Slipstream is defined as the induced airflow caused by the high-speed train (HST) movement, which can cause safety hazards and damage concerns. Therefore, much effort has been invested to numerically predict the slipstream. As the flow over a HST is highly turbulent, a Scale-Resolving Solver (SRS) is essential to accurately capture the dynamic flow features: for example, Large-Eddy Simulation (LES) and Detached-Eddy Simulation (DES). In comparison to the Reynolds-Averaged Navier-Stokes (RANS) simulations, both LES and DES are computationally demanding, especially LES, which is rarely used for real-life industrial flows. This study aims to evaluate the capability of a relative new turbulence model, Scale-Adaptive Simulation (SAS), to predict HST slipstream, which is less computationally demanding than conventional SRSs. The present research systematically studies the performance of SAS under different time-steps, and the results are compared with DES predictions and experimental

Keywords: Aerodynamics, Computational methods, Turbulence