Uncertainty Analysis and Optimization of Quasi-Zero Stiffness Air Suspension Based on Polynomial Chaos Method
作者机构：Automotive Engineering Research InstituteJiangsu University
出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报)
年 卷 期：2022年第35卷第4期
页 面：284-302页
核心收录：
学科分类：0711[理学-系统科学] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学]
基 金：Supported by National Natural Science Foundation of China (Grant No. 51875256) Open Platform Fund of Hunan Institute of Technology of China (Grant No. KFA20009) Hong Kong,Macao and Taiwan Science and Technology Cooperation Project in Jiangsu Province of China (Grant No. BZ2020050)
主 题：Air suspension Quasi-zero stiffness Polynomial chaos Uncertainty analysis Optimization
摘 要：To improve the vibration isolation performance of suspensions, various new structural forms of suspensions have been proposed. However, there is uncertainty in these new structure suspensions, so the deterministic research cannot reflect the performance of the suspension under actual operating conditions. In this paper, a quasi-zero stiffness isolator is used in automotive suspensions to form a new suspensionquasi-zero stiffness air suspension(QZSAS). Due to the strong nonlinearity and structural complexity of quasi-zero stiffness suspensions, changes in structural parameters may cause dramatic changes in suspension performance, so it is of practical importance to study the effect of structural parameter uncertainty on the suspension performance. In order to solve this problem, three suspension structural parameters d0, L0and Pc0are selected as random variables, and the polynomial chaos expansion(PCE) theory is used to solve the suspension performance parameters. The sensitivity of the performance parameters to different structural parameters was discussed and analyzed in the frequency domain. Furthermore, a multi-objective optimization of the structural parameters d0, L0and Pc0of QZSAS was performed with the mean and variance of the root-mean-square(RMS) acceleration values as the optimization objectives. The optimization results show that there is an improvement of about 8%-10% in the mean value and about 40%-55% in the standard deviation of acceleration(RMS) values. This paper verifies the feasibility of the PCE method for solving the uncertainty problem of complex nonlinear systems, which provide a reference for the future structural design and optimization of such suspension systems.
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