机器学习技术在气动优化中的应用

doi:10.7527/S1000-6893.2018.22480

Abstract

Abstract: While design optimization has been widely used in the aerodynamic design in the last decades, its incapability to efficiently obtain applicable solutions has seriously limited its utilization potential. The proposal and utilization of so-called "man-in-loop" methodology (to use expert's experience to interfere the optimization process) in supercritical wing design of commercial airliners has shown impressive performance improvement. Regarding the rapid development of machine learning technology in recent decades and to improve both the efficiency and applicability of the current aerodynamic optimization methods, this article proposes to leverage machine learning technology to imitate and substitute experts' rational interference inside the optimization loop to automatically further utilize essential information generated during optimization. For such purposes, this article reviews the history of the development and utilization of machine learning technology related to aerodynamic optimization and presents several typical cases based on authors' engineering practices. Furthermore, this article discusses possible utilization forms of deep learning technology in aerodynamic optimization.

Key words: aerodynamic optimization, machine learning, aerodynamic design, Computational Fluid Dynamics(CFD), deep learning

CLC Number:

V211.3

CHEN Haixin, DENG Kaiwen, LI Runze. Utilization of machine learning technology in aerodynamic optimization[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(1): 522480-522480.

References

[1] 张宇飞,陈海昕,符松,等. 一种实用的运输类飞机机翼/发动机短舱一体化优化设计方法[J]. 航空学报, 2012, 33(11):1993-2001. ZHANG Y F, CHEN H X, FU S, et al.A practical optimization design method for transport aircraft wing/nacelle integration[J]. Acta Aeronautica et Astronautica Sinica, 2012, 33(11):1993-2001(in Chinese).
[2] 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 aircraft based on advanded CFD[D]. Beijing:Tsinghua University, 2010(in Chinese).
[3] HICKS R N, MURMAN E M, VAN DER PLAATS G N. An assessment of airfoil design by numerical optimization:NASA-TM-X-3092[R]. Washington, D.C.:NASA, 1974.
[4] VAN DER PLAATS G N, HICKS R N, MURMAN E M. Application of numerical optimization techniques to airfoil design[C]//Proceedings of Aerodynamic Analyses Requiring Advanced Computers. Washington, D.C.:NASA, 1975:749.
[5] VAN DER PLAATS G N, HICKS R M. Numerical airfoil optimization using a reduced number of design coordin-ates:NASA-TM-X-73151[R]. Washington, D.C.:NASA, 1976.
[6] HICKS R N, VAN DER PLAATS G N. Application of numerical optimization to the design of supercritical airfoils without drag-creep[EB/OL]. SAE Technical Paper (1977-02-01).https://doi.org/10.4271/770440.
[7] VAN DER PLAATS G N. Approximation concepts for numerical airfoil optimization:NASA-TP-1370[R]. Was-hington, D.C.:NASA, 1979.
[8] 王晓鹏, 高正红. 应用自适应遗传算法的气动优化设计[J]. 计算物理, 2000, 17(5):573-578. WANG X P, GAO Z H.Aerodynamic optimization design through self-adaptive genetic algorithm[J]. Chinese Journal of Computational Physics, 2000, 17(5):573-578(in Chinese).
[9] JONES B R, CROSSLEY W A, LYRINTZIS A S. Aerodynamic and aeroacoustic optimization of rotorcraft airfoils via a parallel genetic algorithm[J]. Journal of Aircraft, 2000, 37(6):1088-1096.
[10] VICINI A, QUAGLIARELLA D. Inverse and direct airfoil design using a multiobjective genetic algorithm[J]. AIAA Journal, 1997, 35(9):1499-1505.
[11] OBAVASHI S, TAKANASHI S. Genetic algorithm for aerodynamic inverse optimization problems[C]//First International Conference on Genetic Algorithms in Engineering Systems:Innovation and Applications.Piscataway,NJ:IEEE Press, 1995:7-12.
