基于改进EGO算法的汽车40％偏置碰撞优化设计

摘要

An optimizational design was investigated at vehicle frontal crash cases for a multi-purpose vehicle (MPV) at 40％ offset frontal crash to improve the optimization accuracy and efficiency.A modified Efficient Global Optimization (EGO) algorithm was built based on Kriging model considering the improved effect of sequence samples of target response and constraints with a new established sequential sampling process at constraint conditions of intrusion and deformation in vehicle crashes.The results show that using the proposed modified EGO algorithm has a mininal sample number of 112 and an error of less than 8.42％ with the target acceleration reducing from 28.48 g to 26.77 g in the case of crash,compared with the algorithm without considering sequential sampling,Jones classical EGO sequential sampling algorithm and Schonlau constraint EGO sequential sampling algorithm;while all the other constrained crash performances meet the requirements with reduced mass of 2.89 kg.Therefore,the accuracy and efficiency of the method are verified.%为提升优化的精度和效率,对某多用途车(MPV)车型进行整车正面偏置碰撞结构优化设计.以整车碰撞后侵量和变形量等为约束条件,考虑了序列样本对目标响应和约束响应的改进效果,建立了基于Kriging模型的改进的高效全局优化(EGO)算法和相应的序列采样优化流程.结果表明:与不考虑序列采样的传统优化方法、Jones经典EGO序列采样算法和Schonlau约束EGO序列采样算法进行对比,该算法可以在最小的112个样本规模下,得到误差小于8.42％的优化解,碰撞案例在减质量2.89 kg,且所有碰撞约束性能均满足要求的情况下,目标碰撞有效加速度从28.48 g下降为26.77 g.从而,验证了该方法的准确性和效率.