战术导弹气动隐身快速多目标优化方法

刘莉; 蒋孟龙; 龙腾; 吴迪; 黄波

doi:10.13700/j.bh.1001-5965.2013.0753

战术导弹气动隐身快速多目标优化方法

doi: 10.13700/j.bh.1001-5965.2013.0753

北京理工大学宇航学院, 北京 100081

基金项目: 航空科学基金资助项目(2011ZA72003);国家自然科学基金资助项目(51105040,11372036)

详细信息

中图分类号: V218
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出版历程
- 收稿日期: 2014-01-09
- 网络出版日期: 2014-12-20

Effective multi-objective optimization for aerodynamic and stealthy performance of tactical missiles

School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China

摘要

摘要: 针对战术导弹外形气动隐身多目标优化问题,提出了一种新的快速优化方法.采用物理规划将多目标问题转化为单目标问题间接求解,利用遗传算法(GA,Genetic Algorithm)对问题进行设计空间搜索.为降低计算成本,通过变量筛选来降低设计变量空间维数,通过构建径向基函数(RBF,Radial Basis Function)代理模型来减少高精度分析模型的调用次数.最后以类BGM-109导弹模型的气动隐身多目标优化为例对该方法进行校验.在满足升力系数不小于初始升力系数的约束下,进行导弹几何外形优化使全弹阻力系数和前向雷达散射截面(RCS,Radar Cross Section)最小.与标准GA相比,在两者优化结果基本相同的情况下,该方法节约了83%的计算成本.
- 战术导弹设计 /
- 气动隐身 /
- 多目标优化 /
- 物理规划 /
- 代理模型 /
- 变量筛选
Abstract: A new effective multi-objective optimization method was employed to solve the aerodynamic and stealthy performance optimization of tactical missile problem. Physical programming was selected to translate the multi-objective problem into the single objective problem. Genetic algorithm (GA) was applied to carrying on the design space search. Variable selection and radial basis function (RBF) were contributed to reducing the design variable space dimension and the number of high fidelity model evaluations. Then, the aerodynamic and stealthy performance optimization of a quasi BGM-109 model was chosen as an example to deliver the whole optimization steps and verify the correctness of the method. The optimization task is to minimize the drag coefficient and the heading radar cross section (RCS) subject to the aerodynamic performance constraint, namely the lift coefficient has to be no less than the initial value. Through application to engineering case, the computational cost of the proposed method decreases by 83% compared to that of GA, while two methods has nearly identical performance.
- tactical missile design /
- aerodynamic and stealthy performance /
- multi-objective optimization /
- physical programming /
- surrogate model /
- variable selection

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参考文献(15)

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