[12] HUTCHINSON M G, UNGER E R, MASON W H, et al. Variable-complexity aerodynamic optimization of a high speed civil transport wing[J]. Journal of Aircraft, 1994, 31(1):110-116.
[13] QUAGLIARELLA D, VICINI A, ITALIANO C, et al. Sub-population policies for a parallel multiobjective genetic algorithm with applications to wing design[C]//IEEE International Conference on Systems, Man, and Cybernetics. Piscataway,NJ:IEEE Press, 1998, 4:3142-3147.
[14] SASAKI D, MORIKAWA M, OBAYASHI S, et al. Aerodynamic shape optimization of supersonic wings by adaptive range multiobjective genetic algorithms[C]//International Conference on Evolutionary Multi-Criterion Optimization. Berlin:Springer, 2001:639-652.
[15] OYAMA A, OBAYASHI S, NAKAHASHI K. Real-coded adaptive range genetic algorithm applied to transonic wing optimization[J]. Applied Soft Computing, 2001, 1(3):179-187.
[16] POLONI C. Hybrid GA for multi-objective aerodynamic shape optimization[C]//Proceedings of Genetic Algorithms in Engineering and Computer Science.New York:Wiley, 1995:397-416.
[17] VICINI A, QUAGLIARELLA D. Multipoint transonic airfoil design by means of a multiobjective genetic algorithm[C]//35th Aerospace Sciences Meeting and Exhibit. Reston, VA:AIAA,1997:82.
[18] MÄKINEN R A E, PERIAUX J, TOIVANEN J. Multidisciplinary shape optimization in aerodynamics and electromagnetics using genetic algorithms[J]. International Journal for Numerical Methods in Fluids, 1999, 30(2):149-159.
[19] LIU Z, LIU X C X. A new hybrid aerodynamic optimization framework based on differential evolution and invasive weed optimization[J]. Chinese Journal of Aeronautics, 2018, 31(7):1437-1448.
[20] JAMESON A, MARTINELLV L. Aerodynamic shape optimization techniques based on control theory[C]//29th AIAA Fluid Dynamics Conference. Reston, VA:AIAA,1998:2538.
[21] REUTHER J, JAMESON A, FARMER J, et al. Aerodynamic shape optimization of complex aircraft configurations via an adjoint formulation[C]//34th Aerospace Sciences Meeting and Exhibit. Reston, VA:AIAA,1996:94.
[22] REUTHER J, AIONSO J J, RIMLINGER M J, et al. Aerodynamic shape optimization of supersonic aircraft configurations via an adjoint formulation on distributed memory parallel computers[J]. Computers & Fluids, 1999, 28(4-5):675-700.
[23] REUTHER J, JAMESON A, ALONSO J J, et al. Constrained multipoint aerodynamic shape optimization using an adjoint formulation and parallel computers, Part I[J]. Journal of Aircraft, 1999, 36(1):51-60.
[24] 黄勇, 陈作斌, 刘刚. 基于伴随方程的翼型数值优化设计方法研究[J]. 空气动力学学报, 1999, 17(4):413-422. HUANG Y, CHEN Z B, LIU G. An investigation of aerodynamic optimization design for airfoil based on adjoint formulation[J]. Acta Aerodynamica Sinica, 1999, 17(4):413-422(in Chinese).
[25] ANDERSON W K, VENKATAKRISHNAN V. Aerodynamic design optimization on unstructured grids with a continuous adjoint formulation[J]. Computers & Fluids, 1999, 28(4):443-480.
[26] ZINGG D W, NEMEC M, PULLIAM T H. A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization[J]. European Journal of Computational Mechanics, 2008, 17(1-2):103-126.
[27] GIUNTA A. Aircraft multidisciplinary design optimization using design of experiments theory and response surface modeling methods[D]. Blacksburg:Virginia Polytechnic Institute and State University, 1997.
