摘要
本文中基于C-NCAP中40%重叠度的偏置碰撞工况对某轿车进行结构耐撞性优化。为提高输出响应的预测精度,使用基于粒子群算法优化的支持向量回归模型来拟合设计变量与输出响应之间的关系,并利用非支配排序多目标遗传算法Ⅱ获得该优化问题的Pareto前沿。在确定性优化的基础上,并考虑产品性能在不确定因素影响下的波动,对其进行稳健性优化设计。最后,对优化结果进行有限元仿真验证。结果表明:优化后,结构质量减轻,耐撞性能明显提升,同时保障了稳定的产品性能。
Based on the condition of 40% overlap offset crash against deformable barrier in C-NCAP,the structural crashworthiness of a car is optimized in this paper.In order to enhance the predicted accuracy of output responses,the support vector regression model based on particle swarm optimization algorithm is employed to simulate the relationship between output responses and design variables,and the Pareto front of the proposed optimization problem is obtained by using non-dominated sorting multi-objective genetic algorithm II.On the basis of deterministic optimization with consideration of the fluctuation of product performance under the influence of uncertain factors,an optimization design for robustness is conducted.Finally,the results of optimization are verified by finite element simulation.The results show that after optimization the mass of structure is reduced,the crashworthiness is obviously enhanced,and the stable product performances are ensured.
作者
张海洋
吕晓江
周大永
夏梁
谷先广
Zhang Haiyang;LüXiaojiang;Zhou Dayong;Xia Liang;Gu Xianguang(Geely Automobile Research Institute,Zhejiang Key Laboratory of Automobile Safety Technology,Hangzhou 311228;Hunan University,State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Changsha 410082;Intelligent Manufacturing Institute,Hefei University of Technology,Hefei 230009)
出处
《汽车工程》
EI
CSCD
北大核心
2020年第2期222-227,277,共7页
Automotive Engineering
基金
中国博士后面上基金(2018M640524)
中国博士后基金特别项目(2019T120460)
浙江省汽车安全技术研究重点实验室开放基金(2009E10013)资助