Current simulations and standards for high-speed train aerodynamics often neglect the critical effect of surface roughness on atmospheric boundary layer wind profiles, assuming idealized uniform flow. This simplification creates a significant gap between simulation conditions and real-world operating environments, leading to systematic deviations in predicting aerodynamic loads. To address this, our study introduces, for the first time, a comprehensive computational wind engineering approach into train aerodynamics. By using a new atmospheric roughness wall function and optimizing the parameters of the turbulence model, four typical atmospheric boundary layer wind fields were successfully simulated considering the surface roughness. An innovative “double wall function” method (the ground surface applied the atmospheric roughness wall function and the train applied the standard wall function) was adopted to effectively solve the scale matching problem between the atmospheric boundary layer and industrial surface flows. Results show that the y+ can maintain good applicability up to the order of 104 when using the atmospheric roughness wall function; the surface roughness has a negative correlation with the aerodynamic load of the train, that is, when the roughness increases from the lowest roughness to the highest roughness considered in this study (increases by 1.5 times), the lateral force coefficients of the front and tail vehicles decrease by 21.59% and 74.31% respectively, and the lift coefficient shows a significant attenuation of 1.08 times and 1.06 times. Thus, it can be known that the risk level of train operation in the lowest roughness surface area is the highest, and priority measures for wind prevention need to be formulated.
Sun et al. (Thu,) studied this question.
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