[1] JAMESON A, VASSBERG J C. Computational fluid dynamics for aerodynamic design:Its current and future impact:AIAA-2001-0538[R]. Reston:AIAA, 2001.
[2] SLOTNICK J, KHODADOUST A, ALONSO J, et al. CFD vision 2030 study:A path to revolutionary computational aerosciences:NASA/CR-2014-218178[R]. Washington, D.C.:NASA, 2014.
[3] 周铸, 黄江涛, 黄勇, 等. CDF技术在航空工程领域的应用、挑战与发展[J]. 航空学报, 2017, 38(3):020891. ZHOU Z, HUANG J T, HUANG Y, et al. CFD technology in aeronautic engineering field:Applications, challenges and development[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(3):020891(in Chinese).
[4] 张淼, 刘铁军, 马涂亮, 等. 基于CFD方法的大型客机高速气动设计[J]. 航空学报, 2016, 37(1):244-254. ZHANG M, LIU T J, MA T L, et al. High speed aerodynamic design of large civil transporter based on CFD method[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(1):244-254(in Chinese).
[5] 白俊强, 雷锐午, 杨体浩, 等. 基于伴随理论的大型客机气动优化设计研究进展[J]. 航空学报, 2019, 40(1):522642. BAI J Q, LEI R W, YANG T H, et al. Progress of adjoint-based aerodynamic optimization design for large civil aircraft[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1):522642(in Chinese).
[6] 高正红, 王超. 飞行器气动外形设计方法研究与进展[J]. 空气动力学学报, 2017, 35(4):516-528. GAO Z H, WANG C. Aerodynamic shape design methods for aircraft:Status and trends[J]. Acta Aerodynamica Sinica, 2017, 35(4):516-528(in Chinese).
[7] 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).
[8] RUDER S. An overview of gradient descent optimization algorithms[EB/OL].(2016-01-19)[2019-08-06].http://sebastianruder.com/optimization-gradient-descent/index.html.[BFY]
[9] WRIGHT S J,NOCEDAL J. Numerical optimization[M]. New York:Springer, 1999.
[10] BYRD R H, HANSEN S L, NOCEDAL J, et al. A stochastic quasi-newton method for large-scale optimiza-tion[J]. SIAM Journal on Optimization, 2016, 26(2):1008-1031.
[11] POWELL M J D. Restart procedures for the conjugate gradient method[J]. Mathematical Programming, 1977, 12(1):241-254.
[12] KLAUS S, YUAN Y. Sequential quadratic programming methods[J]. Wiley Encyclopedia of Operations Research and Management Science, 2012, 154:147-224.
[13] JAMESON A. Aerodynamic design via control theory[J]. Journal of Scientific Computing, 1988, 3(3):233-260.
[14] JAMESON A. Optimum aerodynamic design using CFD and control theory:AIAA-1995-1729[R]. Reston:AIAA, 1995.
[15] KENWAY G K W, MADER C A, HE P, et al. Effective adjoint approaches for computational fluid dynamics[J]. Progress in Aerospace Sciences, 2019, 110:100542.
[16] LYU Z, KENWAY G K W, MARTINS J R R A. Aerodynamic shape optimization investigations of the common research model wing benchmark[J]. AIAA Journal. 2015, 53(4):968-985.
[17] GILL P E, MURRAY W, SAUNDERS M A. SNOPT:An SQP algorithm for large-scale constrained optimiza-tion[J]. SIAM Review, 2005, 47(1):99-131.
[18] MARTINS J R R A, HWANG J T. Review and unification of methods for computing derivatives of multidisciplinary computational models[J]. AIAA Journal, 2013, 51(11):2582-2599.
[19] CHERNUKHIN O, ZINGG D W. Multimodality and global optimization in aerodynamic design[J]. AIAA Journal, 2013, 51(6):1342-1354.
[20] JAISWAL N K. Heuristic optimization[M].Beilin Heidelberg:Springer, 1997:17-30.
[21] LEE K, El-SHARKAWI M. Modern heuristic optimization techniques:Theory and applications to power syst-ems[M]. Piscataway:Wiley-IEEE Press, 2008.
[22] GONG C L, GU L X. Genetic algorithm based global optimization algorithm used to solving system analysis of multidisciplinary optimization[C]//IEEE International Conference on Intelligent Computing and Intelligent Systems. Piscataway:IEEE Press, 2010:325-329.
[23] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
[24] DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I:Solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4):577-601.
