Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model

Abstract

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

Digital and automatic design of free-form single-layer grid structures

“…Since the slack lengths have been defined, some trials need to determine reasonable elastic coefficients of various springs. In the field of virtual simulation, to simulate real objects, a great deal of research has been carried out on the physical parameters of springs to make the characteristics of digitized objects more similar to those of real objects [32][33][34].…”

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

“…To address the challenge of balancing accuracy and efficiency, data-driven modelling methods as a reverse approach have recently received more interest in the engineering field. For instance, Tang et al [8][9][10] proposed a data-driven collision modelling method where useful force-displacement curve models are extracted from the existing FE simulation data, to predict the collision dynamic response under various collision conditions. Mu ¨ller et al [11] proposed a machine learning method to quickly predict and estimate the severity of crushing caused by collisions based on the FE simulation data.…”

A Few-Shot Learning-Based Crashworthiness Analysis and Optimization for Multi-Cell Structure of High-Speed Train

“…Recently, Machine Learning, as a novel developed data-driven approach, has yielded significant improvement in terms of efficiency, and has been extensively used in the engineering area to simulate highly nonlinear interactions between inputs and outputs of crash dynamics. For instance, the stable behavior prediction of a thin-walled box [5], the force-displacement characteristics analysis of train crashes [6,7], the integrated dynamic response prediction of automobile crashes [8], and the crash process estimation of vehiclebarrier frontal crashes [9] were all analyzed by Karimi et al [10]. These approaches could directly analyze and predict crash dynamics characteristics using the trained model, which has significantly increased computing efficiency.…”