Simulation of a Granular Gas with Non-Spherical Particles - A Stochastic Modeling Approach

Many industrial systems that involve particle technology utilize media that contain irregularly shaped particles. However, modeling the fluid dynamics of non-spherical particles (NSPs) is challenging due to the complex nature of the inter-particle interactions. The discrete element method (DEM) can be used to model non-spherical particle flows, but the computational cost associated with contact detection limits the application of DEM to small systems. We develop a Monte Carlo methodology where simulators are used to represented a large number of real particles. Data from high-fidelity deterministic models are used to generate probability distribution functions (PDFs) that predict post-collisional states of particles without the need for costly contact detection algorithms. Such post-collisional state distribution functions capture scattering, translational-rotational energy exchange, and orientation. These PDFs are then integrated into the Monte Carlo framework and the new method predictions are compared to DEM simulations for different particle shapes. Preliminary results of the evolution of rotational energy in a homogeneous system of spherocylinders (A.R = 1.5) versus mean collision time, and a close match between the DEM and DSMC is observed. Due to the elongated nature of high aspect ratio particles, a significant amount of energy is stored in the rotational modes, and the particle shape thus affects how the system cools and clusters over time. Correlations for the cooling rate for different particle shapes are presented.