SURROGATE MODELING APPROACH FOR STATISTICAL SIMULATION OF LINEAR CAR SUSPENSION DYNAMICS

A parametric uncertainty analysis of a car suspension system was conducted in this study. Four degrees-of-freedom (DOF) mathematical models were developed for both passive and semi-active suspension systems, and the uncertainty of these models was analyzed using a surrogate modeling approach. This approach employed polynomial-based surrogate models to simulate uncertainties, offering an alternative to traditional Monte Carlo (MC) simulations. The results indicate that the surrogate modeling method is not only more computationally efficient but also robust in capturing the uncertainty in dynamic systems compared to the MC approach, making it a powerful tool for the statistical simulation of linear car suspension dynamics.

Keywords – surrogate modeling, linear car suspension dynamics, uncertainty analysis, polynomial-based surrogate model, Monte Carlo method