GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator (Open Access)--颗粒学报PARTICUOLOGY

Zheng, C., Govender, N., Zhang, L., & Wu, C.-Y. (2022). GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator. Particuology, 61, 30-40. https://doi.org/10.1016/j.partic.2021.03.007

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

Chao Zheng ^a, Nicolin Govender ^{b c}, Ling Zhang ^a, Chuan-Yu Wu ^a *

a Department of Chemical and Process Engineering, University of Surrey, Guildford GU2 7XH, United Kingdom
b Department of Mechanical Engineering, University of Johannesburg, South Africa
c Research Center Pharmaceutical Engineering GmbH, Graz 8010, Austria

10.1016/j.partic.2021.03.007

Volume 61, February 2022, Pages 30-40

Received 21 January 2021, Revised 2 March 2021, Accepted 16 March 2021, Available online 31 March 2021, Version of Record 27 October 2021.

E-mail: c.y.wu@surrey.ac.uk

Highlights

• GPU-enhanced DEM analysis on the irregularly shaped particles is performed.

• Effects of various particle shapes on the conveying characteristics are analyzed.

• Flow patterns of shaped particles are identified for current numerical conditions.

Abstract

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 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.

Graphical abstract

Keywords

Discrete element method; Twin screw granulation; Non-spherical particle; Residence time distribution; GPU

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