基于卷积神经网络的深度学习流场特征识别及应用进展

doi:10.7527/S1000-6893.2020.24736

综述

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于卷积神经网络的深度学习流场特征识别及应用进展

叶舒然^1,2, 张珍^1,2, 王一伟^1,2, 黄晨光^1,2

1. 中国科学院力学研究所流固耦合系统力学重点实验室, 北京 100190;
2. 中国科学院大学工程科学学院, 北京 100049

收稿日期:2020-09-10 修回日期:2020-10-15 发布日期:2020-12-31
通讯作者: 王一伟 E-mail:wangyw@imech.ac.cn
基金资助:
国家重点研发计划（2016YFC0300800）

Progress in deep convolutional neural network based flow field recognition and its applications

YE Shuran^1,2, ZHANG Zhen^1,2, WANG Yiwei^1,2, HUANG Chenguang^1,2

1. Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China;
2. School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-09-10 Revised:2020-10-15 Published:2020-12-31
Supported by:
National Key R & D Program of China (2016YFC0300800)

摘要/Abstract

摘要： 深度学习架构的出色性能使得机器学习在流体力学中的应用得到新的发展，可以应对流体力学中诸多问题和需求。卷积神经网络（CNN）强大的非线性映射能力以及分层提取信息特征的功能，使其成为当下流场特征研究不容忽视的工具。围绕这一研究前沿与热点问题，概述和归纳了这一研究领域的进展与成果。首先，对深度学习在流体力学中的发展以及卷积神经网络进行了简单的回顾。然后，从卷积神经网络能够识别特征出发，先后介绍了基于卷积的深度学习特征识别在流场预测、流动外形优化、流场可视化精度提升和生成对抗等应用方面的研究进展。最后，对深度学习在流场识别领域的应用进行了展望，为后续的研究提供参考。

关键词: 卷积神经网络, 流场识别, 流动预测, 外形优化, 泊松方程, 生成对抗网络, 深度学习

Abstract: With excellent performance, the deep learning architecture has enabled new developments in application of machine learning in fluid mechanics, and can cope with many challenges and needs in fluid mechanics. Due to powerful nonlinear mapping capabilities and hierarchical extraction of information features, the Convolutional Neural Network (CNN) has become a tool that cannot be ignored in current research on flow features. This paper summarizes the progress and achievements in this research area. First, the developments of deep learning for fluid mechanics and CNNs are briefly reviewed. Then, the research progress of using deep CNN in flow prediction, flow shape optimization, improving the accuracy of flow field visualization, and generation confrontation is introduced. Finally, prospects of application of deep learning in flow field recognition are discussed to provide a reference for subsequent research.

Key words: convolutional neural network, flow field recognition, flow prediction, shape optimization, Poisson equation, generative adversarial network, deep learning

中图分类号:

叶舒然, 张珍, 王一伟, 黄晨光. 基于卷积神经网络的深度学习流场特征识别及应用进展[J]. 航空学报, 2021, 42(4): 524736-524736.

YE Shuran, ZHANG Zhen, WANG Yiwei, HUANG Chenguang. Progress in deep convolutional neural network based flow field recognition and its applications[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524736-524736.

