Open Access
Volume 40, Number 3, June 2022
Page(s) 618 - 627
Published online 19 September 2022
  1. OLIVIU S G, KOREANSCHI A, BOTEZ R M. A new non-linear vortex lattice method: applications to wing aerodynamic optimizations[J]. Chinese Journal of Aeronautics, 2016, 29(5): 1178–1195 [Article] [CrossRef] [Google Scholar]
  2. KOREANSCHI A, GABOR O S, ACOTTO J. Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speed, part Ⅰ-aerodynamic optimization using genetic, bee colony and gradient descent algorithms[J]. Chinese Journal of Aeronautics, 2017, 30(1): 149–163. [Article] [CrossRef] [Google Scholar]
  3. KOREANSCHI A, GABOR O S, ACOTTO J. Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speeds, part Ⅱ-experimental validation using infra-red transition measurement from wind tunnel tests[J]. Chinese Journal of Aeronautics, 2017, 30(1): 164–174. [Article] [CrossRef] [Google Scholar]
  4. LYU Jichan, DONG Yanfei, CHEN Yuankai. Rules of the optimal variable sweep wing in low and medium height[J]. Flight Dynamics, 2016, 32(2): 24–27. [Article] (in Chinese) [Google Scholar]
  5. LIU Lu, DONG Yanfei. Basic analysis of the best sweep variation rule about variable sweep wing-body based on aerodynamics[J]. Journal of Chongqing University of Technology, 2017, 31(8): 76–80. [Article] (in Chinese) [Google Scholar]
  6. LEE I, CHOI K, ZHAO L. Sampling-based RBDO using the dynamic Kriging (d-Kriging) method and stochastic sensitivity analysis[J]. Structural & Multidisciplinary Optimization, 2010, 44(3): 299–317 [Google Scholar]
  7. ALLAIRE D K. Surrogate modeling for uncertainty assessment with application to aviation environmental system models[J]. AIAA Journal, 2010, 48(8): 1791–1791. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  8. WANG Fangpeng, LUO Hong, WANG Haipeng. Research on parameter optimization method of air vehicle gliding trajectory[J]. Aeronautical Manufacturing Technology, 2015(suppl 1): 104–107. [Article] (in Chinese) [Google Scholar]
  9. HUANG Guoqiang, LU Yuping, NAN Ying. A survey of numerical algorithms for trajectory optimization of flight vehicles[J]. Scientia Sinica(Technologica), 2012, 42(9): 1016–1036. [Article] (in Chinese) [NASA ADS] [Google Scholar]
  10. LI Weiming, SUN Ruisheng, WU Junji. Optimization of glide trajectory for aerial bomb with morphing swept wings[J]. Journal of Ballistics, 2012, 24(2): 6–9. [Article] (in Chinese) [Google Scholar]
  11. GUO Jie, TANG Shengjing, LI Xiang, et al. Optimum design of the project trajectory based on an improved particle swarm optimization[J]. Transactions of Beijing Institute of Technology, 2010, 30(6): 682–692. [Article] (in Chinese) [Google Scholar]
  12. GONG Chunlin, CHI Fenghua, GU Liangxian, et al. Optimal control method for distributed morphing aircraft based on Karhunen-Loève expansion[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(2): 121518. [Article] (in Chinese) [Google Scholar]
  13. WANG Na, CHEN Jieqing, MING Chao, et al. Optimization design for trajectory of morphing-wing missile based on hp-adaptive pseudo-spectral method[J]. Journal of Ballistics, 2016(4): 24–29. [Article] (in Chinese) [Google Scholar]
  14. TOAL J. Some considerations regarding the use of multi-fidelity Kriging in the construction of surrogate models[J]. Structural and Multidisciplinary Optimization, 2015, 51(6): 122–1245 [Google Scholar]
  15. ZAHIR M K, GAO Zhenghong. Variable-fidelity optimization with design space reduction[J]. Chinese Journal of Aeronautics, 2013(4): 14–22 [Google Scholar]
  16. HUANG Likeng, GAO Zhenghong, ZHANG Dehu. Research on multi-fidelity aerodynamic optimization methods[J]. Chinese Journal of Aeronautics, 2013, 26(2): 279–286. [Article] [CrossRef] [Google Scholar]
  17. HU Jiexiang, ZHOU Qi, JIANG Ping, et al. An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical Kriging[J]. Engineering Optimization, 2017(3): 1–19 [Google Scholar]
  18. YIN Shi, ZHU Ming, LIANG Haoquan. Multi-disciplinary design optimization with variable complexity modeling for a stratosphere airship[J]. Chinese Journal of Aeronautics, 2019, 32(5): 191–202 [Google Scholar]
  19. KOZIEL S, TESFAHUNEGN Y, LEIFSSON L. Variable-fidelity CFD models and co-Kriging for expedited multi-objective aerodynamic design optimization[J]. Engineering Computations, 2016, 33(8): 2320–2338. [Article] [CrossRef] [Google Scholar]
  20. LE L G. Multi-fidelity Gaussian process regression for computer experiments[D]. Autres: Université Paris-Diderot-Paris VⅡ, 2013 [Google Scholar]
  21. HAN Zhonghua. 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. [Article] (in Chinese) [Google Scholar]
  22. HAN Zhonghua, XU Chenzhou, ZHANG Liang, et al. Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids[J]. Chinese Journal of Aeronautics, 2020, 33(1): 31–47. [Article] [CrossRef] [Google Scholar]
  23. ZHANG Keshi, HAN Zhonghua, GAO Zhongjian, et al. Constraint aggregation for large number of constraints in wing surrogate-based optimization[J]. J Structural and Multidisciplinary Optimization, 2019, 59(2): 421–438. [Article] [CrossRef] [Google Scholar]
  24. LI Chunna, ZHANG Yangkang. An efficient adaptive global optimization method suitable for aerodynamic optimization[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 95–107. [Article] (in Chinese) [Google Scholar]
  25. 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 [Google Scholar]
  26. LI Chunna. Adaptive optimization methodology based on Kriging modeling and a trust region method[J]. Chinese Journal of Aeronautics, 2019, 32(2): 281–295. [Article] [CrossRef] [Google Scholar]
  27. GANDHI M. Trajectory optimization algorithm studies[J]. Mathematics, 2015, 15(2): 267–281 [Google Scholar]
  28. ZHANG Boyuan, ZONG Qun, LU Hanchen, et al. Trajectory optimization of quad-rotor UAV formation using hp-adaptive pseudospectral method[J]. Science & Technology Review, 2017, 35(7): 69–76. [Article] (in Chinese) [Google Scholar]
  29. BOUHLEL M A, HWANG J T, BARTOLI N, et al. A Python surrogate modeling framework with derivatives[J]. Advances in Engineering Software, 2019, 3(5): 9965–9978 [Google Scholar]

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