Stochastic dynamic simulation of railway vehicles collision using data-driven modelling approach.

Abstract

Using stochastic dynamic simulation for railway vehicle collision still faces many challenges, such as high modelling complexity and time-consuming. To address the challenges, we introduce a novel data-driven stochastic process modelling (DSPM) approach into dynamic simulation of the railway vehicle collision. This DSPM approach consists of two steps: (i) process description, four kinds of kernels are used to describe the uncertainty inherent in collision processes; (ii) solving, stochastic variational inferences and mini-batch algorithms can then be used to accelerate computations of stochastic processes. By applying DSPM, Gaussian process regression (GPR) and finite element (FE) methods to two collision scenarios (i.e. lead car colliding with a rigid wall, and the lead car colliding with another lead car), we are able to achieve a comprehensive analysis. The comparison between the DSPM approach and the FE method revealed that the DSPM approach is capable of calculating the corresponding confidence interval, simultaneously improving the overall computational efficiency. Comparing the DSPM approach with the GPR method indicates that the DSPM approach has the ability to accurately describe the dynamic response under unknown conditions. Overall, this research demonstrates the feasibility and usability of the proposed DSPM approach for stochastic dynamics simulation of the railway vehicle collision.