Validation of numerical method of unsteady Reynolds-averaged Navier–Stokes coupled with discrete phase model and optimization for the snow-resistance performance of bogies of a high-speed train

This paper presents an investigation aimed at enhancing the snow resistance of bogies in a three-car grouped alpine high-speed train (HST) that operates in a snowy region. The study employs a combination of the unsteady Reynolds-averaged Navier–Stokes model, coupled with the discrete phase model. This modeling approach has been validated through wind tunnel tests, including flow field tests, two-phase flow tests, and snow-ice tests. The study comprehensively compares the characteristics of the wind-snow flow, spatial snow concentration, and the distribution of snow on the train's underbody between two scenarios: the original train's underbody structure and the multifactor collaborative optimization scheme (MCOS). The results indicate that the MCOS case facilitates the escape of snow particles from the bogie cavities by weakening the upward and reverse flows. Furthermore, the MCOS case significantly reduces the concentration of snow particles around the heat-producing elements and decreases the accumulation of snow on both the lower and upper surfaces of the bogies. As a result, it reduces snow accumulation on the bogies by 50.6%, 56.4%, 60.8%, and 62.4% at train speeds of 200, 250, 300, and 350 km/h, respectively. In summary, this research provides valuable insights for improving the snow resistance of HSTs operating in snowy regions, with potential applications in enhancing railway transportation safety and efficiency.