Towards filter-dependent closure models for dilute and moderately dense particle-laden flow

The tendency of inertial particles to cluster in turbulent flows introduces challenges in developing accurate subgrid-scale models. Filtered equations of particle-laden flows depend on accurately modeling the difference between the filtered and `true' field. Efficient computations of certain two point statistics from `true' particle data may be used to develop or validate closure models across different flow regimes. In particular, any introduction of a mesh or grid results in some amount of implicit filtering at the sub-grid scale. It is common to utilize radial distribution functions (RDFs) as a characterization of clustering and it is well understood how to compute RDFs from particle positions using neighbor finding. We propose an efficient method for computing filter-dependent statistics, thus revealing the effects of filtering on two point statistics. In this talk, we will present a novel algorithm for computing two point statistics from particle fields and their filtered equivalents, provide benchmarks for an implementation of this algorithm, and show how this metric differentiates between different flow regimes (from dilute suspensions of particles in homogeneous turbulence to moderately dense cluster-induced turbulence) and varies as a function of filter width.