(338b) Experimental Validation of a Fast, 3D Physics-Based Cellular Automata Model of Granular Shear Flows

Granular flows continue to be a complex problem in nature and industrial sectors where solid particles exhibit solid, liquid, and gaseous behavior in a manner which is often difficult to predict both locally and globally. In solids processing applications such as pharmaceutical production, food processing, and lawn fertilizer production, the ability to accurately predict particle flows can improve process efficiency and product effectiveness. The “gold standard” for the modeling and prediction of granular flows is the discrete element method (DEM), which provides a rigorous physical treatment of particle interactions in many granular systems. One possible supplement to DEM-based design or engineering analysis is lattice-based cellular automata (CA). The CA modeling framework employed by the authors provides a platform for obtaining fast predictions by employing both rule-based mathematics and high-fidelity physics to rapidly model physical processes, such as granular flows. Several prior physics-based CA models have been developed by the authors with each model’s predictive capabilities being assessed by comparisons to experiments. The present work introduces a three-dimensional (3D) physics-based CA framework which aims to model all three granular flow regimes (kinetic, transitional, and frictional) effectively, include treatments for force chains, and increase the modeling accuracy of CA for granular flows. The predictive capabilities and quantitative accuracy of this CA model have been tested and validated against experimental shear flow results. Results from this work suggest that the model accurately predicts local flow properties. Parametric tests varying the particle-particle coefficient of restitution, macroscopic surface roughness, and global solid fraction have also been performed.