrealengine.com/en-US/digital-twins Accessed 05/07 2022

Eyre JM, Dodd TJ, Freeman C, Lanyon-Hogg R, Lockwood AJ, Scott RW, et al (2018) Demonstration of an Industrial Framework for an Implementation of a Process Digital Twin. ASMEInternational Mechanical Engineering Congress and Exposition (IMECE2018): Amer Soc Mechanical Engineers

Fahim M, Sharma V, Cao TV, Canberk B, Duong TQ (2022) Machine learning-based digital twin for predictive modeling in wind turbines. IEEE Access 10:14184–14194. https://doi.org/10.1109/ACCESS.2022.3147602

Gamma E, Helm R, Johnson R, Vlissides J (1994) Design patterns: elements of reusable object-oriented software

Glaessgen EH, Stargel DS (2012) The digital twin paradigm for future NASA and U.S. Air force vehicles. Collection of Technical Papers—AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. https://doi.org/10.2514/6.2012-1818

Gppert A, Grahn L, Rachner J, Grunert D, Hort S, Schmitt RH (2021) Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems. J Intell Manuf. https://doi.org/10.1007/s10845-021-01860-6

Grieves M, Vickers J (2017) Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen F-J, Flumerfelt S, Alves A (eds) Transdisciplinary perspectives on complex systems: new findings and approaches. Springer International Publishing, Cham, pp 85–113

Howard DA, Ma Z, Jørgensen BN (2021) Digital twin framework for energy efficient greenhouse industry 4.0. Springer International Publishing, Cham, pp 293–297

Howard DA, Ma Z, Veje C, Clausen A, Aaslyng JM, Jørgensen BN (2021) Greenhouse industry 4.0—digital twin technology for commercial greenhouses. Energy Inf 4(2):37. https://doi.org/10.1186/s42162-021-00161-9

Introducing JSON. www.json.org Accessed 05/07 2022

Kong LCW, Harper S, Mitchell D, Blanche J, Lim T, Flynn D (2020) Interactive digital twins framework for asset management through internet. 2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). p. 1–7

Larsen BL (2022) EU-Parlamentet vedtager strategi for havvind. https://www.danskenergi.dk/nyheder/eu-parlamentet-vedtager-strategi-havvind (2022). Accessed 02-07-2022

Lee D, Lee SH, Masoud N, Krishnan MS, Li VC (2021) Integrated digital twin and blockchain framework to support accountable information sharing in construction projects. Autom Constr 127:9. https://doi.org/10.1016/j.autcon.2021.103688

Leskovsk (2020) Proposal of digital twin platform based on 3D rendering and IIoT principles using virtual augmented reality. 2020 Cybernetics & Informatics (K&I). p. 1–8

Ma Z, Billanes JD, Jørgensen BN (2017) A business ecosystem driven market analysis: the bright green building market potential. 2017 IEEE Technology & Engineering Management Conference (TEMSCON). p. 79–85

Ma Z, Værbak M, Rasmussen RK, Jøergensen BN (2019a) Distributed energy resource adoption for campus microgrid. 2019a IEEE 17th International Conference on Industrial Informatics (INDIN). p. 1065–70

Ma Z, Broe M, Fischer A, Sϕrensen TB, Frederiksen MV, Jϕergensen BN (2019b) Ecosystem thinking: creating microgrid solutions for reliable power supply in India’s power system. 2019b 1st Global Power, Energy and Communication Conference (GPECOM). p. 392–7

Ma Z, Korsgaard J, Jørgensen BN (2020) Optimization of greenhouse production process: an investigation of energy efficiency potentials. 2019 6th International Conference on Dependable Systems and Their Applications (DSA). p. 365–70

Malakuti S, Juhlin P, Doppelhamer J, Schmitt J, Goldschmidt T, Ciepal A (2021) An architecture and information meta-model for back-end data access via digital twins. 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). p. 1–8

Moghadam FK, Nejad AR (2022) Online condition monitoring of floating wind turbines drivetrain by means of digital twin. Mech Syst Signal Process 162:108087. https://doi.org/10.1016/j.ymssp.2021.108087

Moghadam FK, Rebouças GFdS, Nejad AR (2021) Digital twin modeling for predictive maintenance of gearboxes in floating offshore wind turbine drivetrains. Forsch Ingenieurwes 85(2):273–286. https://doi.org/10.1007/s10010-021-00468-9

Mostafa F, Tao LQ, Yu WJ (2021) An effective architecture of digital twin system to support human decision making and AI-driven autonomy. Concurr Comput-Pract Exp 33(19):15. https://doi.org/10.1002/cpe.6111

Negri E, Berardi S, Fumagalli L, Macchi M (2020) MES-integrated digital twin frameworks. J Manuf Syst 56:58–71. https://doi.org/10.1016/j.jmsy.2020.05.007

