4.7 Article Proceedings Paper

Comparison of non-cohesive resolved and coarse grain DEM models for gas flow through particle beds

期刊

APPLIED MATHEMATICAL MODELLING
卷 38, 期 17-18, 页码 4197-4214

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ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2014.02.013

关键词

DEM; CFD; Fluidised bed; Coarse grain; Fluid-particle interaction; Multiphase flow

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The Discrete Element Method (DEM) is a widely used approach for modelling granular systems. Currently, the number of particles which can be tractably modelled using DEM is several orders of magnitude lower than the number of particles present in common large-scale industrial systems. Practical approaches to modelling such industrial system therefore usually involve modelling over a limited domain, or with larger particle diameters and a corresponding assumption of scale invariance. These assumption are, however, problematic in systems where granular material interacts with gas flow, as the dynamics of the system depends heavily on the number of particles. This has led to a number of suggested modifications for coupled gas-grain DEM to effectively increase the number of particles being simulated. One such approach is for each simulated particle to represent a cluster of smaller particles and to re-formulate DEM based on these clusters. This, known as a representative or 'coarse grain' method, potentially allows the number of virtual DEM particles to be approximately the same as the real number of particles at relatively low computational cost. We summarise the current approaches to coarse grain models in the literature, with emphasis on discussion of limitations and assumptions inherent in such approaches. The effectiveness of the method is investigated for gas flow through particle beds using resolved and coarse grain models with the same effective particle numbers. The pressure drop, as well as the pre and post fluidisation characteristics in the beds are measured and compared, and the relative saving in computational cost is weighed against the effectiveness of the coarse grain approach. In general, the method is found perform reasonably well, with a considerable saving of computational time, but to deviate from empirical predictions at large coarse grain ratios. Crown Copyright (C) 2014 Published by Elsevier Inc. All rights reserved.

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