[25] VENTER G, SOBIESZCZANSKI-SOBIESKI J. Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization[J]. Structural and Multidisciplinary Optimization, 2004, 26(1-2):121-131.
[26] SHAN S, WANG G G. Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions[J]. Structural and Multidisciplinary Optimization, 2010, 41(2):219-241.
[27] FORRESTER A I J, KEANE A J. Recent advances in surrogate-based optimization[J]. Progress in Aerospace Sciences, 2009, 45(1):50-79.
[28] GIANNAKOGLOU K C. Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence[J]. Progress in Aerospace Sciences, 2002, 38(1):43-76.
[29] SCHMIT L A, FARSHI B. Some approximation concepts for structural synthesis[J]. AIAA Journal, 1974, 12(5):692-699.
[30] GIUNTA A A, WATSON L T. A comparison of approximation modeling techniques:Polynomial versus interpolation models:AIAA-1998-4758[R]. Reston:AIAA, 1998.
[31] SIMPSON T W, BOOKER A J, GHOSH D, et al. Approximation methods in multidisciplinary analysis and optimization:A panel discussion[J]. Structural and Multidisciplinary Optimization, 2004, 27(5):302-313.
[32] SIMPSON T W, PEPLINSKI J, KOCH P N, et al. Metamodels for computer-based engineering design:Survey and recommendations[J]. Engineering with Computers, 2001, 17(2):129-150.
[33] VIANA F A C, SIMPSON T W, BALABANOV V, et al. Metamodeling in multidisciplinary design optimization:How far have we really come?[J] AIAA Journal, 2014, 52(4):670-690.
[34] KRIGE D G. A statistical approach to some basic mine valuation problems on the witwatersrand[J]. Journal of the Southern African Institute of Mining and Metallurgy, 1951, 52(6):119-139.
[35] SACKS J, WELCH W J, MITCHELL T J, et al. Design and analysis of computer experiments[J]. Statistical Science, 1989, 4(4):409-435.
[36] 穆雪峰, 姚卫星, 余雄庆, 等. 多学科设计优化中常用代理模型的研究[J]. 计算力学学报, 2005, 22(5):608-612. MU X F, YAO W X, YU X Q, et al. A survey of surrogate models used in MDO[J]. Chinese Journal of Computational Mechanics, 2005, 22(5):608-612(in Chinese).
[37] HAN Z H, ZHANG K S. Surrogate-based optimizat-ion[M]. InTech Book, 2012:343-362.
[38] FORRESTER A I J, SOBESTER A, KEANE A J. Engineering design via surrogate modeling:A practical guide[M]. Chichester:John Wiley & Sons, 2008.
[39] VAVALLE A, QIN N. Iterative response surface based optimization scheme for transonic airfoil design[J]. Journal of Aircraft, 2005, 42(2):413-420.
[40] KRISHNAMURTHY T. Response surface approximation with augmented and compactly supported radial basis functions:AIAA-2003-1748[R]. Reston:AIAA, 2003.
[41] SOBESTER A, LEARY S J, KEANE A J. On the design of optimization strategies based on global response surface approximation models[J]. Journal of Global Optimization, 2005, 33(1):31-59.
[42] YAO X, LIU Y. A new evolutionary system for evolving artificial neural networks[J]. IEEE Transactions on Neural Networks, 1997, 8(3):694-713.
[43] VATANDAS E. Hybridizing genetic algorithm with artificial neural network in the aerodynamic optimization of the forward swept wing:AIAA-2010-2915[R]. Reston:AIAA, 2010.
[44] BANDLER J W, BIERNACKI R, CHEN S H. Space mapping technique for electromagnetic optimization[J]. IEEE Transactions on Microwave Theory and Techniques, 1994, 42(12):2536-2544.
[45] KHALATPOUR A, AMINEH R K, CHENG Q S, et al. Accelerating space mapping optimization with adjoint sensitivities[J]. IEEE Microwave and Wireless Components Letters, 2011, 21(6):280-282.
[46] SMOLA A J, SCHOLKÖPF B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3):199-222.
[47] YUN Y, YOON M, NAKAYAMA H. Multi-objective optimization based on meta-modeling by using support vector regression[J]. Optimization and Engineering, 2009, 10(2):167-181.
[48] WANG Q, MOIN P, IACCARINO G. A rational interpolation scheme with super-polynomial rate of convergence[J]. SIAM Journal of Numerical Analysis, 2010, 47(6):4073-4097.
[49] WANG Q, MOIN P, IACCARINO G. A high-order multi-variate approximation scheme for arbitrary data sets[J]. Journal of Computational Physics, 2010, 229(18):6343-6361.