[28] KIRN Y, KIM J, JEON Y, et al. Response surface models combining linear and Euler aerodynamics for supersonic transport design[C]//40th AIAA Aerospace Sciences Meeting and Exhibit. Reston, VA:AIAA, 2002.
[29] KNILL D L, GIUNTA A A, BAKER C A, et al. Response surface models combining linear and Euler aerodynamics for supersonic transport design[J]. Journal of Aircraft, 1999, 36(1):75-86.
[30] MADSEN J I, SHYY W, HAFTKA R T. Response surface techniques for diffuser shape optimization[J]. AIAA Journal, 2000, 38(9):1512-1518.
[31] QUEIPO N V, HAFTKA R T, SHYY W, et al. Surrogate-based analysis and optimization[J]. Progress in Aerospace Sciences, 2005, 41(1):1-28.
[32] SEVANT N E, BLOOR M I, WILSON M J. Aerodynamic design of a flying wing using response surface methodology[J]. Journal of Aircraft, 2000, 37(4):562-569.
[33] YAO W, CHEN X, LUO W, et al. Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles[J]. Progress in Aerospace Sciences, 2011, 47(6):450-479.
[34] KUMAR A, KEANE A J, NAIR P B, et al. Robust design of compressor fan blades against erosion[J]. Journal of Mechanical Design, 2006, 128(4):864.
[35] JU Y P, ZHANG C H. Multi-point robust design optimization of wind turbine airfoil under geometric uncertainty[J]. Proceedings of the Institution of Mechanical Engineers Part A:Journal of Power and Energy, 2012, 226(A2):245-261.
[36] HUANG J T,GAO Z H,ZHAO K, et al. Robust design of supercritical wing aerodynamic optimization considering fuselage interfering[J]. Chinese Journal of Aeronautics, 2010, 23(5):523-528.
[37] SHIMOYAMA K, OYAMA A, FUJⅡ K. Robust aerodynamic airfoil design optimization against wind variations for mars exploratory airplane[C]//57th International Astronautical Congress.Reston, VA:AIAA, 2006:1-7.
[38] CLARICH A, PEDIRODA V. A competitive game approach for multi-objective robust design optimization:AIAA-2004-6511[R]. Reston, VA:AIAA, 2004.
[39] LIANG Y, CHENG X, LI Z, et al. Robust multi-objective wing design optimization via CFD approximation model[J]. Engineering Applications of Computational Fluid Mechanics, 2011, 5(2):286-300.
[40] DODSON M, PARKS G T. Robust aerodynamic design optimization using polynomial chaos[J]. Journal of Aircraft, 2009, 46(2):635-646.
[41] PADULO M, CAMPOBASSO M S, GUENOV M D. Novel uncertainty propagation method for robust aerodynamic design[J]. AIAA Journal, 2011, 49(3):530-543.
[42] 白俊强, 王波, 孙智伟,等. 超临界翼型稳健型优化设计研究[J]. 空气动力学学报, 2011, 29(4):459-463. BAI J Q, WANG B, SUN Z W, et al. The research of robust supercritical airfoil design optimization[J]. Acta Aerodynamica Sinica, 2011, 29(4):459-463(in Chinese).
[43] 李焦赞, 高正红. 气动设计问题中确定性优化与稳健优化的对比研究[J]. 航空计算技术, 2010, 40(2):28-31. LI J Z, GAO Z H. Comparison computation of deterministic optimization and robust optimization in aerodynamic design[J]. Aeronautical Computing Technique, 2010, 40(2):28-31(in Chinese).
[44] 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).
[45] MARUSIC I, CANDLER G V, INTERRANTE V, et al. Real time feature extraction for the analysis of turbulent flows[M]//GROSSMAN R L, KAMATH C, KEGELMEYER P, et al. Data mining for scientific and engineering applications. Berlin:Springer, 2001:223-238.
[46] MILANO M, KOUMOUTSAKOS P. Neural network modeling for near wall turbulent flow[J]. Journal of Computational Physics, 2002, 182(1):1-26.
