数据与知识联合赋能的民机智能气动设计

doi:10.7527/S1000-6893.2024.31485

Abstract

Abstract:

With the rapid advancement of high-performance computing and artificial intelligence technologies， data-driven AI models have been extensively researched in the field of civil aircraft aerodynamic design， demonstrating significant potential in design space compression， key feature extraction， flow field prediction， and intelligent optimization design. However， the application of purely data-driven models in engineering design still faces many challenges， including the scarcity and high acquisition cost of domain-specific data， as well as deficiencies in model reliability， generality， interpretability， and usability. Integrating physical knowledge and aerodynamic design experience into model development has become a key approach to addressing these challenges， providing an important direction for advancing technology in this field. This paper， from the perspective of civil aircraft engineering design and supported by relevant practices in intelligent aerodynamic design， reviews recent theories and progress in data- and knowledge-driven AI models in the areas of knowledge embedding， knowledge correction， and knowledge discovery. It further explores the current state of research and application potential of data- and knowledge-driven methods in civil aircraft aerodynamic design， while offering insights into the future of new paradigms in intelligent aerodynamic design.

Key words: aerodynamic design, intelligent design, data-driven, knowledge-driven, artificial intelligence

CLC Number:

V211.4

Guanghui WU, Jing WANG, Hairun XIE, Tuliang MA, Qiang MIAO, Jixin XIANG, Miao ZHANG. Data and knowledge-enabled intelligent aerodynamic design for civil aircraft[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(5): 531485.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

