Coherent clusters of inertial particles in homogeneous turbulence
Coherent clusters of inertial particles in homogeneous turbulence
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DOI：
10.1017/jfm.2017.700
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发表时间：
2016-11
影响因子：
3.7
通讯作者：
Lucia J. Baker;A. Frankel;A. Mani;F. Coletti
中科院分区：
文献类型：
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作者：
Lucia J. Baker;A. Frankel;A. Mani;F. Coletti
尽管湍流驱动的颗粒团簇的重要性已被广泛认可，但湍流分散多相流中颗粒团簇的拓扑定义一直缺乏。在这里，我们引入了一个基于自相似性的相干团的定义，并将其应用于重粒子分布的均匀各向同性湍流的直接数值模拟，有和没有重力加速度。集群显示自相似性已经在长度尺度大于两倍的柯尔莫哥洛夫长度，所示的分形性质的表面和幂律衰减的大小分布。所确定的集群的大小扩展到整体规模，与平均浓度，取决于斯托克斯数，但不上的集群尺寸。与非聚集粒子相比，相干团簇表现出更强的倾向于高应变和低涡度的样品区域。此外，我们发现，集群对准自己的本地涡度矢量。在重力的存在下，它们倾向于垂直排列，它们的下落速度与平均沉降速度显著不同：对于中等下落速度，它们比非聚集颗粒经历更强的沉降增强，而对于大下落速度，它们表现出微弱的沉降减少。所提出的聚类识别方法利用了Voronopathic图方法，但也与其他镶嵌技术，如经典的盒计数方法兼容。
Despite the widely acknowledged significance of turbulence-driven clustering, a clear topological definition of particle cluster in turbulent dispersed multiphase flows has been lacking. Here we introduce a definition of coherent cluster based on self-similarity, and apply it to distributions of heavy particles in direct numerical simulations of homogeneous isotropic turbulence, with and without gravitational acceleration. Clusters show self-similarity already at length scales larger than twice the Kolmogorov length, as indicated by the fractal nature of their surface and by the power-law decay of their size distribution. The size of the identified clusters extends to the integral scale, with average concentrations that depend on the Stokes number but not on the cluster dimension. Compared to non-clustered particles, coherent clusters show a stronger tendency to sample regions of high strain and low vorticity. Moreover, we find that the clusters align themselves with the local vorticity vector. In the presence of gravity, they tend to align themselves vertically and their fall speed is significantly different from the average settling velocity: for moderate fall speeds they experience stronger settling enhancement than non-clustered particles, while for large fall speeds they exhibit weakly reduced settling. The proposed approach for cluster identification leverages the Voronoï diagram method, but is also compatible with other tessellation techniques such as the classic box-counting method.