Some research progress and prospect of Intelligent Aerodynamics

TANG Zhigong; ZHU Linyang; XIANG Xinghao; HE Lei; ZHAO Tun; WANG Yueqing; QIAN Weiqi; YUAN Xianxu

doi:10.7638/kqdlxxb-2023.0128

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Some research progress and prospect of Intelligent Aerodynamics

更新时间：2026-05-28

- Some research progress and prospect of Intelligent Aerodynamics
- Vol. 41, Issue 7, Pages: 1-35(2023)
- 作者机构：
  
  1. 中国空气动力研究与发展中心,绵阳,621000
  2. 空天飞行空气动力科学与技术全国重点实验室,绵阳,621000
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2023.0128
  CLC： O355;V211.25
- Published：2023
- 稿件说明：
移动端阅览
唐志共, 朱林阳, 向星皓, 等. 智能空气动力学若干研究进展及展望[J]. 空气动力学学报, 2023, 41(7): 1−35.

TANG Zhigong, ZHU Linyang, XIANG Xinghao, et al. Some research progress and prospect of Intelligent Aerodynamics[J]. 2023, 41(7): 1-35.
唐志共, 朱林阳, 向星皓, 等. 智能空气动力学若干研究进展及展望[J]. 空气动力学学报, 2023, 41(7): 1−35. DOI： 10.7638/kqdlxxb-2023.0128

TANG Zhigong, ZHU Linyang, XIANG Xinghao, et al. Some research progress and prospect of Intelligent Aerodynamics[J]. 2023, 41(7): 1-35. DOI： 10.7638/kqdlxxb-2023.0128.

摘要

智能空气动力学是人工智能与空气动力学的结合，融入了第四研究范式（数据驱动）的独特研究方法，已逐步发展成为一门独立的交叉学科。本文首先对智能空气动力学的概念和内涵进行了剖析，简述了智能空气动力学的主要研究方法，然后介绍了智能空气动力学在流场预测、转捩/湍流建模、多源数据融合、气动力/热建模、流场特征信息提取等方面研究进展及本团队开展的相关工作，最后展望了智能空气动力学的发展趋势和未来的研究设想。

Abstract

Intelligent Aerodynamics (IA)

a combination of artificial intelligence (AI) and aerodynamics that integrates the fourth research paradigm (Big Data)

is growing to be a distinct cross-discipline. This paper first introduces IA's concept and connotation and sketches the main research methods. Secondly

the research progress and the work carried out in China Aerodynamics Research and Development Center (CARDC) are reviewed regarding AI-enhanced flow prediction

transition/turbulence modeling

multi-source data fusion

aerodynamic/aerothermal modeling

and extraction of critical flow-field information. Finally

the future research work and development trends of IA are prospected.

关键词

Keywords

references

HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. DOI: 10.1162/neco.2006.18.7.1527

SILVER D, HUANG A, MADDISON C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587): 484-489. DOI: 10.1038/nature16961

LU L, JIN P Z, PANG G F, et al. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators[J]. Nature Machine Intelligence, 2021, 3(3): 218-229. DOI: 10.1038/s42256-021-00302-5

BI K F, XIE L X, ZHANG H H, et al. Accurate medium-range global weather forecasting with 3D neural networks[J]. Nature, 2023, 619(7970): 533-538. DOI: 10.1038/s41586-023-06185-3

RAO C P, REN P, WANG Q, et al. Encoding physics to learn reaction-diffusion processes[J]. Nature Machine Intelligence, 2023, 5(7): 765-779. DOI: 10.1038/s42256-023-00685-7

LI Y F, CHANG J T, KONG C, et al. Recent progress of machine learning in flow modeling and active flow control[J]. Chinese Journal of Aeronautics, 2022, 35(4): 14-44. DOI: 10.1016/j.cja.2021.07.027

XIE L J, ZHANG S, ZHANG J F, et al. A feature-based data mining method for fluid dynamics[J]. Acta Aerodynamica Sinica, 2010, 28(5): 540-546. (in Chinese)

QIAN W Q, ZHAO T, HUANG Y, et al. Modeling and data mining of engineering airfoil aerodynamic characteristics[J]. Acta Aerodynamica Sinica, 2021, 39(6): 175−183 (in Chinese).