[47] BERMEJO-MORENO I, PULLIN D I. On the non-local geometry of turbulence[J]. Journal of Fluid Mechanics, 2008, 603:101-135.
[48] LING J, TEMPLETON J. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty[J]. Physics of Fluids, 2015, 27(8):085103.
[49] RAY J, LEFANTZI S, ARUNAJATESAN S, et al. Bayesian parameter estimation of a k-ε model for accurate jet-in-crossflow simulations[J]. AIAA Journal, 2016, 54(8):2432-2448.
[50] EDELING W N, CINNELLA P, DWIGHT R P, et al. Bayesian estimates of parameter variability in the k-ε turbulence model[J]. Journal of Computational Physics, 2014, 258:73-94.
[51] CHEUNG S H, OLIVER T A, PRUDENCIO E E, et al. Bayesian uncertainty analysis with applications to turbulence modeling[J]. Reliability Engineering and System Safety, 2011, 96(9):1137-1149.
[52] OLIVER T A, MOSER R D. Bayesian uncertainty quantification applied to RANS turbulence models[J].Journal of Physics:Conference Series,2011, 318(4):042032.
[53] YARLANKI S, RAJENDRAN B, HAMANN H. Estimation of turbulence closure coefficients for data centers using machine learning algorithms[C]//13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems. Piscataway, NJ:IEEE Press,2012:38-42.
[54] DOW E, WANG Q. Uncertainty quantification of structural uncertainties in RANS simulations of complex flows[C]//20th AIAA Computational Fluid Dynamics Conference. Reston, VA:AIAA, 2011.
[55] DOW E, WANG Q. Quantification of structural uncertainties in the k-w turbulence model[C]//52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston, VA:AIAA, 2011:41-51.
[56] DURAISAMY K, ZHANG Z J, SINGH A P. New approaches in turbulence and transition modeling using data-driven techniques[C]//53rd AIAA Aerospace Sciences Meeting. Reston, VA:AIAA, 2015.
[57] 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.
[58] TRACEY B, DURAISAMY K, ALONSO J. Application of supervised learning to quantify uncertainties in turbulence and combustion modeling[C]//51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, VA:AIAA, 2013.
[59] XIAO H, WU J L, WANG J X, et al. Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations:A data-driven, physics-informed Bayesian approach[J]. Journal of Computational Physics, 2016, 324:115-136.
[60] LING J, RUIZ A, LACAZE G, et al. Uncertainty analysis and data-driven model advances for a jet-in-crossflow[J]. Journal of Turbomachinery, 2016, 139(2):021008.
[61] LING J, JONES R, TEMPLETON J. Machine learning strategies for systems with invariance properties[J]. Journal of Computational Physics, 2016, 318:22-35.
[62] 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.
[63] KUTZ J N. Deep learning in fluid dynamics[J]. Journal of Fluid Mechanics, 2017, 814:1-4.
[64] WANG J, WU J, 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.
[65] WANG J, WU J, LING J, et al. A comprehensive physics-informed machine learning framework for predictive turbulence modeling[EB/OL].(2017-05-24)[2018-08-12]. https://arxiv.org/abs/1701.07102.
[66] XIAO H, WU J, WANG J, et al. Physics-informed machine learning for predictive turbulence modeling:Progress and perspectives[C]//Proceedings of the 2017 AIAA SciTech. Reston, VA:AIAA, 2017.
[67] CHEN X, AGARWAL R. Optimization of flatback airfoils for wind-turbine blades using a genetic algorithm[J]. Journal of Aircraft, 2012, 49(2):622-629.
[68] LAMARSH W. Aerodynamic performance optimization of a rotor blade using a neural network as the analysis[C]//4th AIAA/USAF/NASA/OAI Symposium on Multidisciplinary Analysis and Optimization. Reston, VA:AIAA, 1992:4837.
