Digital Technology Empowered Wind Tunnel Whole Life Cycle System

doi:10.3901/JME.260366

Abstract

Abstract: Wind tunnel is the vital facility for aerodynamic evaluation and research, which plays a pivotal role in advancing the development of aerospace vehicles and the construction of an independent flight equipment development system. It is a tangible representation of a nation's scientific and technological capabilities. In light of the ongoing digitisation and intelligence development of equipment, we have outlined the historical trajectory of complex equipment digitisation. With the emergency of the next generation aircraft evaluation requirement, a digital framework for wind tunnel full life cycle research is proposed. Throughout the requirement, design, construction, operation, maintenance, the framework is to solve the current limitation, which include the unstable quality and inefficient design, simple but inefficient maintenance. In the case of low-speed wind tunnels, the typical applications of wind tunnel digital prototypes 1.0 to 3.0 are presented, and the main application scenarios of digital twins, such as wind tunnel state monitoring, wind tunnel virtual test and wind tunnel prognostics and health management, are outlined. Finally, potential research directions of wind tunnel digitisation are discussed, including interdisciplinary-multiscale modeling and silulation, flight performance evaluation and design integration, model and data standard system. Through this research, we hope to provides a suggestion to conduct further development for the construction of a digital control system for the entire life cycle of wind tunnel equipment.

Key words: wind tunnel, digital prototype, digital twin, machine learning

CLC Number:

YANG Yisheng, LI Ming, YANG Zeyuan, YAN Xiqiang, YAN Sijie, DING Han. Digital Technology Empowered Wind Tunnel Whole Life Cycle System[J]. Journal of Mechanical Engineering, 2026, 62(7): 97-113.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260366

