  The Eulerian-Lagrangian approach based on Large-Eddy Simulation (LES) is one
of the most promising and viable numerical tools to study turbulent dispersed
flows when the computational cost of Direct Numerical Simulation (DNS) becomes
too expensive. The applicability of this approach is however limited if the
effects of the Sub-Grid Scales (SGS) of the flow on particle dynamics are
neglected. In this paper, we propose to take these effects into account by
means of a Lagrangian stochastic SGS model for the equations of particle
motion. The model extends to particle-laden flows the velocity-filtered density
function method originally developed for reactive flows. The underlying
filtered density function is simulated through a Lagrangian Monte Carlo
procedure that solves for a set of Stochastic Differential Equations (SDEs)
along individual particle trajectories. The resulting model is tested for the
reference case of turbulent channel flow, using a hybrid algorithm in which the
fluid velocity field is provided by LES and then used to advance the SDEs in
time. The model consistency is assessed in the limit of particles with zero
inertia, when "duplicate fields" are available from both the Eulerian LES and
the Lagrangian tracking. Tests with inertial particles were performed to
examine the capability of the model to capture particle preferential
concentration and near-wall segregation. Upon comparison with DNS-based
statistics, our results show improved accuracy and considerably reduced errors
with respect to the case in which no SGS model is used in the equations of
particle motion.
