GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator

During twin screw granulation (TSG), small particles, which generally have irregular shapes, agglomerate together to form larger granules with improved properties. However, how particle shape impacts the conveying characteristics during TSG is not explored nor well understood. In this study, a graphic processor units (GPUs) enhanced discrete element method (DEM) adopted examine effect of on in full scale granulator for first time. It found that spherical particles has smallest retention number, mean residence time, and power consumption; while hexagonal prism (Hexp) shaped largest number obtained, cubic requires highest consumption. Furthermore, exhibit flow pattern closer an ideal plug flow, present approaching perfect mixing. demonstrated GPU-enhanced DEM capable simulating complex process full-scale non-spherical particles.