http://www.cjmenet.com.cn/EN/Y2026/V62/I7/97

References

[1] 赖欢,祝长江,陈万华,等. 大型低温风洞结构设计关键技术分析[J]. 实验流体力学,2022,36(1):19-26. LAI Huan,ZHU Changjiang,CHEN Wanhua,et al. Key technology for mechanical design in large-scale cryogenic wind tunnel[J]. Journal of Experiments in Fluid Mechanics,2022,36(1):19-26.
[2] 王文奎. 低速风洞洞体设计[J]. 机床与液压,2008,36(5):93-95. WANG Wenkui. The Design of low speed wind tunnel[J]. Machine Tool & Hydraulics,2008,36(5):93-95.
[3] MANENTE M G. The evolution and implementation of electronic prototyping systems[D]. Cambridge:Massachusetts Institute of Technology,1995.
[4] 李培根,高亮. 智能制造概论[M]. 北京:清华大学出版社,2021. LI Peigen,GAO Liang. Introduction to intelligent manufacturing[M]. Beijing:Tsinghua University Press,2021.
[5] 吴宝贵,黄洪钟,张旭. 复杂机械产品虚拟样机多学科设计优化研究[J]. 计算机集成制造系统,2006(11):1729-1735. WU Baogui,HUANG Hongzhong,ZHANG Xu. Multidisciplinary design optimization of virtual prototype for complex mechanical product[J]. Computer Integrated Manufacturing Systems,2006(11):1729-1735.
[6] GRIEVES M,VICKERS J. Digital twin:Mitigating unpredictable,undesirable emergent behavior in complex systems[M]. Switzerland:Springer,2017:85-113.
[7] 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 Fei,LIU Weiran,LI JIanhua,et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems,2018,24(1):1-18.
[8] 陶飞,马昕,戚庆林,等. 数字孪生连接交互理论与关键技术[J]. 计算机集成制造系统,2023,29(1):1-10. TAO Fei,MA Xin,QI Qinglin,et al. Theory and key technologies of digital twin connection and interaction[J]. Computer Integrated Manufacturing Systems,2023,29(1):1-10.
[9] 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,2011:1687-5966.
[10] GLAESSGEN E,STARGEL D. The digital twin paradigm for future NASA and U.S. air force vehicles[J]. 2012:2687-2693.
[11] HE B,BAI K J. Digital twin-based sustainable intelligent manufacturing:A review[J]. Advances in Manufacturing,2021,9(1):1-21.
[12] PERNO M,HVAM L,HAUG A. Implementation of digital twins in the process industry:A systematic literature review of enablers and barriers[J]. Computers in Industry,2022,134:103558-.
[13] Errandonea I,Beltran S,Arrizabalaga S. Digital twin for maintenance:A literature review[J]. Computers in Industry,2020,123(11).
[14] 戴晟,赵罡,于勇,等. 数字化产品定义发展趋势:从样机到孪生[J]. 计算机辅助设计与图形学学报,2018,30(8):1554-1562. DAI Sheng,ZHAO Gang,YU Yong,et al. Trend of digital product definition:From mock-up to twin[J]. Journal of Computer-Aided Design & Computer Graphics,2018,30(8):1554-1562.
[15] 陶飞,刘蔚然,张萌,等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统,2019,25(1):1-18. TAO Fei,LIU Weiran,ZHANG Meng,et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems,2019,25(1):1-18.
[16] 董雷霆,周轩,赵福斌,等. 飞机结构数字孪生关键建模仿真技术[J]. 航空学报,2021,42(3):113-141. DONG Leiting,ZHOU Xuan,ZHAO Fubin,et al. Key technologies for modeling and simulation of airframe digital twin[J]. Acta Aeronautica et Astronautica Sinica,2021,42(3):023981.
[17] 陶飞,孙清超,孙惠斌,等．航空发动机数字孪生工程:内涵与关键技术[J]．航空学报,2024,45(X):630283. TAO Fei,SUN Qingchao,SUN Huibin,et al. Aero-engine digital twin engineering:Connotation and key technologies[J]. Acta Aeronautica et Astronautica Sinica,2024,45(X):630283.
[18] YUAN Y,TANG X,ZHOU W,et al. Data driven discovery of cyber physical systems[J]. Nature Communications,2019,10(1):4894.
[19] LAI X,HE X,WANG S,et al. Building a lightweight digital twin of a crane boom for structural safety monitoring based on a multifidelity surrogate model[J]. Journal of Mechanical Design,2022,144(6):064502.
[20] LAI X,WANG S,GUO Z,et al. Designing a shape-performance integrated digital twin based on multiple models and dynamic data:A boom crane example[J]. Journal of Mechanical Design,2021,143(7):071703.
[21] WANG S,LAI X,HE X,et al. Building a trustworthy product-level shape-performance integrated digital twin with multifidelity surrogate Model[J]. Journal of Mechanical Design,2022,144(3):031703.
[22] 宋学官,来孝楠,何西旺,等. 重大装备形性一体化数字孪生关键技术[J]. 机械工程学报,2022,58(10):298-325. SONG Xueguan,LAI Xiaonan,HE Xiwang,et al. Key technologies of shape-performance integrated digital twin for major equipment[J]. Journal of Mechanical Engineering,2022,58(10):298-325.
[23] STADTMAN F,RASHEED A,KVAMSDAL T,et al. Digital twins in wind energy:Emerging technologies and industry-informed future directions[J]. IEEE Access,2023,11:110762-110795.
[24] BERGMANN A .The aeroacoustic wind tunnel DNW-NWB[C]//18th AIAA/CEAS Aeroacoustics Conference (33rd AIAA Aeroacoustics Conference),June 04-06,2012 Colorado Springs,2012:17-26.
[25] SYMS G F. Acoustic upgrades to wind tunnels at the National Research Council Canada[C]// 18th AIAA/CEAS Aeroacoustics Conference (33rd AIAA Aeroacoustics Conference). June 04-06,2012 Colorado Springs,2012:1-15.