LIU S S, CHEN J T, GUI Y W, et al. Knowledge discovery for vehicle aerodynamic configuration design using data mining[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524708. (in Chinese)

WANG W Z, ZHENG K P, CHEN G, et al. Application research of data-mining method to flight test data analysis and aerodynamic parameter identification[J]. Acta Aerodynamica Sinica, 2016, 34(6): 778-782. (in Chinese)

ŽEGKLITZ J, POŠÍK P. Benchmarking state-of-the-art symbolic regression algorithms[J]. Genetic Programming and Evolvable Machines, 2021, 22(1): 5-33. DOI: 10.1007/s10710-020-09387-0

LU Q, ZHANG Y. Solving symbol regression based on Monte Carlo tree search[J]. Computer Engineering and Design, 2020, 41(8): 2158-2164. (in Chinese)

AHVANOOEY M T, LI QM, WU M, et al. A survey of genetic programming and its applications[J]. KSII Transactions on Internet and Information Systems, 2019, 13(4): 1765-1794. Doi: 10.3837/tiis.2019.04.002

ZHONG J H, FENG L, ONG Y S. Gene expression programming: a survey[review article[J]. IEEE Computational Intelligence Magazine, 2017, 12(3): 54-72. DOI: 10.1109/MCI.2017.2708618

DURIEZ T, PAREZANOVIC V, LAURENTIE J C, et al. Closed-loop control of experimental shear flows using machine learning[C]// Proceedings of the 7th AIAA Flow Control Conference, Atlanta, GA. Reston, Virginia: AIAA, 2014: AIAA2014-2219. doi: 10.2514/6.2014-2219

ZHAO Y M, AKOLEKAR H D, WEATHERITT J, et al. RANS turbulence model development using CFD-driven machine learning[J]. Journal of Computational Physics, 2020, 411: 109413. DOI: 10.1016/j.jcp.2020.109413

HAMMOND J, MONTOMOLI F, PIETROPAOLI M, et al. Machine learning for the development of data driven turbulence closures in coolant systems[J]. Journal of Turbomachinery, 2022, 144(8): 081003-1-081003-10DOI: 10.1115/1.4053533

MA H P. Model establishments and performance analyses of evolutionary algorithms[D]. Shanghai: Shanghai University, 2014. (in Chinese)

LIU C, FEI W, HU S. Review on research and application of wolf pack algorithm[J]. Science Technology and Engineering, 2020, 20(9): 3378-3386. (in Chinese)

BRUNTON S L, NATHAN KUTZ J, MANOHAR K, et al. Data-driven aerospace engineering: reframing the industry with machine learning[J]. AIAA Journal, 2021: 1-26. DOI: 10.2514/1.j060131

ZHANG W W, KOU J Q, LIU Y L. Prospect of artificial intelligence empowered fluid mechanics[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524689. (in Chinese)

WU Z, FAN D W, ZHOU Y. Advances in control of turbulence by artificial intelligence: systems, algorithms, achievements and data analysis methods[J]. Advances in Mechanics, 2023, 53(2): 273-307. (in Chinese)

ZHANG W W, WANG X, KOU J Q. Prospects of multi-paradigm fusion methods for fluid mechanics research[J]. Advances in Mechanics, 2023, 53(2): 433-467. (in Chinese)

ZHANG T J, QIAN W Q, ZHOU Y, et al. Preliminary thoughts on the combination of artificial intelligence and aerodynamics[J]. Advances in Aeronautical Science and Engineering, 2019, 10(1): 1-11. (in Chinese)

THUEREY N, WEIßENOW K, PRANTL L, et al. Deep learning methods for reynolds-averaged navier–stokes simulations of airfoil flows[J]. AIAA Journal, 2020, 58(1): 25-36. DOI: 10.2514/1.j058291

GUO X X, LI W, IORIO F. Convolutional neural networks for steady flow approximation[C]//Proc of the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA. New York, NY, USA: ACM, 2016. doi: 10.1145/2939672.2939738

DURU C, ALEMDAR H, BARAN Ö U. CNNFOIL: convolutional encoder decoder modeling for pressure fields around airfoils[J]. Neural Computing and Applications, 2021, 33(12): 6835-6849. DOI: 10.1007/s00521-020-05461-x

DURU C, ALEMDAR H, BARAN O U. A deep learning approach for the transonic flow field predictions around airfoils[J]. Computers Fluids, 2022, 236: 105312. DOI: 10.1016/j.compfluid.2022.105312

WU M Y, WU Y, YUAN X Y, et al. Fast prediction of flow field around airfoils based on deep convolutional neural network[J]. Applied Sciences, 2022, 12(23): 12075. DOI: 10.3390/app122312075