[50] CRESTAUX T, MAI[DD(-*2/3] [HT8.]^TRE O L, MARTINEZ J M. Polynomial chaos expansion for sensitivity analysis[J]. Reliability Engineering and System Safety, 2009, 94(7):1161-1172.
[51] NOVÁK L, NOVAK D. Polynomial chaos expansion for surrogate modelling:Theory and software[J]. Beton-und Stahlbetonbau, 2018, 113(2):27-32.
[52] FORRESTER A I J, KEANE A J, BRESSLOFF N W. Design and analysis of noisy computer experiments[J]. AIAA Journal, 2006, 44(10):2331-2339.
[53] LIU J, HAN Z H, SONG W P. Comparison of infill sampling criteria in Kriging-based aerodynamic optimizat-ion[C]//28th ICAS, 2012.
[54] LIU J, SONG W P, HAN Z H, et al. Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models[J]. Structural and Multidisciplinary Optimization, 2017, 55(3):925-943.
[55] ZHANG Y, HAN Z H, ZHANG K S. Variable-fidelity expected improvement for efficient global optimization of expensive functions[J]. Structural and Multidisciplinary Optimization, 2018, 58(4):1431-1451.
[56] JONES D R, SCHONLAU M, WELCH W J. Efficient global optimization of expensive black-box functions[J]. Journal of Global Optimization, 1998, 13(4):455-492.
[57] LOCATELLI M. Bayesian algorithms for one-dimensional global optimization[J]. Journal of Global Optimization, 1997, 10(1):57-76.
[58] BATTITI R, BRUNATO M. The LION way:Machine learning plus intelligent optimization[M]//Create Space Independent Publishing Platform, 2014.
[59] 陈海昕, 邓凯文, 李润泽. 机器学习技术在气动优化中的应用[J]. 航空学报, 2019, 40(1):522480. CHEN H X, DENG K W, LI R Z. Utilization of machine learning technology in aerodynamic optimization[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1):522480(in Chinese).
[60] 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.
[61] KOCH P N, SIMPSON T W, ALLEN J K, et al. Statistical approximations for multidisciplinary design optimization:The problem of the size[J]. Journal of Aircraft, 1999, 36(1):275-286.
[62] 余雄庆. 飞机总体多学科设计优化的现状与发展方向[J]. 南京航空航天大学学报, 2008, 40(4):417-426. YU X Q. Multidisciplinary design optimization for aircraft conceptual and preliminary design:Status and directi-ons[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2008, 40(4):417-426(in Chinese).
[63] SEVANT N E, BLOOR M I G, WILSON M J. Aerodynamic design of a flying wing using response surface methodology[J]. Journal of Aircraft, 2000, 37(4):562-569.
[64] FERNANDEZ G, PARK C Y, KIM N H, et al. Issues in deciding whether to use multifidelity surrogates[J]. AIAA Journal, 2019, 57(5):2039-2054.
[65] PARK C Y, HAFTKA R T, KIM N H. Remarks on multi-fidelity surrogates[J]. Structural and Multidisciplinary Optimization, 2017, 55(3):1029-1050.
[66] HAFTKA R T, VILLANUEVA D, CHAUDHURI A. Parallel surrogate-assisted global optimization with expensive functions-a survey[J]. Structural and Multidisciplinary Optimization, 2016, 54(1):3-13.
[67] VIANA F A C, HAFTKA R T. Surrogate-based optimization with parallel simulations using the probability of improvement:AIAA-2010-9392[R]. Reston:AIAA, 2010.
[68] VIANA F A C, HAFTKA R T, WATSON L T. Efficient global optimization algorithm assisted by multiple surrogate techniques[J]. Journal of Global Optimization, 2013, 56(2):669-689.
[69] BOUHLEL M A, MARTINS J R R A. Gradient-enhanced Kriging for high-dimensional problems[J]. Engineering with Computers, 2018(1):1-17.
[70] BOUHLEL M A, HWANG J T, BARTOLI N, et al. A python surrogate modeling framework with derivatives[J]. Advances in Engineering Software, 2019, 135:102662.
[71] LI J C, BOUHLEL M A, MARTINS J R R A. A data-based approach for fast airfoil analysis and optimization:AIAA-2018-1383[R]. Reston:AIAA, 2018.
[72] MADSEN M H A, ZAHLE F, SØRENSEN N N, et al. Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW wind turbine[J]. Wind Energy Science, 2019, 4:163-192.
