Multiobjective optimization design for an occupant restraint system considering interval correlation

被引:0
作者
Qiong Wang
Zhiwei Liu
Liang Cao
Zhao Xiao
Qunwang Zhang
Shuo Zhang
机构
[1] Hunan Institute of Engineering,Hunan Provincial Key Laboratory of Vehicle Power and Transmission System
[2] Xiangtan University,College of Mechanical Engineering
[3] Hunan University of Science and Technology,School of Mechanical Engineering
来源
Structural and Multidisciplinary Optimization | 2022年 / 65卷
关键词
Multiobjective optimization design; Occupant restraint system; Interval correlation; Multidimensional parallelepiped model;
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中图分类号
学科分类号
摘要
The occupant restraint system (ORS) focuses on automobile crash safety, which can effectively reduce passenger injury. For an ORS, some design parameters are uncertainties, but even they may have correlations. It is noted that the ORS is typically characterized by various occupant injury indices, and uncertainties and their correlations will impact these indices, while most existing design problems have been formulated as a single objective optimization. To address these issues, this paper combines a multidimensional parallelepiped model with a multiobjective genetic algorithm (GA) for the vehicle ORS design. First, a multiobjective optimization model for the ORS design considering interval uncertainties and their correlations is developed to balance the design requirements for multiple objectives. Second, the established multiobjective optimization model considering parametric correlations is converted into an independent interval multiobjective optimization model in the transformed cuboid domain. Third, the interval multiobjective optimization model is converted into a deterministic multiobjective optimization model by the use of the interval order relation and interval possibility degree. Finally, the optimization problem is solved by coupling the multiobjective GA with the interval expansion method. The ORS design of a 100% frontal impact at a speed of 35 mph is exemplified for the proposed model and method, and the application example shows that the optimization results are different for different correlations. If the correlation analysis is neglected, then the optimization results may lead to a deviation of design. Thus, more conservative solutions could have been generated from the ORS design considering interval correlation.
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