HUI X Y, BAI J Q, WANG H, et al. Fast pressure distribution prediction of airfoils using deep learning[J]. Aerospace Science and Technology, 2020, 105: 105949. DOI: 10.1016/j.ast.2020.105949

BHATNAGAR S, AFSHAR Y, PAN S W, et al. Prediction of aerodynamic flow fields using convolutional neural networks[J]. Computational Mechanics, 2019, 64(2): 525-545. DOI: 10.1007/s00466-019-01740-0

WANG M L, LI H X, CHEN X, et al. Deep learning-based model reduction for distributed parameter systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46(12): 1664-1674. DOI: 10.1109/TSMC.2016.2605159

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. DOI: 10.1063/1.5067313

FUKAMI K, FUKAGATA K, TAIRA K. Super-resolution reconstruction of turbulent flows with machine learning[J]. Journal of Fluid Mechanics, 2019, 870: 106-120. DOI: 10.1017/jfm.2019.238

ZUO K J, BU S H, ZHANG W W, et al. Fast sparse flow field prediction around airfoils via multi-head perceptron based deep learning architecture[J]. Aerospace Science and Technology, 2022, 130: 107942. DOI: 10.1016/j.ast.2022.107942

HU J W, ZHANG W W. Mesh-Conv: Convolution operator with mesh resolution independence for flow field modeling[J]. Journal of Computational Physics, 2022, 452: 110896. DOI: 10.1016/j.jcp.2021.110896

GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[J]. Advances in neural information processing systems, 2014, 27.

WU H Z, LIU X J, AN W, et al. A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils[J]. Computers Fluids, 2020, 198: 104393. DOI: 10.1016/j.compfluid.2019.104393

WU H Z, LIU L, AN W, ET AL. . A generative deep learning framework for airfoil flow field prediction with sparse data[J]. Chinese Journal of Aeronautics, 2022, 35(1): 470-484.

KASHEFI A, REMPE D, GUIBAS L J. A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries[J]. Physics of Fluids, 2021, 33(2): 027104. DOI: 10.1063/5.0033376

MIYANAWALA T P, JAIMAN R K. Decomposition of wake dynamics in fluid–structure interaction via low-dimensional models[J]. Journal of Fluid Mechanics, 2019, 867: 723-764. DOI: 10.1017/jfm.2019.140

LI J X, LIU T Y, WANG Y Q, et al. Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery[J]. Energy, 2022, 254: 124440. DOI: 10.1016/j.energy.2022.124440

ZUO K J, YE Z Y, ZHANG W W, et al. Fast aerodynamics prediction of laminar airfoils based on deep attention network[J]. Physics of Fluids, 2023, 35(3): 037127. DOI: 10.1063/5.0140545

CHEN J Q, TU G H, ZHANG Y F, et al. Hypersnonic boundary layer transition: what we know, where shall we go[J]. Acta Aerodynamica Sinica, 2017, 35(3): 311-337. (in Chinese)

SCHMISSEUR J D. Hypersonics into the 21st century: a perspective on AFOSR-sponsored research in aerothermodynamics[J]. Progress in Aerospace Sciences, 2015, 72: 3-16. DOI: 10.1016/j.paerosci.2014.09.009

HU Z Y, WANG Z L, CHEN J Q, et al. Prediction of crossflow transition based on deep neural networks[J]. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(1): 38-51. (in Chinese)

LI B L, YANG Z X, ZHANG X, et al. Using machine learning to detect the turbulent region in flow past a circular cylinder[J]. Journal of Fluid Mechanics, 2020, 905: A10. DOI: 10.1017/jfm.2020.725

MENG D Y, SHI M T, SHI Y P, et al. A machine learning method for transition prediction in hypersonic flows over a cone with angles of attack[J]. AIP Advances, 2022, 12(2): 025116. DOI: 10.1063/5.0077734

YANG M C, XIAO Z X. Improving the k–ω–γ–Ar transition model by the field inversion and machine learning framework[J]. Physics of Fluids, 2020, 32(6): 064101. DOI: 10.1063/5.0008493

DURAISAMY K, ZHANG Z J, SINGH A P. New approaches in turbulence and transition modeling using data-driven techniques[C]//Proceedings of the 53rd AIAA Aerospace Sciences Meeting, Kissimmee, Florida. Reston, Virginia: AIAA, 2015: AIAA2015-1284. doi: 10.2514/6.2015-1284

