| 제목 |
Enhancing Train Coupling Simulation by Incorporating Speed-Dependent Energy Absorber Characteristics Through a Deep Neural Network
|
| 분야 |
Body and Safety |
| 언어 |
English |
| 저자 |
Jun Hyeok Hwang(Korea Railroad Research Institute), Hyun-Seung Jung(Korea Railroad Research Institute), Jin Sung Kim( Korea Railroad Research Institute), Seung Ho Ahn(Korea Railroad Research Institute), Hyung Gyeun Gil(KOBA) |
| Key Words |
Hydrostatic buff er · Speed-dependent characteristics · Train coupling simulation · Deep neural network · Multibody dynamics |
| 초록 |
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. |
| 미리보기 |
다운로드
|
| 원문(PDF) |
다운로드
|