参考文献

[1] WANG Z, XIAO D, FANG F, et al. Model identification of reduced order fluid dynamics systems using deep learning[J]. International Journal for Numerical Methods in Fluids, 2018, 86(4):255-268.
[2] 寇家庆, 张伟伟, 高传强. 基于POD和DMD方法的跨声速抖振模态分析[J]. 航空学报, 2016, 37(9):2679-2689. KOU J Q, ZHANG W W, GAO C Q. Modal analysis of transonic buffet based on POD and DMD method[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(9):2679-2689(in Chinese).
[3] BERKOOZ G, HOLMES P, LUMLEY J L. The proper orthogonal decomposition in the analysis of turbulent flows[J]. Annual Review of Fluid Mechanics, 1993, 25(1):539-575.
[4] RAVINDRAN S S. A reduced-order approach for optimal control of fluids using proper orthogonal decomposition[J]. International Journal for Numerical Methods in Fluids, 2000, 34(5):425-448.
[5] WU X, ZHANG W, PENG X,et al. Benchmark aerodynamic shape optimization with the POD-based CST airfoil parametric method[J]. Aerospace Science and Technology, 2019, 84:632-640.
[6] DUBIEF Y, DELCAYRE F. On coherent-vortex identification in turbulence[J]. Journal of Turbulence, 2000, 1:N11.
[7] XIONG S, YANG Y. Identifying the tangle of vortex tubes in homogeneous isotropic turbulence[J]. Journal of Fluid Mechanics, 2019, 874:952-978.
[8] HINTON G, DENG L, YU D, et al. Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups[J]. IEEE Signal Processing Magazine, 2012, 29(6):82-97.
[9] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6):84-90.
[10] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015:1-9.
[11] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//3rd International Conference on Learning Representations, ICLR 2015- Conference Track Proceedings, 2015.
[12] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016:770-778.
[13] MANNING C, SURDEANU M, BAUER J, et al. The stanford CoreNLP natural language processing toolkit[C]//Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics:System Demonstrations. Stroudsburg:Association for Computational Linguistics, 2014:55-60.
[14] CHEN C, SEFF A, KORNHAUSER A, et al. Deep driving:Learning affordance for direct perception in autonomous driving[C]//2015 IEEE International Conference on Computer Vision (ICCV), 2015:2722-2730.
[15] TRACEY B, DURAISAMY K, ALONSO J. Application of supervised learning to quantify uncertainties in turbulence and combustion modeling[C]//51 st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston:AIAA, 2013.
[16] LING J, KURZAWSKI A, TEMPLETON J. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance[J]. Journal of Fluid Mechanics, 2016, 807:155-166.
[17] WANG J X, WU J L, XIAO H. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data[J]. Physical Review Fluids, 2017, 2(3):034603.
[18] ZHANG Z, SONG X, YE S, et al. Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data[J]. Journal of Hydrodynamics, 2019, 31(1):58-65.
[19] 陈海昕, 邓凯文, 李润泽. 机器学习技术在气动优化中的应用[J]. 航空学报, 2019, 40(1):522480. CHEN H X, DENG K W, LI R Z. Utilization of machine learning technology in aerodynamic optimization[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1):522480(in Chinese).
[20] 韩忠华, 许晨舟, 乔建领, 等. 基于代理模型的高效全局气动优化设计综述方法研究进展[J]. 航空学报, 2020, 41(5):623344. HAN Z H, XU C Z, QIAO J L, et al. Recent progress of efficient global aerodynamic shape optimization using surrogate-based approach[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5):623344(in Chinese).
[21] BRUNTON S L, NOACK B R, KOUMOUTSAKOS P. Machine learning for fluid mechanics[J]. Annual Review of Fluid Mechanics, 2020, 52(1):477-508.
[22] LI T, WU T, LIU Z. Nonlinear unsteady bridge aerodynamics:Reduced-order modeling based on deep LSTM networks[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2020, 198:104116.
[23] WIEWEL S, BECHER M, THUEREY N. Latent space physics:Towards learning the temporal evolution of fluid flow[J]. Computer Graphics Forum, 2019, 38(2):71-82.
[24] LECUN Y, BENGIO Y, HINTON G. Deep learning[Z].2015. DOI:10.1038/nature14539.
[25] KINGMA D P, BA J L. Adam:A method for stochastic gradient descent[C]//International Conference on Learning Representations, 2015.
[26] LIU Y, LU Y, WANG Y, et al. A CNN-based shock detection method in flow visualization[J]. Computers & Fluids, 2019, 184:1-9.
[27] LOVELY D, HAIMES R. Shock detection from computational fluid dynamics results[C]//Proceedings of the AIAA 14th Computational Fluid Dynamics Conference, 1999.
[28] ZHANG Y, AZMAN A N, XU K W, et al. Two-phase flow regime identification based on the liquid-phase velocity information and machine learning[J]. Experiments in Fluids, 2020, 61(10):212.
[29] 魏晓良,潮群,陶建峰, 等. 基于LSTM和CNN的高速柱塞泵故障诊断[J]. 航空学报, 2020, 41(3):423876. WEI X L, CHAO Q, TAO J F, et al. Cavitation fault diagnosis method for high-speed plunger pumps based on LSTM and CNN[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(3):423876(in Chinese).