ZHANG T X, CHEN J Q, ZENG F Z, et al. Improvement of transition prediction model in hypersonic boundary layer based on field inversion and machine learning framework[J]. Physics of Fluids, 2023, 35(2): 064101. DOI: 10.1063/5.0137819

ZHAO Y M, XU X W. Data-driven turbulence modelling based on gene-expression programming[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 2640-2655. (in Chinese)

AKOLEKAR H D, WASCHKOWSKI F, ZHAO Y M, et al. Transition modeling for low pressure turbines using computational fluid dynamics driven machine learning[J]. Energies, 2021, 14(15): 4680. DOI: 10.3390/en14154680

ZHENG T Y, WANG S Y, WANG G X, et al. High-order natural transition simulation method based on deep residual network[J]. Acta Physica Sinica, 2020, 69(20): 204701. (in Chinese)

WU L, CUI B, XIAO Z L. Artificial neural network-based one-equation model for simulation of laminar-turbulent transitional flow[J]. Theoretical and Applied Mechanics Letters, 2023, 13(1): 100387. DOI: 10.1016/j.taml.2022.100387

CHEN J Q, TU G H, WAN B B, et al. Characteristics of flow field and boundary-layer stability of HyTRV[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(6): 124317. (in Chinese)

CROUCH J D, CROUCH I W M, NG L. Transition prediction for three-dimensional boundary layers in computational fluid dynamics applications[J]. AIAA Journal, 2002, 40: 1536-1541. DOI: 10.2514/3.15228

ZAFAR M I, XIAO H, CHOUDHARI M M, et al. Convolutional neural network for transition modeling based on linear stability theory[J]. Physical Review Fluids, 2020, 5(11): 113903. DOI: 10.1103/physrevfluids.5.113903

ZAFAR M I, CHOUDHARI M M, PAREDES P, et al. Recurrent neural network for end-to-end modeling of laminar-turbulent transition[J]. Data-Centric Engineering, 2021, 2: e17. DOI: 10.1017/dce.2021.11

XIANG X H, REN H J, ZHANG Y F, et al. Transition prediction with hypersonic cross-flow model on HIFiRE-5[J]. Journal of Physics: Conference Series, 2021, 1786(1): 012051. DOI: 10.1088/1742-6596/1786/1/012051

XIANG X H, ZHANG Y F, CHEN J Q, et al. Uncertainty quantification analysis and application research on cross-flow transition model parameters[J]. Journal of Astronautics, 2020, 41(9): 1141-1150. (in Chinese)

XIANG X H, CHEN J Q, YUAN X X, et al. Cross-flow transition model predictions of hypersonic transition research vehicle[J]. Aerospace Science and Technology, 2022, 122: 107327. DOI: 10.1016/j.ast.2022.107327

TU G H, WAN B B, CHEN J Q, et al. Investigation on correlation between wind tunnel and flight for boundary layer stability and transition of MF-1 blunt cone[J]. Scientia Sinica (Physica, Mechanica Astronomica), 2019, 49(12): 118-128. (in Chinese)

CHEN S Y, JIANG T, CHANG Y, et al. Hypersonic boundary layer transition over blunt nosetipped bodies: experiment[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(12): 124098. (in Chinese)

REN H J. WANG S, YUAN X X, et al. Prediction of hypersonic transition based on neural network[J]. Physics of Fluids, 2022, 34: 054106.

SLOTNICK J, KHODADOUST A, ALONSO J, et al. CFD vision 2030 study: a path to revolutionary computational aerosciences[J]. Mchenry County Natural Hazards Mitigation Plan, 2014.

PARISH E J, DURAISAMY K. A paradigm for data-driven predictive modeling using field inversion and machine learning[J]. Journal of Computational Physics, 2016, 305: 758-774. DOI: 10.1016/j.jcp.2015.11.012

SINGH A P, DURAISAMY K. Using field inversion to quantify functional errors in turbulence closures[J]. Physics of Fluids, 2016, 28(4): 045110. DOI: 10.1063/1.4947045

SINGH A P, MEDIDA S, DURAISAMY K. Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils[J]. AIAA Journal, 2017, 55(7): 2215-2227. DOI: 10.2514/1.j055595

YAN C Y, ZHANG Y F, CHEN H X. Application of field inversion based on discrete adjoint method in turbulence modeling. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524695.

