Skip to main content

Advertisement

SpringerLink
Log in

Digital twin applications in aviation industry: A review

  • Critical Review
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

As a highly secure and reliable system, aviation faces challenges including digital transformation, high production operation costs, low maintenance efficiency, and intensive technology. One of the most beneficial technologies is the digital twin (DT), which can solve the above issues. This paper investigates aviation industry DT from government authorities, industry, and academia. Firstly, it surveys the development of the definition of DT and compares its concepts with the Internet of Things, cyber-physical systems, and digital thread etc. Next, it shows the research timeline of aviation DT in the past 10 years by authorities such as Air Force Research Laboratory and National Aeronautics and Space Administration. Then, this paper reviews the state-of-the-art research status of DT in the whole lifecycle of the aviation system and concludes that aviation DT is the most widely used in manufacturing and maintenance, and should pay attention to the application of UAV DT. Finally, it summarizes that data fusion, high-fidelity modeling, integration with New IT, and human–machine interaction are the key technologies of aviation DT. The future possible research directions could be digital process twin risk control, interactive DT, and DT data learning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Badea V, Zamfiroiu A, Boncea R (2018) Big Data in the Aerospace Industry. Informatica Economică 22(1):17–24

    Google Scholar 

  2. Lee J, Bagheri B, Kao H (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23

    Google Scholar 

  3. Lasi H, Fettke P, Kemper, HG et al (2014) Industry 4.0. Bus Inf Syst Eng 6:239–242. https://doi.org/10.1007/s12599-014-0334-4

  4. Ling L (2018) China’s manufacturing locus in 2025: with a comparison of ‘Made-in-China 2025’ and ‘Industry 4.0.’ Technol Forecasting Soc Change 135:66–74

    Google Scholar 

  5. Qi Q, Tuegel E (2018) Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison”. IEEE Access 6:3585–3593

    Google Scholar 

  6. Mabkhot M, Al-Ahmari A, Salah B, Alkhalefah H (2018) Requirements of the smart factory system: a survey and perspective. Machines 6(23). https://doi.org/10.3390/machines6020023

  7. Zhou M, Yan J, Feng D (2019) Digital twin framework and its application to power grid online analysis. CSEE J Power Energy 5(3):391–398

    Google Scholar 

  8. Tao F, Zhang H, Liu A, Nee A (2019) Digital twin in industry: state-of-the-Art. IEEE Trans Ind Informatics 15:2405–2415

    Google Scholar 

  9. Kritzinger W, Karner M, Traar G, Henjes J, Sihn W (2018) Digital Twin in manufacturing: a categorical literature review and classification. IFAC- PapersOnLine 51(11):1016–1022

    Google Scholar 

  10. Li L, Aslam S, Wileman A, Perinpanayagam S (2021) Digital twin in aerospace industry:a gentle introduction. IEEE Access 10:9543–9562

    Google Scholar 

  11. Tuegel E, Ingraffea A, Eason T, Spottswood S (2011) Reengineering aircraft structural life prediction using a Digital Twin. Int J Aerosp Eng. https://doi.org/10.1155/2011/154798

    Article  Google Scholar 

  12. Melesse T, Pasquale RS (2020) Digital twin models in industrial operations: a systematic literature review. Procedia Manufacturing 42:267–272

    Google Scholar 

  13. Grieves M, Vickers J (2017) Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems, Springer, Cham, pp 85–113

  14. Boschert S, Rosen R (2016) Digital twin—the simulation aspect. Mechatronic Futures, Switzerland, Springer, Cham, pp. 59–74. https://doi.org/10.1007/978-3-319-32156-1_5

  15. Shafto M, Conroy M, Doyle R, Glaessgen E, Kemp C, LeMoigne J, Wang L (2010) NASA technology roadmap: modeling, simulation, information technology & processing roadmap technology area 11. 2019–04–13

