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Granular object morphological generation with genetic algorithms for discrete element simulations

de Macedo, Robert Buarque and Marshall, Jason P. and Andrade, José E. (2018) Granular object morphological generation with genetic algorithms for discrete element simulations. Granular Matter, 20 (11). Art. No. 73. ISSN 1434-7636 . http://resolver.caltech.edu/CaltechAUTHORS:20181024-135818599

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Abstract

The Discrete Element Method is a popular method for modeling granular materials, however, it is typically limited to geometrically simple objects. A recent extension of this method, the Level Set Discrete Element Method (LS-DEM), overcomes this issue by allowing the use of any particle shape, including morphologically accurate computational grains generated from tomographic images. This method has the ability to provide insight into the physics of granular media that are challenging if granular shape morphology is not accurately represented. One challenge with fully utilizing LS-DEM is gathering the data necessary to reproduce the distinct shapes of grains. In this work, we develop a novel granular generation method that uses genetic algorithms to create new computational grains from a smaller set of input data. This method has the capability of building grains that match any well defined morphological property. We demonstrate the method by generating grains to match sphericity and principal curvature property distributions generated from an existing particle dataset captured with 3D X-Ray tomography.


Item Type:Article
Related URLs:
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https://doi.org/10.1007/s10035-018-0845-7DOIArticle
https://rdcu.be/90kTPublisherFree ReadCube access
Additional Information:© 2018 Springer-Verlag GmbH Germany, part of Springer Nature. Received: 11 May 2018; First Online: 08 October 2018. Research was sponsored by the Army Research Office and was accomplished under Grant Number W911NF-17-1-0212. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. Compliance with ethical standards. The authors declare that they have no conflict of interest.
Funders:
Funding AgencyGrant Number
Army Research Office (ARO)W911NF-17-1-0212
Subject Keywords:Shape morphology; Genetic algorithms; Granular objects
Record Number:CaltechAUTHORS:20181024-135818599
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20181024-135818599
Official Citation:Buarque de Macedo, R., Marshall, J.P. & Andrade, J.E. Granular Matter (2018) 20: 73. https://doi.org/10.1007/s10035-018-0845-7
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:90399
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:25 Oct 2018 16:57
Last Modified:25 Oct 2018 16:57

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