飞机结构数字孪生关键建模仿真技术

doi:10.7527/S1000-6893.2020.23981

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飞机结构数字孪生关键建模仿真技术

董雷霆^1,2, 周轩¹, 赵福斌¹, 贺双新¹, 卢志远¹, 冯建民³

1. 北京航空航天大学航空科学与工程学院, 北京 100083;
2. 先进无人飞行器北京高校高精尖学科中心, 北京 100083;
3. 航空工业飞机强度研究所全尺寸飞机结构静力/疲劳航空科技重点试验室, 西安 710065

收稿日期:2020-03-16 修回日期:2020-04-17 发布日期:2020-06-12
通讯作者: 董雷霆 E-mail:ltdong@buaa.edu.cn
基金资助:
先进无人飞行器北京高校高精尖学科基地种子基金（ADBUAS-2019-SP-05）

Key technologies for modeling and simulation of airframe digital twin

DONG Leiting^1,2, ZHOU Xuan¹, ZHAO Fubin¹, HE Shuangxin¹, LU Zhiyuan¹, FENG Jianmin³

1. School of Aeronautic Science and Engineering, Beihang University, Beijing 100083, China;
2. Beijing Advanced Discipline Center for Unmanned Aircraft System, Beijing 100083, China;
3. Key Laboratory of static and fatigue test of full scale aircraft, AVIC Aircraft Strength Research Institute, Xi'an 710065, China

Received:2020-03-16 Revised:2020-04-17 Published:2020-06-12
Supported by:
Seed Foundation of Beijing Advanced Discipline Center for Unmanned Aircraft System (ADBUAS-2019-SP-05)

摘要/Abstract

摘要： 飞机结构安全性设计思想经历了从静强度设计、安全寿命设计、损伤容限与耐久性设计到单机追踪的演变，未来有进一步向结构数字孪生的方向发展的趋势。飞机结构数字孪生是数字线程驱动的，多学科、多物理、多尺度、多保真度、多概率的模拟仿真系统，采用在线传感器监测、离线地面检查、飞机运行历史等多源数据，反映并预测对应飞机结构实体在全寿命周期内的行为和性能，有望革新现有的飞机结构使用和维护模式。面向疲劳寿命管理，提出飞机结构数字孪生的5项关键建模仿真技术，分别是载荷和损伤的数据获取技术、多尺度建模和力学分析技术、含裂纹复杂结构的精确高效仿真技术、基于降阶的数字孪生高效建模技术和考虑不确定性与多源异构数据的剩余寿命评估技术，并详细探讨这五项关键技术的研究现状与发展方向，为飞机结构数字孪生的系统研究与工程应用提供参考。

关键词: 数字孪生, 结构安全性, 疲劳断裂, 降阶模型, 不确定性

Abstract: The design philosophy for airframe safety has experienced evolution from static strength, safety life, damage tolerance and durability, to individual aircraft tracking, with a trend towards the airframe digital twin in the future. The airframe twin driven by the digital thread is a multidisciplinary, multi-physical, multi-scale, multi-fidelity, and multi-uncertainty virtual simulation system. It uses multi-source data such as online sensor monitoring, offline ground inspection, and aircraft operation history to reflect and predict the behavior and performance of the corresponding airframe physical entity during the entire lifespan. It is expected to reform the existing paradigm of airframe usage and maintenance. Focusing on fatigue life management, this study proposes five key modeling and simulation technologies, which are (a) acquisition of load and damage data, (b) multi-scale mechanical modeling of airframes, (c) high-performance simulation of damage development, (d) efficient digital twin construction based on Reduced Order Modeling (ROM), (e) Remaining Useful Life (RUL) assessment considering multi-source uncertainty and heterogeneous data. This article discusses the state of the art of the proposed five key technologies as well as future research directions, providing reference for the systematic study and engineering applications of the airframe digital twin.

Key words: digital twin, structural safety, fatigue and fracture, Reduced Order Model (ROM), uncertainty

中图分类号:

董雷霆, 周轩, 赵福斌, 贺双新, 卢志远, 冯建民. 飞机结构数字孪生关键建模仿真技术[J]. 航空学报, 2021, 42(3): 23981-023981.

DONG Leiting, ZHOU Xuan, ZHAO Fubin, HE Shuangxin, LU Zhiyuan, FENG Jianmin. Key technologies for modeling and simulation of airframe digital twin[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(3): 23981-023981.

