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Grain-scale discrete analysis methods for real granular matter: Granular element and coupled Level Set-Discrete Element Methods

Lim, K.-W. and Kawamoto, R. and Andrade, J. E. (2015) Grain-scale discrete analysis methods for real granular matter: Granular element and coupled Level Set-Discrete Element Methods. In: Geomechanics from micro to macro : Proceedings of the TC105 ISSMGE International Symposium on Geomechanics from Micro to Macro. CRC Press , Leiden, The Netherlands, pp. 55-60. ISBN 978-1-138-02707-7.

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Motivated by recentadvances in X-Ray Computed Tomography (XRCT) for analyzing phenomenon emanating from the grain-scale, we developed two grain-scale discrete simulation methods for modeling and characterization of real granular materials: the Granular Element Method (GEM) and coupled Level Set-Discrete Element Method (LS-DEM). The technologies underlying these methods are Non-Uniform Rational Basis-Splines (NURBS) and level set methods, which enable the representation of particle morphological features, namely, sphericity and angularity, to their fullest extent. For the first time, emergent properties such as shear strength can be captured entirely through particle geometry, without resorting to further artificial techniques for treating rolling resistance, and thus minimizing parameter calibration of classic DEM models. Through coarse-graining and homogenization techniques, these methods would allow for the extraction of plastic internal variables such as dilatancy and friction directly from the granular microstructure. Independent of image data, these tools can also be for used studying material behavior in what-if scenarios under loading conditions beyond the confines of experimental situations. We demonstrate the capabilities of GEM using realistic grain geometries, followed by a simple numerical example for the LS-DEM.

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Additional Information:© 2015 Taylor & Francis.
Record Number:CaltechAUTHORS:20160901-104009148
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Official Citation:Geomechanics from Micro to Macro Edited by Kenichi Soga, Krishna Kumar, Giovanna Biscontin, and Matthew Kuo CRC Press 2014 Pages 55–60 Print ISBN: 978-1-138-02707-7 eBook ISBN: 978-1-315-73732-4 DOI: 10.1201/b17395-9
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:70113
Deposited By: Tony Diaz
Deposited On:01 Sep 2016 18:46
Last Modified:03 Oct 2019 10:28

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