Skip to main content

A data-driven dynamics simulation framework for railway vehicles.

Nie, Y., Tang, Z., Liu, F., Chang, J. and Zhang, J. J., 2018. A data-driven dynamics simulation framework for railway vehicles. Vehicle System Dynamics, 56 (3), 406-427.

Full text available as:

Data-driven dynamics simulation for railway vehicles.v1.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1080/00423114.2017.1381981


The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy.

Item Type:Article
Uncontrolled Keywords:Dynamics simulation; data-driven modelling; machine learning; surrogate element; co-simulation
Group:Faculty of Media & Communication
ID Code:30123
Deposited By: Symplectic RT2
Deposited On:13 Dec 2017 16:03
Last Modified:14 Mar 2022 14:08


Downloads per month over past year

More statistics for this item...
Repository Staff Only -