Surrogate modelling of railway pantograph-catenary interaction using deep Long-Short-Term-Memory neural networks

Surrogate modelling of railway pantograph-catenary interaction using deep Long-Short-Term-Memory neural networks

Song, Yang (Southwest Jiaotong University; Norwegian University of Science and Technology (NTNU))
Wang, H. (TU Delft Railway Engineering)
Frøseth, Gunnstein (Norwegian University of Science and Technology (NTNU))
Nåvik, Petter (Norwegian University of Science and Technology (NTNU))
Liu, Zhigang (Southwest Jiaotong University)
Rønnquist, Anders (Norwegian University of Science and Technology (NTNU))

2023

The interaction performance of the pantograph-catenary is of great importance as it directly determines the current collection quality and operational safety of trains. The finite element method (FEM) is dominantly used for simulating pantograph-catenary interaction, which is normally computationally heavy. In this work, addressing the tremendous computational cost of FEM models, a surrogate model for fast simulations of pantograph-catenary interaction is proposed using deep learning. A dataset containing 30,000 cases of pantograph-catenary interaction is generated by a validated FEM model. A Long-Short-Term-Memory (LSTM) neural network is proposed to learn the inherent nonlinearity between the input model parameters and the output pantograph-catenary contact force from data. The resulting prediction performance indicates that contact forces predicted by the surrogate model are consistent with those simulated by FEM, while the computational efforts of the surrogate model are negligible compared with FEM. Prediction performances using different network architectures and configurations are compared to determine the optimal setting for a pantograph-catenary system. The LSTM-based surrogate model shows high efficiency for simulating pantograph-catenary interactions and promising practicability in optimising catenary structural parameters for design or upgrade.

Contact force
Deep learning
High-speed railway
LSTM
Pantograph-catenary interaction
Surrogate model

http://resolver.tudelft.nl/uuid:cb21b0be-0063-434f-a6ee-ec5c4d30c8f1

https://doi.org/10.1016/j.mechmachtheory.2023.105386

0094-114X

Mechanism and Machine Theory, 187

Institutional Repository

journal article

© 2023 Yang Song, H. Wang, Gunnstein Frøseth, Petter Nåvik, Zhigang Liu, Anders Rønnquist