The predictive capability of two way--coupled point-particle Euler-Lagrange
model in accurately capturing particle-flow interactions under grid refinement,
wherein the particle size can be comparable to the grid size, is systematically
evaluated. Two situations are considered, (i) uniform flow over a stationary
particle, and (ii) decaying isotropic turbulence laden with Kolmogorov-scale
particles. Particle-fluid interactions are modeled using only the standard drag
law, typical of large density-ratio systems. A zonal,
advection-diffusion-reaction (Zonal-ADR) model is used to obtain the
undisturbed fluid velocity needed in the drag closure. Two main types of
interpolation kernels, grid-based and particle size--based, are employed. The
effect of interpolation kernels on capturing the particle-fluid interactions,
kinetic energy, dissipation rate, and particle acceleration statistics are
evaluated in detail. It is shown that the interpolation kernels whose width
scales with the particle size perform significantly better under grid
refinement than kernels whose width scales with the grid size. Convergence with
respect to spatial resolution is obtained with the particle size--based kernels
with and without correcting for the self-disturbance effect. While the use of
particle size--based interpolation kernels provide spatial convergence and
perform better than kernels that scale based on grid size, small differences
can still be seen in the converged results with and without correcting for the
particle self-disturbance. Such differences indicate the need for
self-disturbance correction to obtain the best results, especially when the
particles are larger than the grid size.Comment: Submitted to International Journal of Multiphase Flo