Multiresolution analysis on tessellation graphs for inertial particle dynamics
For researchers studying particle clustering in turbulent flows, this provides a new tool to analyze scale-dependent statistics from Lagrangian particle data, though it is an incremental extension of wavelet analysis to tessellation graphs.
The paper proposes a multiresolution technique on tessellation graphs to analyze scale-dependent statistics of inertial particle dynamics in turbulent flows, enabling extraction of caustic effects from particle velocity divergence. The method is verified on synthetic data and DNS data, showing agreement with Fourier-based energy spectra.
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.