参考文献

[1] 邱志平, 王晓军. 飞机结构强度分析和设计基础[M]. 北京:北京航空航天大学出版社, 2012:5-7. QIU Z P, WANG X J. Fundamentals of aircraft structural strength analysis and design[M]. Beijing:Beihang University Press, 2012:5-7(in Chinese).
[2] 杜永恩. 概率损伤容限分析体系及其关键技术的研究[D]. 西安:西北工业大学, 2014:1-2. DU Y E. Research on probabilistic damage tolerance analysis system and key technologies[D]. Xi'an:Northwestern Polytechnical University, 2014:1-2(in Chinese).
[3] LEE H, CHO H, PARK S. Review of the F-16 individual aircraft tracking program[J]. Journal of Aircraft, 2012, 49(5):1398-1405.
[4] LEE H, PARK S, KIM H. Estimation of aircraft structural fatigue life using the crack severity index methodology[J]. Journal of Aircraft, 2010, 47(5):1672-1678.
[5] 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, 2011:154798.
[6] LI C, MAHADEVAN S, LING Y, et al. Dynamic Bayesian network for aircraft wing health monitoring digital twin[J]. AIAA Journal, 2017, 55(3):930-941.
[7] GRIEVES M. Digital twin:Manufacturing excellence through virtual factory replication[M]. 2014:1-7.
[8] GRIEVES M W. Product lifecycle management:the new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2(1-2):71-84.
[9] GITHENS G. Product lifecycle management:driving the next generation of lean thinking by Michael Grieves[J]. Journal of Product Innovation Management, 2007, 24(3):278-280.
[10] 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1):1-18. TAO F, LIU W R, LIU J H, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1):1-18(in Chinese).
[11] GLAESSGEN E, STARGEL D. The digital twin paradigm for future NASA and US Air Force vehicles[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 2012:1818.
[12] WANG Z, YANG S, CHEN P-C. Nonlinear gust reduced order modeling based on FUN3D and Volterra theory[C]//2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2018:1211.
[13] RENAUD G, LIAO M, BOMBARDIER Y. Demonstration of an airframe digital twin framework using a CF-188 full-scale component test[C]//International Committee on Aeronautical Fatigue, 2019:176-186.
[14] 刘亚威. 管窥美军数字工程战略——迎接数字时代的转型[J]. 科技中国, 2018(3):30-33. LIU Y W. A look at the US army's digital engineering strategy——embrace the transformation of the digital age[J]. China Scitechnology Business, 2018(3):30-33(in Chinese).
[15] 周瑜, 刘春成. 雄安新区建设数字孪生城市的逻辑与创新[J]. 城市发展研究, 2018, 25(10):60-67. ZHOU Y, LIY C C. The logic and innovation of building digital twin city in Xiong'an new area[J]. Urban Development Studies, 2018, 25(10):60-67(in Chinese).
[16] 许静, 周磊, 陈平录, 等. 基于数字孪生的模块粒度优化分析方法[J]. 计算机集成制造系统, 2019, 25(6):1419-1431. XU J, ZHOU L, CHEN P L, et al. Module granularity optimization analysis model based on digital twin[J]. Computer Integrated Manufacturing Systems, 2019, 25(6):1419-1431(in Chinese).
[17] 尹福炎. 电阻应变计技术六十年(4)结构应变测量用各种电阻应变计[J]. 传感器世界, 1999(1):15-25. YIN F Y. Sixty years of electric resistance strain gages technique(4)[J]. Sensor World, 1999(1):15-25(in Chinese).
[18] 宋扬. 基于无线传感器网络的飞机结构健康监测研究[C]//2019航空装备服务保障与维修技术论坛暨中国航空工业技术装备工程协会年会, 2019:280-282. SONG Y. Aircraft structural health monitoring based on wireless sensor networks[C]//2019 Aviation Equipment Service Support and Maintenance Technical Forum & Annual Conference of China Aviation Industries Technical Equipment Engineering Association, 2019:280-282(in Chinese).
[19] GRATTAN K, SUN T. Fiber optic sensor technology:an overview[J]. Sensors and Actuators A:Physical, 2000, 82(1-3):40-61.
[20] CLAUS R, GUNTHER M, WANG A, et al. Extrinsic Fabry-Perot sensor for strain and crack opening displacement measurements from-200 to 900 degrees C[J]. Smart Materials and Structures, 1992, 1(3):237-242.
[21] GüEMES A, FERNANDEZ-LOPEZ A, FERNANDEZ P. Damage detection in composite structures from fiber optic distributed strain measurements[C]//EWSHM-7th European Workshop on Structural Health Monitoring, 2014:528-535.
[22] 顾钧元, 徐廷学, 余仁波, 等. 基于FBG传感器的飞行器结构健康监测系统研究[J]. 质量与可靠性, 2011(4):25-28. GU J Y, XU T X, YU R B, et al. Research on aircraft structural health monitoring system based on FBG sensor[J]. Quality and Reliability, 2011(4):25-28(in Chi-nese).
[23] WADE J C, CLAUS R O. Interferometric techniques using embedded optical fibers for the quantitative NDE of composites[C]//Review of Progress in Quantitative Nondestructive Evaluation, 1983:1731-1738.