  16. Tuegel E (2012) The airframe digital twin: some challenges to realization. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, pp. 1812. https://doi.org/10.2514/6.2012-1812

  17. Glaessgen E. Stargel D (2012) The digital twin paradigm for future NASA and US Air Force vehicles”, in Proc. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA

  18. Syafrudin M, Alfian G, Fitriyani N, Rhee J (2018) Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing.  Sensors -Basel 18(9):2946

  19. Darko A, Chan A, Adabre M, Edwards D, Hosseini M, Ameyaw E (2020) Artificial intelligence in the AEC industry: scientometric analysis and visualization of research activities. Autom ConStruct 112:103081

    Google Scholar 

  20. Tao F, Zhang H, Liu Q, Zhang M, Liu W et al (2010) Ten question towards digital twin: analysis and thinking. Comput Integr Manuf Syst 26(1):1–17

    Google Scholar 

  21. Parris C (2017) Digital twin Predix, GE. China. https://www.ge.com/cn/sites/www.ge.com.cn/files/8-15-4%20Predix-Digital-Twin-Technology.pdf

  22. Zhao M, Ning Z (2020) Software definition and digital twin. Casting the Soul: Software defined manufacturing. China Machine Press, Beijing, China, pp 226–230

    Google Scholar 

  23. SIEMENS (2017) Digital twin of respiratory products. https://new.siemens.com/global/en/company/stories/industry/digitaltwin-simulation-vyairemedical. Accessed 15 Feb 2022

  24. Dassault Group (2019) South Australia to share vision for the future economy. https://govinsider.asia/digital-gov/south-australia-share-vision-future-economy/. Accessed 15 Feb 2022

  25. PTC Group (2019) PTC digital center. https://www.ptc.com/cn/industry-insights/zhongtai. Accessed 15 Feb 2022

  26. ESI Group (2019) Hybrid Twin™ by ESI in Composite Factory 4.0. https://www.esi-group.com/resources/webinar/transformation-towards-the-industry-4-0-with-hybrid-twintm. Accessed 15 Feb 2022

  27. Talkhestani BA, Jung T, Lindemann B, Sahlab N, Jazdi N, Schloegl W, Weyrich M (2019) An architecture of an intelligent digital twin in a cyber-physical production system. at-Autom 67(9):762–82

  28. Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94:3563–3576

    Google Scholar 

  29. Zhang H, Liu Q, Chen X, Zhang D, Leng J (2017) A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5:26901–26911

    Google Scholar 

  30. Lu Y, Liu C, Kevin I, Wang K, Huang H, Xu X (2020) Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics Com-Int Manuf 61:101837. https://doi.org/10.1016/j.rcim.2019.101837

    Article  Google Scholar 

  31. Uhlemann T, Lehmann C, Steinhilper R (2017) The digital twin: realizing the cyber-physical production system for industry 4.0. Procedia CIRP 335–40

  32. Bagheri B, Lee J (2015) Big future for cyber-physical manufacturing systems. Design World 23. http://www.designworldonline.com/big-future-for-cyber-physical-manufacturing-systems/

  33. Li H, Wei M, Huang J et al (2019) Survey on cyberphysical system. A CTA Automatica Sinica 45(1):37–50

    Google Scholar 

  34. Tao F, Liu W, Liu J et al (2018) Digital twin and its potential application exploration. Comput Integr Manuf Syst 24(01):1–18

    Google Scholar 

  35. Zhao Z, Liu F, Cai Z, Xiao N (2018) Edge com putting: platforms, applications and challenges. Journal of Computer Research and Development 55(2):327–337

    Google Scholar 

  36. Systems Engineering Initiatives (2021) http://www.acq.osd.mil/se/initiatives/init_de.html. Accessed 8 Jun 2021

  37. Liu Y (2018) Digital clue helps US Air Force equipment development. Measurement & Control Technology 37(9):1–4

    Google Scholar 

  38. Zhang B, Li X, Wang X (2019) From digital twin to digital engineering modeling and simulation entering a new era. Journal of System Simulation 31(03):369–376