References 125

1	MARTINS J R R A. Aerodynamic design optimization： Challenges and perspectives［J］. Computers & Fluids， 2022， 239： 105391.
2	MARTINS J R R A. A coupled-adjoint method for high-fidelity aero-structural optimization［D］. California： Stanford University， 2003.
3	KIM H J， NAKAHASHI K， OBAYASHI S， et al. Aerodynamic optimization of supersonic transport wing using unstructured adjoint method［J］. AIAA Journal， 2001， 39（6）： 1011-1020.
4	ARIAS-MONTAÑO A， COELLO COELLO C A， MEZURA-MONTES E. Evolutionary algorithms applied to multi-objective aerodynamic shape optimization［M］∥Computational optimization， methods and algorithms. Berlin， Heidelberg： Springer， 2011： 211-240.
5	LI R Z， ZHANG M， ZHANG M H， et al. Multi-point aerodynamic optimization design of a dual-aisle airplane wing［C］∥31st Congress of the International Council of the Aeraonautical Sciences. Beijing： ICAS， 2018.
6	HUANG J T， ZHU Z， GAO Z H， et al. Aerodynamic multi-objective integrated optimization based on principal component analysis ［J］. Chinese Journal of Aeronautics， 2017， 30（4）： 1336-1348.
7	OBAYASHI S， JEONG S， CHIBA K. Multi-objective design exploration for aerodynamic configurations［C］∥35th AIAA Fluid Dynamics Conference and Exhibit. Reston： AIAA， 2005.
8	ZUHAL L R， PALAR P S， SHIMOYAMA K. A comparative study of multi-objective expected improvement for aerodynamic design［J］. Aerospace Science and Technology， 2019， 91： 548-560.
9	WANG HC， FU TF， DU Y， et al. Scientific discovery in the age of artificial intelligence［J］. Nature， 2023， 620（7972）： 47-60.
10	LI J C， BOUHLEL M A， MARTINS JOAQUIM R R A. Data-based approach for fast airfoil analysis and optimization ［J］. AIAA Journal， 2019， 57（2）： 581-596.
11	LIU R L， HUA Y， ZHOU Z F， et al. Prediction and optimization of airfoil aerodynamic performance using deep neural network coupled Bayesian method ［J］. Physics of Fluids， 2022， 34（11）： 117116.
12	HERNÁNDEZ-LOBATO J M， ADAMS R P. Probabilistic backpropagation for scalable learning of Bayesian neural networks［C］∥International Conference on Machine Learning. 2015.
13	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.
14	WANG J， HE C， LI R Z， et al. Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework［J］. Physics of Fluids， 2021， 33（8）： 086108.
15	LI J C， DU X S， MARTINS JOAQUIM R R A. Machine learning in aerodynamic shape optimization［J］. Progress in Aerospace Sciences， 2022， 134： 100849.
16	KOU J Q， ZHANG W W. Data-driven modeling for unsteady aerodynamics and aeroelasticity［J］. Progress in Aerospace Sciences， 2021， 125： 100725.
17	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. Application of machine learning techniques in aerodynamic optimization［J］. Acta Aeronautica et Astronautica Sinica， 2019， 40（1）： 522480 （in Chinese）.
18	孙刚，王聪，王立悦，等. 人工智能在气动设计中的应用与展望［J］. 民用飞机设计与研究， 2021（3）： 1-9， 147.
	SUN G， WANG C， WANG L Y， et al. Application and prospect of artificial intelligence in aerodynamic design［J］. Civil Aircraft Design & Research， 2021（3）： 1-9， 147 （in Chinese）.
19	DONG Y Q， TAO J， ZHANG Y M， et al. Deep learning in aircraft design， dynamics， and control： Review and prospects［J］. IEEE Transactions on Aerospace and Electronic Systems， 2021， 57（4）： 2346-2368.
20	LE CLAINCHE S， FERRER E， GIBSON S， et al. Improving aircraft performance using machine learning： A review［J］. Aerospace Science and Technology， 2023， 138： 108354.
21	GUIDOTTI R， MONREALE A， RUGGIERI S， et al. A survey of methods for explaining black box models［J］. ACM computing surveys （CSUR）， 2018， 51（5）： 1-42.
22	SHAFIQUE M， NASEER M， THEOCHARIDES T， et al. Robust machine learning systems： Challenges， current trends， perspectives， and the road ahead［J］. IEEE Design & Test， 2020， 37（2）： 30-57.
23	JOURDAN N， SEN S， HUSOM E J， et al. On the reliability of machine learning applications in manufacturing environments［DB/OL］. arXiv preprint： 2112.06986， 2021.
24	DOSHI-VELEZ F， KIM B. Considerations for evaluation and generalization in interpretable machine learning［M］∥Explainable and interpretable models in computer vision and machine learning. Cham： Springer， 2018： 3-17.
25	BARBIERO P， SQUILLERO G， TONDA A. Modeling generalization in machine learning： A methodological and computational study［DB/OL］. arXiv preprint： 2006.15680， 2020.