[24] AUSTIN R. The X-33 program:proving feasibility of the next generation reusable launch vehicles[C]//The Proceeding of Structural Health Monitoring, 1999:3-22.
[25] STASZEWSKI W, BOLLER C, TOMLINSON G R. Health monitoring of aerospace structures:smart sensor technologies and signal processing[M]. New York:John Wiley & Sons, 2004:66-70.
[26] KABASHIMA S, OZAKI T, TAKEDA N. Damage detection of satellite structures by optical fiber with small diameter[C]//Smart Structures and Materials 2000:Smart Structures and Integrated Systems, 2000:343-351.
[27] LEE J-R, RYU C-Y, KOO B-Y, et al. In-flight health monitoring of a subscale wing using a fiber Bragg grating sensor system[J]. Smart Materials and Structures, 2003, 12(1):147-155.
[28] READ I, FOOTE P. Sea and flight trials of optical fibre Bragg grating strain sensing systems[J]. Smart Materials and Structures, 2001, 10(5):1085-1094.
[29] 刘文珽, 王智, 隋福成. 单机寿命监控技术指南[M]. 北京:国防工业出版社, 2010. LIU W T, WANG Z, SUI F C. Individual aircraft life monitoring technical guidelines[M]. Beijing:National Defense Industry Press, 2010(in Chinese).
[30] 李映颖, 朱立贵, 张德全, 等. 基于BP和RBF神经网络对试飞数据预处理比较研究[J]. 计量与测试技术, 2009, 36(2):1-2. LI Y Y, ZHU L G, ZHANG D Q, et al. The pretreatment of the pre-processing data with BP and RBF neural network[J]. Metrology & Measurement Technique, 2009, 36(2):1-2(in Chinese).
[31] 顾宇轩, 隋福成, 宋恩鹏. 神经网络技术在单机应变寿命监控中的应用研究[J]. 装备环境工程, 2018, 15(12):74-77. GU Y X, SUI F C, SONG E P. Application of neural network technique in individual strain life monitoring[J]. Equipment Environmental Engineering, 2018, 15(12):74-77(in Chinese).
[32] HAAS D J, IMBER R. Identification of helicopter component loads using multiple regression[J]. Journal of Aircraft, 1994, 31(4):929-935.
[33] HUNT S, HEBDEN I. Eurofighter 2000:an integrated approach to structural health and usage monitoring//The NATO Research and Technology Organisation Meeting, 1998.
[34] O'HIGGINS E, GRAHAM K, DAVERSCHOT D, et al. Machine learning application on aircraft fatigue stress predictions[C]//International Committee on Aeronautical Fatigue, 2019:1031-1042.
[35] GOCKEL B, TUDOR A, BRANDYBERRY M, et al. Challenges with structural life forecasting using realistic mission profiles[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 2012:1813.
[36] MICHAEL K. AFOSR spring review 2014 test and evaluation (T&E)[R]. 2014.
[37] 张卫方, 何晶靖, 阳劲松, 等. 面向飞行器结构的健康监控技术研究现状[J]. 航空制造技术, 2017(19):38-47. ZHANG W F, HE J J, YANG J S, et al. Research status on structural health monitoring technology for aircraft structures[J]. Aeronautical Manufacturing Technology, 2017(19):38-47(in Chinese).
[38] WORLTON D. Experimental confirmation of Lamb waves at megacycle frequencies[J]. Journal of Applied Physics, 1961, 32(6):967-971.
[39] DEMER L, FENTNOR L. Lamb wave techniques in nondestructive testing(Lamb waves behavior applied to defect evaluation in nondestructive tests of solid elongated cylindrical objects)[J]. International Journal of Nondestructive Testing, 1969, 1:251-283.
[40] KIM Y, HA S, CHANG F-K. Time-domain spectral element method for built-in piezoelectric-actuator-induced lamb wave propagation analysis[J]. AIAA Journal, 2008, 46(3):591-600.
[41] MURAYAMA R, KOBAYASHI M. Pipe inspection system by guide wave using a long distance waveguide[C]//AIP Conference Proceedings. AIP Publishing, 2016:140002.
[42] TUA P, QUEK S, WANG Q. Detection of crack in thin cylindrical pipes using piezo-actuated lamb waves[C]//Smart Structures and Materials 2005:Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 2005:820-831.
[43] GIURGIUTIU V, XU B, CHAO Y, et al. Smart sensors for monitoring crack growth under fatigue loading conditions[J]. Smart Structures and Systems, 2006, 2(2):101-113.
[44] 施越红, 杨卓君, 杜金强, 等. 柔性矩形涡流阵列传感器裂纹检测性能研究[J]. 传感器与微系统, 2015, 34(11):42-44. SHI Y H, YANG Z J, DU J Q, et al. Research on crack inspecting capability of flexible rectangle eddy current array sensor[J]. Transducer and Microsystem Technologies, 2015, 34(11):42-44(in Chinese).
[45] 杜金强, 何宇廷, 李培源. 矩形柔性涡流阵列传感器裂纹检测研究[J]. 传感器与微系统, 2014, 33(5):12-14. DU J Q, HE Y T, LI P Y. Research on crack inspecting of rectangular flexible eddy current array sensor[J]. Transducer and Microsystem Technologies, 2014, 33(5):12-14(in Chinese).
