面向抗冲击结构尺寸优化的自训练分类判断优化设计方法
A SELF-TRAINING CLASSIFICATION JUDGEMENT OPTIMIZATION METHOD FOR THE IMPACT-RESISTANT STRUCTURAL SIZE OPTIMIZATION
王春彭 1高汝鑫 1廉艳平 1成湛山 1李明健1
作者信息
- 1. 北京理工大学先进结构技术研究院,北京 100081
- 折叠
摘要
抗冲击结构在军民安全领域有着广泛且重要的应用,但其优化设计因涉及极端载荷下的强非线性而面临结构响应求解耗时与灵敏度分析困难的挑战.针对该问题,提出了一种自训练分类判断优化设计方法.该方法采用基于支持向量机的自训练分类判断代理模型和遗传算法,求解抗冲击结构尺寸优化列式.与基于回归代理模型的传统方法不同,自训练分类判断代理模型可以减少样本集构建计算成本.同时,提出了面向分类判断代理模型的遗传算法约束处理策略及个体适应度计算方法.基于该方法开展的波纹夹芯结构抗爆性能和聚脲/陶瓷复合板抗侵彻性能优化设计,验证了其有效性和高效性.为抗冲击结构设计提供了一种新的高效优化方法,以期保障极端冲击载荷下装备结构服役性能和人员安全.
Abstract
Impact-resistant structures have a broad application and play a crucial role in military and civilian safety.However,such structure optimization suffers from the time-consuming issue of the structure dynamic response analysis and finds sensitivity analysis difficult due to the strong non-linearity associated with the extreme loading.In this study,a self-training classification judgment optimization method is proposed.This method utilizes a self-training classification judgment surrogate model based on support vector machines and a genetic algorithm to solve the size optimization problem of impact-resistant structures.Different from the regression-based conventional surrogate models,the self-training classification judgment surrogate model reduces the computational cost of the sample dataset.Moreover,a constraint-handling strategy and a fitness calculation method are introduced to integrate the classification judgment surrogate model into the genetic algorithm.Two examples,including the optimization of the blast-resistant corrugated sandwich structures and the penetration-resistant polyurea/ceramic composite plate,are presented to showcase the effectiveness and efficiency of the proposed method.It is expected that the proposed novel method with high efficiency for impact-resistant structures optimization is capable of ensuring the service performance of equipment structures and personnel safety under extreme impact loadings.
关键词
极端载荷/数据驱动/代理模型/序列优化/结构优化Key words
extreme loading/data-driven/surrogate model/sequence optimization/structure optimization引用本文复制引用
基金项目
国家自然科学基金(11972086)
中央高校基本科研业务费专项(2024CX06075)
出版年
2024
NETL
NSTL
万方数据