26	ROSCHER R， BOHN B， DUARTE M F， et al. Explainable machine learning for scientific insights and discoveries［J］. IEEE Access， 2020， 8： 42200-42216.
27	LI R Z， ZHANG Y F， CHEN H X. Physically interpretable feature learning of supercritical airfoils based on variational autoencoders［J］. AIAA Journal， 2022， 60（11）： 6168-6182.
28	李润泽，张宇飞，陈海昕. “人在回路” 思想在飞机气动优化设计中演变与发展［J］. 空气动力学学报， 2017， 35（4）： 529-543.
	LI R Z， ZHANG Y F， CHEN H X. Evolution and development of “man-in-loop” in aerodynamic optimization design［J］. Acta Aerodynamica Sinica， 2017， 35（4）： 529-543 （in Chinese）.
29	CAI S， MAO Z， WANG Z， et al. Physics-informed neural networks （PINNs） for fluid mechanics： A review［J］. Acta Mechanica Sinica， 2021， 37（12）： 1727-1738.
30	ZHAO T， ZHANG Y F， CHEN H X， et al. Supercritical wing design based on airfoil optimization and 2.75D transformation［J］. Aerospace Science and Technology， 2016， 56： 168-182.
31	INGER G R. Application of Oswatitsch’s theorem to supercritical airfoil drag calculation［J］. Journal of aircraft， 1993， 30（3）： 415-416.
32	PEARCEY H H. Some effects of shock-induced separation of turbulent boundary layers in transonic flow past aerofoils： R. & M. No. 3108［R］. London： Her Majesty’s Stationery Office， 1959.
33	MASON W. Analytic models for technology integration in aircraft design［C］∥Aircraft Design， Systems and Operations Conference. Reston： AIAA， 1990.
34	LI R Z， ZHANG Y F， CHEN H X. Knowledge discovery with computational fluid dynamics： Supercritical airfoil database and drag divergence prediction［J］. Physics of Fluids， 2023， 35（1）： 016113
35	陈迎春，张美红，张淼，等. 大型客机气动设计综述［J］. 航空学报， 2019， 40（1）： 522759.
	CHEN Y C， ZHANG M H， ZHANG M， et al. Review of large civil aircraft aerodynamic design ［J］. Acta Aeronautica et Astronautica Sinica， 2019， 40（1）： 522759 （in Chinese）.
36	李润泽，张宇飞，陈海昕. 超临界机翼多目标气动优化设计的策略与方法［J］. 航空学报， 2020， 41（5）： 623409.
	LI R Z， ZHANG Y F， CHEN H X. Strategies and methods for multi-objective aerodynamic optimization design for supercritical wings［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（5）： 623409 （in Chinese）.
37	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.
38	YAN C Y， LI H R， ZHANG Y F， et al. Data-driven turbulence modeling in separated flows considering physical mechanism analysis［J］. International Journal of Heat and Fluid Flow， 2022， 96： 109004.
39	DENG Z W， WANG J， LIU H S， et al. Prediction of transonic flow over supercritical airfoils using geometric-encoding and deep-learning strategies［J］. Physics of Fluids， 2023， 35（7）： 075146.
40	CHEN L W， THUEREY N. Towards high-accuracy deep learning inference of compressible flows over aerofoils［J］. Computers & Fluids， 2023， 250： 105707.
41	LONG Z， LU Y， MA X， et al. PDE-net： Learning PDEs from data［C］∥International Conference on Machine Learning. 2018.
42	QU J G， CAI W H， ZHAO Y J. Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network［J］. Journal of Computational Physics， 2022， 453： 110928.
43	RAO C P， REN P， WANG Q， et al. Encoding physics to learn reaction-diffusion processes［J］. Nature Machine Intelligence， 2023， 5： 765-779.
44	YE Z H， HUANG X， CHEN L H， et al. PDEFORMER： Towards a foundation model for one-dimensional partial differential equations［DB/OL］. arXiv preprint： 2402.12652， 2024.
45	YU Y， YAO H P， LIU Y M. Aircraft dynamics simulation using a novel physics-based learning method［J］. Aerospace Science and Technology， 2019， 87： 254-264.
46	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.
47	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.
48	YANG Y J， LI R Z， ZHANG Y F， et al. Flow field prediction of airfoil off-design conditions based on a modified variational autoencoder［J］. AIAA Journal， 2022， 60（10）： 5805-5820.
49	CHEN Y T， HUANG D， ZHANG D X， et al. Theory-guided hard constraint projection （hcp）： A knowledge-based data-driven scientific machine learning method［J］. Journal of Computational Physics， 2021， 445： 110624.
50	DU M G， CHEN Y T， ZHANG D X. Autoke： An automatic knowledge embedding framework for scientific machine learning［J］. IEEE Transactions on Artificial Intelligence， 2023； 4（6）： 1564-1578.
51	HOLLAND J R， BAEDER J D， DURAISAMY K. Field inversion and machine learning with embedded neural networks： Physics-consistent neural network training［C］∥AIAA Aviation 2019 Forum. Reston： AIAA， 2019.
