A Data-Driven Dynamics Simulation Model for Railway Vehicles Based on Lightweight 3DCNN With Physics-Informed Constraints

Abstract

The dynamics simulation of complex railway vehicles requires a dedicated vehicle model, such as multi-body dynamics model. However, the multi-body model is time-consuming in long-distance simulation due to its computational complexity. This issue can be alleviated by using a data-driven vehicle dynamics model due to its effective generalization and computational speed. Firstly, the construction of the physical model of the vehicle system is carried out to obtain the coupling relationship between the components. Secondly, the coupling relationship between the components is embedded into the loss function of the deep neural network as physics-informed constraints. Further, the network parameters satisfying certain physical laws are obtained by minimizing the loss function. Finally, the proposed lightweight 3D convolutional neural network is used to predict the vibration state of the vehicle system. The dynamic response resulting from both the data-driven simulation model and the multi-body simulation model are investigated and compared. The simulation results show that the data-driven dynamics simulation model can accurately predict the vibration state of the vehicle system. The data-driven simulation model has smaller size and faster operation speed, which can be applied to long-distance prediction research of vehicle systems.