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

• 作者: Chao Zheng Nicolin Govender Ling Zhang Chuan-Yu Wu
• 来源期刊: 颗粒学报（英文版）
• 年，卷（期）: 2022,（2）
• 页码范围: 30-40
• 页数: 11页
• 语种: 英文

摘要

During twin screw granulation (TSG),small particles,which generally have irregular shapes,agglomer-ate 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 proces-sor 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 TSG with hexagonal prism (Hexp) shaped particles the largest particle retention number is obtained,and TSG with cubic particles requires the highest 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.