论文标题
壁构成湍流中惯性颗粒的沉降速度和空间分布的机制
Mechanisms governing the settling velocities and spatial distributions of inertial particles in wall-bounded turbulence
论文作者
论文摘要
我们使用理论和直接数值模拟(DNS)来探索在壁挂式湍流中沉降的惯性颗粒的平均垂直速度和空间分布。该理论基于描述粒子位置和速度的概率密度函数的确切相位方程。这使我们能够确定管理颗粒传输的不同物理机制。然后,我们检查了壁附近粒子运动的渐近行为,揭示了在结合重力沉降时产生的近壁行为的基本差异。当平均垂直粒子质量通量为零时,由于颗粒优先采样了流体速度为正的颗粒,平均垂直粒子速度离墙壁为零,这与向下Stokes沉降速度平衡。当平均质量通量为负时,湍流和粒子惯性的综合作用会导致平均垂直粒子速度,可以显着超过Stokes沉降速度,高达多达十倍。远离墙壁足够远的是,增强的垂直速度是由于优先清扫机制所致。但是,随着颗粒接近墙壁,优先扫描机制的贡献变得很小,涡轮速度的向下贡献占主导地位。靠近墙壁,粒子浓度随着幂律而生长,但该功率定律的性质取决于粒子的stokes数字。最后,我们的结果强调了如何为有限惯性的颗粒修改颗粒浓度的隆起模型。
We use theory and Direct Numerical Simulations (DNS) to explore the average vertical velocities and spatial distributions of inertial particles settling in a wall-bounded turbulent flow. The theory is based on the exact phase-space equation for the Probability Density Function describing particle positions and velocities. This allowed us to identify the distinct physical mechanisms governing the particle transport. We then examined the asymptotic behavior of the particle motion near the wall, revealing the fundamental differences to the near wall behavior that is produced when incorporating gravitational settling. When the average vertical particle mass flux is zero, the averaged vertical particle velocity is zero away from the wall due to the particles preferentially sampling regions where the fluid velocity is positive, which balances with the downward Stokes settling velocity. When the average mass flux is negative, the combined effects of turbulence and particle inertia lead to average vertical particle velocities that can significantly exceed the Stokes settling velocity, by as much as ten times. Sufficiently far from the wall, the enhanced vertical velocities are due to the preferential sweeping mechanism. However, as the particles approach the wall, the contribution from the preferential sweeping mechanism becomes small, and a downward contribution from the turbophoretic velocity dominates the behavior. Close to the wall, the particle concentration grows as a power-law, but the nature of this power law depends on the particle Stokes number. Finally, our results highlight how the Rouse model of particle concentration is to be modified for particles with finite inertia.