    Google Scholar 

  39. Glossary of Defense Acquisition Acronyms and Terms (2019) Defense Acquisition Portal, Defense Acquisition University, Fort Belvoir, VA. https://dap.dau.mil/glossary/Pages/Default.aspx. Accessed 27 Jun 2021

  40. Zweber J, Kolonay R, Kobryn P, Tuegel E (2017) Digital thread and twin for systems engineering: Pre-MDD through TMRR. In Proceedings of the 55th AIAA Aerospace Sciences Meeting. http://arc.aiaa.org. https://doi.org/10.2514/6.2017-0875

  41. Margaria T, Schieweck A (2019) The Digital Thread in Industry 4.0. In: Ahrendt W., Tapia Tarifa S. (eds) Integrated Formal Methods. IFM 2019. Lecture Notes in Computer Science, vol 11918. Springer, Cham. https://doi.org/10.1007/978-3-030-34968-4_1

  42. Kobryn P, Tuegel E, Zweber J, Kolonay R (2017) Digital thread and twin for systems engineering: EMD to disposal. In Proceedings of the 55th AIAA Aerospace Sciences Meeting. http://arc.aiaa.org, https://doi.org/10.2514/6.2017-0876

  43. Phanden R, Sharma P, Dubey A (2021) A review on simulation in digital twin for aerospace, manufacturing and robotics. Mater Today: Proc 38:174–178

    Google Scholar 

  44. Wang L (2020) Application and development prospect of digital twin technology in aerospace. IFAC-PapersOnLine 53(5):732–737

    Google Scholar 

  45. Hochhalter, Jacob D et al (2014) Coupling damage-sensing particles to the digitial twin concept. NASA/TM–2014–218257, National Aeronautics and Space Administration, Langley Research Center, 2014. http://www.sti.nasa.gov

  46. Tuegel E, Kobryn P, Henderson D (2016) The Airframe Digital Twin Spiral 1 Program. In 2016 Aircraft Airworthiness and Sustainment Conference, March 21–24, pp 9032. http://www.meetingdata.utcdayton.com/agenda/airworthiness/2016/agenda.htm

  47. Wang L, Asher I, Ryan K, Khan G, Ball D (2016) AIRFRAME DIGITAL TWIN (ADT) Delivery Order 0001: Scalable, Accurate, Flexible, Efficient, Robust, Prognostic and Probabilistic Individual Aircraft Tracking (SAFER-P2IAT) 1. The General Electric Company Niskayuna United States

  48. Anagnostou E, Engel S, Madsen J, Engel K, Nardiello J et al (2016) Developing the Airframe Digital Twin Prognostic & Probabilistic Individual Aircraft Tracking Paradigm. In 2016 Aircraft Airworthiness and Sustainment Conference, March 21–24, pp 9038. http://www.meetingdata.utcdayton.com/agenda/airworthiness/2016/agenda.htm

  49. Liao M, Guillaume R, Yan B (2020) Airframe digital twin technology adaptability assessment and technology demonstration Eng Fract Mech 225. https://doi.org/10.1016/j.engfracmech.2019.106793

  50. Qiu Z, WANG X, (2012) Comprehensive design ideas of modern aircraft structure. Fundamentals of aircraft structural strength analysis and design, Beijing. University of Aeronautics and Astronautics Press, China, pp 5–7

    Google Scholar 

  51. Du Y (2014) Research on probabilistic damage tolerance analysis system and key technologies. Ph.D. dissertation, Dept. Solid mechanics, Northwestern Polytechnical University, Xi’an, Shanxi, China 1–2

  52. Lee H, Cho H, PARK S, (2012) Review of the F-16 individual aircraft tracking program. J Aircraft 49(5):1398–1405

    Google Scholar 

  53. Tao F, Liu W, Zhang M et al (2019) Five dimension digital twin model and its ten applications. Comput Integr Manuf Syst 25(1):1–18

