Response analysis and optimization of the air spring with epistemic uncertainties

Traditional methods for the optimization design of the air spring are based on the deterministic assumption that the parameters are fixed. However, uncertainties widely exist during the manufacturing stage of the air spring. To model the uncertainties in air springs, evidence theory is introduced. For the response analysis of the air spring system under evidence theory, an evidence theory-based polynomial chaos method, called the sparse grid quadrature-based arbitrary orthogonal polynomial (SGQ-AOP) method, is proposed. In the SGQ-AOP method, the response of the air spring is approximated by AOP expansion, and the sparse grid quadrature is introduced to calculate the expansion coefficient. For optimization of the air spring, a reliability-based optimization model is established under evidence theory. To improve the efficiency of optimization, the SGQ-AOP method is used to establish the surrogate model for the response of the air spring. The proposed response analysis and the optimization method were employed to optimize an air spring with epistemic uncertainties, and its effectiveness has been demonstrated by comparing it with the traditional evidence theory-based AOP method.