[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).