Simulation of the influence of physical crust development on the aerodynamic entrainment of sand particles on a sloping bed

Wind erosion is a major cause of desertification, dust release and landscape reshaping, with the aerodynamic entrainment of particles being a key physical process triggering aeolian transport. While existing parameterizations predominantly consider flat bare soils, the role of physical crust in modulating entrainment thresholds remains not comprehensive enough. This study innovatively uses the discrete element method to quantify inter-particle cohesion and precisely simulate the trajectory of aerodynamic entrainment of each sand grain. It obtains microscopic characteristics like the response time and energy accumulation process of aerodynamic entrainment, which are hard to measure accurately in experiments. The results show that the threshold friction velocity for aerodynamic entrainment escalates exponentially with the increase in crust strength and thickness. Specifically, it can increase up to 3.6 times from 0.13 ms⁻¹ to 0.60 ms⁻¹ with enhanced crust strength, and 2.5 times from 0.20 ms⁻¹ to 0.71 ms⁻¹ with greater thickness. The aerodynamic entrainment laws of physical crusts on slopes with different gradients are basically consistent. Under varying crust strength conditions, the entrainment rate decreases exponentially with an increase in slope, and the reduction can exceed 40% compared to a flat bed. However, it is worth noting that as the crust strengthens, the influence of the slope on the entrainment rate gradually diminishes and the difference in the entrainment rates among different slopes ranges from 54% to almost zero. Through a detailed analysis of the mechanical evolution process, the underlying variation law by which the crust affects the aerodynamic entrainment of surface particles was elucidated. The innovative quantification and parameterization approach proposed herein not only furnishes a more precise and in-depth comprehension of this intricate process, but also contributes to improving the simulation accuracy of large and meso-scale wind erosion prediction models.