飞行器结构布局与尺寸混合优化方法

doi:10.7527/S1000-6893.2021.25363

固体力学与飞行器总体设计

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飞行器结构布局与尺寸混合优化方法

胡嘉欣¹, 芮姝², 高瑞朝², 苟建军¹, 龚春林¹

1. 西北工业大学航天学院, 陕西省空天飞行器设计重点实验室, 西安 710072;
2. 北京空天技术研究所, 北京 100074

收稿日期:2021-02-04 修回日期:2021-03-18 发布日期:2021-04-29
通讯作者: 龚春林 E-mail:leonwood@nwpu.edu.cn
基金资助:
国家自然科学基金青年基金（51806175）；中央高校基本科研业务费专项（3102019HTXM004）

Hybrid optimization method for structural layout and size of flight vehicles

HU Jiaxin¹, RUI Shu², GAO Ruichao², GOU Jianjun¹, GONG Chunlin¹

1. Shaanxi Aerospace Flight Vehicle Design Key Laboratory, School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Beijing Aerospace Technology Institute, Beijing 100074, China

Received:2021-02-04 Revised:2021-03-18 Published:2021-04-29
Supported by:
National Natural Science Foundation of China (51806175); Fundamental Research Funds for the Central Universities (3102019HTXM004)

摘要/Abstract

摘要： 针对飞行器结构轻量化设计中的布局和尺寸优化问题，提出了一种高效的混合优化方法。在传统的基结构优化方法中引入尺寸变量，构建结构布局与尺寸混合优化模型，并以遗传算法为基本搜索方法，设计了统一描述0-1型离散布局变量和连续尺寸变量的染色体，建立了结构布局与尺寸混合优化的求解流程。由于种群规模较大，在求解过程中引入了神经网络代理模型，通过预估种群个体的优劣，缩减每一代中需要精确计算的个体数目。以含有55个布局变量和55个尺寸变量的机翼结构优化问题作为算例，验证了该方法的可行性，优化结果相比传统基结构法减重13%，且引入代理模型可使计算成本降低约50%。

关键词: 飞行器结构设计, 混合优化, 布局优化, 尺寸优化, 遗传算法(GA), 代理模型

Abstract: An efficient hybrid optimization method is proposed to solve the problem of layout and size optimization in lightweight structural design of aircraft. A layout-size hybrid optimization model is constructed by introducing size variables into the traditional ground structure method. The Genetic Algorithm (GA) is used as the basic search method, and the chromosome is designed to uniformly describe the 0-1 type discrete layout variables and continuous size variables, and the solution process of the hybrid layout-size optimization of the structure is established. The neural network agent model is introduced in the solution process because of the large size of the population. By predicting the individual population, the number of individuals that need to be accurately calculated in each generation is reduced. A wing structure optimization design with 55 layout variables and 55 size variables is taken as an example, and the results verify the feasibility of the method. Compared with the traditional ground structure method, the new method can further reduce the weight by 13%. Furthermore, the introduction of the agent model reduces the computational cost by about 50%.

Key words: flight vehicle structure design, hybrid optimization, layout optimization, size optimization, genetic algorithm (GA), agent model

中图分类号:

胡嘉欣, 芮姝, 高瑞朝, 苟建军, 龚春林. 飞行器结构布局与尺寸混合优化方法[J]. 航空学报, 2022, 43(5): 225363-225363.

HU Jiaxin, RUI Shu, GAO Ruichao, GOU Jianjun, GONG Chunlin. Hybrid optimization method for structural layout and size of flight vehicles[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 225363-225363.

