A hybrid stochastic-deconvolution model for LES of particle-laden flow

We develop a hybrid model for large-eddy simulation of particle-laden turbulent flow, which is a combination of the approximate deconvolution model for the resolved scales and a stochastic model for the sub-grid scales. The stochastic model incorporates a priori results of direct numerical simulation of turbulent channel flow, which showed that the parameters in the stochastic model are quite independent of Reynolds and Stokes number. In order to correctly predict the flux of particles towards the walls an extra term should be included in the stochastic model, which corresponds to the term related to the well-mixed condition in Langevin models for particle dispersion in inhomogeneous turbulent flow. The model predictions are compared with results of direct numerical simulation of channel flow at a frictional Reynolds number of 950. The inclusion of the stochastic forcing is shown to yield a significant improvement over the approximate deconvolution model for the particles alone when combined with a Stokes dependent weight-factor for the well-mixed term