[73] GRAY J S, HWANG J T, MARTINS J R R A, et al. OpenMDAO:An open-source framework for multidisciplinary design, analysis, and optimization[J]. Structural and Multidisciplinary Optimization, 2019, 59(4):1075-1104.
[74] HE X L, LI J C, MADER C, et al. Robust aerodynamic shape optimization-From a circle to an airfoil[J]. Aerospace Science and Technology, 2019, 87:48-61.
[75] BONS N P, HE X L, MADER C, et al. Multimodality in aerodynamic wing design optimization[J]. AIAA Journal, 2019, 57(3):1004-1018.
[76] CHEN S, LYU P Z J, KENWAY G, et al. Aerodynamic shape optimization of the common research model wing-body-tail configuration[J]. Journal of Aircraft, 2016, 53(1):276-293.
[77] DU X S, REN J, LEIFSSON L. Aerodynamic inverse design using multifidelity models and manifold mapping[J]. Aerospace Science and Technology, 2018, 85:371-385.
[78] THELEN A, LEIFSSON L, SHARMA A, et al. Variable-fidelity shape optimization of dual-rotor wind turbines[J]. Engineering Computations, 35(7):2514-2542.
[79] AMRIT A, LEIFSSON L, KOZIEL S. Multi-fidelity aerodynamic design trade-off exploration using point-by-point pareto set identification[J]. Aerospace Science and Technology, 2018, 79:399-412.
[80] LEIFSSON L, KOZIEL S, OWEN D R J. Adaptive response prediction for aerodynamic shape optimization[J]. Engineering Computations, 2017, 34(5):1485-1500.
[81] AMRIT A, LEIFSSON L, KOZIEL S. Design strategies for multi-objective optimization of aerodynamic surfaces[J]. Engineering Computations, 2017, 34(5):1724-1753.
[82] AMRIT A, LEIFSSON L, KOZIEL S T, et al. Efficient multi-objective aerodynamic optimization by design space dimension reduction and co-Kriging:AIAA-2016-3515[R]. Reston:AIAA, 2016.
[83] KOZIEL S, LEIFSSON L. Simulation-driven design by knowledge-based response correction techniques[M]. Switzerland:Springer International Publishing Press, 2016.
[84] REN J, LEIFSSON L, KOZIEL S, et al. Multi-fidelity aerodynamic shape optimization using manifold mapping:AIAA-2016-0419[R]. Reston:AIAA, 2016.
[85] LEIFSSON L, KOZIEL S, TESFAHUNEGN Y A. Multiobjective aerodynamic optimization by variable-fidelity models and response surface surrogates[J]. AIAA Journal, 2016, 54(2):531-541.
[86] LEIFSSON L, KOZIEL S. Aerodynamic shape optimization by variable-fidelity computational fluid dynamics models:A review of recent progress[J]. Journal of Computational Science, 2015, 10:45-54.
[87] LEIFSSON L, KOZIEL S. Wing aerodynamic shape optimization by space mapping[J]. Advances in Intelligent Systems and Computing, 2014, 256:319-332.
[88] KOZIEL S, LEIFSSON L. Surrogate-based aerodynamic shape optimization by variable-resolution models[J]. AIAA Journal, 2013, 51(1):94-106.
[89] KOZIEL S, LEIFSSON L. Statistical-analysis-based setup of physics-based surrogates and optimization process resolution for variable-fidelity aerodynamic design:AIAA-2017-3328[R]. Reston:AIAA, 2017.
[90] KOZIEL S, LEIFSSON L. Multi-level surrogate-based airfoil shape optimization:AIAA-2013-0778[R]. Reston:AIAA, 2013.
[91] LEIFSSON L, KOZIEL S. Variable-resolution shape optimization:Low-fidelity model setup and algorithm scalability:AIAA-2012-5604[R]. Reston:AIAA, 2012.
[92] KOZIEL S, LEIFSSON L. Multi-level CFD-based airfoil shape optimization with automated low-fidelity model selection[J]. Procedia Computer Science, 2013, 18(1):889-898.
[93] LEIFSSON L, KOZIEL S. Multi-fidelity design optimization of transonic airfoils using shape-preserving response prediction[J]. Journal of Computational Science, 2010, 1(2):98-106.
[94] KOZIEL S, LEIFSSON L. Surrogate-based modeling and optimization:Applications in engineering[M]. Heidelberg:Springer Nature Press, 2013.