[30] STROFER C M, WU J L, XIAO H, et al. Data-driven, physics-based feature extraction from fluid flow fields using convolutional neural networks[J]. Communications in Computational Physics, 2019, 25(3):625-650.
[31] GUPTA K, VOELKER L, BACH C. CFD-based aeroelastic analysis of the X-43 hypersonic flight vehicle[C]//39th Aerospace Sciences Meeting and Exhibit. Reston:AIAA, 2001.
[32] SILVA W. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques[J]. 1997.
[33] 陈刚, 李跃明. 非定常流场降阶模型及应用研究进展与展望[J]. 力学进展, 2011, 41(6):686-701. CHEN G, LI Y M. Advances and prospects of the reduced order model for unsteady flow and its application[J]. Advances in Mechanics, 2011, 41(6):686-701(in Chinese).
[34] GHOMAN S S, WANG Z, CHEN P C. Hybrid optimization framework with proper-orthogonal-decomposition-based order reduction and design-space evolution scheme[J]. Journal of Aircraft, 2013, 50(6):1776-1786.
[35] DUAN Y, CAI J, LI Y. Gappy proper orthogonal decomposition-based two-step optimization for airfoil design[J]. AIAA Journal, 2012, 50(4):968-971.
[36] CARLSON H A, VERBERG R, HARRIS C A. Aeroservoelastic modeling with proper orthogonal decomposition[J]. Physics of Fluids, 2017, 29(2):020711.
[37] LIU H F S, WOLF W R. Construction of reduced-order models for fluid flows using deep feedforward neural networks[J]. Journal of Fluid Mechanics, 2019, 872:963-994.
[38] WU P, SUN J, CHANG X. Data-driven reduced order model with temporal convolutional neural network[J]. Computer Methods in Applied Mechanics and Engineering, 2020, 360:112766.
[39] MIYANAWALA T P, JAIMAN R K. A hybrid data-driven deep learning technique for fluid-structure interaction[C]//American Society of Mechanical Engineers, CFD and FSI, 2019.
[40] RENGANATHAN S A, MAULIK R, RAO V. Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil[J]. Physics of Fluids, 2020, 32(4):047110.
[41] WANG M, CHEUNG S W, LEUNG W T, et al. Reduced-order deep learning for flow dynamics. The interplay between deep learning and model reduction[J]. Journal of Computational Physics, 2020, 401:108939.
[42] PAWAR S, RAHMAN S M, VADDIREDDY H. A deep learning enabler for nonintrusive reduced order modeling of fluid flows[J]. Physics of Fluids, 2019, 31(8):085101.
[43] ALLA A, KUTZ J N. Nonlinear model order reduction via dynamic mode decomposition[J]. SIAM Journal on Scientific Computing, 2017, 39(5):B778-B796.
[44] HAN J, TAO J, WANG C. FlowNet:a deep learning framework for clustering and selection of streamlines and stream surfaces[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 26(4):1-1.
[45] OMATA N, SHIRAYAMA S. A novel method of low-dimensional representation for temporal behavior of flow fields using deep autoencoder[J]. AIP Advances, 2019, 9(1):015006.
[46] FONDA E, PANDEY A, SCHUMACHER J. Deep learning in turbulent convection networks[J]. Proceedings of the National Academy of Sciences, 2019, 116(18):8667-8672.
[47] LORE K G, STOECKLEIN D, DAVIES M. A deep learning framework for causal shape transformation[J]. Neural Networks, 2018, 98:305-317.
[48] KIM B, GÜNTHER T. Robust reference frame extraction from unsteady 2D vector fields with convolutional neural networks[J]. Computer Graphics Forum, 2019, 38(3):285-295.
[49] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[M]//Computer Vision-ECCV 2014. Cham:Springer International Publishing, 2014:818-833.
[50] MURATA T, FUKAMI K, FUKAGATA K. Nonlinear mode decomposition with convolutional neural networks for fluid dynamics[J]. Journal of Fluid Mechanics, 2020, 882:A13.
[51] HADIKHANI P, BORHANI N, H. HASHEMI S M. Learning from droplet flows in microfluidic channels using deep neural networks[J]. Scientific Reports, 2019, 9(1):8114.
[52] TENNEY A S, GLAUSER M N, RUSCHER C J. Application of artificial neural networks to stochastic estimation and jet noise modeling[J]. AIAA Journal, 2020, 58(2):647-658.
[53] CHANG C W, DINH N T. Classification of machine learning frameworks for data-driven thermal fluid models[J]. International Journal of Thermal Sciences, 2019, 135:559-579.
[54] FUKAMI K, FUKAGATA K, TAIRA K. Assessment of supervised machine learning methods for fluid flows[J]. Theoretical and Computational Fluid Dynamics, 2020, 34(4):497-519.
[55] MIYANAWALA T P, JAIMAN R K. An efficient deep learning technique for the navier-stokes equations:application to unsteady wake flow dynamics[DB/OL]. arXiv e-prints:1710.09099,2017.
[56] BHATNAGAR S, AFSHAR Y, PAN S. Prediction of aerodynamic flow fields using convolutional neural networks[J]. Computational Mechanics, 2019, 64(2):525-545.
[57] YE S, ZHANG Z, SONG X. A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network[J]. Scientific Reports, 2020, 10(1):4459.
[58] JIN X, CHENG P, CHEN W L. Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder[J]. Physics of Fluids, 2018, 30(4):047105.
[59] ZHU L, ZHANG W, KOU J. Machine learning methods for turbulence modeling in subsonic flows around airfoils[J]. Physics of Fluids, 2019, 31(1):015105.
