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The Eulerian-Lagrangian approach based on Large-Eddy Simulation (LES) is oneof the most promising and viable numerical tools to study turbulent dispersedflows when the computational cost of Direct Numerical Simulation (DNS) becomestoo expensive. The applicability of this approach is however limited if theeffects of the Sub-Grid Scales (SGS) of the flow on particle dynamics areneglected. In this paper, we propose to take these effects into account bymeans of a Lagrangian stochastic SGS model for the equations of particlemotion. The model extends to particle-laden flows the velocity-filtered densityfunction method originally developed for reactive flows. The underlyingfiltered density function is simulated through a Lagrangian Monte Carloprocedure that solves for a set of Stochastic Differential Equations (SDEs)along individual particle trajectories. The resulting model is tested for thereference case of turbulent channel flow, using a hybrid algorithm in which thefluid velocity field is provided by LES and then used to advance the SDEs intime. The model consistency is assessed in the limit of particles with zeroinertia, when "duplicate fields" are available from both the Eulerian LES andthe Lagrangian tracking. Tests with inertial particles were performed toexamine the capability of the model to capture particle preferentialconcentration and near-wall segregation. Upon comparison with DNS-basedstatistics, our results show improved accuracy and considerably reduced errorswith respect to the case in which no SGS model is used in the equations ofparticle motion.

Lagrangian filtered density function for LES-based stochastic modelling of turbulent dispersed flows