Clustering of Inertial Particles in Turbulent Flow Through a Porous Unit Cell

TL;DR

This study uses direct numerical simulation to analyze how inertial particles cluster and deposit in turbulent flow within a porous unit cell, revealing effects of particle inertia and wall collisions on clustering patterns.

Contribution

It adapts clustering analysis tools for curved pore geometries and uncovers new insights into particle-wall interactions and multiscale clustering behavior in porous turbulence.

Findings

Cluster formation increases with Stokes number.

Wall collisions lead to small volume clusters at all Stokes numbers.

Clustering shifts to larger scales as Stokes number increases.

Abstract

Direct numerical simulation is used to investigate effects of turbulent flow in the confined geometry of a face-centered cubic porous unit cell on the transport, clustering, and deposition of fine particles at different Stokes numbers ($St = 0.01, 0.1, 0.5, 1, 2$) and at a pore Reynolds number of 500. Particles are advanced using one-way coupling and collision of particles with pore walls is modeled as perfectly elastic with specular reflection. Tools for studying inertial particle dynamics and clustering developed for homogeneous flows are adapted to take into account the embedded, curved geometry of the pore walls. The pattern and dynamics of clustering are investigated using the volume change of Voronoi tesselation in time to analyze the divergence and convergence of the particles. Similar to the case of homogeneous, isotropic turbulence, the cluster formation is present at large volumes, while cluster destruction is prominent at small volumes and these effects are amplified with Stokes number. However, unlike homogeneous, isotropic turbulence, formation of large number of very small volumes was observed at all Stokes numbers and is attributed to the collision of particles with the pore wall. Multiscale wavelet analysis of the particle number density showed peak of clustering shifts towards larger scales with increase in Stokes number. Scale-dependent skewness and flatness quantify the intermittent void and cluster distribution, with cluster formation observed at small scales for all Stokes numbers, and void regions at large scales for large Stokes numbers.