    Google Scholar 

  54. Besch H (2015) Large aircraft landing gears—a brief overview. In Proceedings of the 6th International Munich Chassis Symposium 2015, Berlin, Germany: Springer-Verlag 685–15

  55. Kuruppu K, Hettiarachchi C (2018) Case study on aircraft tyre wear in Y12 aircraft tyres. Aeronaut J 122:1–22

    Google Scholar 

  56. Rudd J (2013). Airframe digital twin. In Proceedings of the 27th Symposium of the International Committee on Aeronautical Fatigue and Structural Integrity, Jerusalem, Israel 5:15

  57. Graham K, Artim M, Daverschot D (2017) Aircraft fatigue analysis in the digital age. In Proceedings of the 29th Symposium of the International Committee on Aeronautical Fatigue and Structural Integrity (ICAF2017), pp. 61

  58. Dong L, Zhou X, Zhao F et al (2021) Key technologies for modeling and simulation of airframe digital twin. Acta Aeronautica et Astronautica Sinica 42(3):113–141

    Google Scholar 

  59. Bachelor G, Brusa E, Ferretto D, Mitschke A (2019) Model-based design of complex aeronautical systems through digital twin and thread concepts. IEEE Syst J 14(2):1568–1579

    Google Scholar 

  60. Tadeja S, Seshadri P, Kristensson P (2020) AeroVR: An immersive visualisation system for aerospace design and digital twinning in virtual reality. Aeronaut J 124(1280):615–1635

    Google Scholar 

  61. Unnikrishnakurup S, Dash J, Ray S, Pesala B, Balasubramaniam K (2020) Nondestructive evaluation of thermal barrier coating thickness degradation using pulsed IR thermography and THz-TDS measurements: a comparative study. NDT and E Int 116:102367. https://doi.org/10.1016/j.ndteint.2020.102367

    Article  Google Scholar 

  62. Seon G, Nikishkov Y, Makeev A, Ferguson L (2020) Towards a digital twin for mitigating void formation during debulking of autoclave composite parts Eng Fract Mech 225. https://doi.org/10.1016/j.engfracmech.2019.106792

  63. Cai H, Zhang W, Zhu Z (2019) Quality management and analysis of aircraft final assembly based on digital twin. In IEEE 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) 1:202–205

  64. Zhuang C, Gong J, Liu J (2021) Digital twin-based assembly data management and process traceability for complex products. J Manuf Syst 58:118–131

    Google Scholar 

  65. Demartini M, Galluccio F, Mattis P, Abusohyon I, Tonelli LR, F, (2019) Closed-loop manufacturing for aerospace industry: An integrated PLM-MOM solution to support the wing box assembly process. IFIP International Conference on Advances in Production Management Systems. Springer, Cham, pp 423–430

    Google Scholar 

  66. Zhuang C, Liu J, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Tech 96:1149–1163

    Google Scholar 

  67. Guo F, Zou F, Liu J, Wang Z (2018) Working mode in aircraft manufacturing based on digital coordination model. Int J Adv Tech 98(5):1547–1571

    Google Scholar 

  68. Cai H, Zhu J, Zhang W (2021) Quality deviation control for Aircraft Using Digital. Twin J Comput Inf Sci Eng 21(3). https://doi.org/10.1115/1.4050376

  69. Jiang Y, Huang X, Li S, Deng Z (2019) A coordination modelling approach for assembly of multi-constrained objects based on measured skin model. Assembly Autom. 39(2). https://www.emerald.com/insight/content/doi/https://doi.org/10.1108/AA-04-2018-058/full/html

  70. Liang B, Liu W, Liu K, Zhou M, Zhang Y, Jia Z (2020) A displacement field perception method for component digital twin in aircraft assembly. Sensors 20(18). https://doi.org/10.3390/s20185161

  71. Chang X, Jia X, Liu K, Hu H (2020) Knowledge-enabled digital twin for smart designing of aircraft assembly line. Assembly Autom. https://www.emerald.com/insight/content/doi/https://doi.org/10.1108/AA-09-2020-0133/full/html.