[46] GOLDFINE N, SCHLICKER D, WASHABAUGH A. Surface-mounted eddy-current sensors for on-line monitoring of fatigue tests and for aircraft health monitoring[C]//Second Joint NASA/FAA/DoD Conference on Aging Aircraft, 1998:1-16.
[47] 曹俊. 裂纹扩展的实时健康监测技术研究[D]. 南京:南京航空航天大学, 2006:12-13. CAO J. Research on real-time structural health monitoring for crack growth[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2006:12-13(in Chinese).
[48] 常琦, 杨维希, 赵恒, 等. 基于多传感器的裂纹扩展监测研究[J]. 航空学报, 2020, 41(2):223336. CHANG Q, YANG W X, ZHAO H, et al. A multi-sensor based crack propagation monitoring research[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(2):223336(in Chinese).
[49] PARK H G, CANNON H, BAJWA A, et al. Hybrid diagnostic system:beacon-based exception analysis for multimissions-Livingstone integration[C]//Society for Machinery Failure Prevention Technology Conference Proceedings, 2004:1-10.
[50] HE J, YANG J, WANG Y, et al. Probabilistic model updating for sizing of hole-edge crack using fiber bragg grating sensors and the high-order extended finite element method[J]. Sensors, 2016, 16(11):1956.
[51] HUANG G, WEI C, CHEN S, et al. Reconstruction of structural damage based on reflection intensity spectra of fiber Bragg gratings[J]. Measurement Science and Technology, 2014, 25(12):125109.
[52] 邱雷, 袁慎芳, 苗苗. 基于FBG的机翼盒段结构健康监测系统功能验证研究[J]. 压电与声光, 2009, 31(3):350-353. QIU L, YUAN S F, MIAO M. An evaluation research on the wing box structural health monitoring system based on FBG sensor[J]. Piezoelectrics & Acoustooptics, 2009, 31(3):350-353(in Chinese).
[53] 张利绍. 基于lamb波的机翼蒙皮结构损伤检测技术研究[D]. 杭州:浙江理工大学, 2011:1-2. ZHANG L S. Study on damage detection in wing cover structure based on lamb wave[D]. Hangzhou:Zhejiang Sci-Tech University, 2011:1-2(in Chinese).
[54] 郦正能, 张玉珠, 方卫国. 飞行器结构学[M]. 北京:北京航空航天大学出版社, 2005:23-26. Li Z N, ZHANG Y Z, FANG W G. Aircraft structure[M]. Beijing:Beihang University Press, 2005:23-26(in Chinese).
[55] LEE U. Equivalent dynamic beam-rod models of aircraft wing structures[J]. The Aeronautical Journal, 1995, 99(990):450-457.
[56] YU W, HODGES D H, VOLOVOI V, et al. On Timoshenko-like modeling of initially curved and twisted composite beams[J]. International Journal of Solids and Structures, 2002, 39(19):5101-5121.
[57] PALACIOS R, CEA A. Nonlinear modal condensation of large finite element models:application of Hodges's intrinsic theory[J]. AIAA Journal, 2019, 57(10):4255-4268.
[58] HODGES D H. Nonlinear composite beam theory[M]. Reston:AIAA, 2006:5-17.
[59] PALACIOS R. Asymptotic models of integrally-strained slender structures for high-fidelity nonlinear aeroelastic analysis[D]. East Lansing:University of Michigan, 2005:31-42.
[60] PALACIOS R S, CESNIK C E. Cross-sectional analysis of nonhomogeneous anisotropic active slender structures[J]. AIAA Journal, 2005, 43(12):2624-2638.
[61] BOROWIEC Z. Application of surfaces of ultimate strength for thin-walled beams[J]. Thin-walled structures, 2005, 43(8):1312-1323.
[62] CAVAGNA L, RICCI S, RICCOBENE L. A fast tool for structural sizing, aeroelastic analysis and optimization in aircraft conceptual design[J]. Journal of Aircraft, 2011, 48(6):1840-1855.
[63] YUAN J, ALLEGRI G, SCARPA F, et al. Novel parametric reduced order model for aeroengine blade dynamics[J]. Mechanical Systems and Signal Processing, 2015, 62-63:235-253.
[64] GILES G L. Further generalization of an equivalent plate representation for aircraft structural analysis[J]. Journal of Aircraft, 1989, 26(1):67-74.
[65] GILES G L. Design-oriented analysis of aircraft fuselage structures using equivalent plate methodology[J]. Journal of Aircraft, 1999, 36(1):21-28.
[66] LIVNE E, LI W-L. Aeroservoelastic aspects of wing/control surface planform shape optimization[J]. AIAA Journal, 1995, 33(2):302-311.
[67] KAPANIA R K, LIU Y. Static and vibration analyses of general wing structures using equivalent-plate models[J]. AIAA Journal, 2000, 38(7):1269-1277.
[68] KRISHNAMURTHY T. Frequencies and flutter speed estimation for damaged aircraft wing using scaled equivalent plate analysis[C]//51 st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2010:2769.
[69] ZIENKIEWICZ O C, ZHU J Z. The superconvergent patch recovery and a posteriori error estimates. Part 1:The recovery technique[J]. International Journal for Numerical Methods in Engineering, 1992, 33(7):1331-1364.
