Parallel computing aided analyses of dynamic buckling for railway track infrastructure

Abstract

This paper presents a scalable parallel computing framework for simulating track buckling under dynamic train loads, enabling large-scale railway track stability analysis. A three-dimensional (3D) track model is developed using finite element-based Euler–Bernoulli beam formulations for rails, dynamic force inputs, and nonlinear interactions at the sleeper–ballast interface to capture dynamic buckling behavior. To address computational challenges in simulating extended track sections, the framework employs message passing interface–based parallelization, optimizing load balancing, and minimizing interprocess communication overhead. Unlike approaches that simulate long tracks virtually by recycling a small domain, the proposed method maintains complete dynamic and structural detail across the entire track length. It dynamically adjusts lateral rail stiffness and incorporates thermal compression effects to enable simulation of buckling behavior, while efficiently scaling across high-performance computing clusters. Case studies demonstrate the framework's ability to simulate large-scale tracks under combined thermal gradients and dynamic train loads, achieving near-linear speedup and reducing runtime by up to 90% compared to serial approaches. Additionally, a machine learning–based buckling risk assessment is presented as a use case, where a model trained on long-track simulation results predicts buckling risk across extended sections. By integrating 3D track dynamics, parallel computing, and data-driven risk assessment, this work provides a powerful tool for evaluating railway infrastructure resilience under extreme operational conditions.