  72. Ho GTS, Tang Y et al (2021) A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management Expert Syst Appl 179. https://doi.org/10.1016/j.eswa.2021.115101

  73. Li S, Liang Y, Bai S, Zhuang C, Cao Y (2021) Research on intelligent assembly modes of aerospace products based on digital twin. J Phys: Conf Ser 1756:26–29

    Google Scholar 

  74. Sun X, Bao J, Li J, Zhang Y, Liu S, Zhou B (2020) A digital twin-driven approach for the assembly-commissioning of high precision products. Robot Cim-Int Manuf 61. https://doi.org/10.1016/j.rcim.2019.101839

  75. Schleich B, Anwer N, Mathieu L, Wartzack S (2017) Shaping the digital twin for design and production engineering. CIRP Ann 66(1):141–144

    Google Scholar 

  76. GE (2021) Digital Twin Software, GE Digital. https://www.ge.com/digital/applications/digital-twin. Accessed 15 Feb 2022

  77. GE (2021) Compressing Time-to-V alue for Digital Industrial Companies. https://www.ge.com/digital/sites/default/files/download_assets/The-Digital-Twin_Compressing-Time-to-Value-for-Digital-Industrial-Companies.pdf. Accessed 15 Feb 2022

  78. Bolcavage A, Brown P, Cedoz R, Cooper N, Deaton C, Hartman D, Keskin A, Ma K, Matlik J, Modgil G, Stillinger J (2014) Integrated computational materials engineering from a gas turbine engine perspective. Integrating Mater Manuf Innov 3(1):181–204

    Google Scholar 

  79. Hänel A, Schnellhardt T, Wenkler E, Nestler A, Brosius A, Corinth C, Fay A, Ihlenfeldt S (2020) The development of a digital twin for machining processes for the application in aerospace industry. Procedia CIRP 93:1399–1404

    Google Scholar 

  80. Zhan X, Zhu W (2019) Application framework of digital twin-driven product smart manufacturing system: a case study of aeroengine blade manufacturing. Int J Adv Robot Syst 16(5). https://doi.org/10.1177/1729881419880663

  81. Qu S, Zhao J, Wang T (2017) Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II. Int J Adv Manuf Technol 89:2399–2409

    Google Scholar 

  82. Xu G, Chen J, Zhou H et al (2019) Multi-objective feedrate optimization method of end milling using the internal data of the CNC system. Int J Adv Manuf Technol 101:715–731

    Google Scholar 

  83. Stavropoulos P, Papacharalampopoulos A, Michail C, Chryssolouris G (2021) Robust additive manufacturing performance through a control oriented digital twin. Metals 11(5):708. https://doi.org/10.3390/met11050708

    Article  Google Scholar 

  84. Wang Z, Wagner T, Guo C (2020) Smart machining process monitoring enabled by contextualized process Profiles for Synchronization. Smart Sustain Manuf Syst 4(2):199–209

    Google Scholar 

  85. Xu Z, Ji F, Ding S, Zhao Y, Zhou Y, Zhang Q, Du F (2021) Digital twin-driven optimization of gas exchange system of 2-stroke heavy fuel aircraft engine. J Manuf Syst 58:132–145

    Google Scholar 

  86. Cao X, Zhao G, Xiao W (2020) Digital Twin–oriented real-time cutting simulation for intelligent computer numerical control machining. In PI Mech Eng B-J Eng. https://doi.org/10.1177/0954405420937869

  87. Liu S, Bao J, Lu Y, Li J, Lu S, Sun X (2021) Digital twin modeling method based on biomimicry for machining aerospace components. J Manuf Syst 58:180–195