[60] THUEREY N, WEISSENOW K, PRANTL L. Deep learning methods for reynolds-averaged navier-stokes simulations of airfoil flows[J]. AIAA Journal, 2020, 58(1):25-36.
[61] HUI X, BAI J, WANG H. Fast pressure distribution prediction of airfoils using deep learning[J]. Aerospace Science and Technology, 2020, 105:105949.
[62] GUO X, LI W, IORIO F. Convolutional neural networks for steady flow approximation[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2016:481-490.
[63] SEKAR V, KHOO B C. Fast flow field prediction over airfoils using deep learning approach[J]. Physics of Fluids, 2019, 31(5):057103.
[64] UMETANI N. Exploring generative 3D shapes using autoencoder networks[C]//SIGGRAPH Asia 2017 Technical Briefs on-SA'17. New York:ACM Press, 2017:1-4.
[65] UMETANI N, BICKEL B. Learning three-dimensional flow for interactive aerodynamic design[J]. ACM Transactions on Graphics, 2018, 37(4):1-10.
[66] GAYMANN A, MONTOMOLI F. Deep neural network and monte carlo tree search applied to fluid-structure topology optimization[J]. Scientific Reports, 2019, 9(1):15916.
[67] SEKAR V, ZHANG M, SHU C. Inverse design of airfoil using a deep convolutional neural network[J]. AIAA Journal, 2019, 57(3):993-1003.
[68] CUNNINGHAM J, TUCKER C S. A Validation Neural Network (VNN) metamodel for predicting the performance of deep generative designs[C]//ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2018.
[69] HU L, ZHANG J, XIANG Y. Neural networks-based aerodynamic data modeling:A comprehensive review[J]. IEEE Access, 2020, 8:90805-90823.
[70] LIU B, TANG J, HUANG H. Deep learning methods for super-resolution reconstruction of turbulent flows[J]. Physics of Fluids, 2020, 32(2):025105.
[71] CAI S, LIANG J, GAO Q. Particle image velocimetry based on a deep learning motion estimator[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6):3538-3554.
[72] CHU M, THUEREY N. Data-driven synthesis of smoke flows with CNN-based feature descriptors[J]. ACM Transactions on Graphics, 2017, 36(4):1-14.
[73] FUKAMI K, FUKAGATA K, TAIRA K. Super-resolution reconstruction of turbulent flows with machine learning[J]. Journal of Fluid Mechanics, 2019, 870:106-120.
[74] FERDIAN E, SUINESIAPUTRA A, DUBOWITZ D J. 4DFlowNet:super-resolution 4D flow MRI using deep learning and computational fluid dynamics[J]. Frontiers in Physics, 2020, 8.
[75] YANG C, YANG X, XIAO X. Data-driven projection method in fluid simulation[J]. Computer Animation and Virtual Worlds, 2016, 27(3-4):415-424.
[76] TOMPSON J, SCHLACHTER K, SPRECHMANN P. Accelerating Eulerian fluid simulation with convolutional networks[C]//PRECUP D, TEH Y W, ed. International Convention Centre. Sydney:PMLR, 2017:3424-3433.
[77] OZBAY A G, LAIZET S, TZIRAKIS P. Poisson CNN:Convolutional neural networks for the solution of the poisson equation with varying meshes and dirichlet boundary conditions[DB/OL]. arXiv e-prints:1910.08613, 2019.
[78] LONG Z, LU Y, MA X. PDE-Net:Learning PDEs from data[C]//35th International Conference on Machine Learning, 2018.
[79] LONG Z, LU Y, DONG B. PDE-Net 2.0:Learning PDEs from data with a numeric-symbolic hybrid deep network[J]. Journal of Computational Physics, 2019, 399:108925.
[80] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M. Generative adversarial nets[C]//Advances in Neural Information Processing Systems 27(NIPS 2014), 2014.
[81] KARRAS T, AILA T, LAINE S. Progressive growing of gans for improved quality, stability, and variation[DB/OL]. arXiv eprints:1710.10196,2017.
[82] ISOLA P, ZHU J Y, ZHOU T. Image-to-image translation with conditional adversarial networks[Z]. 2016.
[83] WANG X, YU K, WU S. ESRGAN:Enhanced super-resolution generative adversarial networks[Z]. 2018.
[84] FARIMANI A B, GOMES J, PANDE V. Deep learning fluid mechanics[C]//70th Annual Meeting of the APS Division of Fluid Dynamics, 2017.
[85] LIU Y, WANG Y, DENG L. A novel in situ compression method for CFD data based on generative adversarial network[J]. Journal of Visualization, 2019, 22(1):95-108.
[86] WU H, LIU X, AN W. A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils[J]. Computers & Fluids, 2020, 198:104393.
[87] XIE Y, FRANZ E, CHU M. tempoGAN[J]. ACM Transactions on Graphics, 2018, 37(4):1-15.
[88] LEE J, LEE S, YOU D. Deep learning approach in multi-scale prediction of turbulent mixing-layer[Z]. 2018.
[89] LEE S, YOU D. Data-driven prediction of unsteady flow over a circular cylinder using deep learning[J]. Journal of Fluid Mechanics, 2019, 879:217-254.
[90] WU J, KASHINATH K, ALBERT A. Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems[J]. Journal of Computational Physics, 2020, 406:109209.
[91] LIN J, LENSINK K, HABER E. Fluid flow mass transport for generative networks[Z]. 2019.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于卷积神经网络的深度学习流场特征识别及应用进展

