Comparison of multi-sphere and superquadric particle representation for modelling shearing and flow characteristics of granular assemblies

Behzad Soltanbeigi, Alexander Podlozhnyuk, Jin Y. Ooi, Christoph Kloss, Stefanos Aldo Papanicolopulos

Research output: Contribution to journalArticlepeer-review

Abstract

In the current study, complex-shaped particles are simulated with the Discrete Element Method (DEM) using two different approaches, namely Multi-spheres (MS) and Superquadrics (SQ). Both methods have been used by researchers to represent the shape of real particles. However, despite the growing popularity of utilizing MS and SQ particles in DEM simulations, few insights have been given on the comparison of the macro scale characteristics arising from the two methods. In this respect, initially the characteristics of the two shape representation methods are evaluated in a direct shear test simulation. The results suggest that controlling the sharpness of the edges for SQ particles can lead to a good agreement with the results of MS particles. This way, a set of SQ and MS particles, which are numerically calibrated in the shear tester, are obtained. Furthermore, the macro-scale responses of the numerically calibrated particles are assessed during a slow shearing scenario, which is achieved through simulating quasi-static flow of the particles from a flat-bottom silo. The results for mass discharge, flow profile and wall pressure show a good quantitative agreement. These findings suggest that the numerically calibrated MS and SQ particles in the shear tester can provide similar bulk-scale flow properties. Moreover, the results highlight that surface bumpiness for MS particles and corner sharpness for SQ particles change the characteristics of particles and play a significant role in the shear strength of the material composed of these particles.

Original languageEnglish
Article number06015
JournalEPJ Web of Conferences
Volume140
Early online date30 Jun 2017
DOIs
Publication statusE-pub ahead of print - 30 Jun 2017

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