    Google Scholar 

  88. Zhang Z, Guan Z, Gong Y, Luo D, Yue L (2020) Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor. Int J Prod Res 1–20

  89. Mandolla C, Petruzzelli A, Percoco G, Urbinati A (2019) Building a digital twin for additive manufacturing through the exploitation of blockchain: a case analysis of the aircraft industry. Compu Ind 109:134–152

    Google Scholar 

  90. Zörrer H, Steringer R, Zambal S, Eitzinger C (2019) Using business analytics for decision support in zero defect manufacturing of composite parts in the aerospace industry. IFAC-PapersOnLine 52(13):1461–1466

    Google Scholar 

  91. Zhang H, Yan Q, Zheng H (2020) Information modeling for cyber-physical production system based on digital twin and AutomationML. Int J Adv Manuf Technol 10:1927–1945

    Google Scholar 

  92. Matyas K, Nemeth T, Kovacs K, Glawar R (2017) A procedural approach for realizing prescriptive maintenance planning in manufacturing industries. CIRP Ann Manuf Technol 66:461–464

    Google Scholar 

  93. Aivaliotis P, Georgoulias K, Alexopoulos K (2019) Using digital twin for maintenance applications in manufacturing: State of the Art and Gap analysis. In 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1–5). IEEE

  94. Errandonea I, Beltrán S, Arrizabalaga S (2020) Digital twin for maintenance: a literature review Compu Ind 123. https://doi.org/10.1016/j.compind.2020.103316

  95. Ezhilarasu C, Skaf Z, Jennions I (2019) Understanding the role of a digital twin in integrated vehicle health management (IVHM). In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE 1484–91

  96. Zaccaria V, Stenfelt M, Aslanidou I, Kyprianidis K (2018) Fleet monitoring and diagnostics framework based on digital twin of aero-engines. In Turbo Expo: Power for Land, Sea, and Air, American Society of Mechanical Engineers 51128 https://doi.org/10.1115/GT2018-76414

  97. Kraft J, Kuntzagk S (2017) Engine fleet-management: the use of digital twins from a MRO perspective. In Turbo Expo: Power for Land, Sea, and Air American Society of Mechanical Engineers 50770. https://doi.org/10.1115/GT2017-63336

  98. Wang J, Ye L, Gao R, Li C, Zhang L (2018) Digital twin for rotating machinery fault diagnosis in smart manufacturing. Int J Prod Res 1–15

  99. Liu Z, Chen W, Zhang C, Yang C, Chu H (2019) Data super-network fault prediction model and maintenance strategy for mechanical product based on digital twin. IEEE Access 7:177284–177296

    Google Scholar 

  100. Ezhilarasu C, Jennions I (2020) A system-level failure propagation detectability using anfis for an aircraft electrical power system. Appl Sci 10(8). https://doi.org/10.3390/app10082854

  101. Ellis M, Bojdo N, Filippone A, Clarkson R (2021) Monte Carlo predictions of aero-engine performance degradation due to particle ingestion. Aerospace 8(6). https://doi.org/10.3390/aerospace8060146

  102. Ezhilarasu C, Skaf Z, Jennions I (2021) A generalised methodology for the diagnosis of aircraft systems. IEEE Access 9:11437–11454

    Google Scholar 

  103. Li C, Mahadevan S, Ling Y, Choze S, Wang L (2017) Dynamic Bayesian network for aircraft wing health monitoring digital twin. AIAA J 55(3):930–941

    Google Scholar 

  104. Bielefeldt B, Hochhalter J, Hartl D (2015) Computationally efficient analysis of SMA sensory particles embedded in complex aerostructures using a substructure approach. In Smart Materials, Adaptive Structures and Intelligent Systems, American Society of Mechanical Engineers, Colorado Springs. 57298:V001T02A007