参考文献

[1] 张明, 刘文斌, 李闯, 等. 优化驱动的起落架结构设计方法[J]. 航空学报, 2015, 36(3):857-864. ZHANG M, LIU W B, LI C, et al. Optimization-driven design method of landing gear structure[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(3):857-864(in Chinese).
[2] 何林涛, 万小朋, 赵美英, 等. 布局优化和尺寸优化相结合的复合材料机翼优化设计[J]. 中国机械工程, 2008, 19(17):2077-2080. HE L T, WAN X P, ZHAO M Y, et al. Composite aircraft wing structure optimization using combination of layout and sizing[J]. China Mechanical Engineering, 2008, 19(17):2077-2080(in Chinese).
[3] STANFORD B. Shape, sizing, and topology design of a wingbox under aeroelastic constraints:AIAA-2020-3147[R]. Reston:AIAA, 2020.
[4] QIN Z B, LUO Y G, ZHUANG W C, et al. Simultaneous optimization of topology, control and size for multi-mode hybrid tracked vehicles[J]. Applied Energy, 2018, 212:1627-1641.
[5] AN H C, HUANG H. Topology and sizing optimization for frame structures with a two-level approximation method[J]. AIAA Journal, 2017, 55(3):1044-1057.
[6] SAVSANI V J, TEJANI G G, PATEL V K, et al. Modified meta-heuristics using random mutation for truss topology optimization with static and dynamic constraints[J]. Journal of Computational Design and Engineering, 2017, 4(2):106-130.
[7] ASSIMI H, JAMALI A. A hybrid algorithm coupling genetic programming and Nelder-Mead for topology and size optimization of trusses with static and dynamic constraints[J]. Expert Systems With Applications, 2018, 95:127-141.
[8] FERNANDEZ F, BARKER A T, KUDO J, et al. Simultaneous material, shape and topology optimization[J]. Computer Methods in Applied Mechanics and Engineering, 2020, 371:113321.
[9] 周丹发. 一种结构尺寸、形状及拓扑综合优化设计方法[D]. 南京:南京航空航天大学, 2019:17-18. ZHOU D F. An integrated optimization method of structural size, shape and topology[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2019:17-18. (in Chinese).
[10] QUERIN O M, VICTORIA M, ALONSO C, et al. Topology design methods for structural optimization[M]. London:Academic Press, 2017.
[11] 郭中泽, 张卫红, 陈裕泽. 结构拓扑优化设计综述[J]. 机械设计, 2007, 24(8):1-6. GUO Z Z, ZHANG W H, CHEN Y Z. An overview on the topological optimization design of structures[J]. Journal of Machine Design, 2007, 24(8):1-6(in Chinese).
[12] HERBERT J DORN W S, GOMORY R E, et al. Automatic design of optimal structures[J]. Journal e Mécanique, 1964, 3:25-52.
[13] SLEESONGSOM S, BUREERAT S, TAI K. Aircraft morphing wing design by using partial topology optimization[J]. Structural and Multidisciplinary Optimization, 2013, 48(6):1109-1128.
[14] WANG W, GUO S J, YANG W. Simultaneous partial topology and size optimization of a wing structure using ant colony and gradient based methods[J]. Engineering Optimization, 2011, 43(4):433-446.
[15] 陈秀, 葛文杰, 张永红, 等. 基于遗传算法的柔性机构形状变化综合优化研究[J]. 航空学报, 2007, 28(5):1230-1235. CHEN X, GE W J, ZHANG Y H, et al. Investigation on synthesis optimization for shape morphing compliant mechanisms using GA[J]. Acta Aeronautica et Astronautica Sinica, 2007, 28(5):1230-1235(in Chinese).
[16] 钱令希. 工程结构优化设计[M]. 北京:科学出版社, 2011. QIAN L X. Optimization design for engineering structures[M]. Beijing:Science Press, 2011(in Chinese).
[17] 辛馡, 朱鳌鑫. 遗传算法的适应度函数研究[J]. 系统工程与电子技术, 1998, 20(11):58-62. XIN F, ZHU A X. Research on fitness function in genetic algorithms[J]. Systems Engineering and Electronics, 1998, 20(11):58-62(in Chinese).
[18] 许素强, 夏人伟. 桁架结构拓扑优化与遗传算法[J]. 计算结构力学及其应用, 1994, 11(4):436-446. XU S Q, XIA R W. Topological optimization of truss structure via the genetic algorithm[J]. Chinese Journal of Computational Mechanics, 1994, 11(4):436-446(in Chinese).
[19] WHITLEY L D. The GENITOR algorithm and selection pressure:Why rank-based allocation of reproductive trials is best[C]//The 3rd International Conference on Genetic Algorithms (ICGA). Fairfax:George Mason University, 1989:116-123.
[20] CHIPPERFIELD A, FLEMING P, POHLHEIM H, et al. Genetic algorithm toolbox user's guide[M]. Sheffield:Department of Automatic Control and Systems Engineering, Unversity of Sheffield, 1994.
[21] FORRESTER A I J, KEANE A J. Recent advances in surrogate-based optimization[J]. Progress in Aerospace Sciences, 2009, 45(1-3):50-79.
[22] ADELI H, YEH C. Perceptron learning in engineering design[J]. Computer-Aided Civil and Infrastructure Engineering, 2008, 4(4):247-256.
[23] 王伟, 赵美英, 赵锋, 等. 基于人工神经网络技术的结构布局优化设计[J]. 机械设计, 2006, 23(12):7-10. WANG W, ZHAO M Y, ZHAO F, et al. Optimization design of structural layout based on techniques of artificial neural network[J]. Journal of Machine Design, 2006, 23(12):7-10(in Chinese).
[24] ER M J, WU S Q, LU J W, et al. Face recognition with radial basis function (RBF) neural networks[J]. IEEE Transactions on Neural Networks, 2002, 13(3):697-710.
[25] KATTAN A, GALVAN E. Evolving radial basis function networks via GP for estimating fitness values using surrogate models[C]//2012 IEEE Congress on Evolutionary Computation. Piscataway:IEEE Press, 2012:1-7.
[26] 张振伟, 姚卫星. 机翼结构型式和选型参数的统计分析[J]. 飞机设计, 2011, 31(2):16-19. ZHANG Z W, YAO W X. Statistical analysis of wing configuration and related parameters[J]. Aircraft Design, 2011, 31(2):16-19(in Chinese).

E-mail：hkxb@buaa.edu.cn

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飞行器结构布局与尺寸混合优化方法

Hybrid optimization method for structural layout and size of flight vehicles

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