A geometry-based algorithm for cloning real grains 2.0

David A. Medina, Alex X. Jerves

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

10 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Número de artículo2
PublicaciónGranular Matter
Volumen21
N.º1
DOI
EstadoPublicada - 1 feb. 2019

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