  105. Zakrajsek A, Mall S (2017) The development and use of a digital twin model for tire touchdown health monitoring. In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, pp 08637. http://arc.aiaa.org/, https://doi.org/10.2514/6.2017-0863

  106. Liu Z, Meyendorf N, Mrad N (2018) The role of data fusion in predictive maintenance using digital twin. In AIP Conference Proceedings, AIP Publishing LLC 37(1):1–6

    Google Scholar 

  107. Damme C, Allen M, Hollkamp J (2020) Updating geometrically nonlinear reduced-order models using nonlinear modes and harmonic balance. AIAA J 58(8):3553–3568

    Google Scholar 

  108. Guivarch D, Mermoz E, Marino Y, Sartor M (2019) Creation of helicopter dynamic systems digital twin using multibody simulations. CIRP Ann 68(1):133–136

    Google Scholar 

  109. Garza P, Perinpanayagam S, Aslam S, Wileman A (2020) Qualitative validation approach using digital model for the health management of electromechanical actuators. Appl Sci 10:10217809. https://doi.org/10.3390/app10217809

    Article  Google Scholar 

  110. Chowdhury S, Ali F, Jennions I (2019) A methodology for the experimental validation of an aircraft ECS digital twin targeting system level diagnostics. In Annual Conference of the PHM Society 11(1):21–26

    Google Scholar 

  111. Utzig S, Kaps R, Azeem S, Gerndt A (2019) Augmented reality for remote collaboration in aircraft maintenance tasks. In 2019 IEEE Aerospace Conference, IEEE 1–10

  112. Rashidi M, Samali B (2021) Health monitoring of bridges using RPAs. In: Wang C.M., Dao V., Kitipornchai S. (eds) EASEC16. Lecture Notes in Civil Engineering 101:209–218. Springer, Singapore. https://doi.org/10.1007/978-981-15-8079-6_20

  113. Delgado J, Short N, Roberts D, Vandenberg B (2019) Big data analysis for sustainable agriculture on a geospatial cloud framework. Front. Sustain. Food Syst, 3(54). https://doi.org/10.3389/fsufs.2019.00054

  114. Tsolakis N, Bechtsis D, Bochtis D (2019) Agros: a robot operating system based emulation tool for agricultural robotics. Agronomy 9(7). https://doi.org/10.3390/agronomy9070403. ISSN 20734395

  115. Deng T, Zhang K, Shen Z (2021) A systematic review of a digital twin city: a new pattern of urban governance toward smart cities. J Manag Sci Eng 6(2):125–34

  116. Fan C, Zhang C, Yahja A, Mostafavi A (2021) Disaster City Digital Twin: a vision for integrating artificial and human intelligence for disaster management. Int J Inf Manage 56:102049. https://doi.org/10.1016/j.ijinfomgt.2019.102049

    Article  Google Scholar 

  117. Hollenbeck D, Chen Y (2020) Characterization of ground-to-air emissions with sUAS using a digital twin framework. In 2020 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE 1162–1166

  118. Lei L, Shen G, Zhang L, Li Z (2020) Toward intelligent cooperation of UAV swarms: when machine learning meets digital twin. IEEE Network 35(1):386–392

    Google Scholar 

  119. Kapteyn M, Knezevic D, Huynh D, Tran M, Willcox K (2020) Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models. Int J Numer Meth Eng. https://doi.org/10.1002/nme.6423

  120. Zohdi T (2020) A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety. Comput Method Appl M 373:113446. https://doi.org/10.1016/j.cma.2020.113446

    Article  MathSciNet  MATH  Google Scholar 

  121. Gusev M, Nikolaev S, Papulov A, Belov S, Padalitsa D, Uzhinsky I (2019) Optimization-driven product development to support digital twin creation on the small UAV case study. In Proceedings of the 2019 Summer Simulation Conference 1–12

