Multi-objective optimization of suspension parameters for rail vehicles based on a virtual prototype surrogate model

Abstract This research is intended to develop a suspension parameter optimization approach based on a virtual prototype surrogate model of rail vehicles considering the coupling effects of suspension parameters. In order to analyze the effects on the dynamic indexes, which were affected by the suspension parameters, a virtual prototype model of a rail vehicle was established. The indexes of lateral ride quality and motion stability were obtained under different combinations of suspension parameters by design of experiment and simulation of virtual prototype. For constructing objective function of multi-objective optimization model for suspension parameters, the suspension parameters that have significant effects on ride quality and motion stability simultaneously were taken as the design variables, and thereafter Kriging models of lateral ride quality index, derailment coefficient, and reduction ratio of wheel load were obtained. On this basis, the multi-objective optimization model of suspension parameters was established, in which the objective function was combined with the three Kriging models. Then, the Pareto optimal solution set and concrete value of suspension parameters were sought using the NSGA-II algorithm. The dynamic simulation results indicated that both ride quality and motion stability of the rail vehicle had been improved after the multi-objective optimization of suspension parameters.

[1]  YuanTong Gu,et al.  Analysis of microelectromechanical systems (mems) by meshless local kriging (lokriging) method , 2004 .

[2]  Wei Chen,et al.  Lightweight design of vehicle parameters under crashworthiness using conservative surrogates , 2013, Comput. Ind..

[3]  Martyn Pinfold,et al.  The application of KBE techniques to the FE model creation of an automotive body structure , 2001 .

[4]  Jafar Roshanian,et al.  Latin hypercube sampling applied to reliability-based multidisciplinary design optimization of a launch vehicle , 2013 .

[5]  Philippe Renard,et al.  Distance-based Kriging relying on proxy simulations for inverse conditioning , 2013 .

[6]  Wei Zeng,et al.  CF-Kriging surrogate model based on the combination forecasting method , 2016 .

[7]  Chun‐Liang Lin,et al.  An evolutionary approach to active suspension design of rail vehicles , 2006 .

[8]  Mohamed Nejlaoui,et al.  Analytical modeling of rail vehicle safety and comfort in short radius curved tracks , 2009 .

[9]  Chun-Liang Lin,et al.  Optimal design for passive suspension of a light rail vehicle using constrained multiobjective evolutionary search , 2005 .

[10]  Ruan Xue-yu.  The Reliability Estimation Based on Kriging Model , 2007 .

[11]  Kong Wei-jian.  Survey on large-dimensional multi-objective evolutionary algorithms , 2010 .

[12]  Alexandros A. Taflanidis,et al.  Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment , 2013 .