[70] KITAMURA M, OHTSUBO H, AKIYAMA A, et al. Submodeling analysis of ship structure with superconvergent patch recovery method[J]. International Journal of Offshore and Polar Engineering, 2003, 13(03):85-93.
[71] MARENI? E, SKOZRIT I, TONKOVI? Z. On the calculation of stress intensity factors and J-integrals using the submodeling technique[J]. Journal of Pressure vessel Technology, 2010, 132(4):041203.
[72] GENDRE L, ALLIX O, GOSSELET P, et al. Non-intrusive and exact global/local techniques for structural problems with local plasticity[J]. Computational Mechanics, 2009, 44(2):233-245.
[73] RAJASEKARAN R, NOWELL D. On the finite element analysis of contacting bodies using submodelling[J]. The Journal of Strain Analysis for Engineering Design, 2005, 40(2):95-106.
[74] 李录贤, 王铁军. 扩展有限元法(XFEM)及其应用[J]. 力学进展, 2005, 35(1):5-20. LI L X, WANG T J. The extended finite element method and it's applications-a review[J]. Advances in Mechanics, 2005, 35(1):5-20(in Chinese).
[75] KUMAR K K, SRINIVAS P, RAO D S. Modeling and stress analysis of aerospace bracket using ANSYS And FRANC3D[J]. International Journal of Engineering Research and Technology, 2012, 1(8):1-11.
[76] 张卫国, 宓为建, 刘海洋. 基于Zencrack的岸边集装箱起重机圆管构件疲劳裂纹扩展分析[J]. 计算机辅助工程, 2008, 17(1):16-20. ZHANG W G, MI W J, LIU H Y. Analysis on fatigue crack propagation of pipe components of container cranes based on Zencrack[J]. Computer Aided Engineering, 2008, 17(1):16-20(in Chinese).
[77] CERRONE A, HOCHHALTER J, HEBER G, et al. On the effects of modeling as-manufactured geometry:toward digital twin[J]. International Journal of Aerospace Engineering, 2014, 2014:439278.
[78] BELYTSCHKO T, BLACK T. Elastic crack growth in finite elements with minimal remeshing[J]. International Journal for Numerical Methods in Engineering, 1999, 45(5):601-620.
[79] CHESSA J, WANG H, BELYTSCHKO T. On the construction of blending elements for local partition of unity enriched finite elements[J]. International Journal for Numerical Methods in Engineering, 2003, 57(7):1015-1038.
[80] MOЁS N, DOLBOW J, BELYTSCHKO T. A finite element method for crack growth without remeshing[J]. International Journal for Numerical Methods in Engineering, 1999, 46(1):131-150.
[81] DUAN Q, SONG J H, MENOUILLARD T, et al. Element-local level set method for three-dimensional dynamic crack growth[J]. International Journal for Numerical Methods in Engineering, 2010, 80(12):1520-1543.
[82] 庄茁, 成斌斌. 发展基于CB壳单元的扩展有限元模拟三维任意扩展裂纹[J]. 工程力学, 2012, 29(6):12-21. ZHUANG Z, CHENG B B. Development of X-FEM on CB shell element for simulating 3D arbitrary crack growth[J]. Engineering Mechanics, 2012, 29(6):12-21(in Chinese).
[83] 余天堂. 扩展有限单元法:理论,应用及程序[M]. 北京:科学出版社, 2014. YU T T. The extended finite element method theory, application and program[M]. Beijing:Science Press, 2014(in Chinese).
[84] 方修君, 金峰. 基于ABAQUS平台的扩展有限元法[J]. 工程力学, 2007, 24(7):6-10. FANG X J, JIN F. Extended finite element method based on ABAQUS[J]. Engineering Mechanics, 2007, 24(7):6-10(in Chinese).
[85] 郭历伦, 陈忠富, 罗景润, 等. 扩展有限元方法及应用综述[J]. 力学季刊, 2011, 32(4):612-625. GUO L L, CHEN Z F, LUO J R, et al. A review of the extended finite element method and its applications[J]. Chinese Quarterly of Mechanics, 2011, 32(4):612-625(in Chinese).
[86] 龙述尧. 无网格方法及其在固体力学中的应用[M]. 北京:科学出版社, 2014. LONG S Y. Meshless method and its application in in solid mechanics[M]. Beijing:Science Press, 2014(in Chinese).
[87] 程玉民. 无网格方法[M]. 北京:科学出版社, 2015. CHENG Y M. Meshless method[M]. Beijing:Science Press, 2015(in Chinese).
[88] BELYTSCHKO T, LU Y Y, GU L. Element-free galerkin methods[J]. International Journal for Numerical Methods in Engineering, 1994, 37(2):229-256.
[89] BELYTSCHKO T. Crack propagation by element-free galerkin methods[J]. Engineering Fracture Mechanics, 1995, 51(2):295-315.
[90] ATLURI S N, ZHU T L. The meshless local Petrov-Galerkin (MLPG) approach for solving problems in elasto-statics[J]. Computational Mechanics, 2000, 25(2-3):169-179.
[91] PROVATAS N, ELDER K. Phase-field methods in materials science and engineering[M]. 2010.
[92] 张豪, 于继东, 裴晓阳, 等. 相场断裂方法发展概况[J]. 高压物理学报, 2019, 33(3):128-139. ZHANG H, YU J D, PEI X Y, et al. An overview of phase field approach to fracture[J]. Chinese Journal of High Pressure Physics, 2019, 33(3):128-139(in Chinese).