YAN C Y, ZHANG Y F, CHEN H X. Data augmented turbulence modeling for three-dimensional separation flows[J]. Physics of Fluids, 2022, 34(7): 075101. DOI: 10.1063/5.0097438

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. DOI: 10.1103/physrevfluids.2.034603

WANG J X, HUANG J J, DUAN L, et al. Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning[J]. Theoretical and Computational Fluid Dynamics, 2019, 33(1): 1-19. DOI: 10.1007/s00162-018-0480-2

WU J L, XIAO H, PATERSON E. Physics-informed machine learning approach for augmenting turbulence models: a comprehensive framework[J]. Physical Review Fluids, 2018, 3(7): 074602. DOI: 10.1103/physrevfluids.3.074602

XIAO H, WU J, WANG J, et al. Physics informed machine learning for predictive turbulence modeling: Progress and perspectives: 2017-1712 [J]. Reston: AIAA, 2017.

XIAO H, WU J L, LAIZET S, et al. Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations[J]. Computers Fluids, 2020, 200: 104431. DOI: 10.1016/j.compfluid.2020.104431

LING J L, KURZAWSKI A, TEMPLETON J. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance[J]. Journal of Fluid Mechanics, 2016, 807: 155-166. DOI: 10.1017/jfm.2016.615

NOVATI G, DE LAROUSSILHE H L, KOUMOUTSAKOS P. Automating turbulence modelling by multi-agent reinforcement learning[J]. Nature Machine Intelligence, 2021, 3(1): 87-96. DOI: 10.1038/s42256-020-00272-0

SUN X X, CAO W B, LIU Y L, et al. High Reynolds number airfoil turbulence modeling method based on machine learning technique[J]. Computers Fluids, 2022, 236: 105298. DOI: 10.1016/j.compfluid.2021.105298

ZHU L Y, SUN X X, LIU Y L, et al. One neural network approach for the surrogate turbulence model in transonic flows[J]. Acta Mechanica Sinica, 2022, 38(3): 1-14. DOI: 10.1007/s10409-021-09057-z

ZHU L Y, ZHANG W W, KOU J Q, et al. Machine learning methods for turbulence modeling in subsonic flows around airfoils[J]. Physics of Fluids, 2019, 31(1): 015105. DOI: 10.1063/1.5061693

ZHU L Y, ZHANG W W, SUN X X, et al. Turbulence closure for high Reynolds number airfoil flows by deep neural networks[J]. Aerospace Science and Technology, 2021, 110: 106452. DOI: 10.1016/j.ast.2020.106452

XIE C Y, WANG J C, LI H, et al. Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence[J]. Physics of Fluids, 2019, 31(8): 085112. DOI: 10.1063/1.5110788

XIE C Y, XIONG X M, WANG J C. Artificial neural network approach for turbulence models: a local framework[J]. Physical Review Fluids, 2021, 6(8): 084612. DOI: 10.1103/physrevfluids.6.084612

WANG Y P, YUAN Z L, XIE C Y, et al. Artificial neural network-based spatial gradient models for large-eddy simulation of turbulence[J]. AIP Advances, 2021, 11(5): 055216. DOI: 10.1063/5.0053590

WEATHERITT J, SANDBERG R. A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship[J]. Journal of Computational Physics, 2016, 325: 22-37. DOI: 10.1016/j.jcp.2016.08.015

WEATHERITT J, SANDBERG R D, LING J L, et al. A comparative study of contrasting machine learning frameworks applied to RANS modeling of jets in crossflow[C]// ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, Charlotte, North Carolina, USA. 2017. doi: 10.1115/GT2017-63403

BRUNTON S L, NOACK B R, KOUMOUTSAKOS P. Machine learning for fluid mechanics[J]. Annual Review of Fluid Mechanics, 2020, 52: 477-508. DOI: 10.1146/annurev-fluid-010719-060214

DURAISAMY K, IACCARINO G, XIAO H. Turbulence modeling in the age of data[J]. Annual Review of Fluid Mechanics, 2019, 51: 357-377. DOI: 10.1146/annurev-fluid-010518-040547

ZHANG W W, ZHU L Y, LIU Y L, et al. Progresses in the application of machine learning in turbulence modeling[J]. Acta Aerodynamica Sinica, 2019, 37(3): 444-454. (in Chinese)

BECK A, KURZ M. A perspective on machine learning methods in turbulence modeling[J]. GAMM-Mitteilungen, 2021, 44(1): e202100002. DOI: 10.1002/gamm.202100002

MENTER F R. Improved two-equation k-omega turbulence models for aerodynamic flows: NASA-TM-103975[R]. NTRS, 1992.

