Robust-based design optimization of powertrain mounting system based on full vehicle model involving parametric uncertainty and correlation

Abstract

In engineering practice, the parametric uncertainty and correlation may coexist in the powertrain mounting system (PMS). An effective robust-based design optimization approach is proposed for uncertain PMS based on full vehicle model, where both the parametric uncertainty and correlation are considered. The uncertain parameters of PMS are firstly treated as probabilistic variables, and the Unscented Transformation Inspired (UTI) transformation is introduced to quantify the correlation of uncertain parameters. Then, to perform the uncertainty and correlation analysis, the UTI-Monte Carlo (UMC) method is developed based on UTI transformation and Monte Carlo sampling to estimate the means, standard deviations, variation ranges and correlation coefficients of PMS responses. Meanwhile, an efficient method named UTI-Arbitrary Polynomial Chaos Expansion (UAPCE) method is derived for the uncertainty and correlation analysis of PMS responses by combining UTI transformation and arbitrary polynomial chaos expansion. Next, an optimization model considering parametric uncertainty and correlation is formulated to perform the robust-based design of PMS, in which the weight coefficients of optimization components are calculated by principal component analysis. Finally, the numerical example is investigated to verify the effectiveness of the proposed methods.