代理模型在飞行器多学科优化中的应用研究

蒋鲁佳; 辛万青; 张鸣; 王威; 陈红波

doi:10.15892/j.cnki.djzdxb.2023.03.008

弹箭与制导学报 >

2023 , Vol. 43 >Issue 3: 56 - 61

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.03.008

代理模型在飞行器多学科优化中的应用研究

蒋鲁佳 ,
辛万青 ,
张鸣 ,
王威 ,
陈红波

展开

北京宇航系统工程研究所,北京 100076

蒋鲁佳(1979—),男,高级工程师,博士,研究方向:飞行器设计。

收稿日期: 2022-08-10

网络出版日期: 2025-02-07

收起

Application of Surrogate Model in Aircraft Multidisciplinary Design Optimization

JIANG Lujia ,
XIN Wanqing ,
ZHANG Ming ,
WANG Wei ,
CHEN Hongbo

Expand

Beijing Institute of Astronautical Systems Engineering,Beijing 100076,China

Received date: 2022-08-10

Online published: 2025-02-07

Fold

摘要

飞行器多学科优化能够提高飞行器总体设计的质量和综合性能,然而,在优化设计过程中,庞大的数据量与计算量、复杂的信息交互、非线性设计空间等会耗费大量的优化计算时间,从而降低优化效率及优化结果的准确性。文中针对飞行器多学科设计模型的特点,从工程应用的角度出发,通过构造代理模型,简化了优化迭代过程中需要进行的复杂的学科分析,有效提升了飞行器多学科设计优化的效率。

关键词： 飞行器总体设计; 多学科设计优化; 代理模型; 总体设计

本文引用格式

蒋鲁佳 , 辛万青 , 张鸣 , 王威 , 陈红波 . 代理模型在飞行器多学科优化中的应用研究[J]. 弹箭与制导学报, 2023 , 43(3) : 56 -61 . DOI: 10.15892/j.cnki.djzdxb.2023.03.008

Abstract

Aircraft system multidisciplinary design optimization can improve design quality and integrated performance. However, huge amounts of calculation, complex data exchanges, and complicated nonlinearities require a lot of calculation time, which may reduce optimization efficiency and accuracy of optimization. An engineering way to simplify complex subjects analysis during optimization iteration with surrogate model is presented, according to the characteristics of system multidisciplinary design model of aircraft. Results show that the method can improve aircraft system multidisciplinary design optimization efficiency.

Key words： aircraft system design; MDO; surrogate model; system design

[an error occurred while processing this directive]

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

[1]	王振国, 陈小前, 罗文采, 等. 飞行器多学科设计优化理论与应用研究[M]. 北京: 国防工业出版社, 2006: 1-25. WANG Z G, CHEN X Q, LUO W C, et al. Research on the theory and application of multidisciplinary design optimization for aircraft[M]. Beijing: National Defense Industry Press, 2006: 1-25.

[2]	ERFORT G, BACKSTROM T W V, VENTER G. Numerical optimisation of a small-scale wind turbine through the use of surrogate modelling[J]. Journal of Energy in Southern Africa, 2017, 28: 79-91.

[3]	GUO S Z, ZHENG X M, ANG H S, et al. Cruise missile head shape optimisation using an adaptive sampling surrogate model[J]. The Aeronautical Journal, 2018, 122: 1145-1162.

[4]	李育超, 齐婵颖, 高通锋. 基于Kriging代理模型的运输机机翼多学科优化设计[J]. 航空科学技术, 2018, 29(3): 20-24. LI Y C, QI C Y, GAO T F. Multi-disciplinary optimization of transport aircraft wing based on kriging surrogate model[J]. Aeronautical Science and Technology, 2018, 29(3): 20-24.

[5]	MULLER J. SOCEMO: surrogate optimization of computationally expensive multiobjective problems[J]. Informs Journal on Computing, 2017, 29: 581-596.

[6]

LEE

Y H

, CORMAN

R E

, EWOLDT

R H

, et al. A multiobjective adaptive surrogate modeling-based optimization (Mo-Asmo) framework using efficient sampling strategies[C]// ASME. Proceedings of the 2017 International Design Engineering Technical Conferences & Computers & Information in Engineering Conference. New York: ASME, 2017: 1127-1139.

[7]	李伟华. 基于遗传算法的翼型性能优化[D]. 武汉: 华中科技大学, 2008. LI W H. Optimization of aerofoil performance based on the genetic algorithm[D]. Wuhan: Huazhong University of Science and Technology, 2008.

[8]	余德海, 宋保维, 李家旺, 等. 鱼雷多学科设计优化中的代理模型研究[J]. 机械科学与技术, 2007, 27(2): 184-187. YU D H, SONG B W, LI J W, et al. Surrogate models for the multi-disciplinary design optimization of a torpedo[J]. Mechanical Science and Technology for Aerospace Engineering, 2007, 27(2): 184-187.

[9]	LIU X Y, YUAN Q Q, ZHAO M, et al. Multiple objective multidisciplinary design optimization of heavier-than-water underwater vehicle using CFD and approximation model[J]. Journal of Marine Science and Technology, 2017, 22(1): 135-148.

