(147h) A Monte Carlo Approach to Modeling Fluidized Systems

The ability to accurately and efficiently model fluidized systems is of great practical interest to researchers and engineers. Models such as the Multiphase Particle-in-Cell (MP-PIC) method have leveraged statistical properties and interpolation functions to simulate large-scale systems. An alternative statistically-driven approach is presented here, based on the Direct Simulation Monte Carlo (DSMC) method. Typically used to model rarefied gases, the DSMC method has been extended to solving the revised Enskog equation for dilute granular flows. By directly solving this equation, the DSMC method does not rely on solid stress closures that are needed for other continuum modeling approaches. Previous attempts to apply DSMC theory to granular flow systems have encountered difficulties with over-packing because the collisional pressures were not properly accounted for. In our previous work, overpacking in granular systems was prevented by the implementation of a random walk algorithm during time integration. This addition greatly improves the outcomes of a DSMC-based method when simulating dense to moderately dense flows. In this work, we assess the validity of the DSMC method across several fluidization regimes. The DSMC predictions are compared against those from DEM simulations, and metrics such as the bed pressure drop and defluidization curves are compared. It is demonstrated that the DSMC approach provides a reasonable approximation of the results from DEM in several applications for a lower computational cost. Additionally, areas where this method is currently deficient are identified and avenues of improvement for future work are explored.