[26] POTT-POLLENSKE M,VON H W,BERGMANN A. Acoustical preexamination work and characterization of the low noise wind tunnel DNW-NWB[C]// 18th AIAA/CEAS Aeroacoustics Conference (33rd AIAA Aeroacoustics Conference). June 04-06,2012 Colorado Springs,2012:156-178.
[27] GUO J,LENG J,ZHAO J L,et al. Industrial metaverse towards Industry 5.0:Connotation,architecture,enablers,and challenges[J]. Journal of Manufacturing Systems,2024,76:25-42.
[28] 石小林,王为. 数字空间站建设及其应用[J]. 航天器工程,2022,31(6):76-85. SHI Xiaolin,WANG Wei. Digital space station and its application[J]. Space Craft Engineering,2022,31(6):76-85.
[29] ZHU Q,ZHAO Z,YAN J. Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement[J]. Computational Mechanics,2023,71(3):481-491.
[30] JIA H,HU C,KIKUMOTO H. Effects of sensor configuration optimization on airflow estimation in urban environment:A case study with a building group model[J]. Sustainable Cities and Society,2023,98:104840.
[31] GAO H,LIU J,LIN P,et al. An optimal sensor placement scheme for wind flow and pressure field monitoring[J]. Building and Environment,2023,244:110803.
[32] CHEN D,KAISER F,HU J,et al. Sparse pressure-based machine learning approach for aerodynamic loads estimation during gust encounters[J]. AIAA Journal,2023:1-16.
[33] KRITHIKA M,BINGNI W,BRUNTON J,et al. Data-driven sparse sensor placement for reconstruction:Demonstrating the benefits of exploiting known patterns[J]. IEEE Control Systems,2018,38(3):63-86.
[34] PENG X,LI X,CHEN X,et al. A hybrid deep learning framework for unsteady periodic flow field reconstruction based on frequency and residual learning[J]. Aerospace Science and Technology,2023,141:108539.
[35] RENGANATHAN S A,HARADA K,MAVRIS D N. Aerodynamic data fusion toward the digital twin paradigm[J]. AIAA Journal,2020,58(9):3902-3918.
[36] NAGATA T,NAKAI K,YAMADA K,et al. Seismic wavefield reconstruction based on compressed sensing using data-driven reduced-order model[J]. Geophysical Journal International,2022,233(1):33-50.
[37] QIN S,YUAN Y,HAN S,et al. A novel multiobjective function for finite-element model updating of a long-span cable-stayed bridge using in situ static and dynamic measurements[J]. Journal of Bridge Engineering,2023,28(1):04022131.
[38] 贾政楠,王欣,高顺德,等. 基于数字孪生的塔式起重机结构性能在线监测方法[J]. 计算机集成制造系统,2024,30(12):4468-4476. JIA Zhengnan,WANG Xin,GAO Shunde,et al. Online structural performance monitoring method of tower crane based on digital twin[J]. Computer Integrated Manufacturing Systems,2024,30(12):4468-4476..
[39] GAO X,SHI M,SONG X,et al. Recurrent neural networks for real-time prediction of TBM operating parameters[J]. Automation in Construction,2019,98:225-235.
[40] ZHANG L,YU C,LIU B. Surrogate-based structural optimization design of large-scale rectangular pressure vessel using radial point interpolation method[J]. International Journal of Pressure Vessels and Piping,2022,197:104638.
[41] YISHENG Y,ZEYUAN Y,XIQIANG Y,et al. Multi-fidelity surrogate model based wind tunnel structure design optimization[C]//16th International Conference on Advanced Computational Intelligence,ICACI 2024,May 16,2024- May 19,2024. Zhangjiajie,China:Institute of Electrical and Electronics Engineers Inc.,2024:72-78.
[42] QI G,JIUTAO H,SUIAN W,et al. Design optimization of variable stiffness composites by using multi-fidelity surrogate models[J]. Structural and Multidisciplinary Optimization,2021,63(1):439-461.
[43] 李多,曹军义,张征宇,等. 跨声速风洞现代试验设计方法应用研究[J]. 空气动力学学报,2018,36(1):26-30. LI Duo,CAO Junyi,ZHANG Zhengyu,et al. Applications of modern design of experiment method in transonic wind tunnel[J]. Acta Aerodynamica Sinica,2018,36(1):26-30.
[44] 尤文佳,王慧杰,韩仁坤,等. 高超声速风洞现代试验设计方法研究[J]. 实验流体力学,2022,36(3):20-32. YOU Wenjia,WANG Huijie,HAN Renkun,et al. Using modern design of experiments method for hypersonic wind tunnel test[J]. Journal of Experiments in Fluid Mechanics,2022,36(3):20-32
[45] 高金吉. 人工自愈概论[J]. 机械工程学报,2021,57(2):1-10. GAO Jinji. Overview on artificial self-recovery[J]. Journal of Mechanical Engineering,2021,57(2):1-10.
[46] ZHANG H, LIU Z, LIAO W,et al. Research on the health intelligent management system of large wind tunnel[J]. Journal of Physics Conference Series,2020,1626:012118.
[47] 党宏杰,余文广,杨华晖,基于数字孪生与平行试验的装备体系试验鉴定数字化应用[J]. 系统仿真学报,2024,36(7):1729-1736. DANG Hongjie,YU Wenguang,YANG Huahui. Digital application of equipment system test and evaluation based on digital twin and parallel experiment theory[J]. Journal of System Simulation,2024,36(7):1729-1736.
