Settling and clustering of snow particles in atmospheric turbulence
Settling and clustering of snow particles in atmospheric turbulence
复制标题
大气湍流中雪颗粒的沉降和聚集
DOI：
10.1017/jfm.2020.1153
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发表时间：
2021
影响因子：
3.7
通讯作者：
Hong, Jiarong
中科院分区：
文献类型：
--
作者：
Li, Cheng;Lim, Kaeul;Berk, Tim;Abraham, Aliza;Heisel, Michael;Guala, Michele;Coletti, Filippo;Hong, Jiarong
湍流对降雪的影响在目前的天气预报模式中没有考虑。在这里，我们证明了湍流实际上是对降雪速度和空间分布的关键影响。我们考虑三个降雪事件下的大气湍流水平有很大的不同。我们表征的大小和形态的雪颗粒，我们同时成像的速度，加速度和相对浓度在垂直平面上近似的面积。我们发现，顺磁驱动的沉降增强解释否则颗粒尺寸和速度之间的矛盾趋势。Stokes数的估计和垂直速度与当地浓度之间的相关性是一致的，认为增强沉降是植根于优先清扫机制。当雪的垂直速度比特征湍流速度大时，交叉轨迹效应导致强烈的加速度。当满足优先扫掠条件时，浓度场高度不均匀，在很大尺度范围内出现聚集现象。这些集群，第一次在自然发生的流中识别，显示在规范设置中看到的签名特征：幂律尺寸分布，分形形状，垂直伸长和随集群大小增加的大下降速度。这些研究结果表明，粒子负载湍流的基本现象可以被利用到一个更好的预测了解雪降水和地面积雪。他们还演示了如何环境流量可以用来调查分散的多相流在雷诺数在实验室实验或数值模拟中无法访问。
The effect of turbulence on snow precipitation is not incorporated into present weather forecasting models. Here we show evidence that turbulence is in fact a key influence on both fall speed and spatial distribution of settling snow. We consider three snowfall events under vastly different levels of atmospheric turbulence. We characterize the size and morphology of the snow particles, and we simultaneously image their velocity, acceleration and relative concentration over vertical planes approximately in area. We find that turbulence-driven settling enhancement explains otherwise contradictory trends between the particle size and velocity. The estimates of the Stokes number and the correlation between vertical velocity and local concentration are consistent with the view that the enhanced settling is rooted in the preferential sweeping mechanism. When the snow vertical velocity is large compared to the characteristic turbulence velocity, the crossing trajectories effect results in strong accelerations. When the conditions of preferential sweeping are met, the concentration field is highly non-uniform and clustering appears over a wide range of scales. These clusters, identified for the first time in a naturally occurring flow, display the signature features seen in canonical settings: power-law size distribution, fractal-like shape, vertical elongation and large fall speed that increases with the cluster size. These findings demonstrate that the fundamental phenomenology of particle-laden turbulence can be leveraged towards a better predictive understanding of snow precipitation and ground snow accumulation. They also demonstrate how environmental flows can be used to investigate dispersed multiphase flows at Reynolds numbers not accessible in laboratory experiments or numerical simulations.
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