[95] KRAMER B, MARQUES A, PEHERSTORFER B, et al. Multifidelity probability estimation via fusion of estimators[J]. Journal of Computational Physics, 2019, 392:385-402.
[96] PEHERSTORFER B, WILLCOX K E, GUNZBURGER M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization[J]. SIAM Review, 2018, 60(3):550-591.
[97] CHAUDHURI A, LAM R, WILLCOX K E. Multifidelity uncertainty propagation via adaptive surrogates in coupled multidisciplinary systems[J]. AIAA Journal, 2018, 56(1):235-249.
[98] PEHERSTORFER B, KRAMER B, WILLCOX K E. Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models[J]. Journal of Computational Physics, 2017, 341:61-75.
[99] PEHERSTORFER B, CUI T, MARZOUK Y, et al. Multifidelity importance sampling[J]. Computer Methods in Applied Mechanics and Engineering, 2016, 300:490-509.
[100] NG L W T, WILLCOX K E. Multifidelity approaches for optimization under uncertainty[J]. International Journal for Numerical Methods in Engineering, 2014, 100(10):746-772.
[101] MARCH A, WILLCOX K E. Constrained multifidelity optimization using model calibration[J]. Structural and Multidisciplinary Optimization, 2012, 46:93-109.
[102] ROBINSON T D, ELDRED M S, WILLCOX K E, et al. Surrogate-based optimization using multifidelity models with variable parameterization and corrected space mapping[J]. AIAA Journal, 2008, 46(11):2814-2822.
[103] ROBINSON T D, WILLCOX K E, ELDRED M S, et al. Multifidelity optimization for variable-complexity design:AIAA-2006-7114[R]. Reston:AIAA, 2006.
[104] SONG W B, KEANE A J. Surrogate-based aerodynamic shape optimization of a civil aircraft engine nacelle[J]. AIAA Journal, 2007, 45(10):2565-2574.
[105] ONG Y S, NAIR P B, KEANE A J. Evolutionary optimization of computationally expensive problems via surrogate modeling[J]. AIAA Journal, 2003, 41(4):687-696.
[106] KEANE A J. Wing optimization using design of experiment, response surface, and data fusion methods[J]. Journal of Aircraft, 2003, 40(4):741-750.
[107] LEARY S J, BHASKAR A, KEANE A J. A knowledge-based approach to response surface modeling in multifidelity optimization[J]. Journal of Global Optimization, 2003, 26(3):297-319.
[108] TOAL D J J, KEANE A J. Efficient multipoint aerodynamic design optimization via coKriging[J]. Journal of Aircraft, 2011, 48(5):1685-1695.
[109] KEANE A J. CoKriging for robust design optimization[J]. AIAA Journal, 2012, 50(11):2351-2364.
[110] KUYA Y, TAKEDA K, ZHANG X, et al. Multifidelity surrogate modeling of experimental and computational aerodynamic data sets[J]. AIAA Journal, 2011, 49(2):289-298.
[111] AHMED M Y M, QIN N. Surrogate-based aerodynamic design optimization:Use of surrogates in aerodynamic design optimization[C]//13th International Conference on Aerospace Sciences and Aviation Technology, 2009.
[112] 邓枫, 覃宁, 伍贻兆. 基于并行EGO算法的激波控制鼓包减阻优化[J]. 南京航空航天大学学报, 2013, 45(4):485-490. DENG F, QIN N, WU Y Z. Shock control bump optimization for drag reduction using parallel efficient global optimization algorithm[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2013, 45(4):485-490(in Chinese).
[113] AHMED M Y M, QIN N. Surrogate-based multi-objective aerothermodynamic design optimization of hypersonic spiked bodies[J]. AIAA Journal, 2012, 50(4):797-810.
[114] AHMED M Y M, QIN N. Metamodels for aerothermodynamic design optimization of hypersonic spiked blunt bodies[J]. Aerospace Science and Technology, 2010, 14(5):364-376.
[115] REIST T A, ZINGG D W, RAKOWITZ M, et al. Multifidelity optimization of hybrid wing-body aircraft with stability and control requirements[J]. Journal of Aircraft, 2018, 56(2):442-456.
[116] KHOSRAVI S, ZINGG D W. Aerostructural optimization of drooped wings[J]. Journal of Aircraft, 2017, 55(3):1261-1268.
[117] CHAU T, ZINGG D W. Aerodynamic shape optimization of a box-wing regional aircraft based on the reynolds-averaged navier-stokes equations:AIAA-2017-3258[R]. Reston:AIAA, 2017.