[48] JARDINE A K S,LIN D,BANJEVIC D. A review on machinery diagnostics and prognostics implementing condition-based maintenance[J]. Mechanical Systems and Signal Processing,2006,20(7):1483-1510.
[49] LI H,MI S,LI Q,et al. A scheduling optimization method for maintenance,repair and operations service resources of complex products[J]. Journal of Intelligent Manufacturing,2020,31(7):1673-1691.
[50] RUSCHEL E,SANTOS E A P,LOURES E,et al. Industrial maintenance decision-making:A systematic literature review[J]. Journal of Manufacturing Systems,2017,45:180-194.
[51] YANG Z,XU X,LI C,et al. Information diffusion for few-shot learning in robotic residual errors compensation[C]//Springer Science and Business Media Deutschland GmbH,2022:637-647.
[52] TAO H,CHENG L,QIU J,et al. Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic[J]. Measurement Science and Technology,2022,33(11):115005.
[53] ZHANG J,ZHANG K,AN Y,et al. An integrated multitasking intelligent bearing fault diagnosis scheme based on representation learning under imbalanced sample condition[J]. IEEE Transactions on Neural Networks and Learning Systems,2023:1-12.
[54] MAGARGLE R,JOHNSON L,MANDLOI P,et al. A simulation-based digital twin for model-driven health monitoring and predictive maintenance of an automotive braking system[C]//International Modelica Conference. 2017:35-46.
[55] 严如强,周峥,杨远贵,等. 可解释人工智能在工业智能诊断中的挑战和机遇:归因解释[J]. 机械工程学报,2024,60(12):21-40. YAN Ruqiang,ZHOU Zheng,YANG Guiyuan,et al,Challenges and opportunities of XAI in industrial intelligent diagnosis:Attribution interpretation[J]. Journal of Mechanical Engineering,2024,60(12):21-40.
[56] 严如强,商佐港,王志颖,等. 可解释人工智能在工业智能诊断中的挑战和机遇:先验赋能[J]. 机械工程学报,2024,60(12):1-20. YAN Ruqiang,SHANG Zuogang,WANG Zhiying,et al,Challenges and opportunities of XAI in industrial intelligent diagnosis:Priori-empowered[J]. Journal of Mechanical Engineering,2024,60(12):1-20.
[57] TAO F,XIAO B,QI Q,et al. Digital twin modeling[J]. Journal of Manufacturing Systems,2022,64:372-389.
[58] LIU Y,FENG J,LU J,et al. A review of digital twin capabilities,technologies,and applications based on the maturity model[J]. Advanced Engineering Informatics,2024,62:102592.
[59] LE J,YANG M,GUO M,et al. Progress and prospects of artificial intelligence development and applications in supersonic flow and combustion[J]. Progress in Aerospace Sciences,2024,151:101046.
[60] 国防科学技术工业委员会. HB 7757-2005飞机数字样机通用要求[S]. 北京:中国标准出版社,2005. Commission of Science,Technology and Industry for National Defence. HB 7757-2005 The general specification of airplane digital mockup[S]. Beijing:Standards Press of China,2005.
[61] 国防科学技术工业委员会. HB 7798-2005飞机数字样机评审要求[S]. 北京:中国标准出版社,2005. Commission of Science,Technology and Industry for National Defence. HB 7757-2005 Requirements for review of airplane digital mockup[S]. Beijing:Standards Press of China,2005.
[62] 陶飞,马昕,胡天亮,等. 数字孪生标准体系[J]. 计算机集成制造系统,2019,25(10):14. TAO Fei,MA Xin,HU Tianliang,et al. Research on digital twin standard system[J]. Computer Integrated Manufacturing Systems,2019,25(10):14.
[63] 覃文波,周诚,陈健.城市轨道交通数字孪生标准体系研究[J]. 土木建筑工程信息技术,2024,16(1):8-14. QIN Wenbo,ZHOU Cheng,Chen Jian,Research on the digital twin standard system for urban rail transit[J]. Journal of Information Technology in Civil engineering and Architecture,2024,16(1):8-14.
[64] 薛瑞娟,黄祖广,王金江,等. 数控机床数字孪生标准体系研究[J]. 制造技术与机床,2023(3):39-50. XUE Ruijuan,HUANG Zuguang,WANG Jinjiang,et al,Research on digital twin standards system of CNC machine tool[J]. Manufacturing Technology & Machine Tool,2023(3):39-50.
[65] 国家市场监督管理总局,国家标准化管理委员会. GB/T 45109.1-2024智慧城市城市数字孪生第1部分:技术参考架构[S]. 北京:中国标准出版社,2024. State Administration for Market Regulation,Standardization Administration of the People's Republic of China. GB/T 45109.1-2024 Smart city—city digital twin—part 1: technical reference architecture[S]. Beijing:Standards Press of China,2024.
[66] 国家市场监督管理总局,国家标准化管理委员会. GB/T 41732—2022自动化系统与集成复杂产品数字孪生体系架构[S]. 北京:中国标准出版社,2022. State Administration for Market Regulation,Standardization Administration of the People's Republic of China. GB/T 41732-2022 Automation systems and integration—digital twin architecture of complex product[S]. Beijing:Standards Press of China,2022.
[67] 国家市场监督管理总局,国家标准化管理委员会. GB/Z 44267—2024自动化系统与集成工业数据数字孪生的可视化元素[S]. 北京:中国标准出版社,2024. State Administration for Market Regulation,Standardization Administration of the People's Republic of China. GB/Z 44267-2024 Automation systems and integration—industrial data—visualization elements of digital twins[S]. Beijing:Standards Press of China,2024.