52	DOHERTY P， LUKASZEWICZ W， SZALAS A. Knowledge representation techniques： A rough set approach［M］∥Studies in Fuzziness and Soft Computing. Berlin： Springer， 2006.
53	PAWAR S， SAN O， AKSOYLU B， et al. Physics guided machine learning using simplified theories［J］. Physics of Fluids， 2021， 33（1）： 011701.
54	WANG X， KOU J Q， ZHANG W W， et al. Incorporating physical models for dynamic stall prediction based on machine learning［J］. AIAA Journal， 2022， 60（7）： 4428-4439.
55	XIE H R， WANG J， ZHANG M. Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils［J］. Expert Systems with Applications， 2023， 233： 121002.
56	LI S P， SNAIKI R， WU T. A knowledge-enhanced deep reinforcement learning-based shape optimizer for aerodynamic mitigation of wind-sensitive structures［J］. Computer-Aided Civil and Infrastructure Engineering， 2021， 36（6）： 733-746.
57	LI R Z， ZHANG Y F， CHEN H X. Transfer learning from two-dimensional supercritical airfoils to three-dimensional transonic swept wings［J］. Chinese Journal of Aeronautics， 2023， 36（9）： 96-110.
58	ANDRÉS-PÉREZ E. Data mining and machine learning techniques for aerodynamic databases： Introduction， methodology and potential benefits［J］. Energies， 2020， 13（21）： 5807.
59	MAIMON O， ROKACH L. Data mining and knowledge discovery handbook： Vol. 2［M］. New York： Springer-Verlag， 2005.
60	HO T B. Knowledge discovery［J］. Knowledge Science， 2016： 70-93.
61	UDRESCU S M， TEGMARK M. AI Feynman： A physics-inspired method for symbolic regression［J］. Science Advances， 2020， 6（16）： eaay2631.
62	PETER J E， DWIGHT R P. Numerical sensitivity analysis for aerodynamic optimization： A survey of approaches［J］. Computers & Fluids， 2010， 39（3）： 373-391.
63	CHIBA K， JEONG S， OBAYASHI S， et al. Knowledge discovery in aerodynamic design space for flyback-booster wing using data mining［C］∥14th AIAA/AHI Space Planes and Hypersonic Systems and Technologies Conference. Reston： AIAA， 2006.
64	CHIBA K， JEONG S， OBAYASHI S， et al. Data mining for multidisciplinary design space of regional-jet wing［C］∥2005 IEEE Congress on Evolutionary Computation. Piscataway： IEEE， 2005.
65	LUCHINI P， BOTTARO A. Adjoint equations in stability analysis［J］. Annual Review of Fluid Mechanics， 2014， 46： 493-517.
66	LIU G C， YAN S C. Active subspace： Toward scalable low-rank learning［J］. Neural Computation， 2012， 24 （12）： 3371-3394.
67	Xu J S， Lou F Y. A rapid method for turbomachinery aerodynamic design and optimization using active subspace［J］. Journal of the Global Power and Propulsion Society， 2024， 8： 24-38.
68	LUKACZYK T W， CONSTANTINE P， PALACIOS F， et al. Active subspaces for shape optimization［C］∥10th AIAA Multidisciplinary Design Optimization Conference. Reston： AIAA， 2014.
69	LUO J Q， LIU F. Statistical evaluation of performance impact of manufacturing variability by an adjoint method［J］. Aerospace Science and Technology， 2018， 77： 471-484.
70	ANGELIS D， SOFOS F， KARAKASIDIS T E. Artificial intelligence in physical sciences： Symbolic regression trends and perspectives［J］. Archives of Computational Methods in Engineering， 2023， 30（6）： 3845-3865.
71	MAKKE N， CHAWLA S. Interpretable scientific discovery with symbolic regression： A review［J］. Artificial Intelligence Review， 2024， 57（1）： 2.
72	KEREN L S， LIBERZON A， LAZEBNIK T. A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge［J］. Scientific Reports， 2023， 13： 1249.
73	SCHMELZER M， DWIGHT R， CINNELLA P. Data-driven deterministic symbolic regression of nonlinear stress-strain relation for RANS turbulence modelling［C］∥2018 Fluid Dynamics Conference. Reston： AIAA， 2018.
74	VADDIREDDY H， RASHEED A， STAPLES A E， et al. Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data［J］. Physics of Fluids， 2020， 32（1）： 015113.
75	WU C Y， ZHANG Y F. Enhancing the shear-stress-transport turbulence model with symbolic regression： A generalizable and interpretable data-driven approach［J］. Physical Review Fluids， 2023， 8（8）： 084604.
76	SARRADJ E， GEYER T. Symbolic regression modeling of noise generation at porous airfoils［J］. Journal of Sound and Vibration， 2014， 333（14）： 3189-3202.
77	GUO S Q， LIU W， ZHANG C N， et al. Aerodynamic optimization of hypersonic blunted waveriders based on symbolic regression［J］. Aerospace Science and Technology， 2024， 144： 108801.
