Divergence and convergence of inertial particles in high Reynolds number turbulence

TL;DR

This study analyzes inertial particles in high Reynolds number turbulence using Voronoi tessellation, revealing how divergence varies with clustering and Stokes number, and introduces a finite-time divergence measure.

Contribution

It introduces a finite-time measure of particle velocity divergence and analyzes its distribution in turbulent flows, linking divergence behavior to clustering and Stokes number effects.

Findings

Divergence PDF deviates for inertial particles compared to fluid particles.

Divergence is most prominent in cluster regions and less in voids.

Larger Stokes numbers lead to greater divergence values.

Abstract

Inertial particle data from three-dimensional direct numerical simulations of particle-laden homogeneous isotropic turbulence at high Reynolds number are analyzed using Voronoi tessellation of the particle positions, 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 (PDF) of the divergence deviates from that for fluid particles. Joint PDFs 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. Theoretical analyses further show that the divergence for random particles in random flow satisfies a PDF corresponding to the ratio of two independent variables following normal and gamma distributions in one dimension. Extending this model to three dimensions, the predicted PDF agrees reasonably well with Monte-Carlo simulations and DNS data of fluid particles.