[10]	高轶男. 基于组合代理模型的汽车碰撞安全性多目标优化研究[D]. 重庆: 重庆大学, 2016. GAO Y N. Research on multi-objective optimization of vehicle crash safety based on combined surrogate model[D]. Chongqing: Chongqing University, 2016.

[11]	张磊. 基于高斯过程代理模型的粒子群算法研究[D]. 杭州: 浙江科技学院, 2019. ZHANG L. Research on particle swarm optimization based on gaussian process surrogate model[D]. Hangzhou: Zhejiang University of Science and Technology, 2019.

[12]	吴梦阳. 某型装甲车底部防护组件结构材料一体化设计技术研究[D]. 南京: 南京理工大学, 2019. WU M Y. Research on integrated design technology of structural materials for bottom protection components of a certain armored vehicle[D]. Nanjing: Nanjing University of Science and Technology, 2019.

[13]	刘彬, 肖守讷, 朱涛, 等. 自由外翻式组合吸能装置的设计及优化[J]. 中国机械工程, 2018, 29(17): 2052-2059. LIU B, XIAO S N, ZHU T, et al. Design and optimization of combined energy absorption devices based on free inversion[J]. China Mechanical Engineering, 2018, 29(17): 2052-2059.

[14]	赵涛. 基于代理模型验证的混合不确定性优化方法及其在空间绳系设计中的应用[D]. 成都: 电子科技大学, 2015. ZHAO T. Hybrid uncertainty optimization method based on surrogate model verification and its application in space rope system design[D]. Chengdu: University of Electronic Science and Technology of China, 2015.

[15]	KOCH P N, WUJEK B. Facilitating probabilistic multidisciplinary design optimization using kriging approximation models: AIAA 2002-5415[R]. Reston: AIAA, 2002.

[16]	杨路春, 胡迟, 高阳, 等. 基于多目标优化和多属性决策的数据采集装置总体优化设计[J]. 舰船科学技术, 2021, 43(1): 164-169. YANG L C, HU C, GAO Y, et al. Data acquisition unit optimal design based on multi-objective optimization and multi-attribute decision making[J]. Ship Science and Technology, 2021, 43(1): 164-169.

[17]	杨华. 机翼气动力双向代理模型设计[D]. 南京: 南京航空航天大学, 2007. YANG H. Design of a two-way surrogate model aerodynamic model for wings[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2007.

[18]	王洪元, 史国栋. 人工神经网络技术及其应用[M]. 北京: 中国石化出版社, 2002: 21-58. WANG H Y, SHI G D. Artificial neural network technology and its applications[M]. Beijing: China Petrochemical Press, 2002: 21-58.

[19]	梁晗. 复合地层小半径曲线隧道盾构掘进关键技术研究[D]. 北京: 北京交通大学, 2020. LIANG H. Study on the key technology of shield tunneling in small radius curve tunnel in composite stratum[D]. Beijing: Beijing Jiaotong University, 2020.

[20]	葛学伟. 气体辅助注射成型穿透机理及工艺优化方法研究[D]. 上海: 上海交通大学, 2010. GE X W. Mechanism study and process optimization on gas penetration behavior for gas-assisted injection molding[D]. Shanghai: Shanghai Jiao Tong University, 2010.

[21]	周军, 林鹏, 周敏, 等. 降低气动耦合特性的飞行器总体随控优化方法: 201310060969.3[P]. 2013-07-10. ZHOU J, LIN P, ZHOU M, et al. Optimization method for overall control of aircraft to reduce aerodynamic coupling characteristics: 201310060969.3[P]. 2013-07-10.

[22]	蒋鲁佳, 辛万青, 布向伟. 导弹总体多学科设计优化耦合关系处理方法[J]. 导弹与航天运载技术, 2009(6): 5-7. JIANG L J, XIN W Q, BU X W. Processing methods of coupled relationship of multidisciplinary design optimization for missile[J]. Missiles and Space Vehicles, 2009(6): 5-7.

[23]	刘峰. 载人潜器总体设计优化方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2014. LIU F. Research on general design optimization method of human occupied vehicle[D]. Harbin: Harbin Engineering University, 2014.

[24]	蒋鲁佳. 多学科设计优化在飞行器总体设计中的应用研究[J]. 弹箭与制导学报, 2012, 32(3): 39-43. JIANG L J. The application of multidisciplinary design optimization in aircraft system design[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2012, 32(3): 39-43.

[25]	赵选民. 试验设计方法[M]. 北京: 科学出版社, 2006: 123-169. ZHAO X M. Experimental design method[M]. Beijing: Science Press, 2006: 123-169.

[26]	施雨阳. 远程空空反辐射导弹总体优化设计技术研究[D]. 西安: 西北工业大学, 2015. SHI Y Y. Research on long range air-to-air anti radiation missile general design and optimization[D]. Xi'an: Northwestern Polytechnical University, 2015.

[27]	马燕芳. 显式模型预测控制的多胞体近似方法及其应用[D]. 杭州: 浙江工业大学, 2014. MA Y F. Polytopic approximation of explicit model predictive control and its applications[D]. Hangzhou: Zhejiang University of Technology, 2014.

Options

文章导航