78	RICHMOND-NAVARRO G， CALDERÓN-MUÑOZ W R， LEBOEUF R， et al. A Magnus wind turbine power model based on direct solutions using the blade element momentum theory and symbolic regression［J］. IEEE Transactions on Sustainable Energy， 2017， 8（1）： 425-430.
79	PEARL J. Causal inference in statistics： An overview［J］. Statistics Surveys， 2009， 3： 96-146.
80	QUAN F X， SUN X M， ZHAO H Y， et al. Detection of rotating stall inception of axial compressors based on deep dilated causal convolutional neural networks［J］. IEEE Transactions on Automation Science and Engineering， 2024， 21（2）： 1235-1243.
81	NEGREIRO D M P， JOKSIMOVIC A. Simple causal-network descriptive framework for aeroplane systems and the application to propulsive system sizing［C］∥AIAA Scitech 2024 Forum. Reston： AIAA， 2024.
82	WANG W， MO R， FAN Q M. Knowledge extraction from aerodynamic simulation data of compressor rotor［J］. Procedia Engineering， 2011， 15： 1792-1796.
83	VISWANATH A， FORRESTER A I J， KEANE A J. Dimension reduction for aerodynamic design optimization［J］. AIAA Journal， 2011， 49（6）： 1256-1266.
84	QIU Y S， BAI J Q， LIU N， et al. Global aerodynamic design optimization based on data dimensionality reduction［J］. Chinese Journal of Aeronautics， 2018， 31（4）： 643-659.
85	OYAMA A， NONOMURA T， FUJII K. Data mining of pareto-optimal transonic airfoil shapes using proper orthogonal decomposition［J］. Journal of Aircraft， 2010， 47（5）： 1756-1762.
86	WANG J， XIE H， ZHANG M， et al. Physics-assisted reduced-order modeling for identifying dominant features of transonic buffet［J］. Physics of Fluids， 2023， 35（6）： 066124.
87	WANG G G， SHAN S. Review of metamodeling techniques in support of engineering design optimization［C］∥Proceedings of ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. New York： ASME， 2006.
88	MACKMAN T J， ALLEN C B， GHOREYSHI M， et al. Comparison of adaptive sampling methods for generation of surrogate aerodynamic models［J］. AIAA Journal， 2013， 51（4）： 797-808.
89	AHMED M， QIN N. Surrogate-based aerodynamic design optimization： Use of surrogates in aerodynamic design optimization［J］. International Conference on Aerospace Sciences and Aviation Technology， 2009， 13： 1-26.
90	FORRESTER A I J， KEANE A J. Recent advances in surrogate-based optimization［J］. Progress in Aerospace Sciences， 2009， 45（1）： 50-79.
91	MACKMAN T J， ALLEN C B. Investigation of an adaptive sampling method for data interpolation using radial basis functions［J］. International Journal for Numerical Methods in Engineering， 2010， 83（7）： 915-938.
92	DENG K W， CHEN H X. A hybrid aerodynamic optimization algorithm based on differential evolution and RBF response surface［C］∥Proceedings of the 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston： AIAA， 2016.
93	LI R Z， DENG K W， ZHANG Y F， et al. Pressure distribution guided supercritical wing optimization［J］. Chinese Journal of Aeronautics， 2018， 31（9）： 1842-1854.
94	LIU J， HAN Z H， SONG W P. Comparison of infill sampling criteria in kriging-based aerodynamic optimization［C］∥28th Congress of the International Council of the Aeronautical Sciences. 2012.
95	TOAL D J J， KEANE A J. Efficient multipoint aerodynamic design optimization via cokriging［J］. Journal of Aircraft， 2011， 48（5）： 1685-1695.
96	HAN Z H， ZHANG Y， SONG C X， et al. Weighted gradient-enhanced Kriging for high-dimensional surrogate modeling and design optimization［J］. AIAA Journal， 2017， 55（12）： 4330-4346.
97	DASGUPTA S， HSU D. Hierarchical sampling for active learning［C］∥Proceedings of the 25th International Conference on Machine Learning-ICML ’08. New York： ACM， 2008.
98	MAYER C， TIMOFTE R. Adversarial sampling for active learning［C］∥2020 IEEE Winter Conference on Applications of Computer Vision （WACV）. Piscataway： IEEE Press， 2020.
99	LI R Z， ZHANG Y F， CHEN H X. Pressure distribution feature-oriented sampling for statistical analysis of supercritical airfoil aerodynamics［J］. Chinese Journal of Aeronautics， 2022， 35（4）： 134-147.
100	XU M F， SONG S F， SUN X X， et al. Machine learning for adjoint vector in aerodynamic shape optimization［J］. Acta Mechanica Sinica， 2021， 37（9）： 1416-1432.
101	LI J C， ZHANG M Q， MARTINS J R R A， et al. Efficient aerodynamic shape optimization with deep-learning-based geometric filtering［J］. AIAA Journal， 2020， 58（10）： 4243-4259.
