Crashworthiness design of vehicle by using multiobjective robust optimization
Crashworthiness design of vehicle by using multiobjective robust optimization
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DOI：
10.1007/s00158-010-0601-z
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发表时间：
2011-07
影响因子：
3.9
通讯作者：
Guangyong Sun;Guangyao Li;Shiwei Zhou;Hongzhou Li;Shujuan Hou;Qing Li
中科院分区：
文献类型：
--
作者：
Guangyong Sun;Guangyao Li;Shiwei Zhou;Hongzhou Li;Shujuan Hou;Qing Li
虽然确定性优化方法已在相当程度上成功地应用于各种耐撞性设计中，以提高乘客安全性和降低车辆成本，但当考虑到设计变量和系统参数噪声的摄动时，设计可能变得不那么有意义甚至不可接受。为了克服这一缺点，我们提出了一个多目标鲁棒优化方法来解决多个耐撞性标准的参数不确定性的影响，其中采用几个不同的西格玛标准来衡量的变化。以汽车正面全碰撞为例，以增加能量吸收和减轻结构重量为设计目标，以峰值减速度为约束条件。多目标粒子群优化算法被应用于生成鲁棒Pareto解，它不再需要制定一个单一的成本函数，通过使用加权因子或其他手段。从这个例子中，可以观察到帕累托确定性设计和稳健设计之间的明显折衷。结果表明，使用多目标鲁棒优化的优点，不仅在能量吸收的增加和减少结构重量从基线设计，但也显着改善的鲁棒优化。
Although deterministic optimization has to a considerable extent been successfully applied in various crashworthiness designs to improve passenger safety and reduce vehicle cost, the design could become less meaningful or even unacceptable when considering the perturbations of design variables and noises of system parameters. To overcome this drawback, we present a multiobjective robust optimization methodology to address the effects of parametric uncertainties on multiple crashworthiness criteria, where several different sigma criteria are adopted to measure the variations. As an example, a full front impact of vehicle is considered with increase in energy absorption and reduction of structural weight as the design objectives, and peak deceleration as the constraint. A multiobjective particle swarm optimization is applied to generate robust Pareto solution, which no longer requires formulating a single cost function by using weighting factors or other means. From the example, a clear compromise between the Pareto deterministic and robust designs can be observed. The results demonstrate the advantages of using multiobjective robust optimization, with not only the increase in the energy absorption and decrease in structural weight from a baseline design, but also a significant improvement in the robustness of optimum.