Discrete element simulations of granular pile formation: Method for calibrating discrete element models

Authors

Andrew GrimaFollow
Peter W. Wypych, University of WollongongFollow

RIS ID

35779

Publication Details

Grima, A. P. Wypych, P. W. (2011). Discrete element simulations of granular pile formation: Method for calibrating discrete element models. Engineering Computations: International Journal for Computer-Aided Engineering and Software, 28 (3), 314-339.

Abstract

Purpose – The purpose of this paper is to examine several calibration techniques that have been developed to determine the discrete element method (DEM) parameters for slow and rapid unconfined flow of granular conical pile formation. This paper also aims to discuss some of the methods currently employed to scale particle properties to reduce computational resources and time to solve large DEM models.

Design/methodology/approach – DEM models have been calibrated against simple bench-scale experimental results to examine the validity of selected parameters for the contact, material and mechanical models to simulate the dynamic and static behaviour of cohesionless polyethylene pellets. Methods to determine quantifiable single particle parameters such as static friction and the coefficient of restitution have been highlighted. Numerical and experimental granular pile formation has been investigated using different slumping and pouring techniques to examine the dependency of the type of flow mechanism on the DEM parameters.

Findings – The proposed methods can provide cost effective and simple techniques to determine suitable input parameters for DEM models. Rolling friction and particle shape representation has shown to have a significant influence on the bulk flow characteristics via a sensitivity analysis and needs to be accessed based on the environmental conditions.

Originality/value – This paper describes several effective known and novel methodologies to characterise granular materials that are needed to accurately model granular flow using the DEM to provide valuable quantitative data. For the DEM to be a viable predictive tool in industrial applications which often contain huge quantities of particles with random particle shapes and irregular properties, quick and validated techniques to “tune” DEM models are necessary.