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.
Highlights
Among railway accidents, train crashes are considered to be one of the most serious rail disasters, leading to a great number of casualties as well as property losses, as in the case of the railway traffic accident that occurred on the Ningzhou-Wenzhou line in China, resulting in 40 deaths and 172 injuries, and the South Carolina train crash accident, resulting in 2 deaths and 116 injuries
Many different models have been proposed to describe the crash process, including the finite element (FE) model and the lumped parameter model (LPM) [6, 7]. e essential difference between these two lies in the parameters incorporated in the vehicle model: LPM uses mass, spring, and damping to describe the vehicle’s structure only, while the FE model takes into consideration the geometry, material, and connection of every component of the train
Stoffel et al [11] take into account the strain-rate and high dynamic deformation in nonlinear structural deformations and propose an intelligent finite element, where an Artificial Neural Network (ANN) is used instead of viscoelastic constitutive equations
Summary
Train crashes are considered to be one of the most serious rail disasters, leading to a great number of casualties as well as property losses, as in the case of the railway traffic accident that occurred on the Ningzhou-Wenzhou line in China, resulting in 40 deaths and 172 injuries, and the South Carolina train crash accident, resulting in 2 deaths and 116 injuries. Stoffel et al [11] take into account the strain-rate and high dynamic deformation in nonlinear structural deformations and propose an intelligent finite element, where an Artificial Neural Network (ANN) is used instead of viscoelastic constitutive equations In this way, a finite element theory is combined with Machine Learning, which substitutes a physically nonlinear constitutive law and leads to low fidelity simulation (see Figure 1(a)). Ese methods investigate whether it is possible to accurately estimate the basic crashworthiness parameters by using the earlier proposed LPM modeling combined with Machine Learning (see Figure 1(b)). Ey introduce a new piecewise model structure to describe the acceleration of vehicles during frontal crash, in this way allowing for an effective estimation of the crash process under different working conditions. The train frontal crash process is estimated under different conditions, and lastly the model is verified
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