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Published November 2018 | public
Journal Article

Granular object morphological generation with genetic algorithms for discrete element simulations


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.

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.

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