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A geometry-based algorithm for cloning real grains 2.0

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Abstract

We introduce an improved version of a computational algorithm that “clones”/generates an arbitrary number of new digital grains from a sample of real digitalized granular material. Our improved algorithm produces “cloned” grains that more accurately approach the morphological features displayed by their parents. Now, the “cloned” grains were also included in a discrete element method simulation of a triaxial test and showed similar mechanical behavior compared to the one displayed by the original (parent) sample. Thus, the present work is divided in four parts. First, we compute multivariable probability density functions from the parents’ morphological parameters (morphological DNA), i.e., aspect ratio, roundness, volume-surface ratio, and particle diameter. Second, an improved, now parallelized and better tuned version of the geometric stochastic cloning algorithm (Jerves et al. in Granul Matter, 2017. https://doi.org/10.1007/s10035-017-0716-7), which is based on the aforementioned multivariable distributions and that, in the same way, introduces an enhanced radii sampling process, as well as a new quality control test based on the volume-surface ratio is discussed. Third, morphological DNA of the grains (i.e., aspect ratio, roundness, volume-surface ratio and particle diameter) is also extracted from the new “cloned” grains and compared to the one obtained from the parent sample. Fourth, clones and parents are subjected to a triaxial compression tests using a level set discrete element scheme (3DLS-DEM), and then, compared in terms of their mechanical response. Finally, the error of the “clones” in the morphology and mechanical behavior is analyzed and discussed for future improvements.

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Acknowledgements

This work was possible thanks to the research computing facilities (Quinde) and advisory services offered by the Scientific Computing Laboratory of the Research Center on Mathematical Modeling: MODEMAT, Escuela Politécnica Nacional-Quito and the facilities as well as support of Yachay E.P. with their supercomputer Quinde 1 at Urcuqui, Imbabura, Ecuador. The authors also want to recognize and thank the help provided by Reid Y. Kawamoto (California Institute of Technology, USA), and Lino M. Mediavilla (Universidad San Francisco de Quito, Ecuador) without whom compiling and running the 3D LS-DEM C++ code [21] used for the triaxial compression test simulations would have not been possible.

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  1. Universidad San Francisco de Quito, USFQ, Diego de Robles y Vía Interoceánica, 1712841, Quito, Pichincha, Ecuador

    David A. Medina & Alex X. Jerves

  2. Facultad de Ciencias Naturales y Matemáticas, Escuela Superior, Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Velasco km 30.5 vıa Perimetral, Guayaquil, Guayas, Ecuador

    Alex X. Jerves

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  1. David A. Medina
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Correspondence to Alex X. Jerves.

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Medina, D.A., Jerves, A.X. A geometry-based algorithm for cloning real grains 2.0. Granular Matter 21, 2 (2019). https://doi.org/10.1007/s10035-018-0851-9

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