We introduce a computational algorithm to “clone” the grain morphologies of a sample of real grains that have been digitalized. This cloning algorithm allows us to generate an arbitrary number of cloned grains that satisfy the same distributions of morphological features displayed by their parents and can be included into a numerical Discrete Element Method simulation. This study is carried out in three steps. First, distributions of morphological parameters such as aspect ratio, roundness, principal geometric directions, and spherical radius, called the morphological DNA, are extracted from the parents. Second, the geometric stochastic cloning (GSC) algorithm, relying purely on statistical distributions of the aforementioned parameters, is explained, detailed, and used to generate a pool of clones from its parents’ morphological DNA. Third, morphological DNA is extracted from the pool of clones and compared to the one obtained from a similar pool of parents, and the distribution of volume-surface ratio is used to perform quality control. Then, from these results, the error (mutation) in the GSC process is analyzed and used to discuss the algorithm’s drawbacks, knobs (parameters) tuning, as well as potential improvements.
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Jerves, A.X., Kawamoto, R.Y. & Andrade, J.E. A geometry-based algorithm for cloning real grains. Granular Matter 19, 30 (2017). https://doi.org/10.1007/s10035-017-0716-7
- Geometric stochastic cloning algorithm
- Grain’s morphological parameters
- Monte Carlo sampling
- Level sets
- Discrete element method