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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.

Due to a lot of uncertainties in the robust optimization process, especially in multiple response problems, many random factors can cost doubt on results. The aim of this paper is to propose a robust optimization method for multiple response considering the random factors in the robust optimization design to solve the aforementioned problem. In this paper, we research the multi-response robustness optimization of the anti-rolling torsion bar using a stochastic model. First, the quality loss function of the anti-rolling torsion bar is determined as the optimization object, and the diameters of the anti-rolling torsion bar are determined as the design variables. Second, the multi-response robust optimization model, considering random factors (such as the loads), is established by using the stochastic model. Finally, the Monte Carlo sampling method combined with a non-dominated sorting genetic algorithm II (NSGA II) is adopted to solve this robust optimization problem, and then the robust optimization solution is obtained. The research results indicate that the anti-rolling torsion bar weight decreases, and the stiffness and fatigue strength increase. Furthermore, the quality performance of the anti-rolling torsion bar gets better, and the anti-disturbance ability of the anti-rolling torsion bar gets stronger.

Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different\ud mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation.\ud ,is paper proposes a nonlinear spring-mass-damper model of train frontal crash, which achieves high accuracy and maintains\ud low complexity. ,e Convolutional Long-short-term-memory Generation Adversarial Network (CLGAN) model is applied to\ud study the nonlinear parameters dynamic variation of the key components of a rail vehicle (e.g., the head car, anticlimbing energy\ud absorber, and the coupler buffer devices). Firstly, the nonlinear lumped model of train frontal crash is built, and then the physical\ud parameters are deduced in twenty different cases using D’Alembert’s principle. Secondly, the input/output relationship of the\ud CLGAN model is determined, where the inputs are the nonlinear physical parameters in twenty initial conditions, and the output\ud is the nonlinear relationship between the train crash nonlinear parameters under other initial cases. Finally, the train crash\ud dynamic characteristics are accurately estimated during the train crash processes through the training of the CLGAN model, and\ud then the crash processes under different given conditions can be described effectively. ,e estimation results exhibit good\ud agreement with finite element (FE) simulations and experimental results. Furthermore, the CLGAN model shows great potential\ud in nonlinear estimation, and CLGAN can better describe the variation of nonlinear spring damping compared with the traditional\ud model. ,e nonlinear spring-mass-damper modeling is involved in improving the speed and accuracy of the train crash estimation, as well as being able to offer guidance for structure optimization in the early design stage.