[118] ZHANG Z J, ZINGG D W. Efficient monolithic solution algorithm for high-fidelity aerostructural analysis and optimization[J]. AIAA Journal, 2018, 56(3):1251-1265.
[119] RASHAD R, ZINGG D W. Aerodynamic shape optimization for natural laminar flow using a discrete-adjoint approach[J]. AIAA Journal, 2016, 54(11):3321-3337.
[120] REIST T A, ZINGG D W. High-fidelity aerodynamic shape optimization of a lifting-fuselage concept for regional aircraft[J]. Journal of Aircraft, 2017, 54(3):1085-1097.
[121] REIST T A, ZINGG D W, Aerodynamic shape optimization of a blended-wing-body regional transport for a short range mission:AIAA-2013-2414[R]. Reston:AIAA, 2013.
[122] LEUNG T M, ZINGG D W. Aerodynamic shape optimization of wings using a parallel Newton-Krylov approach[J]. AIAA Journal, 2012, 50(3):540-550.
[123] WANG X J, WANG G G, SONG B W, et al. A novel evolutionary sampling assisted optimization method for high dimensional expensive problems[J]. IEEE Transactions on Evolutionary Computation, 2019, 23(5):815-827.
[124] SAFARI A, YOUNIS A, WANG G G, et al. Development of a metamodel assisted sampling approach to aerodynamic shape optimization problems[J]. Journal of Mechanical Science and Technology, 2015, 29(5):2013-2024.
[125] LONG T, WU D, GUO X S, et al. Efficient adaptive response surface method using intelligent space exploration strategy[J]. Structural and Multidisciplinary Optimization, 2015, 51(6):1335-1362.
[126] SHAN S Q, WANG G G. Metamodeling for high dimensional simulation-based design problems[J]. Journal of Mechanical Design, 2010, 132(5):1-11.
[127] WANG G G, SHAN S Q. Review of metamodeling techniques in support of engineering design optimization[J]. Journal of Mechanical Design, 2007, 129(4):370-380.
[128] NAMURA N, SHIMOYAMA K, OBAYASHI S, et al. Multipoint design optimization of vortex generators on transonic swept wings[J]. Journal of Aircraft, 2019, 56(4):1291-1302.
[129] NAMURA N, SHIMOYAMA K, OBAYASHI S. Expected improvement of penalty-based boundary intersection for expensive multiobjective optimization[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(6):898-913.
[130] NAMURA N, SHIMOYAMA K, OBAYASHI S. Kriging surrogate model with coordinate transformation based on likelihood and gradient[J]. Journal of Global Optimization, 2017, 68(4):827-849.
[131] NAMURA N, OBAYASHI S, JEONG S. Efficient global optimization of vortex generators on a supercritical infinite wing[J]. Journal of Aircraft, 2016, 53(6):1670-1679.
[132] LUO C, SHIMOYAMA K, OBAYASHI S. A study on many-objective optimization using the Kriging-surrogate-based evolutionary algorithm maximizing expected hypervolume improvement[J]. Mathematical Problems in Engineering, 2015, 4:1-15.
[133] NAMURA N, SHIMOYAMA K, JEONG S, et al. Kriging/RBF-hybrid response surface methodology for highly nonlinear functions[J]. Journal of Computational Science and Technology, 2012, 6(3):81-96.
[134] PALAR P, SHIMOYAMA K. On the accuracy of Kriging model in active subspaces:AIAA-2018-0913[R]. Reston:AIAA, 2018.
[135] 聂雪媛, 刘中玉, 杨国伟. 基于Kriging代理模型的飞行器结构刚度气动优化设计[J]. 气体物理, 2017, 2(2):8-16. NIE X Y, LIU Z Y, YANG G W. Aircraft structure stiffness and aerodynamic optimization design based on Kriging surrogate model[J]. Physics of Gases, 2017, 2(2):8-16(in Chinese).
[136] 姚拴宝, 郭迪龙, 孙振旭, 等. 基于Kriging代理模型的高速列车头型多目标优化设计[J]. 中国科学:技术科学, 2013, 43(2):186-200. YAO S B, GUO D L, SUN Z X, et al. Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model[J]. Science China Technological Sciences, 2013, 43(2):186-200(in Chinese).
[137] YANG G W, CHEN D W, CUI K. Response surface technique for static aeroelastic optimization on a high-aspect-ratio wing[J]. Journal of Aircraft, 2009, 46(4):1444-1450.
[138] 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):49-61.
[139] 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).
[140] 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.