[69] SU W, ZUO Y, GAO Z. Preliminary aerodynamic shape optimization using genetic algorithm and neural network[C]//AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, VA:AIAA, 2006:576-581.
[70] 朱莉, 高正红. 基于神经网络的翼型优化设计方法研究[J]. 航空计算技术, 2007, 37(3):33-36. ZHU L, GAO Z H. Aerodynamic optimization design of airfoil based on neural networks[J]. Aeronautical Computing Technique, 2007, 37(3):33-36(in Chinese).
[71] MENGISTU T, GHALY W. Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based Surrogate models[J]. Optimization and Engineering, 2008, 9(3):239-255.
[72] 蒙文巩, 马东立, 楚亮. 基于神经网络响应面的机翼气动稳健性优化设计[J]. 航空学报, 2010, 31(6):1134-1140. MENG W G, MA D L, CHU L. Wing aerodynamic robustness optimization based on neural network response surface[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(6):1134-1140(in Chinese).
[73] HARYANTO I, UTOMO T S, SINAGA N,et al. Optimization of maximum lift to drag ratio on airfoil design based on artificial neural network utilizing genetic algorithm[J]. Applied Mechanics and Materials, 2014, 493(2014):123-128.
[74] 周晨, 王志瑾, 支骄杨. 基于Isight的自适应翼型前缘气动优化设计[J]. 上海交通大学学报, 2014, 48(8):1122-1133. ZHOU C, WANG Z J, ZHI J Y. Aerodynamic optimization design of adaptive airfoil leading edge based on Isight[J]. Journal of Shanghai Jiaotong University, 2014, 48(8):1122-1133(in Chinese).
[75] 王宏亮, 席光. 多目标优化设计方法在翼型气动优化中的应用研究[J]. 工程热物理学报, 2008, 29(7):51-54. WANG H L, XI G. Investigation of multi-objective optimization method of airfoil design[J]. Journal of Engineering Thermophysics, 2008, 29(7):51-54(in Chinese).
[76] ZHANG B, WANG T, GU C G, et al. An integrated blade optimization approach based on parallel ANN and GA with hierarchical fair competition dynamic-niche[J]. Journal of Mechanical Science and Technology, 2011, 25(6):1457-1463.
[77] 黄江涛, 高正红, 白俊强,等. 基于任意空间属性FFD技术的融合式翼稍小翼稳健型气动优化设计[J]. 航空学报, 2013, 34(1):37-45. HUANG J T, GAO Z H, BAI J Q, et al. The study of winglet robust design based on arbitrary space shape FFD technique[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(1):37-45(in Chinese).
[78] 张彬乾, 罗烈, 陈真利,等. 飞翼布局隐身翼型优化设计[J]. 航空学报, 2014, 35(4):957-967. ZHANG B Q, LUO L, CHEN Z L, et al.On stealth airfoil optimization design for flying wing configuration[J].Acta Aeronautica et Astronautica Sinica, 2014, 35(4):957-967(in Chinese).
[79] LIU X, ZHU Q L H. Modeling multiresponse surfaces for airfoil design with multiple-output-Gaussian-process regression[J]. Journal of Aircraft, 2014, 51(3):740-747.
[80] 韩忠华. Kriging模型及代理优化算法研究进展[J]. 航空学报, 2016, 37(11):3197-3225. HAN Z H. Kriging surrogate model and its application to design optimization:A review of recent progress[J].Acta Aeronautica et Astronautica Sinica, 2016, 37(11):3197-3225(in Chinese).
[81] JU Y, ZHANG C, MA L. Artificial intelligence metamodel comparison and application to wind turbine airfoil uncertainty analysis[J]. Advances in Mechanical Engineering, 2016, 8(5):1-14.
[82] 倪昂修. Kriging响应面和GA相结合的气动优化设计方法[D]. 北京:清华大学, 2014. NI A X. Aerodynamic optimization method composed of genetic algorithm and Kriging resopnse surface[D]. Beijing:Tsinghua University, 2014(in Chinese).
