Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation. This paper proposes a nonlinear spring-mass-damper model of train frontal crash, which achieves high accuracy and maintains low complexity. The Convolutional Long-short-term-memory Generation Adversarial Network (CLGAN) model is applied to study the nonlinear parameters dynamic variation of the key components of a rail vehicle (e.g., the head car, anticlimbing energy absorber, and the coupler buffer devices). Firstly, the nonlinear lumped model of train frontal crash is built, and then the physical parameters are deduced in twenty different cases using D’Alembert’s principle. Secondly, the input/output relationship of the CLGAN model is determined, where the inputs are the nonlinear physical parameters in twenty initial conditions, and the output is the nonlinear relationship between the train crash nonlinear parameters under other initial cases. Finally, the train crash dynamic characteristics are accurately estimated during the train crash processes through the training of the CLGAN model, and then the crash processes under different given conditions can be described effectively. The estimation results exhibit good agreement with finite element (FE) simulations and experimental results. Furthermore, the CLGAN model shows great potential in nonlinear estimation, and CLGAN can better describe the variation of nonlinear spring damping compared with the traditional model. The nonlinear spring-mass-damper modeling is involved in improving the speed and accuracy of the train crash estimation, as well as being able to offer guidance for structure optimization in the early design stage.

中文翻译：

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基于CLGAN模型的列车正面碰撞非线性弹簧-阻尼器建模与参数估计。

由于火车撞车事故的复杂性，数学描述和估算是一个具有挑战性的过程。尽管已经开发了不同的数学模型，但是仍然难以平衡模型的复杂性和估计的准确性。本文提出了一种列车前部碰撞的非线性弹簧-质量-阻尼器模型，该模型可以实现较高的精度并保持较低的复杂度。卷积长短期记忆对抗网络（CLGAN）模型用于研究轨道车辆关键部件（例如头车，防爬能量吸收器和车钩缓冲装置）的非线性参数动态变化。首先建立了列车正面碰撞的非线性集总模型，然后利用D'Alembert原理推导了二十种不同情况下的物理参数。其次，确定CLGAN模型的输入/输出关系，其中输入是二十个初始条件下的非线性物理参数，输出是在其他初始情况下列车碰撞非线性参数之间的非线性关系。最后，通过CLGAN模型的训练，可以准确地估计出列车碰撞过程中的碰撞动力学特性，从而可以有效地描述不同给定条件下的碰撞过程。估计结果与有限元（FE）仿真和实验结果具有很好的一致性。此外，CLGAN模型在非线性估计中显示出很大的潜力，与传统模型相比，CLGAN可以更好地描述非线性弹簧阻尼的变化。