数字孪生及其在航空航天中的应用

doi:10.7527/S1000-6893.2020.23615

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数字孪生及其在航空航天中的应用

孟松鹤¹, 叶雨玫¹, 杨强^1,2, 黄震³, 解维华¹

1. 哈尔滨工业大学特种环境复合材料技术国家级重点实验室, 哈尔滨 150080;
2. 哈尔滨工业大学材料科学与工程博士后流动站, 哈尔滨 150080;
3. 中国空间技术研究院载人航天总体部, 北京 100094

收稿日期:2019-10-28 修回日期:2019-12-12 出版日期:2020-09-15 发布日期:2020-03-13
通讯作者: 杨强 E-mail:young@hit.edu.cn
基金资助:
国家自然科学基金青年科学基金（11902101）；中国博士后科学基金（2018M641815）；国家自然科学基金面上项目（11672088）

Digital twin and its aerospace applications

MENG Songhe¹, YE Yumei¹, YANG Qiang^1,2, HUANG Zhen³, XIE Weihua¹

1. National Key Laboratory of Science and Technology for National Defense on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150080, China;
2. Postdoctoral Research Center for Material Science and Engineering, Harbin Institute of Technology, Harbin 150080, China;
3. Institute of Manned Space System Engineering, China Academy of Space Technology, Beijing 100094, China

Received:2019-10-28 Revised:2019-12-12 Online:2020-09-15 Published:2020-03-13
Supported by:
National Natural Science Foundation of China (11902101); China Postdoctoral Science Foundation Funded Project (2018M641815); National Natural Science Foundation of China (11672088)

摘要/Abstract

摘要： 数字孪生已引起国内外的广泛重视，可看作是连接物理世界和数字世界的纽带。其通过建立物理系统的数字模型、实时监测系统状态并驱动模型动态更新实现系统行为更准确的描述与预报，从而在线优化决策与反馈控制。本文分析表明数字孪生体相比一般的模拟模型，具有集中性、动态性和完整性的突出特点。数字孪生的发展需要复杂系统建模、传感与监测、大数据、动态数据驱动分析与决策和数字孪生软件平台技术的支撑。在航空航天领域，数字孪生可应用于飞行器的设计研发、制造装配和运行维护。重点讨论了应用机身数字孪生进行寿命预测与维护决策的案例，相比于周期性维护，具有检修次数更少、维护成本更低的优势。最后，给出了数字孪生在空间站、可重复使用飞船的地面伴飞系统中的初步应用框架。

关键词: 数字孪生, 数据驱动, 模型更新, 飞行器, 维护

Abstract: Digital twin, the link between the physical and digital worlds, has currently attracted extensive attention both at home and abroad. Through establishment of a digital model of a physical system, real-time monitoring of the system status, and driving of the dynamic model update to describe and forecast system behaviors more accurately, the online decision optimization and feedback control are realized. In this paper, analyses show that compared with a general simulation model, the digital twin has outstanding characteristics of being centralized, dynamic, and integral. The development of the digital twin requires the support of complex system modeling, sensing and monitoring, big data, dynamic data-driven analysis and decision, and software platform technology. In the field of aerospace, digital twin can be applied to the design, development, manufacturing, assembly, operation and maintenance of aircraft. This article focuses on a case of application of the airframe digital twin to life prediction and maintenance decision, which exhibits advantages of lower maintenance frequencies and maintenance costs compared with the periodic maintenance. Finally, a preliminary application framework of a ground accompanying flight system established for space stations and reusable spacecraft is demonstrated.

Key words: digital twin, data driven, model update, aircraft, maintenance

中图分类号:

孟松鹤, 叶雨玫, 杨强, 黄震, 解维华. 数字孪生及其在航空航天中的应用[J]. 航空学报, 2020, 41(9): 23615-023615.

MENG Songhe, YE Yumei, YANG Qiang, HUANG Zhen, XIE Weihua. Digital twin and its aerospace applications[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(9): 23615-023615.