[93] MIEHE C, WELSCHINGER F, HOFACKER M. Thermodynamically consistent phase-field models of fracture:Variational principles and multi-field FE implementations[J]. International Journal for Numerical Methods in Engineering, 2010, 83(10):1273-1311.
[94] BORDEN M J, VERHOOSEL C V, SCOTT M A, et al. A phase-field description of dynamic brittle fracture[J]. Computer Methods in Applied Mechanics and Engineering, 2012, 217:77-95.
[95] RAMULU M, KOBAYASHI A S. Mechanics of crack curving and branching-a dynamic fracture analysis[J]. International Journal of Fracture, 1985, 27(3-4):187-201.
[96] PARTRIDGE P W, BREBBIA C A. Dual reciprocity boundary element method[M]. 2012.
[97] SUTRADHAR A, PAULINO G, GRAY L J. Symmetric Galerkin boundary element method[M]. 2008.
[98] 姚振汉, 王海涛. 边界元法[M]. 北京:高等教育出版社, 2010. YAO Z H, WANG H T. Boundary element method[M]. Beijing:Higher Education Press, 2010(in Chinese).
[99] VIJAYAKUMAR K, ATLURI S N. An embedded elliptical crack, in an infinite solid, subject to arbitrary crack-face tractions[J]. Journal of Applied Mechanics, 1981, 48(1):88-96.
[100] NISHIOKA T, ATLURI S. Analytical solution for embedded elliptical cracks, and finite element alternating method for elliptical surface cracks, subjected to arbitrary loadings[J]. Engineering Fracture Mechanics, 1983, 17(3):247-268.
[101] NIKISHKOV G, PARK J, ATLURI S. SGBEM-FEM alternating method for analyzing 3D non-planar cracks and their growth in structural components[J]. Computer Modeling in Engineering and Sciences, 2001, 2(3):401-422.
[102] HAN Z, ATLURI S. On simple formulations of weakly-singular traction & displacement BIE, and their solutions through Petrov-Galerkin approaches[J]. Computer Modeling in Engineering and Sciences, 2003, 4(1):5-20.
[103] HAN Z, ATLURI S. SGBEM (for cracked local subdomain)-FEM (for uncracked global structure) alternating method for analyzing 3D surface cracks and their fatigue-growth[J]. Computer Modeling in Engineering and Sciences, 2002, 3(6):699-716.
[104] DONG L, ATLURI S N. SGBEM(using non-hyper-singular traction BIE), and super elements, for non-collinear fatigue-growth analyses of cracks in stiffened panels with composite-patch repairs[J]. Computer Modeling in Engineering & Sciences, 2012, 89(5):415-456.
[105] DONG L, ATLURI S N. Sgbem voronoi cells (svcs), with embedded arbitrary-shaped inclusions, voids, and/or cracks, for micromechanical modeling of heterogeneous materials[J]. CMC:Computers, Materials & Continua, 2013, 33(2):111-154.
[106] TIAN L, DONG L, PHAN N, et al. Three-dimensional SGBEM-FEM alternating method for analyzing fatigue-crack growth in and the life of attachment lugs[J]. Journal of Engineering Mechanics, 2014, 141(4):04014142.
[107] TIAN L, DONG L, BHAVANAM S, et al. Mixed-mode fracture & non-planar fatigue analyses of cracked I-beams, using a 3D SGBEM-FEM Alternating Method[J]. Theoretical and Applied Fracture Mechanics, 2014, 74:188-199.
[108] PEHERSTORFER B, WILLCOX K, GUNZBURGER M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization[J]. SIAM Review, 2018, 60(3):550-591.
[109] RATHINAM M, PETZOLD L R. A new look at proper orthogonal decomposition[J]. SIAM Journal on Numerical Analysis, 2003, 41(5):1893-1925.
[110] MOORE B. Principal component analysis in linear systems:Controllability, observability, and model reduction[J]. IEEE Transactions on Automatic Control, 1981, 26(1):17-32.
[111] GUGERCIN S, ANTOULAS A C. A survey of model reduction by balanced truncation and some new results[J]. International Journal of Control, 2004, 77(8):748-766.
[112] GALLIVAN K, GRIMME E, VAN DOOREN P. Padé approximation of large-scale dynamic systems with Lanczos methods[C]//Proceedings of 1994 33rd IEEE Conference on Decision and Control, 1994:443-448.
[113] LIU C-S. A double optimal iterative algorithm in an affine Krylov subspace for solving nonlinear algebraic equations[J]. Computers & Mathematics with Applications, 2015, 70(10):2376-2400.
[114] SCHMID P J, LI L, JUNIPER M, et al. Applications of the dynamic mode decomposition[J]. Theoretical and Computational Fluid Dynamics, 2011, 25(1-4):249-259.
