摘要
为提高前纵梁耐撞性,首先探究了加强筋组数和宽度对其压溃位移、吸能和初始峰值力的影响,综合分析选择设置加强筋长宽高为70 mm×20 mm×1 mm且组数为1的前纵梁为研究对象;然后选取其内外板厚度、材料及加强筋高度作为参数化变量,以质量比吸能(SEA)最大化,质量、初始峰值力最小化为目标,以压溃位移小于180mm为约束,采用哈默斯雷采样方法进行试验设计,并基于该采样数据进行响应面拟合;最后通过全局响应面法进行多目标优化求解。优化结果表明,位移增大25.3 mm,质量比吸能增加143.86 J/kg,初始峰值力下降128823.1 N,质量下降了0.098 kg。优化后的前纵梁耐撞性提高且实现了轻量化,对提升汽车被动安全具有重要应用价值。
In order to improve the crashworthiness of the front longitudinal beam,the effects of the number of groups and width of the stiffeners on the crushing displacement,energy absorption and initial peak force were firstly investigated.Then,the front longitudinal beam with the length,width and height of stiffeners as 70 mm×20 mm×1mm and the number of groups of stiffeners as 1 were selected as the follow-up research object.The thickness and material of the inner and outer plates,the height of the stiffeners were selected as the parameterized variables.The maximum SEA,the minimum mass and initial peak force were taken as the objectives,and the crushing displacement was restricted below 180 mm.The Hammersley sampling method was used for experimental design.Based on the sampling data,the response surface fitting was carried out,and lastly the global response surface method was used to solve the multi-objective optimization.The optimization results showed that the displacement increased by 25.3 mm,the SEA increased by 143.86 J/kg,the initial peak force decreased by 128823.1 N,and the mass decreased by 0.098 kg.The optimized front longitudinal beam has improved crashworthiness and lightweight,which has important application value for improving vehicle passive safety.
作者
李志扬
莫秋云
庞毅
姜玉秀
石恒鹏
LI Zhiyang;MO Qiuyun;PANG Yi;JIANG Yuxiu;SHI Hengpeng(College of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guilin 541004,Guangxi,China;Guangxi Digital Simulation Technology Co.,Ltd.,Liuzhou 545000,Guangxi,China)
出处
《农业装备与车辆工程》
2023年第11期33-38,共6页
Agricultural Equipment & Vehicle Engineering
基金
校企合作项目“M6汽车结构耐撞仿真分析”。
关键词
前纵梁
加强筋
哈默斯雷采样
响应面拟合
多目标优化
front longitudinal beam
stiffeners
Hamersley sampling
response surface fitting
multiple objective optimization
