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International Journal of Automotive Technology > Volume 25(3); 2024 > Article
International Journal of Automotive Technology 2024;25(3): 663-672.
doi: https://doi.org/10.1007/s12239-024-00052-4
Enhancing Train Coupling Simulation by Incorporating Speed-Dependent Energy Absorber Characteristics Through a Deep Neural Network
Jun Hyeok Hwang 1,2, Hyun Seung Jung 1,3, Jin Sung Kim 1, Seung Ho Ahn 1, Hyung Gyeun Gil 4
1Railroad Accident Research Department , Korea Railroad Research Institute
2Department of Robotics and Virtual Engineering , Korea University of Science and Technology
3Department of Transportation System Engineering , Korea University of Science and Technology
4Technical Research Center , KOBA
PDF Links Corresponding Author.  Hyun Seung Jung  , Email. jhs@krri.re.kr
ABSTRACT
Recently, hydrostatic buffers have emerged as energy-absorbing components in railway vehicles. These buffers exhibit speed-dependent characteristics, with their reaction forces contingent upon compression displacement and speed. However, when dealing with a hydrostatic buffer with an unknown characteristic function in dynamic simulations, accommodating its speed-dependent attributes becomes a challenging task. In this study, we proposed a method for simulating train couplings that incorporates the speed-dependent characteristics of a hydrostatic buffer by utilizing a deep neural network (DNN). Our methodology involved the training of a DNN-based speed-dependent buffer model using empirical data obtained from dynamic buffer tests. Subsequently, this model was applied to a multibody dynamics simulation for train coupling analysis. A critical aspect of this study involved comparing speed-dependent and speed-independent models in a train coupling scenario. This comparison reveals a significant insight: neglecting speed-dependent characteristics in coupling simulations can lead to inaccurate train-coupling safety assessments. The DNN-based method demonstrated its effectiveness, even with limited test data and when the mathematical speed-dependent characteristic function of the buffer is unknown.
Key Words: Hydrostatic buff er · Speed-dependent characteristics · Train coupling simulation · Deep neural network · Multibody dynamics
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