Stochastic Modeling of Dilute and Moderately Dense Particle Flows for Large Scale Systems

The direct simulation Monte Carlo method is used to simulate particle flows by stochastically solving the governing revised Enskog equation. Unlike deterministic Lagrangian modeling approaches, collisions between particles are simulated stochastically. Nearby particles are randomly selected to collide based on a kinetic theory-based collision probability. After the collision, particles can scatter and dissipate energy via inelastic losses. By performing the collisions stochastically, the DSMC approach is shown to be more than an order of magnitude faster than deterministic modeling approaches. Although the traditional DSMC method is able to accurately capture the solid stresses in dilute granular flows, traditional DSMC does not accurately capture the collisional and virial stresses when the solid concentration becomes moderately dense. As a result, traditional DSMC is susceptible to inelastic collapse, and this phenomenon can lead to unphysical over-packing. We find that the virial pressure is not recovered in traditional DSMC because the convection of particles does not consider volume exclusion effects. To include volume exclusion effects in the convective phase, we implement a random walk method. It is found that by making a relatively simple correction to how particles convect, we are now able to recover the correct collisional pressures for moderately dense flows and prevent unphysical over-packing of particles. The new method is compared to coarse grained DEM and DEM simulations for a bubbling bed.