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) 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.

在双螺杆造粒（Twin Screw Granulation, TSG）过程中，通常呈不规则形状的细小颗粒会相互团聚，形成性能更优异的较大颗粒。然而，颗粒形状对双螺杆造粒过程中输送特性的影响尚未得到研究，也未被充分理解。本研究首次采用基于图形处理器（Graphics Processing Units, GPUs）加速的离散元法（Discrete Element Method, DEM），针对全尺寸双螺杆造粒机探究颗粒形状对输送特性的影响。研究发现，采用球形颗粒的双螺杆造粒体系拥有最小的颗粒留存数、平均停留时间与能耗；而采用六角形颗粒的体系颗粒留存数最高，采用立方体颗粒的体系则需要最大的能耗。此外，球形颗粒的流动模式更接近理想活塞流，而立方体颗粒的流动模式则趋近于完全混合。研究证实，基于GPU加速的离散元法能够有效模拟全尺寸双螺杆造粒机内非球形颗粒参与的复杂双螺杆造粒过程。