航空工程进展2025，Vol.16Issue(4)：48-62,92,16.DOI:10.16615/j.cnki.1674-8190.2025.04.05
基于物理知识驱动的敏感性分析及分区代理模型优化方法
Sensitivity analysis and optimization method of partition surrogate model based on physical knowledge driven
摘要
Abstract
The problem of"dimensionality disaster"of design variable is a key technical problem that restricts the ap-plication of current agent-based optimization algorithms.In order to solve the problem of declining accuracy and poor optimization effect of surrogate model caused by dimensionality disaster problem,a sensitivity analysis parti-tion surrogate model optimization method based on physical knowledge driven is improved.The sensitivity of differ-ent partition design variables to the objective function is studied.On the basis of the sequential partition optimiza-tion,the sensitivity is taken as the order of the partition surrogate model optimization.The results show that the method can solute the high-dimensional design space into a series of low-dimensional subspaces,improve the pre-diction accuracy of surrogate model,realize the efficiency configuration,so as to realize the global search.In com-parison with traditional surrogate optimization method,the time spent on establishing the surrogate model is much lower,and the ability of surrogate optimization method to solve the high-dimensional aerodynamics design problem.关键词
分区优化/高效全局优化方法/维数灾难/敏感性分析/代理模型精度Key words
partition optimization/efficient global optimization method/dimensionality disaster/sensitivity analy-sis/accuracy of surrogate model分类
航空航天引用本文复制引用
邵梦莹,夏露,张伟,赵轲..基于物理知识驱动的敏感性分析及分区代理模型优化方法[J].航空工程进展,2025,16(4):48-62,92,16.基金项目
飞行器基础布局全国重点实验室稳定支持项目(614220121020128) （614220121020128）
飞行器基础布局全国重点实验室基金(JBGS-2024-02,2023-JCJQ-LB-070) （JBGS-2024-02,2023-JCJQ-LB-070）
