Abstract
In addressing snowdrift disasters in high-speed railway cutting areas, this study utilized the improved delayed detached eddy simulation turbulence model, synthetic eddy method, and Eulerian two-phase flow model (Euler–Euler). Coupled with grid dynamic deformation techniques, the investigation facilitated extracting temporal variations in snow particle accumulation profiles within the cutting areas. The predicting accuracy of the numerical method was validated through the wind tunnel and wind–snow experimental data. Building upon this foundation, the study investigated how uniform and turbulent flow conditions impact snow distribution in the high-speed railway cutting areas. The analysis examined the turbulent flow structures, friction velocity distribution, and snow accretion contours within the cutting area. Findings indicate that the snow mass on the windward slope in the cutting area remains consistent under both flow conditions. However, there are significant differences in the rate of snow mass growth and the characteristics of snow accretion distribution. The disappearance of vortex structures at the base of the leeward slope is crucial for indicating the transition in the accumulation states of the surrounding snow particles. Under turbulent incoming flow conditions, the transition from large-scale vortex systems to vortex pairs on the leeward slope of the cutting is delayed, resulting in a postponed stable growth period for snow accretion. Additionally, strong erosion between the track bed and the leeward slope results in reduced snow accretion mass in these areas. In particular, at the simulation time of t = 150 min, under turbulent incoming flow conditions, the overall snow accumulation in the cutting decreased by 20.8% compared to the uniform incoming flow conditions. The snow mass in the leeward slope area decreased by 38.3%, and the track bed area experienced a 23.3% reduction in snow accumulation. In contrast, the snow quantity on the windward slope remained relatively consistent.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hunan Province