Divergence and convergence of inertial particles in high-Reynolds-number turbulence

Inertial particle data from three-dimensional direct numerical simulations of particle-laden homogeneous isotropic turbulence at high Reynolds number are analysed using Voronoi tessellation of the particle positions and considering different Stokes numbers. A finite-time measure to quantify the dive...

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Bibliographic Details
Published in:Journal of fluid mechanics 2020-12, Vol.905, Article A14
Main Authors: Oujia, Thibault, Matsuda, Keigo, Schneider, Kai
Format: Article
Language:English
Subjects:
Cluster analysis
Clusters
Computational fluid dynamics
Convergence
Direct numerical simulation
Distribution
Distribution functions
Divergence
Flow velocity
Fluid flow
Fluid mechanics
High Reynolds number
Isotropic turbulence
JFM Papers
Orbital velocity
Probability distribution
Probability distribution functions
Probability theory
Regions
Reynolds number
Stokes number
Tessellation
Turbulence
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Summary:Inertial particle data from three-dimensional direct numerical simulations of particle-laden homogeneous isotropic turbulence at high Reynolds number are analysed using Voronoi tessellation of the particle positions and considering different Stokes numbers. A finite-time measure to quantify the divergence of the particle velocity by determining the volume change rate of the Voronoi cells is proposed. For inertial particles, the probability distribution function of the divergence deviates from that for fluid particles. Joint probability distribution functions of the divergence and the Voronoi volume illustrate that the divergence is most prominent in cluster regions and less pronounced in void regions. For larger volumes, the results show negative divergence values which represent cluster formation (i.e. particle convergence) and, for small volumes, the results show positive divergence values which represents cluster destruction/void formation (i.e. particle divergence). Moreover, when the Stokes number increases the divergence takes larger values, which gives some evidence why fine clusters are less observed for large Stokes numbers.
ISSN:0022-1120
1469-7645
DOI:10.1017/jfm.2020.672