Key Takeaways & Executive Findings
- •• A novel co-simulation (CS) approach coupling CFD and MBD is proposed to capture fluid-structure interactions in high-speed trains, overcoming limitations of offline simulation (OS) that neglects vehicle motion effects on airflow. • The CS method is validated against field test data, demonstrating its accuracy and reliability for analyzing aerodynamic and dynamic responses in complex scenarios such as tunnel exit under crosswind. • Compared to OS, CS reveals significantly severer transient wheel-rail impacts and larger differences in aerodynamic moments (up to 29.6 kN·m in yawing), highlighting the importance of considering coupled effects for safety assessment. • The CS approach offers superior computational efficiency and stability, making it a practical tool for studying high-speed train performance under extreme wind conditions and aiding in the design of safer railway operations.
Abstract
The interaction between the airflow and train influences the aerodynamic characteristics and dynamic performance of high-speed trains. This study focused on the fluid-solid coupling effect of airflow and HST, and proposed a co-simulation (CS) approach between computational fluid dynamics and multi-body dynamics. Firstly, the aerodynamic model was developed by employing overset mesh technology and the finite volume method, and the detailed train-track coupled dynamic model was established. Then the User Data Protocol was adopted to build data communication channels. Moreover, the proposed CS method was validated by comparison with a reported field test result. Finally, a case study of the HST exiting a tunnel subjected to crosswind was conducted to compare differences between CS and offline simulation (OS) methods. In terms of the presented case, the changing trends of aerodynamic forces and car-body displacements calculated by the two methods were similar. Differences mainly lie in aerodynamic moments and transient wheel-rail impacts. Maximum pitching and yawing moments on the head vehicle in the two methods differ by 21.1 kN∙m and 29.6 kN∙m, respectively. And wheel-rail impacts caused by sudden changes in aerodynamic loads are significantly severer in CS. Wheel-rail safety indices obtained by CS are slightly greater than those by OS. This research proposes a CS method for aerodynamic characteristics and dynamic performance of the HST in complex scenarios, which has superiority in computational efficiency and stability.
1. Introduction
High-speed trains (HSTs) are widely utilized for passenger transportation due to the high levels of ride comfort, stability, and reliability. With increasing travel demand, high-speed railway constructions in the western mountainous regions, which are key areas affected by strong winds in China, have been progressing. Strong winds pose a significant safety risk for the operation of high-speed railways, as inducing severe aerodynamic loads on vehicles and affecting their dynamic performance [1, 2]. Consequently, wind-related operational safety and comfort issues for HSTs have garnered increasing attention in recent years.
To address these challenges, significant efforts have been devoted to studying the operational quality of HSTs under wind conditions. In Europe, extensive field tests had been conducted to investigate the effects of wind environments on the running safety of HSTs, culminating in a series of practical standards established by CEN [3]. In China, field tests along the Xinjiang railway lines had identified effective measures to mitigate wind-induced risks, including critical wind-vehicle speed relationships and optimized parameters for windbreak structures [4]. While field tests are direct and effective for analyzing the impact of crosswinds on HSTs, conducting such tests under extreme wind conditions remains challenging due to safety and controllability concerns.
Wind tunnel experiments, which offer greater design flexibility and controllability, have been extensively developed as an alternative for studying the aerodynamic characteristics of trains [5–8]. Furthermore, advancements in computational fluid dynamics (CFD), multi-body dynamics (MBD), and computing technologies enhanced the reliability of numerical simulations for analyzing the aerodynamic characteristics and dynamic performance of HSTs. Typically, aerodynamic loads obtained from CFD models were converted into concentrated forces and moments applied to MBD models to calculate the dynamic response of the vehicle. This approach, known as the off-line simulation (OS) method, has been widely used to study aerodynamic and dynamic challenges for HSTs in various scenarios, such as entering a tunnel [9, 10], passing by windbreaks [11, 12], transiting through the bridge [13–16], etc. Some studies also introduced vehicle-track coupled dynamics models [17, 18] to enhance the analysis of train dynamics under crosswind conditions.
In the OS method, the effect of vehicle motions on the flow field surrounding the HST is ignored. How
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Yanlin Hu, Qinghua Chen, Xin Ge, Wentao He, Haowei Yu, Liang Ling, Kaiyun Wang (2025). A CFD-MBD Co-Simulation Approach for Studying Aerodynamic Characteristics and Dynamic Performance of High-Speed Trains. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01352-1
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a co-simulation (CS) approach coupling computational fluid dynamics (CFD) and multi-body dynamics (MBD) to study the aerodynamic characteristics and dynamic performance of high-speed trains, capturing fluid-structure interactions that are neglected in offline simulation (OS) methods.
How does the co-simulation method differ from offline simulation?
In offline simulation, aerodynamic loads are precomputed and applied to the dynamic model without considering the effect of vehicle motion on the airflow. The co-simulation method couples CFD and MBD in real-time, allowing the vehicle motion to influence the flow field, leading to more accurate predictions of aerodynamic moments and transient wheel-rail impacts.
What are the key findings of the case study?
The case study of a high-speed train exiting a tunnel under crosswind shows that while trends in aerodynamic forces and car-body displacements are similar between CS and OS, significant differences exist in aerodynamic moments (up to 29.6 kN·m in yawing) and wheel-rail impacts, with CS predicting severer transient impacts and slightly higher safety indices.
Why is the co-simulation approach considered superior in computational efficiency and stability?
The co-simulation approach is designed to maintain computational efficiency by using efficient data communication protocols and stable coupling algorithms, making it practical for complex scenarios while providing stable and reliable results compared to traditional methods.
What are the practical implications of this research?
The research provides a validated tool for assessing the safety and performance of high-speed trains under strong wind conditions, which is crucial for the design and operation of railways in windy mountainous regions, potentially leading to improved safety standards and operational guidelines.
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