102	LI J C， HE S C， ZHANG M Q， et al. Physics-based data-driven buffet-onset constraint for aerodynamic shape optimization［J］. AIAA Journal， 2022， 60（8）： 4775-4788.
103	LI J C， HE S C， MARTINS J R R A. Data-driven constraint approach to ensure low-speed performance in transonic aerodynamic shape optimization［J］. Aerospace Science and Technology， 2019， 92： 536-550.
104	YANG Y J， LI R Z， ZHANG Y F， et al. Buffet onset optimization for supercritical airfoils with prior-based pressure profile prediction model［C］∥Proceedings of the AIAA Scitech 2024 Forum. Reston： AIAA， 2024.
105	WU H， LUO H， WANG H， et. al . Transolver： A fast transformer solver for PDEs on general geometries［C］∥Forty-first International Conference on Machine Learning. 2024.
106	CAMPBELL R L， LYNDE M N. A knowledge-based optimization method for aerodynamic design［C］∥Proceedings of the AIAA Scitech 2019 Forum. Reston： AIAA， 2019.
107	李润泽. 流场结构-性能指标共同导向的优化设计［D］. 北京：清华大学， 2021.
	LI R Z. Optimization design jointly guided by flow field structures and performance metrics ［D］. Beijing： Tsinghua University， 2021 （in Chinese）.
108	张宇飞. 基于先进CFD方法的民用客机气动优化设计［D］. 北京：清华大学， 2010.
	ZHANG Y F. Aerodynamic optimization design of civil passenger aircraft based on advanced CFD method［D］. Beijing： Tsinghua University， 2010 （in Chinese）.
109	KINGMA D P， WELLING M. Auto-encoding variational bayes［C］∥2nd International Conference on Learning Representations. 2014.
110	GOODFELLOW I， POUGET-ABADIE J， MIRZA M， et al. Generative adversarial nets［C］∥NIPS’14： Proceedings of the 28th International Conference on Neural Information Processing Systems. 2014（2）： 2672-2680.
111	HO J， JAIN A， ABBEEL P. Denoising diffusion probabilistic models［J］. Advances in Neural Information Processing Systems， 2020， 33： 6840-6851.
112	SUN K B， WANG W T， CHENG R， et al. Evolutionary generative design of supercritical airfoils： An automated approach driven by small data［J］. Complex & Intelligent Systems， 2024， 10（1）： 1167-1183.
113	WANG C， WANG S Y， WANG L Y， et al. Framework of nacelle inverse design method based on improved generative adversarial networks［J］. Aerospace Science and Technology， 2022， 121： 107365.
114	CHEN W， CHIU K， FUGE M D. Airfoil design parameterization and optimization using Bézier generative adversarial networks［J］. AIAA Journal， 2020， 58（11）： 4723-4735.
115	CAMPBELL R L， LYNDE M N. A knowledge-based optimization method for aerodynamic design［C］∥AIAA Scitech 2019 Forum. Reston： AIAA， 2019.
116	LIU J， WU J Y， XIE H R， et al. AFBench： A large-scale benchmark for airfoil design［DB/OL］. arXiv preprint： 2406.18846， 2024.
117	WESTON D， ZEUNE C. Exercise of knowledge-based aerodynamic design process on the AFRL SUGAR model［C］∥AIAA Aviation 2022 Forum. Reston： AIAA， 2022.
118	李润泽，张宇飞，陈海昕. 针对超临界翼型气动修型策略的强化学习［J］. 航空学报， 2021， 42（4）： 523810.
	LI R Z， ZHANG Y F， CHEN H X. Reinforcement learning method for supercritical airfoil aerodynamic design［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（4）： 523810 （in Chinese）.
119	YAN X H， ZHU J H， KUANG M C， et al. Aerodynamic shape optimization using a novel optimizer based on machine learning techniques［J］. Aerospace Science and Technology， 2019， 86： 826-835.
120	QIN S， WANG S Y， WANG L Y， et al. Multi-objective optimization of cascade blade profile based on reinforcement learning［J］. Applied Sciences， 2020， 11（1）： 106.
121	LIU Z Y， ZHANG M， SUN D， et al. A deep reinforcement learning optimization framework for supercritical airfoil aerodynamic shape design［J］. Structural and Multidisciplinary Optimization， 2024， 67（3）： 34.
122	LI R Z， ZHANG Y F， CHEN H X. Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning［J］. AIAA Journal， 2021， 59（10）： 3988-4001.
123	HUI X Y， WANG H， LI W Q， et al. Multi-object aerodynamic design optimization using deep reinforcement learning［J］. AIP Advances， 2021， 11（8）： 085311.
124	VIQUERAT J， RABAULT J， KUHNLE A， et al. Direct shape optimization through deep reinforcement learning［J］. Journal of Computational Physics， 2021， 428： 110080.
125	DUSSAUGE T P， SUNG W J， PINON FISCHER O J， et al. A reinforcement learning approach to airfoil shape optimization［J］. Scientific Reports， 2023， 13： 9753.

