2026-10-14T02:11:15Z https://www.scidb.cn/oai

10.57760/sciencedb.Partic.00093 2024-10-22T09:41:43Z

2024-10-22 GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator doi:10.57760/sciencedb.partic.00093 en 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) is adopted to examine the effect of particle shape on the conveying characteristics in a full scale twin screw granulator for the first time. It is found that, TSG with spherical particles has the smallest particle retention number, mean residence time, and power consumption; while for Hexp shaped particles the largest particle retention number is obtained, and TSG with cubic particles requires the maximum power consumption. Furthermore, spherical particles exhibit a flow pattern closer to an ideal plug flow, while cubic particles present a flow pattern approaching a perfect mixing. It is demonstrated that the GPU-enhanced DEM is capable of simulating the complex TSG process in a full-scale twin screw granulator with non-spherical particles. Discrete element method; Twin screw granulation; Non-spherical particle; Residence time distribution; GPU Chao Zheng Nicolin Govender Ling Zhang Chuan-Yu Wu PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/https://doi.org/10.1016/j.partic.2021.03.007 Science Data Bank