(493g) A Physics-Based Cellular Automata Approach for Modeling Industrial Granular Flow Problems

Granular flows are prevalent in nature and industrial sectors where they present complex problems due to their inherently nonlinear and multiphase behavior. In solids processing industries such as pharmaceuticals, food processing, and fertilizer production, granular flow phenomena (e.g. segregation, jamming, etc.) can adversely affect product effectiveness, yield, and cost. Thus, the accurate prediction of particle flow inside of solids processing equipment, such as hoppers, silos, mixers, and rotating drums, becomes important in being able to design against these effects. The discrete element method (DEM) provides for a rigorous physical treatment of particle interactions, and as such has been shown to be the “gold standard” for simulating many granular systems. One possible supplement to a DEM-based design or engineering analysis framework is cellular automata (CA). CA provides a platform for obtaining ultra-fast first-order predictions of the behavior of granular flow systems. Several prior physics-based CA models have been developed by the authors to predict granular flow inside of annular Couette shear cells. However, in this work, the CA modeling framework is extended to more industrially relevant solids processing problems and geometries. Results from CA models are compared against data from the literature and comments are offered about the merits of leveraging the capabilities of CA for solving problems in industrially-relevant solid processing applications.