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基于ANSA对吸能盒防撞梁结构多目标优化设计

Multi-objective optimization design of anti-collision beam structure based on ANSA energy absorbing box
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摘要 在车辆低速撞击过程中,车体前端吸能盒的屈服塑性变形吸收了大部分的动能,起到了对其连接的前纵梁等较为重要的车辆部件的保护作用,降低了车辆的维护费用.本项目拟以“田字型”截面吸能盒为研究对象,采用非线性有限元程序LS-DYNA,研究吸能盒截面参数、壁厚、诱导槽等参数对吸能盒抗撞性能的影响规律,并在lsopt软件中建立多目标优化任务,采用自适应模拟退火算法(ASA),以吸能盒截面力和单侧吸能盒吸能为目标函数进行多目标优化设计.优化结果表明:优化后的吸能盒刚性墙反力峰值降低28%,单侧吸能盒吸能量增加10.7%,吸能盒截面力降低21.4%. During the process of a vehicle's low-speed impact,the yielding plastic deformation of the energyabsorbing box at the front end of the vehicle body absorbs most of the kinetic energy,playing a protective role for relatively important vehicle components such as the front longitudinal beam it is connected to,and reducing the vehicle's maintenance costs.This project intends to take the energy-absorbing box with a"tian-shaped"cross-section as the research object.By using the nonlinear finite element program LS-DYNA,the influence laws of parameters such as the cross-sectional parameters,wall thickness,and induction grooves of the energy-absorbing box on its crashworthiness performance will be studied.And a multi-objective optimization task will be established in the lsopt software.Using the Adaptive Simulated Annealing Algorithm(ASA),a multi-objective optimization design will be carried out with the cross-sectional force of the energy-absorbing box and the energy absorption of a single-sided energy-absorbing box as the objective functions.The optimization results show that:after optimization,the peak value of the reaction force of the energy-absorbing box against the rigid wall is reduced by 28%,the energy absorption amount of a single-sided energy-absorbing box is increased by 10.7%,and the cross-sectional force of the energy-absorbing box is reduced by 21.4%.
作者 周子恒 张宏远 张浩 张昕 ZHOU Ziheng;ZHANG Hongyuan;ZHANG Hao;ZHANG Xin(School of Automobile and Transportation,Shenyang Ligong University,Shenyang 110159,China)
出处 《车辆与动力技术》 2025年第1期29-33,共5页 Vehicle & Power Technology
关键词 正面碰撞 吸能盒 多目标优化 非线性 frontal collision energy absorbing box multi objective optimization nonlinear
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