Costly equipment and specialized personal is needed to perform this 3D X-ray CT scans. In addition, the size of the scanned samples is small (a few thousand grains) compared to the amount of the material required to reproduce macroscopic/engineering behavior. To ameliorate the costly, time consuming, and sample size related constraints of 3DXRCT scanning, a first computational algorithm was developed to obtain morphological features from a sample of real grains, (i.e., aspect ratio, principal geometric directions, roundness and spherical radius). This first algorithm successfully reached its goal of generating new grains (clones) similar to the ones from a parent sample.


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 displayed by the original (parent) sample. Thus, the present work is divided in four parts. First, we compute multivariable probability density functions (PDF) 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 (GSC) algorithm, 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 (LS) 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.

Benefit to the community

This enhanced algorithm enable us to generate an arbitrary number of digital grains similar from a real sample to include the into simulations and predict more accurately geological scale phenomena like earthquakes, landslides and lahar flow.