[141] DENG K W, CHEN H X. A hybrid aerodynamic optimization algorithm based on differential evolution and RBF response surface:AIAA-2016-3671[R]. Reston:AIAA, 2016.
[142] ZHAO T, ZHANG Y F, CHEN H X. Multi-objective aerodynamic-structural optimization of supercritical wing of wide body aircraft:AIAA-2016-1555[R]. Reston:AIAA, 2016.
[143] ZHANG Y F, FANG X M, CHEN H X, et al. Supercritical natural laminar flow airfoil optimization for regional aircraft wing design[J]. Aerospace Science and Technology, 2015, 43:152-164.
[144] ZHAO T, ZHANG Y F, CHEN H X. Multi-objective aerodynamic optimization of supercritical wing with substantial pressure constraints:AIAA-2015-0763[R]. Reston, VA:AIAA, 2015.
[145] 赵童, 张宇飞, 陈海昕, 等. 面向三维机翼性能的超临界翼型优化设计方法[J]. 中国科学:物理学力学天文学, 2015, 45(10):84-96. HAO T, ZHANG Y F, CHEN H X, et al. Aerodynamic optimization method of supercritical airfoil geared to the performance of swept and tapered wing[J]. Scientia Sinica Physica, Mechanica and Astronomica, 2015, 45(10):84-96(in Chinese).
[146] 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).
[147] 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研究[J]. 航空学报, 2020,41(5):623409. LI R Z, ZHANG Y F, CHEN H X. Study of reinforcement learning method for supercritical airfoil aerodynamic design[J]. Acta Aeronautica et Astronautica Sinica, 2020,41(5):623409(in Chinese).
[148] 余永刚, 黄勇, 周铸, 等. 飞翼布局气动外形设计[J]. 空气动力学学报, 2017, 35(6):832-836, 878. YU Y G, HUANG Y, ZHOU Z, et al. Aerodynamic design of a flying-wing aircraft[J]. Acta Aerodynamica Sinica, 2017, 35(6):832-836, 878(in Chinese).
[149] 郑传宇, 黄江涛, 周铸, 等. 飞翼翼型高维目标空间多学科综合优化设计[J]. 空气动力学学报, 2017, 35(4):587-597. ZHENG C Y, HUANG J T, ZHOU Z, et al. Multidisciplinary optimization design of high dimensional target space for flying wing airfoil[J]. Acta Aerodynamica Sinica, 2017, 35(4):587-597(in Chinese).
[150] 黄江涛, 刘刚, 周铸, 等. 基于离散伴随方程求解梯度信息的若干问题研究[J]. 空气动力学学报, 2017, 35(4):554-562. HUANG J T, LIU G, ZHOU Z, et al. Investigation of gradient computation based on discrete adjoint method[J]. Acta Aerodynamica Sinica, 2017, 35(4):554-562(in Chinese).
[151] HUANG J T, ZHOU 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.
[152] HUANG J T, GAO Z H, ZHOU Z, et al. An improved adaptive sampling and experiment design method for aerodynamic optimization[J]. Chinese Journal of Aeronautics, 2015, 28(5):1391-1399.
[153] WANG Q, ZHAO Q J. Rotor airfoil profile optimization for alleviating dynamic stall characteristics[J]. Aerospace Science and Technology, 2018, 72:502-515.
[154] WANG Q, ZHAO Q J, WU Q. Aerodynamic shape optimization for alleviating dynamic stall characteristics of helicopter rotor airfoil[J]. Chinese Journal of Aeronautics, 2015, 28(2):346-356.
[155] JIANG X W, ZHAO Q J, ZHAO G Q, et al. Integrated optimization analyses of aerodynamic/stealth characteristics of helicopter rotor based on surrogate model[J]. Chinese Journal of Aeronautics, 2015, 28(3):737-748.
[156] ZHAO G Q, ZHAO Q J. Dynamic stall control optimization of rotor airfoil via variable droop leading-edge[J]. Aerospace Science and Technology, 2015, 43:406-414.
[157] 朱正, 招启军. 低HSI噪声旋翼桨尖外形优化设计方法[J]. 航空学报, 2015, 36(5):1442-1452. ZHU Z, ZHAO Q J. Optimization design method for rotor blade-tip shape with low HSI noise characteristics[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(5):1442-1452(in Chinese).
[158] SUN L, AN W, LIU X J, et al. On developing data-driven turbulence model for DG solution of RANS[J]. Chinese Journal of Aeronautics, 2019, 32(8):1869-1884.