[115] ROZZA G, HUYNH D B P, PATERA A T. Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations[J]. Archives of Computational Methods in Engineering, 2007, 15(3):1-47.
[116] LIU Y W, MOSES F. A sequential response surface method and its application in the reliability analysis of aircraft structural systems[J]. Structural Safety, 1994, 16(1-2):39-46.
[117] KAYMAZ I. Application of kriging method to structural reliability problems[J]. Structural Safety, 2005, 27(2):133-151.
[118] 韩忠华. Kriging模型及代理优化算法研究进展[J]. 航空学报, 2016, 37(11):3197-3225. HAN Z H. Kriging surrogate model and its application to design optimization:A review of recent progress[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(11):3197-3225(in Chinese).
[119] DRUCKER H, BURGES C J, KAUFMAN L, et al. Support vector regression machines[C]//Advances in neural Information Processing Systems, 1997:155-161.
[120] HAJELA P, BERKE L. Neural networks in structural analysis and design:an overview[J]. Computing Systems in Engineering, 1992, 3(1-4):525-538.
[121] RENGASAMY D, MORVAN H P, FIGUEREDO G P. Deep learning approaches to aircraft maintenance, repair and overhaul:a review[C]//201821 st International Conference on Intelligent Transportation Systems (ITSC), 2018:150-156.
[122] AVERSANO G, BELLEMANS A, LI Z, et al. Application of reduced-order models based on PCA & Kriging for the development of digital twins of reacting flow applications[J]. Computers & Chemical Engineering, 2019, 121:422-441.
[123] SWISCHUK R, MAININI L, PEHERSTORFER B, et al. Projection-based model reduction:Formulations for physics-based machine learning[J]. Computers & Fluids, 2019, 179:704-717.
[124] GUO M, HESTHAVEN J S. Reduced order modeling for nonlinear structural analysis using gaussian process regression[J]. Computer Methods in Applied Mechanics and Engineering, 2018, 341:807-826.
[125] 龙腾, 李学亮, 黄波, 等. 基于自适应代理模型的翼型气动隐身多目标优化[J]. 机械工程学报, 2016, 52(22):101-111. LONG T, LI X L, HUANG B, et al. Aerodynamic and stealthy performance optimization of airfoil based on adaptive surrogate model[J]. Journal of Mechanical Engineering, 2016, 52(22):101-111(in Chinese).
[126] LONG T, WU D, GUO X, et al. Efficient adaptive response surface method using intelligent space exploration strategy[J]. Structural and Multidisciplinary Optimization, 2015, 51(6):1335-1362.
[127] JONES D R, SCHONLAU M, WELCH W J. Efficient global optimization of expensive black-box functions[J]. Journal of Global optimization, 1998, 13(4):455-492.
[128] WANG L, SHAN S, WANG G G. Mode-pursuing sampling method for global optimization on expensive black-box functions[J]. Engineering Optimization, 2004, 36(4):419-438.
[129] PEHERSTORFER B, WILLCOX K. Dynamic data-driven reduced-order models[J]. Computer Methods in Applied Mechanics and Engineering, 2015, 291:21-41.
[130] DEGROOTE J, VIERENDEELS J, WILLCOX K. Interpolation among reduced-order matrices to obtain parameterized models for design, optimization and probabilistic analysis[J]. International Journal for Numerical Methods in Fluids, 2010, 63(2):207-230.
[131] PANZER H, MOHRING J, EID R, et al. Parametric model order reduction by matrix interpolation[J]. Automatisierungstechnik, 2010, 58(8):475-484.
[132] AMSALLEM D, ZAHR M J, FARHAT C. Nonlinear model order reduction based on local reduced-order bases[J]. International Journal for Numerical Methods in Engineering, 2012, 92(10):891-916.
[133] KAULMANN S, HAASDONK B. Online greedy reduced basis construction using dictionaries[C]//VI International Conference on Adaptive Modeling and Simulation (ADMOS 2013), 2013:365-376.
[134] MADAY Y, STAMM B. Locally adaptive greedy approximations for anisotropic parameter reduced basis spaces[J]. SIAM Journal on Scientific Computing, 2013, 35(6):A2417-A2441.
[135] PEHERSTORFER B, WILLCOX K. Dynamic data-driven model reduction:adapting reduced models from incomplete data[J]. Advanced Modeling and Simulation in Engineering Sciences, 2016, 3(1):1-22.
[136] CARLBERG K. Adaptive h-refinement for reduced-order models[J]. International Journal for Numerical Methods in Engineering, 2015, 102(5):1192-1210.
[137] O'HARA P J, HOLLKAMP J J. Modeling fatigue crack propagation in a ti-alloy at elevated temperature within a reduced-order model framework[C]//57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2016:1710.
