Wavelet based modeling of subgrid-scales in LES of particle-laden turbulent flows

Abstract

We propose a novel model to obtain the subgrid-scale velocity in the context of large-eddy simulation (LES) of particle-laden turbulent flows, to recover accurate particle statistics. In the new wavelet enrichment model, the subgrid-scale velocity is discretized with a divergence-free wavelet vector basis, and the coefficients of the expansion are obtained by minimizing the squared error of the linearized subfilter Navier-Stokes equations (SFNSE). The compact support of the wavelet basis is exploited to achieve continuously varying subgrid-scale velocity statistics across the domain. The performance of the new wavelet enrichment model is evaluated in single-phase and particle-laden flow simulations, comparing the results with the results of direct numerical simulations (DNS). The simulations show that the model can generate inhomogeneous and anisotropic velocity statistics, accurate strain-rotation relations, and a good approximation of the kinetic energy spectrum of the corresponding DNS. Furthermore, the model significantly improves the prediction of the particle-pair dispersion, the clustering of the particles, and the turbulence modulation by particles in two-way coupled simulations. The proposed model recovers the most important interactions between fluid turbulence and the behavior of the particles, while maintaining the computational cost on the order of a LES.