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A continuum approach for predicting segregation in flowing polydisperse granular materials

Published online by Cambridge University Press:  16 May 2016

Conor P. Schlick
Affiliation:
Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA
Austin B. Isner
Affiliation:
Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
Ben J. Freireich
Affiliation:
The Dow Chemical Company, Midland, MI 48667, USA
Yi Fan
Affiliation:
The Dow Chemical Company, Midland, MI 48667, USA
Paul B. Umbanhowar
Affiliation:
Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA
Julio M. Ottino
Affiliation:
Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA The Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, IL 60208, USA
Richard M. Lueptow*
Affiliation:
Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA The Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, IL 60208, USA
*
Email address for correspondence: r-lueptow@northwestern.edu

Abstract

Segregation of polydisperse granular materials occurs in many natural and industrial settings, but general theoretical modelling approaches with predictive power have been lacking. Here we describe a model capable of accurately predicting segregation for both discrete and continuous particle size distributions based on a generalized expression for the percolation velocity. The predictions of the model depend on the kinematics of the flow and other physical parameters such as the diffusion coefficient and the percolation length scale, quantities that can be determined directly from experiment, simulation or theory and that are not arbitrarily adjustable. The model is applied to heap and chute flow, and the resulting predictions are consistent with experimentally validated discrete element method (DEM) simulations. Several different continuous particle size distributions are considered to demonstrate the broad applicability of the approach.

Type
Papers
Copyright
© 2016 Cambridge University Press 

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