Reproducing segregation and particle dynamics in Large Eddy Simulation of particle-laden flows

Lagrangian simulations are today widely used for simulating aeronautical chambers. The way droplets are spatially distributed strongly affects the combustion, and accurate modeling and simulation strategies are required. The objective of the present contribution is to investigate how to correctly reproduce preferential concentration in Large Eddy Simulation (LES) of particle-laden flows. Looking for a way to recover the DNS statistics, we highlight that stochastic models can fail in retrieving the tracer limit for non-inertial particles. We suggest a new strategy in the spirit of kinematic modeling of turbulence, which makes use of a random field with enforced divergence-free condition and spatial and temporal correlations. We show that the model can retrieve some Lagrangian statistics and in particular, particle segregation. We also suggest another approach for the LES particle model, based on retrieving not DNS statistics but filtered DNS statistics. We show that in this case, stochastic models can be relevant and appropriate.