Redevelopment of liftoff rates of saltating sand grains based on a simple optimization model

• Published in: Science China. Earth sciences 2010-04, Vol.53 (4), p.568-574
• Main Authors: Cheng, Hong, Zou, XueYong
• Format: Article
• Language: English
• Subjects: Earth and Environmental Science; Earth Sciences; Optimization; Research Paper; Saltation; Sand; Sand & gravel; Simulation; Velocity; Wind; Wind speed; Wind tunnels
• ISSN: 1674-7313; 1869-1897
• DOI: 10.1007/s11430-010-0008-0

This paper is a redevelopment result of liftoff rates of saltating sand grains based on our previous work. Aeolian sand flow is a complex multi-phase flow because of a special two-phase gas-solid flow near ground surface. Despite extensive research on the movement of blowing sand, no model fully characterizes aeolian sand flow, and large differences often exist between simulations of aeolian sand movement and field observations. One key problem is a few of sufficient research on liftoff rates of saltating sand grains (also called the number of liftoff sand grains per unit time and per unit bed area). It is necessary to research in advance liftoff rates of saltating sand grains. We redeveloped liftoff rates of saltating sand grains by establishing an optimization model based on the flux of aeolian sand flow at different heights of the sampler in wind tunnel and the simulated capture of saltating sand grains by different heights of the sampler that are from different liftoff position (distance from the sampler) in order to revise previous inversion condition of liftoff rates of saltating sand grains. Liftoff rates increased rapidly with increasing wind speed. For frictional wind velocities of u * = 0.67, 0.77, 0.82, 0.83, and 0.87 m s −1 , liftoff rates were 3840, 954502, 5235114, 5499407, and 7696291 sand grain s −1 m −2 , respectively. These rates could be expressed as the square of the instantaneous frictional wind velocity and a constant (0.663) that differs from the critical (threshold) frictional wind velocity at which saltation begins. Although our results require additional experimental validation and the simple optimization model must be improved, they nonetheless provide a strong basis for future research.