  122. Alvarez O, Zeaa L, Bilb C, Napolitano M, Fravolini M (2019) Digital twin concept for aircraft sensor failure. In Transdisciplinary Engineering for Complex Socio-technical Systems: Proceedings of the 26th ISTE International Conference on Transdisciplinary Engineering, IOS Press10:370–80

  123. Yu Z, Zhang Y (2020) An evolution-simulation-verification framework of aviation products in the future. In 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), IEEE 1004–7

  124. Renganathan S, Harada K, Mavris D (2020) Aerodynamic Data Fusion Toward the Digital Twin Paradigm. AIAA J 58(9):3902–3918

    Google Scholar 

  125. Borgen K, Ropp T, Weldon W (2021) Assessment of augmented reality technology’s impact on speed of learning and task performance in aeronautical engineering technology education. Int J Aerospace Psychol 1–11

  126. Jonkers R, Shahroudi K (2020) A Design Change, Knowledge, and Project Management Flight Simulator for Product and Project Success. IEEE Syst J 15(1):1130–1139

    Google Scholar 

  127. Siyaev A, Jo G (2021) Towards aircraft maintenance metaverse using speech interactions with virtual objects in mixed reality. Sensors 21(6):2066. https://doi.org/10.3390/s21062066

    Article  Google Scholar 

  128. Ahmed S, Pawar S, San O, Rasheed A, Tabib M (2021) A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction. Comput Fluids 221:104895. https://doi.org/10.1016/j.compfluid.2021.104895

    Article  MathSciNet  MATH  Google Scholar 

  129. Piroumian V (2021) Digital twins: universal interoperability for the digital age. Computer 54(1):61–69

    Google Scholar 

  130. Walter D (2000) Competency-based on-the-job training for aviation maintenance and inspection–a human factors approach. Int J Ind Ergonom 26(2):249–259

    Google Scholar 

  131. Castanedo F (2013) A review of data fusion techniques. Sci World J 1–19

  132. Tao F, Qi Q (2019) Make more digital twins. Nature 573:490–491

    Google Scholar 

  133. Singh S, Shehab E, Higgins N, Fowler K, Erkoyuncu J, Gadd P (2021) Towards Information Management Framework for Digital Twin in Aircraft Manufacturing. Procedia CIRP 96:163–168

    Google Scholar 

  134. Alexopoulos K, Nikolakis N, Chryssolouris G (2020) Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. Int J Comput Integr Manuf 33:429–439

    Google Scholar 

  135. Meng T, Jing X, Yan Z, Pedrycz W (2020) A survey on machine learning for data fusion. Inform Fusion 57:115–129

    Google Scholar 

  136. Ma X, Tao F, Zhang M, Wang T, Zuo Y (2019) Digital twin enhanced human-machine interaction in product lifecycle. Procedia CIRP 83:789–793

    Google Scholar 

  137. Oyekan J, Hutabarat W, Tiwari A, Grech R, Aung M, Mariani M et al (2019) The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans. Robot CIM-Int Manuf 55:41–54

    Google Scholar 

  138. Brunner P, Denk F, Huber W, Kates R (2019) Virtual safety performance assessment for automated driving in complex urban traffic scenarios. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) IEEE 679–85

Download references

Acknowledgements

Thanks for the editors, referees, and all the workmates who dedicated their precious time to this research and provided insightful suggestions. All their work contributes greatly to this article.

Funding

This work was supported by National Natural Science Foundation of China (Grant No. U1833110).

Author information

Authors and Affiliations

  1. School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People’s Republic of China

    Minglan Xiong & Huawei Wang

Authors
  1. Minglan Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huawei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Minglan Xiong: background research, methodology, validation writing—original draft, editing. Huawei Wang: review and editing, supervision.

Corresponding author

Correspondence to Huawei Wang.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiong, M., Wang, H. Digital twin applications in aviation industry: A review. Int J Adv Manuf Technol 121, 5677–5692 (2022). https://doi.org/10.1007/s00170-022-09717-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-09717-9

Keywords

Search

Navigation