Negrin DAM, Cleophas L, Brand MVD (2021) Using Ptolemy II as a Framework for Virtual Entity Integration and Orchestration in Digital Twins. 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). p. 233–6

Örs E, Schmidt R, Mighani M, Shalaby M (2020) A conceptual framework for AI-based operational digital twin in chemical process engineering. 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). p. 1–8

Ospina-Bohórquez A, López-Rebollo J, Muñoz-Sánchez P, González-Aguilera D (2022) A digital twin for monitoring the construction of a wind farm. Eng Proc. https://doi.org/10.3390/engproc2022017003

Pereira JG, Ellman A (2020) From CAD to physics-based digital twin: framework for real-time simulation of virtual prototypes. Proceedings of the Design Society: DESIGN Conference. p. 335–44

Perforce: What Is Keeping Automotive Software Developers Up at Night? https://www.perforce.com/resources/sca/2022-state-automotive-software-development-report#what-is-keeping-automotive-software-developers-up-at-night Accessed 12/07 2022

Redeker M, Weskamp JN, Rssl B, Pethig F (2021) Towards a digital twin platform for Industrie 4.0. 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). p. 39–46

Rolle RP, Martucci VO, Godoy EP (2021) Modular framework for digital twins: development and performance analysis. J Control Autom Elect Syst 32(6):1485–1497. https://doi.org/10.1007/s40313-021-00830-w

Shahsavari S, Immonen E, Rabah M, Haghbayan MH, Plosila J (2021) MCX—an open-source framework for digital twins. Proceedings—European Council for Modelling and Simulation, ECMS. p. 119–24

Sharotry A, Jimenez JA, Wierschem D, Mediavilla FAM, Koldenhoven RM, Valles D, et al (2020) A digital twin framework for real-time analysis and feedback of repetitive work in the manual material handling industry. Winter Simulation Conference: IEEE; p. 2637–48

Steindl G, Stagl M, Kasper L, Kastner W, Hofmann R (2020) Generic digital twin architecture for industrial energy systems. Appl Sci-Basel 10:20. https://doi.org/10.3390/app10248903

Studios N: MQTT Utilities plugin for Unreal Engine. https://github.com/NinevaStudios/mqtt-utilities-unreal (2022). Accessed 15/07 2022

Talkhestani BA, Jung T, Lindemann B, Sahlab N, Jazdi N, Schloegl W et al (2019) An architecture of an intelligent digital twin in a cyber-physical production system. At-Automatisierungstechnik 67(9):762–782. https://doi.org/10.1515/auto-2019-0039

Touma JS (1977) Dependence of the wind profile power law on stability for various locations. J Air Pollut Control Assoc 27(9):863–866. https://doi.org/10.1080/00022470.1977.10470503

Traore MK (2021) Unifying Digital Twin Framework: Simulation-Based Proof-of-Concept. In: Control M, Logist IFACTCIa, editors. 17th IFAC Symposium on Information Control Problems in Manufacturing (INCOM): Elsevier; p. 886–93

Unity: Digital Twin. https://unity.com/solutions/digital-twin Accessed 05/07 2022

Værbak M, Ma Z, Christensen K, Demazeau Y, Jørgensen BN (2019) Agent-based modelling of demand-side flexibility adoption in reservoir pumping. 2019 IEEE Sciences and Humanities International Research Conference (SHIRCON). p. 1–4

wind-turbine-models: Enercon E-126 7.580. https://en.wind-turbine-models.com/turbines/14-enercon-e-126-7.580 Accessed 12/07 2022

wind-turbine-models: Vestas V164-8.0. https://en.wind-turbine-models.com/turbines/318-vestas-v164-8.0 Accessed 12/07 2022

Worden K, Cross EJ, Barthorpe RJ, Wagg DJ, Gardner P (2020) On digital twins, mirrors, and virtualizations: frameworks for model verification and validation. ASCE-ASME J Risk Uncert Eng Syst Part B Mech Eng. https://doi.org/10.1115/1.4046740

51World: 51World. https://www.51vr.com.au/ Accessed 12/707 2022

Yun S, Park JH, Kim WT (2017) IEEE. Data-centric Middleware based Digital Twin Platform for Dependable Cyber-Physical Systems. 9th International Conference on Ubiquitous and Future Networks (ICUFN): Ieee; p. 922–6

Zhang H, Xi Y, Wang S, Wang K, Liu Q, Wei L, et al (2021) Hybrid data-physics based digital twin modeling framework for the power system of bobsleigh and tobogganing venue for Beijing winter Olympics. 2021 6th International Conference on Power and Renewable Energy (ICPRE). p. 865–71

Zohdi TI (2021) A digital-twin and machine-learning framework for the design of multiobjective agrophotovoltaic solar farms. Comput Mech 68:357–370. https://doi.org/10.1007/s00466-021-02035-z

Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Tập 5 Số S4

Thông tin tác giả