[83] 倪昂修, 张宇飞, 陈海昕. NSGA-Ⅱ算法的改进及其在多段翼型缝道参数优化中的应用[J]. 空气动力学学报, 2014, 32(2):252-257. NI A X, ZHANG Y F, CHEN H X. An improvement to NSGA-Ⅱ algorithm and its application in optimization design of multi-element airfoil[J]. Acta Aerodynamica Sinica, 2014, 32(2):252-257(in Chinese).
[84] 方晓明. 跨音速喷流短舱的气动优化设计研究[D]. 北京:清华大学, 2018. FANG X M. Astudy on powered-on transonic nacelle aerodynamic optimization[D]. Beijing:Tsinghua University, 2018(in Chinese).
[85] 邓凯文, 陈海昕. 基于差分进化和RBF响应面的混合优化算法[J]. 力学学报, 2017, 49(2):441-455. DENG K W, CHEN H X. Hybrid optimization algorithm based on differential evolution and RBF response surface[J]. Chinese Journal of Theoretical and Applied Mechanics, 2017, 49(2):441-455(in Chinese).
[86] DENG K W, CHEN H X. A hybrid aerodynamic optimization algorithm based on differential evolution and RBF response surface[C]//17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, VA:AIAA, 2016.
[87] GIANNAKOGLOU K C, GIOTIS A P, KARAKASIS M K. Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters[J]. Inverse Problems in Engineering, 2001, 9(4):389-412.
[88] PRAVEEN C, DUVIGNEAU R. Low cost PSO using metamodels and inexact pre-evaluation:Application to aerodynamic shape design[J]. Computer Methods in Applied Mechanics and Engineering, 2009, 198(9-12):1087-1096.
[89] ASOUTI V G, KAMPOLIS I C, GIANNAKOGLOU K C. A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization[J]. Genetic Programming and Evolvable Machines, 2009, 10(4):373-389.
[90] HACIOGLU A. Fast evolutionary algorithm for airfoil design via neural network[J]. AIAA Journal, 2007, 45(9):2196-2203.
[91] SHAHROKHI A, JAHANGIRIAN A. A surrogate assisted evolutionary optimization method with application to the transonic airfoil design[J]. Engineering Optimization, 2010, 42(6):497-515.
[92] PEHLIVANOGLU Y V, YAGIZ B. Aerodynamic design prediction using surrogate-based modeling in genetic algorithm architecture[J]. Aerospace Science and Technology, 2012, 23(1):479-491.
[93] IULIANO E, PÉREZ E A. Application of surrogate-based global optimization to aerodynamic design[M]. Berlin:Springer, 2015.
[94] RAI M M. Aerodynamic design using neural networks[J]. AIAA Journal, 2000, 38(1):173-182.
[95] PAPILA N, SHYY W, GRIFFIN L W, et al. Shape optimization of supersonic turbines using response surface and neural network methods[C]//39th Aerospace Sciences Meeting and Exhibit. Reston, VA:AIAA, 2001:1065.
[96] SU W, GAO Z, ZUO Y. Application of RBF neural network ensemble to aerodynamic optimization[C]//46th AIAA Aerospace Sciences Meeting and Exhibit. Reston, VA:AIAA, 2008.
[97] CHIBA K, OBAYASHI S. Knowledge discovery for flyback-booster aerodynamic wing design using data mining[J]. Journal of Spacecraft and Rockets, 2008, 45(5):975-987.
[98] GUO Z, SONG L, LI J, et al. Research on meta-model based global design optimization and data mining methods[J]. Journal of Engineering for Gas Turbines and Power, 2016, 138(28):092604.
[99] JUNG S, CHOI W, MARTINS-FILHO L S, et al. An implementation of self-organizing maps for airfoil design exploration via multi-objective optimization technique[J]. Journal of Aerospace Technology and Management, 2016, 8(2):193-202.