WU J L, XIAO H, SUN R, et al. Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned[J]. Journal of Fluid Mechanics, 2019, 869: 553-586. DOI: 10.1017/jfm.2019.205

HE K F, QIAN W Q, WANG Q, et al. Application ofdata fusion technique in aerodynamics studies[J]. ACTA Aerodynamica Sinica, 2014, 32(6): 777-782.

PETTERSSON K, RIZZI A. Aerodynamic scaling to free flight conditions: past and present[J]. Progress in Aerospace Sciences, 2008, 44(4): 295-313. DOI: 10.1016/j.paerosci.2008.03.002

CHEN J Q, ZHANG Y R, ZHANG Y F, et al. Review pf cprrelatipn analysis pf aerpdynamic data between flight and grpund predictipn fpr hyperspnic vehicle[J]. Acta Aerodynamica Sinica, 2014, 32(5): 587-599. (in Chinese)

HE L, QIAN W Q, WANG Q, et al. Applications of machine learning for aerodynamic characteristics modeling[J]. Acta Aerodynamica Sinica, 2019, 37(3): 470-479. (in Chinese)

BREVAULT L, BALESDENT M, HEBBAL A. Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities, application to aerospace systems[J]. Aerospace Science and Technology, 2020, 107: 106339. DOI: 10.1016/j.ast.2020.106339

CHEN S, DING Y, SUN H, et al. Methods and applications of physical information deep learning in wave numerical simulation[J]. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(1): 272-282. (in Chinese)

MENG X H, KARNIADAKIS G E. A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems[J]. Journal of Computational Physics, 2020, 401: 109020. DOI: 10.1016/j.jcp.2019.109020

HAN C Z, ZHU H Y, DUAN Z S. Multi-source information fusion[M]. 2nd ed. Beijing: Tsinghua University Press, 2010. (in Chinese)

MENG T, JING X Y, YAN Z, et al. A survey on machine learning for data fusion[J]. Information Fusion, 2020, 57: 115-129. DOI: 10.1016/j.inffus.2019.12.001

LIU C S, TANG C L, MU H B, et al. A data fusion approach with high spatiotemporal resolution for wall temperature measurement upon jet impingement[J]. International Journal of Heat and Mass Transfer, 2022, 183: 122084. DOI: 10.1016/j.ijheatmasstransfer.2021.122084

KOU J Q, ZHANG W W. Dynamic mode decomposition and its applications in fluid dynamics[J]. Acta Aerodynamica Sinica, 2018, 36(2): 163-179. (in Chinese)

ZHAO Y. High-precision control method for wind speed of large transonic wind tunnel[D]. Shenyang: Northeastern University, 2019.

WANG X, NING C J, WANG W Z, et al. Intelligent fusion method of multi-source aerodynamic data for flight tests[J]. Acta Aerodynamica Sinica, 2023, 41(2): 12-20. (in Chinese)

HE L, QIAN W Q, ZHAO T, et al. Multi-fidelity aerodynamic data fusion with a deep neural network modeling method[J]. Entropy, 2020, 22(9): 1022. DOI: 10.3390/e22091022

MENG X H, BABAEE H, KARNIADAKIS G E. Multi-fidelity Bayesian neural networks: Algorithms and applications[J]. Journal of Computational Physics, 2021, 438: 110361. DOI: 10.1016/j.jcp.2021.110361

LADICKÝ L, JEONG S, SOLENTHALER B, et al. Data-driven fluid simulations using regression forests[J]. ACM Transactions on Graphics, 34(6)Article No. 199. doi: 10.1145/2816795.2818129

WANG Y Q, DENG L, WAN Y B, et al. An intelligent method for predicting the pressure coefficient curve of airfoil-based conditional generative adversarial networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(7): 3538-3552. DOI: 10.1109/TNNLS.2021.3111911

SEKAR V, JIANG Q H, SHU C, et al. Fast flow field prediction over airfoils using deep learning approach[J]. Physics of Fluids, 2019, 31(5): 057103. DOI: 10.1063/1.5094943

RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707. DOI: 10.1016/j.jcp.2018.10.045

RAISSI M, WANG Z C, TRIANTAFYLLOU M S, et al. Deep learning of vortex-induced vibrations[J]. Journal of Fluid Mechanics, 2019, 861: 119-137. DOI: 10.1017/jfm.2018.872

RAISSI M, YAZDANI A, KARNIADAKIS G E. Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations[J]. Science, 2020, 367(6481): 1026-1030. DOI: 10.1126/science.aaw4741