参考文献 43

[1]	LEE J, GHAFFARI M, ELMELIGY S. Self-maintenance and engineering immune systems:Towards smarter machines and manufacturing systems[J]. Annual Reviews in Control, 2011, 35(1):111-122.
[2]	夏唐斌. 面向制造系统健康管理的动态预测与预知维护决策研究[D]. 上海:上海交通大学, 2014. XIA T B. Research on dynamic prognosis and predictive maintenance scheduling for health management of manufacturing systems[D]. Shanghai:Shanghai Jiao Tong University, 2014(in Chinese).
[3]	GRIEVES M, VICKERS J. Digital twin:Mitigating unpredictable, undesirable emergent behavior in complex systems[M]//Transdisciplinary perspectives on complex systems, 2017:85-113.
[4]	刘大同, 郭凯, 王本宽, 等. 数字孪生技术综述与展望[J]. 仪器仪表学报, 2018, 39(11):1-10. LIU D T, GUO K, WANG B K, et al. Summary and perspective survey on digital twin technology[J]. Chinese Journal of Scientific Instrument, 2018, 39(11):1-10(in Chinese).
[5]	庄存波, 刘检华, 熊辉, 等. 产品数字孪生体的内涵, 体系结构及其发展趋势[J]. 计算机集成制造系统, 2017, 23(4):753-768. ZHUANG C B, LIU J H, XIONG H, et al., Connotation, architecture and trends of product digital twin[J]. Computer Integrated Manufacturing Systems, 2017, 23(4):753-768(in Chinese).
[6]	ROSEN R, WICHERT G V, LO G, et al. About the importance of autonomy and digital twins for the future of manufacturing[J]. IFAC-Papers On Line, 2015, 48(3):567-572.
[7]	SHAFTO M, CONROY M, DOYLE R, et al. Modeling, simulation, information technology & processing roadmap[C]//National Aeronautics and Space Administration, 2010:5-7.
[8]	TUEGEL E J. The airframe digital twin:some challenges to realization[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston:AIAA, 2012:1-9.
[9]	HE Y, GUO J C, ZHENG X L. From surveillance to digital twin:Challenges and recent advances of signal processing for industrial internet of things[J]. IEEE Signal Processing Magazine, 2018, 35(5):120-129.
[10]	陶飞, 张萌, 程江峰, 等. 数字孪生车间-一种未来车间运行新模式[J]. 计算机集成制造系统, 2017, 23(1):1-9. TAO F, ZHANG M, CHENG J F, et al. Digital twin workshop:A new paradigm for future workshop[J]. Computer Integrated Manufacturing Systems, 2017, 23(1):1-9(in Chinese).
[11]	KUHN T S. 科学革命的结构[M]. 金吾伦,胡新和, 译. 北京:北京大学出版社, 2012:10-23. KUHN T S. The structure of scientific revolutions[M]. JIN W L, HU X H, translated. Beijing:Peking University Press, 2012:10-23(in Chinese).
[12]	CEARLEY D W, BURKE B, SEARLE S, et al. Top 10 strategic technology trends for 2018[EB/OL]. (2017-10-03)[2020-03-08]. http://brilliantdude.com/solves/content/GartnerTrends2018.pdf.
[13]	CHEN Y B. Integrated and intelligent manufacturing:Perspectives and enablers[J]. Engineering, 2017, 3(5):588-595.
[14]	MACDONALD C. Creating a digital twin for a pump[EB/OL].[2019-10-23]. https://www.ansys.com/zh-tw/about-ansys/advantage-magazine/volume-xi-issue-1-2017/creating-a-digital-twin-for-a-pump.
[15]	TAO F, CHENG J F, QI Q L, et al. Digital twin-driven product design, manufacturing and service with big data[J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(9-12):3563-3576.
[16]	于勇, 范胜廷, 彭关伟, 等. 数字孪生模型在产品构型管理中应用探讨[J]. 航空制造技术, 2017, 60(7):41-45. YU Y, FAN S T, PENG G W, et al. Study on application of digital twin model in product configuration management[J]. Aeronautical Manufacturing Technology, 2017, 60(7):41-45(in Chinese).
[17]	陶飞, 程颖, 程江峰, 等. 数字孪生车间信息物理融合理论与技术[J]. 计算机集成制造系统, 2017, 23(8):160-1611. TAO F, CHENG Y, CHENG J F, et al. Theories and technologies for cyber-physical fusion in digital twin shopfloor[J]. Computer Integrated Manufacturing Systems, 2017, 23(8):1603-1611(in Chinese).
[18]	ZHUANG C B, LIU J H, XIONG H. Digital twin-based smart production management and control framework for the complex product assembly shop-floor[J]. The International Journal of Advanced Manufacturing Technology, 2018, 96(1-4):1149-1163.
[19]	LENG J W, ZHANG H, YAN D X, et al. Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3):1155-1166.
[20]	ZHANG C Y, XU W J, LIU J Y, et al. A reconfigurable modeling approach for digital twin-based manufacturing system[J]. Procedia CIRP, 2019, 83:118-125.
[21]	ROLLS-ROYCE. Rolls-Royce launches intelligent engine[EB/OL]. (2018-02-05)[2019-10-23]. https://www.rolls-royce.com/media/press-releases/2018/05-02-2018-rr-launches-intelligentengine.aspx.
[22]	DOROTHY P. The French connection:digital twins from Paris will protect wind turbines against battering North Atlantic gales[EB/OL]. (2018-04-26)[2019-10-23]. https://www.ge.com/reports/french-connection-digital-twins-paris-will-protect-wind-turbines-battering-north-atlantic-gales/.
