摘要
为提高汽车正面抗撞性,提出了精确收敛于当前设计点的改进的响应面方法,并将该方法与最优拉丁方试验设计方法相结合,建立了汽车全宽正面碰撞过程中B柱加速度峰值的代理模型.基于该代理模型使用序列二次规划算法对多组结构参数进行优化.结果表明,使用改进的响应面法建立的代理模型具有较高精度,基于代理模型优化后汽车B柱的加速度峰值降低18.2%.该研究为汽车正面抗撞性优化提供了一种快速便捷的方法.
To increase the frontal crashworthiness of automobiles,an improved response surface method（RSM） which has accurate convergence characteristics at the current design is presented,and the methodology is firstly used with optimal Latin hypercube sampling（LHS） to build the surrogate model of B-pillar acceleration peak value in full frontal crash.Many structure parameters are optimized using sequential quadratic program（SQP） based on the surrogate model.The results show that the improved RSM has high accuracy,and the acceleration peak value of B-pillar decreases nearly 18.2% after optimization.The research provides a facilitating approach for the optimization design of automotive frontal crashworthiness.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2009年第12期1076-1079,1084,共5页
Transactions of Beijing Institute of Technology
基金
国家"八六三"计划项目(2006AA04Z119)
关键词
车辆工程
响应面法
代理模型
正面碰撞
优化
vehicle engineering
response surface method（RSM）
surrogate model
frontal crash
optimization