ZHU Y H, ZABARAS N, KOUTSOURELAKIS P S, et al. Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data[J]. Journal of Computational Physics, 2019, 394: 56-81. DOI: 10.1016/j.jcp.2019.05.024

GENEVA N, ZABARAS N. Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks[J]. Journal of Computational Physics, 2020, 403: 109056. DOI: 10.1016/j.jcp.2019.109056

KOU J Q, ZHANG W W. Data-driven modeling for unsteady aerodynamics and aeroelasticity[J]. Progress in Aerospace Sciences, 2021, 125: 100725.DOI: 10.1016/j.paerosci.2021.100725

WANG Z, WANG Z Y, WANG X, et al. A data-driven aeroheating prediction model[J]. Acta Aerodynamica Sinica, 2023, 41(5): 12-19 (in Chinese).doi: 10.7638/kqdlxxb-2022.0010

LI T, GUO L, YANG Z G, et al. An automatic shape-aware method for predicting heat flux of supersonic aircraft based on a deep learning approach[J]. Physics of Fluids, 2022, 34(7). doi: 10.1063/5.0098341

LIANG Z. A novel intelligent feature detection and recognition method of fluid fields[J]. Computer Engineering Science. 2009, 31 (5): 27–30.

ZHANG L, DENG Q, MACHIRAJU R, et al. Boosting techniques for physics-based vortex detection[J]. Computer Graphics Forum, 2014, 33(1): 282-293. DOI: 10.1111/cgf.12275

BISWAS A, THOMPSON D, HE W B, et al. An uncertainty-driven approach to vortex analysis using oracle consensus and spatial proximity[C]// 2015 IEEE Pacific Visualization Symposium (PacificVis), Hangzhou, China. IEEE, 2015: 223-230. doi: 10.1109/PACIFICVIS.2015.7156381

BIN T. CNN-based flow field feature visualization method[J]. International Journal of Performability Engineering, 2018: 434. doi: 10.23940/ijpe.18.03.p4.434444

LGUENSAT R, SUN M, FABLET R, et al. EddyNet: a deep neural network for pixel-wise classification of oceanic eddies[C]// IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain. IEEE, 2018: 1764-1767. doi: 10.1109/IGARSS.2018.8518411

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. DOI: 10.4208/cicp.oa-2018-0035

RAJENDRAN V, KELLY K Y, LEONARDI E, et al. Vortex detection on unsteady CFD simulations using recurrent neural networks[C]// 2018 Fluid Dynamics Conference, Atlanta, Georgia. Reston, Virginia: AIAA, 2018: AIAA2018-3724. doi: 10.2514/6.2018-3724

LIU F Y, ZHOU H, WEN B Y. DEDNet: offshore eddy detection and location with HF radar by deep learning[J]. Sensors, 2020, 21(1): 126. DOI: 10.3390/s21010126

JAKOB J, GROSS M, GÜNTHER T. A fluid flow data set for machine learning and its application to neural flow map interpolation[J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(2): 1279-1289. DOI: 10.1109/TVCG.2020.3028947

POPINET S. Free Computational Fluid Dynamics [J]. ClusterWorld. 2004, 2 (6): 7.

KASHIR B, RAGONE M, RAMASUBRAMANIAN A, et al. Application of fully convolutional neural networks for feature extraction in fluid flow[J]. Journal of Visualization, 2021, 24(4): 771-785. DOI: 10.1007/s12650-020-00732-0

ABRAS J, HARIHARAN N S. Application of machine learning to automate flow-physics identification in computed solutions: hover rotor wake vortex identification and breakdown analysis[C]// AIAA Scitech 2021 Forum, VIRTUAL EVENT. Reston, Virginia: AIAA, 2021: AIAA 2021-0474. doi: 10.2514/6.2021-0474

CHEN T Q, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proc of the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA. New York: ACM, 2016: 785-794. doi: 10.1145/2939672.2939785

YE Z X, CHEN Q A, LI B H, et al. Flow structure segmentation for vortex identification using butterfly convolutional neural networks[J]. International Journal of Modern Physics B, 2020, 34(14n16): 2040121. DOI: 10.1142/s0217979220401219

LUO Y Y, SHAO Y H, CHU H Y, et al. CNN-based blade tip vortex region detection in flow field[C]//Proc SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 2020, 11373: 182-187. doi: 10.1117/12.2557248