[1]	Yu ZENG, Hongbo WANG, Chengyue LIAN, Yixin YANG, Dapeng XIONG, Mingbo SUN, Weidong LIU. Applicability analysis of two improved methods of SST turbulence model [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(S1): 730574-730574.
[2]	Guangjia LI, Hongbo WANG, Kai ZHANG, Zhisheng YI. Lift enhancement and drag reduction technologies of solar powered unmanned aerial vehicles in near space: Review [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(5): 529644-529644.
[3]	Shengzhe SHAN, Weiwei ZHANG. Air combat intelligent decision-making method based on self-play and deep reinforcement learning [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(4): 328723-328723.
[4]	Qi WANG, Long WU, Zhen LIU, Jianxia LIU, Liang XIA. Topology optimization design of thermoelastic multi-configuration gradient lattice structures [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(23): 230367-230367.
[5]	Mingqi LIU, Zhonghua HAN, Tao DU, Chenzhou XU, Han ZENG, Keshi ZHANG, Wenping SONG. Optimal control efficiency characteristics and wide-speed-range aerodynamic design optimization method for grid fins of launch vehicle [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(20): 129887-129887.
[6]	Junshen LI, Xiangyan MENG, Nuannuan SHI, Wei LI, Ninghua ZHU, Ming LI. Intelligent processing of optical neural networks: Technological evolution and future prospects [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(20): 630439-630439.
[7]	Xiaofei YANG, Tailu SUN, Dejun MENG, Haibao YIN, Yongmei WANG. Aerodynamic design method for core⁃driven fan stage considering multiple modes [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(2): 128625-128625.
[8]	Lizhuo DONG, Siqi ZHANG, Zhao ZHANG, Baohai WU. Prediction method of blade machining deformation driven by mechanism⁃data hybrid [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(13): 629037-629037.
[9]	Ximing CUI, Zhipeng QIU, Jia WEI, Chi ZHANG, Kai SONG, Zhe LI, Shupeng WANG. Data-driven method for characterization of structural steel surface stress of magnetic Barkhausen noise [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(8): 427237-427237.
[10]	Chunpeng LI, Zhansen QIAN, Xiasheng SUN. Trailing edge deformation matrix aerodynamic design for long-range civil aircraft variable camber wing [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(7): 127335-127335.
[11]	Linkun HE, Wenchao XUE, Ran ZHANG, Huifeng LI. Guidance and control for powered descent and landing of launch vehicles: Overview and outlook [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(23): 628462-628462.
[12]	Xuhui ZHANG, Chunlei XIE, Sijia LIU, Ming YAN, Siyuan XING. Development needs and difficulty analysis for smart morphing aircraft [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(21): 529302-529302.
[13]	Xiaofeng WANG, Feng QU, Junjie FU, Zeyu WANG, Chaoyu LIU, Junqiang BAI. Discrete adjoint-based aerodynamic design optimization for hypersonic inward turning inlet [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(19): 128352-128352.
[14]	Yuwei LIU, Yuqiang CHENG, Jianjun WU. Research progress of intelligent control methods in space propulsion systems [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(15): 528505-528505.
[15]	Haoge LI, Hua YANG, Yuxin YANG, Weifang CHEN. Refinement optimization design for heat reduction on windward surface of hypersonic lifting body [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S2): 124-137.

Data and knowledge-enabled intelligent aerodynamic design for civil aircraft

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 125

Related Articles 15

Recommended Articles

Metrics

Comments