Progress in deep convolutional neural network based flow field recognition and its applications

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	苏翎菲, 化永朝, 董希旺, 任章. 人与无人机集群多模态智能交互方法[J]. 航空学报, 2022, 43(S1): 727001-727001.
[2]	孙秀一, 胡绍海, 马晓乐. 基于无监督深度学习的红外与可见光图像融合[J]. 航空学报, 2022, 43(S1): 726938-726938.
[3]	赵良玉, 李丹, 赵辰悦, 蒋飞. 无人机自主降落标识检测方法若干研究进展[J]. 航空学报, 2022, 43(9): 25882-025882.
[4]	曹明, 王鹏, 左洪福, 曾海军, 孙见忠, 杨卫东, 魏芳, 陈雪峰. 民用航空发动机故障诊断与健康管理现状、挑战与机遇Ⅱ: 地面综合诊断、寿命管理和智能维护维修决策[J]. 航空学报, 2022, 43(9): 625574-625574.
[5]	刘佳奇, 冯蕴雯, 路成, 薛小锋, 潘维煌. 基于智能神经网络的航空发动机运行安全分析[J]. 航空学报, 2022, 43(9): 625375-625375.
[6]	韩淞宇, 邵海东, 姜洪开, 张笑阳. 基于提升卷积神经网络的航空发动机高速轴承智能故障诊断[J]. 航空学报, 2022, 43(9): 625479-625479.
[7]	孙灿飞, 黄林然, 沈勇. 复杂工况下的直升机行星传动轮系故障诊断[J]. 航空学报, 2022, 43(8): 625480-625480.
[8]	张晟斐, 李天梅, 胡昌华, 杜党波, 司小胜. 基于深度卷积生成对抗网络的缺失数据生成方法及其在剩余寿命预测中的应用[J]. 航空学报, 2022, 43(8): 225708-225708.
[9]	邵怡韦, 陈嘉宇, 林翠颖, 万程, 葛红娟, 石智龙. 小训练样本下齿轮箱故障诊断:一种基于改进深度森林的方法[J]. 航空学报, 2022, 43(8): 625429-625429.
[10]	康玉祥, 陈果, 尉询楷, 周磊. 深度残差对冲网络及其在滚动轴承故障诊断中的应用[J]. 航空学报, 2022, 43(8): 625201-625201.
[11]	刘子超, 王江, 何绍溟, 李宇飞. 基于预测校正的落角约束计算制导方法[J]. 航空学报, 2022, 43(8): 325433-325433.
[12]	吕帅帅, 杨宇, 王彬文, 殷晨飞. 基于改进FaceNet的飞行器结构裂纹识别方法[J]. 航空学报, 2022, 43(6): 525463-525463.
[13]	刘天元, 郑杭彬, 杨长祺, 鲍劲松, 汪俊亮, 顾俊. 面向激光焊接缺陷识别的可解释性深度学习方法[J]. 航空学报, 2022, 43(4): 524961-524961.
[14]	康硕, 柯臻铮, 王璇, 朱伟东. 基于红外和可见光图像融合的铺丝缺陷检测方法[J]. 航空学报, 2022, 43(3): 425187-425187.
[15]	战庆亮, 白春锦, 张宁, 葛耀君. 基于时程卷积自编码的机翼绕流特征识别方法[J]. 航空学报, 2022, 43(11): 526531-526531.