[138] O'HARA P J, HOLLKAMP J J. Modeling crack propagation within a reduced-order model framework[C]//55th AIAA/ASME/ASCE/AHS/SC Structures, Structural Dynamics, and Materials Conference, 2014:0150.
[139] HOMBAL V, MAHADEVAN S. Surrogate modeling of 3D crack growth[J]. International Journal of Fatigue, 2013, 47:90-99.
[140] SPEAR A D, PRIEST A R, VEILLEUX M G, et al. Surrogate modeling of high-fidelity fracture simulations for real-time residual strength predictions[J]. AIAA Journal, 2011, 49(12):2770-2782.
[141] LESER P E, HOCHHALTER J D, WARNER J E, et al. Probabilistic fatigue damage prognosis using surrogate models trained via three-dimensional finite element analysis[J]. Structural Health Monitoring, 2017, 16(3):291-308.
[142] KEPRATE A, RATNAYAKE R, SANKARARAMAN S. Comparing different metamodelling approaches to predict stress intensity factor of a semi-elliptic crack[C]//ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, 2017:62333.
[143] KEPRATE A, RATNAYAKE R C, SANKARARAMAN S. Adaptive Gaussian process regression as an alternative to FEM for prediction of stress intensity factor to assess fatigue degradation in offshore pipeline[J]. International Journal of Pressure Vessels and Piping, 2017, 153:45-58.
[144] LIU Z, MEYENDORF N, MRAD N. The role of data fusion in predictive maintenance using digital twin[C]//AIP Conference Proceedings, 2018:020023.
[145] CASTILLO E, GUTIERREZ J M, HADI A S. Expert systems and probabilistic network models[M]. 2012:481-484.
[146] LERNER U, PARR R, KOLLER D, et al. Bayesian fault detection and diagnosis in dynamic systems[C]//Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, 2000:531-537.
[147] SANKARARAMAN S, LING Y, MAHADEVAN S. Uncertainty quantification and model validation of fatigue crack growth prediction[J]. Engineering Fracture Mechanics, 2011, 78(7):1487-1504.
[148] STRAUB D. Stochastic modeling of deterioration processes through dynamic Bayesian networks[J]. Journal of Engineering Mechanics, 2009, 135(10):1089-1099.
[149] LI S, LIU G, TANG X, et al. An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis[J]. Sensors, 2017, 17(8):1729.
[150] YANG J, HUANG H-Z, HE L-P, et al. Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster-Shafer evidence theory under uncertainty[J]. Engineering Failure Analysis, 2011, 18(8):2084-2092.
[151] TAY F E, SHEN L. Fault diagnosis based on rough set theory[J]. Engineering Applications of Artificial Intelligence, 2003, 16(1):39-43.
[152] WANG Q H, LI J R. A rough set-based fault ranking prototype system for fault diagnosis[J]. Engineering Applications of Artificial Intelligence, 2004, 17(8):909-917.
[153] MOHANTY J R, VERMA B B, RAY P K, et al. Prediction of mode-I overload-induced fatigue crack growth rates using neuro-fuzzy approach[J]. Expert Systems with Applications, 2010, 37(4):3075-3087.
[154] SARIDAKIS K, CHASALEVRIS A, DENTSORAS A, et al. Fusing neural networks, genetic algorithms and fuzzy logic for diagnosis of cracks in shafts[C]//Intelligent Production Machines and Systems, 2006:332-337.
[155] WANG H-K, HAYNES R, HUANG H-Z, et al. The use of high-performance fatigue mechanics and the extended Kalman/particle filters, for diagnostics and prognostics of aircraft structures[J]. Computer Modeling in Engineering & Sciences, 2015, 105(1):1-24.
[156] WANG Y, BINAUD N, GOGU C, et al. Determination of Paris' law constants and crack length evolution via Extended and Unscented Kalman filter:An application to aircraft fuselage panels[J]. Mechanical Systems and Signal Processing, 2016, 80:262-281.
[157] ROBINSON E I, MARZAT J, RA? SSI T. Model-based prognosis of fatigue crack growth under variable amplitude loading[J]. IFAC-PapersOnLine, 2018, 51(24):176-183.
[158] LIM H J, SOHN H, KIM Y. Data-driven fatigue crack quantification and prognosis using nonlinear ultrasonic modulation[J]. Mechanical Systems and Signal Processing, 2018, 109:185-195.
[159] TOBON-MEJIA D A, MEDJAHER K, ZERHOUNI N. CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks[J]. Mechanical Systems and Signal Processing, 2012, 28:167-182.
[160] PARK J M, KANG H T. Prediction of fatigue life for spot welds using back-propagation neural networks[J]. Materials & Design, 2007, 28(10):2577-2584.

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