Multiresolution analysis on tessellation graphs for inertial particle dynamics

TL;DR

This paper introduces a multiresolution wavelet-based method on tessellation graphs to analyze inertial particle dynamics, enabling scale-dependent characterization of clustering in turbulent flows.

Contribution

It presents a novel multiresolution technique using tessellation graphs and wavelet transforms for analyzing particle clustering in turbulence.

Findings

Successfully verified with synthetic data of random particles.

Extracted scale-dependent particle velocity divergence from turbulence data.

Compared divergence spectrum with Fourier-based results for validation.

Abstract

A multiresolution technique on tessellation graphs for particle dynamics is proposed. This allows to split spatial field data given on millions of discrete particle positions into scale-dependent contributions. The Delaunay tessellation is used to define the graph, and Voronoi cell volumes are used to satisfy volume conservation. Our approach enables computation of the scale-dependent statistics of particle dynamics by leveraging a wavelet transformation of Lagrangian point particle data and is useful for characterizing particle clustering in turbulent flows. The technique is systematically verified by using synthetic data of randomly distributed particles in a two-dimensional plane. Then the applicability of the technique is demonstrated by extracting the scale-dependent particle velocity divergence of inertial particles in homogeneous isotropic turbulence from direct numerical simulation data. The result is verified by comparing the energy spectrum of the divergence with that obtained by a Fourier-based approach. Finally, the wavelet-based filtering to the particle velocity divergence is demonstrated to extract the effect of caustics in inertial particle clustering.