DENG L, BAO W C, WANG Y Q, et al. Vortex-U-Net: an efficient and effective vortex detection approach based on U-Net structure[J]. Applied Soft Computing, 2022, 115: 108229.DOI: 10.1016/j.asoc.2021.108229

AKIBA H, FOUT N, MA K. Simultaneous classification of time-varying volume data based on the time histogram [C]// Eurographics Conference on Visualization, 2006. doi: 10.2312/VisSym/EuroVis06/171-178

WANG C L, YU H F, MA K L. Importance-driven time-varying data visualization[J]. IEEE Transactions on Visualization and Computer Graphics, 2008, 14(6): 1547-1554. DOI: 10.1109/TVCG.2008.140

WOODRING J, SHEN H W. Multiscale time activity data exploration via temporal clustering visualization spreadsheet[J]. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(1): 123-137. DOI: 10.1109/TVCG.2008.69

MYERS K, LAWRENCE E, FUGATE M, et al. Partitioning a large simulation as it runs[J]. Technometrics, 2016, 58(3): 329-340. DOI: 10.1080/00401706.2016.1158740

LING J L, KEGELMEYER W P, ADITYA K, et al. Using feature importance metrics to detect events of interest in scientific computing applications[C]// 2017 IEEE 7th Symposium on Large Data Analysis and Visualization (LDAV), Phoenix, AZ, USA. IEEE, 2017: 55-63. doi: 10.1109/LDAV.2017.8231851

PORTER W P, XING Y H, VON OHLEN B R, et al. A deep learning approach to selecting representative time steps for time-varying multivariate data[C]//Proc of the 2019 IEEE Visualization Conference (VIS), Vancouver, BC, Canada. IEEE, 2019: 1-5. doi: 10.1109/VISUAL.2019.8933759

LIU Y, LU Y T, WANG Y Q, et al. Key time steps selection for CFD data based on deep metric learning[J]. Computers Fluids, 2019, 195: 104318. DOI: 10.1016/j.compfluid.2019.104318

BROMLEY J, GUYON I, LECUN Y, et al. Signature verification using a “siamese” time delay neural network [J]. Advances in Neural Information Processing Systems (NIPS). 1993, 6: 737–744.

TONG X, LEE T Y, SHEN H W. Salient time steps selection from large scale time-varying data sets with dynamic time warping[C]//Proc of the IEEE Symposium on Large Data Analysis and Visualization (LDAV), Seattle, WA, USA. IEEE, 2012: 49-56. doi: 10.1109/LDAV.2012.6378975

FREY S, ERTL T. Flow-based temporal selection for interactive volume visualization[J]. Computer Graphics Forum, 2017, 36(8): 153-165. DOI: 10.1111/cgf.13070

DA SILVA L T, GIRALDI G A. Fluid flow summarization technique for computational fluid dynamics analysis [J]. Journal of Magnetohydrodynamics and Plasma Research. 2016, 21 (4): 471–509.

LU K C, WANG C L, WU K Q, et al. A unified framework for exploring time-varying volumetric data based on block correspondence[J]. Visual Informatics, 2019, 3(4): 157-165. DOI: 10.1016/j.visinf.2019.10.001

LIU C, LI Y D, YANG C H, et al. Event-based exploration and comparison on time-varying ensembles[J]. Journal of Visualization, 2020, 23(1): 111-123. DOI: 10.1007/s12650-019-00608-y

CHEN L C, FU D Y. Survey on machine learning methods for small sample data[J]. Computer Engineering, 2022, 48(11): 1-13. (in Chinese)

LI F C, LIU Y, WU P X, et al. A survey on recent advances in meta-learning[J]. Chinese Journal of Computers, 2021, 44(2): 422-446. (in Chinese)

XU Z B. Ten fundamental problems for artificial intelligence: mathematical and physical aspects[J]. Scientia Sinica (Informationis), 2021, 51(12): 1967-1978. (in Chinese)

O’CONNELL M, SHI G Y, SHI X C, et al. Neural-Fly enables rapid learning for agile flight in strong winds[J]. Science Robotics, 2022, 7(66): eabm6597. DOI: 10.1126/scirobotics.abm6597

FAWZI A, BALOG M, HUANG A, et al. Discovering faster matrix multiplication algorithms with reinforcement learning[J]. Nature, 2022, 610(7930): 47-53. DOI: 10.1038/s41586-022-05172-4

LI K L, YANG W D, CHEN C, et al. Efficient computing for artificial intelligence and big data[J]. Frontiers of Data Computing, 2020, 2(1): 27-37. (in Chinese)

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