[100] 司景喆, 孙刚. 基于神经网络的风机叶片叶尖翼型设计[J]. 力学季刊, 2012, 33(4):672-678. SI J Z, SUN G. Design of the wind turbine blade airfoil based on SOM[J]. Chinese Quarterly of Mechanics, 2012, 33(4):672-678(in Chinese).
[101] KAPSOULIS D, TSIAKAS K, TROMPOUKIS X, et al. Evolutionary multi-objective optimization assisted by metamodels, kernel PCA and multi-criteria decision making techniques with applications in aerodynamics[J]. Applied Soft Computing, 2018, 64:1-13.
[102] TOAL D J J, BRESSLOFF N W, KEANE A J. Geometric filtration using POD for aerodynamic design optimization[C]//26th AIAA Applied Aerodynamics Conference. Reston, VA:AIAA, 2008:6584.
[103] LEGRESLEY P A, ALONSO J J. Investigation of non-linear projection for POD based reduced order models for aerodynamics[C]//39th AIAA Aerospace Sciences Meeting and Exhibit. Reston, VA:AIAA, 2001:926.
[104] PARK K H, JUN S O, BAEK S M, et al. Reduced-order model with an artificial neural network for aerostructural design optimization[J]. Journal of Aircraft, 2013, 50(4):1106-1116.
[105] THOMAS J P, DOWELL E H, HALL K C. Three-dimensional transonic aeroelasticity using proper orthogonal decomposition based reduced order models[J]. Journal of Aircraft, 2012, 40(40):544-551.
[106] AGARWAL A, BIEGLER L T. A trust-region framework for constrained optimization using reduced order modeling[J]. Optimization and Engineering, 2013, 14(1):3-35.
[107] ZAHR M J, FARHAT C. Progressive construction of a parametric reduced-order model for PDE-constrained optimization[J]. International Journal for Numerical Methods in Engineering, 2015, 102(5):1111-1135.
[108] 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.
[109] KANI J N, ELSHEIKH A H. DR-RNN:A deep residual recurrent neural network for model reduction[EB/OL]. (2017-09-04). https://arxiv.org/abs/1709.00939.
[110] FARIMANI A B, GOMES J, PANDE V S. Deep learning the physics of transport phenomena[EB/OL]. (2017-09-07)[2018-08-12]. http://arxiv.org/abs/1709.02432.
[111] 邓凯文. 基于机器学习的流场结构导向气动优化设计[D]. 北京:清华大学, 2018. DENG K W. Machine learning based flow field structure oriented aerodynamic optimization design[D]. Beijing:Tsinghua University, 2018(in Chinese).
[112] 陈海, 钱炜祺, 何磊. 基于深度学习的翼型气动系数预测[J]. 空气动力学学报, 2018, 36(2):294-299. CHEN H, QIAN W Q, HE L. Aerodynamic coefficient prediction of airfoils based on deep learning[J]. Acta Aerodynamica Sinica, 2018, 36(2):294-299(in Chinese).

Utilization of machine learning technology in aerodynamic optimization

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[1]	Haifeng WANG, Kunpeng LIU, Hongxin JIANG, Chenxi DU. Aerodynamic optimization method of propeller multi⁃design points and variable pitch angle strategy [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(9): 528831-528831.
[2]	Jiaqi LIU, Rongqian CHEN, Jinhua LOU, Xu HAN, Hao WU, Yancheng YOU. Aerodynamic shape optimization of high-speed helicopter rotor airfoil based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(9): 529828-529828.
[3]	Pengbo SUN, Zhou ZHOU, Xu LI, Kelei WANG. Influence analysis and optimization of distribution-propulsion-wing parameters with target aerodynamic characteristics [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 629368-629368.
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[10]	Jinyi MA, Can WANG, Tao XUE, Jianliang AI, Yiqun DONG. Development and illustrative applications of an air combat engagement database [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S1): 727538-727538.
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