[159] LYU H Q, XU Y D, GAO Y K, et al. A high-order discontinuous galerkin method for the two-dimensional time-domain Maxwell's equations on curved mesh[J]. Advances in Applied Mathematics and Mechanics, 2016, 8(1):104-116.
[160] 吴宽展, 刘学军, 吕宏强. 基于多输出高斯过程的超临界翼型优化[J]. 空气动力学学报, 2015, 33(6):728-732. WU K Z, LIU X J, LYU H Q. A supercritical airfoil design based on multi-output surrogate model[J]. Acta Aerodynamica Sinica, 2015, 33(6):728-732(in Chinese).
[161] LIU Z J, LIU X J, CAI X Y. A new hybrid aerodynamic optimization framework based on differential evolution and invasive weed optimization[J]. Chinese Journal of Aeronautics, 2018, 31(7):1437-1448.
[162] CHAI X, YU X Q, WANG Y. Multipoint optimization on fuel efficiency in conceptual design of wide-body aircraft[J]. Chinese Journal of Aeronautics, 2018, 31(1):99-106.
[163] 邢宇, 罗东明, 余雄庆. 超临界层流翼型优化设计策略[J]. 北京航空航天大学学报, 2017, 43(8):1616-1624. XIN Y, LUO D M, YU X Q. Optimization strategy of supercritical laminar flow airfoil design[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(8):1616-1624(in Chinese).
[164] YU X Q, DU X P. Reliability-based multidisciplinary optimization for aircraft wing design[J]. Structure and Infrastructure Engineering, 2006, 2(3-4):277-289.
[165] WANG Y, YIN H L, ZHANG S, et al. Multi-objective optimization of aircraft design for emission and cost reductions[J]. Chinese Journal of Aeronautics, 2014, 27(1):52-58.
[166] 胡婕, 王如华, 王稳江, 等. 客机机翼气动/结构多学科优化方法[J]. 南京航空航天大学学报, 2012, 44(4):458-463. HU J, WANG R H, WANG W J, et al. Multidisciplinary optimization of transport wing aerodynamic/structural integrated design[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2012, 44(4):458-463(in Chinese).
[167] 胡添元, 余雄庆. 多学科设计优化在非常规布局飞机总体设计中的应用[J]. 航空学报, 2011, 32(1):117-127. HU T Y, YU X Q. Preliminary design of unconventional configuration aircraft using multidisciplinary design optimization[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(1):117-127(in Chinese).
[168] HU T Y, YU X Q. Aerodynamic/stealthy/structural multidisciplinary design optimization of unmanned combat air vehicle[J]. Chinese Journal of Aeronautics, 2009, 22(4):380-386.
[169] WANG S, SUN G, CHEN W, et al. Database self-expansion based on artificial neural network:An approach in aircraft design[J]. Aerospace Science and Technology, 2017, 72:77-83.
[170] TAO J, SUN G, SI J Z, et al. A robust design for a winglet based on NURBS-FFD method and PSO algorithm[J]. Aerospace Science and Technology, 2017, 70:568-577.
[171] TAO J, SUN G. An artificial neural network approach for aerodynamic performance retention in airframe noise reduction design of a 3D swept wing model[J]. Chinese Journal of Aeronautics, 2016, 29(5):1213-1225.
[172] TAO J, SUN G. A novel optimization method for maintaining aerodynamic performances in noise reduction design[J]. Aerospace Science and Technology, 2015, 43:415-422.
[173] SUN G, SUN Y J, WANG S Y. Artificial neural network based inverse design:Airfoils and wings[J]. Aerospace Science and Technology, 2015, 42:415-428.
[174] 陶俊, 孙刚, 徐康乐. 基于人工神经网络的缝翼凹槽填充降噪设计, 空气动力学学报, 2015, 33(4):515-522. TAO J. SUN G, XU K L. Slat cove filler design for noise reduction based on artificial neural network[J]. Acta Aerodynamica Sinica, 2015, 33(4):515-522(in Chinese).
[175] JIN X, SUN G, LIU C J. Neural network and data mining method in aerodynamic optimization[J]. Applied Mechanics and Materials, 2011, 52-54:1421-1426.
[176] SONG X G, SUN G Y, LI G Y, et al. Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models[J]. Structural and Multidisciplinary Optimization, 2013, 47(2):221-231.
[177] SONG X G, ZHANG J, KANG S H, et al. Surrogate-based analysis and optimization for the design of heat sinks with jet impingement[J]. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2013, 4(3):429-437.