[23]	MAVRIS D N, BALCHANOS M, SUNG W J, et al. A data mining and visual analytics perspective on sustainability-oriented infrastructure planning[C]//International Conference on Data Mining and Big Data, 2016:330-341.
[24]	LU V Q, PARLIKAD A K, WOODALL P, et al. Developing a dynamic digital twin at a building level:Using Cambridge campus as case study[C]//International Conference on Smart Infrastructure and Construction 2019(ICSIC):Driving Data-informed Decision-making, 2019:67-75.
[25]	本刊编辑部. 美欧军工领域发力数字孪生技术应用[J]. 国防科技工业, 2019, 224(2):35-36. EDITOR. The application of digital twin technology in American and European military industry[J]. Defense Science and Technology Industry, 2019, 224(2):35-36(in Chinese).
[26]	刘亚威. 数字线索助力美国空军航空装备生命周期决策[J]. 国际航空, 2017(9):48-52. LIU Y W. Digital thread assists US Air Force aviation equipment life cycle decisions[J]. International Aviation, 2017(9):48-52(in Chinese).
[27]	TAO F, ZHANG H, LIU A, et al. Digital twin in industry:State-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2018, 15(4):2405-2415.
[28]	KRAFT E M. HPCMP CREATETM-AV and the Air Force digital thread[C]//53rd AIAA Aerospace Sciences Meeting. Reston:AIAA, 2015:1-13.
[29]	KRAFT E M. The US Air Force digital thread/digital twin-life cycle integration and use of computational and experimental knowledge[C]//54th AIAA Aerospace Sciences Meeting. Reston:AIAA, 2016:1-22.
[30]	孙侠生, 肖迎春. 飞机结构健康监测技术的机遇与挑战[J]. 航空学报, 2014, 35(12):3199-3212. SUN X S, XIAO Y C. Opportunities and challenges of aircraft structural health monitoring[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(12):3199-3212(in Chinese).
[31]	孔祥芬, 蔡峻青, 张利寒, 等. 大数据在航空系统的研究现状与发展趋势[J]. 航空学报, 2018, 39(12):022311. KONG X F, CAI J Q, ZHANG L H, et al. Research status and development trend of big data in aviation system[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(12):022311(in Chinese).
[32]	PRUDENCIO E E, BAUMAN P T, WILLIAMS S V, et al. A dynamic data driven application system for re-al-time monitoring of stochastic damage[J]. Procedia Computer Science, 2013, 18:2056-2065.
[33]	GRIEVES M. Product lifecycle management:The new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2(1-2):71-84.
[34]	GLAESSGEN E H, STARGEL D S. The digital twin paradigm for future NASA and U.S. Air Force vehicles[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston:AIAA, 2012:1-14.
[35]	RAMEZANI S, MOINI A, RIAHI M. Prognostics and health management in machinery:A review of methodologies for RUL prediction and roadmap[J]. International Journal of Industrial Engineering and Management Science, 2019, 6(1):38-61.
[36]	MORRISON J H, AMBUR M Y, BAUER S X. Comprehensive digital transformation NASA Langley Research Center[R]. Washington, D.C.:NASA, 2016.
[37]	TUEGEL E J, INGRAFFEA A R, EASON T G, et al. Reengineering aircraft structural life prediction using a digital twin[J]. International Journal of Aerospace Engineering, 2011, 2011(3):1-14.
[38]	WANG L P, ASHER I, RYAN K, et al. Airframe digital twin (ADT), delivery order 0001:Scalable, accurate, flexible, efficient, robust, prognostic and probabilistic individual aircraft tracking (SAFER-P2IAT), volume 1[R]. 2016.
[39]	WANG L P, ASHER I, RYAN K, et al. Airframe digital twin spiral 1, task order 0002:Scalable accurate flexible efficient robust-prognostic and probabilistic individual aircraft tracking (SAFER-P2IAT) full scale wing experiment plans, requirements, and development[R]. 2017.
[40]	LI C Z, MAHADEVAN S, LING Y, et al. Dynamic Bayesian network for aircraft wing health monitoring digital twin[J]. AIAA Journal, 2017, 55(3):930-941.
[41]	ANAGNOSTOU E, ENGEL S. Airframe digital twin (ADT), delivery order 0001:prognostic and probabilistic individual aircraft tracking (P2IAT), volume 1[R]. 2016.
[42]	ANAGNOSTOU E, ENGEL S. Airframe digital twin (ADT), delivery order 0002:demonstration of prognostic and probabilistic individual aircraft tracking (P2IAT), final report and appendices A (test requirements document) and B (instrumentation plan)[R]. 2017.
[43]	周建平. 我国空间站工程总体构想[J]. 载人航天, 2013, 19(2):5-14. ZHOU J P. Chinese space station project overall vision[J]. Manned Spaceflight, 2013, 19(2):5-14(in Chinese).

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数字孪生及其在航空航天中的应用

Digital twin and its aerospace applications

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