TY - JOUR
T1 - DEM modeling of the one-dimensional compression of sands incorporating a statistical particle fragmentation scheme
AU - Wu, Mengmeng
AU - Wang, Jianfeng
AU - Zhao, Budi
PY - 2022/1
Y1 - 2022/1
N2 - This paper presents a novel framework of modeling crushable granular materials under mechanical loadings based on the discrete element method (DEM). The framework is featured with the construction of the one-to-one model in which every particle in a physical experiment has its own numerical twin and allows the modeling of irregular shaped fragments during the continuous breakage process. First, image processing techniques and spherical harmonic (SH) analysis were adopted, respectively, to segment and label particles and to construct a one-to-one model mathematically in DEM. Then, a particle crushing criterion based on the maximum inter-particle contact force was used to predict the crushing events, showing fitting results that agreed very well with a large number of single particle crushing tests. Next, a statistical approach for the generation of particle fragmentation modes of a given type of sand particles based on the principal component analysis (PCA) was proposed. The aim of the PCA was to analyze the statistical trends of the coefficient matrix, which was composed of the SH coefficients of all the particles involved in the analysis. Finally, a successful modeling of a particle crushing event was achieved by replacing the particle, which was judged by the crushing criterion to undergo crushing, with a few sub-particles chosen randomly from a specific fragment template constructed using the micro-computed tomography (micro-CT) data.
AB - This paper presents a novel framework of modeling crushable granular materials under mechanical loadings based on the discrete element method (DEM). The framework is featured with the construction of the one-to-one model in which every particle in a physical experiment has its own numerical twin and allows the modeling of irregular shaped fragments during the continuous breakage process. First, image processing techniques and spherical harmonic (SH) analysis were adopted, respectively, to segment and label particles and to construct a one-to-one model mathematically in DEM. Then, a particle crushing criterion based on the maximum inter-particle contact force was used to predict the crushing events, showing fitting results that agreed very well with a large number of single particle crushing tests. Next, a statistical approach for the generation of particle fragmentation modes of a given type of sand particles based on the principal component analysis (PCA) was proposed. The aim of the PCA was to analyze the statistical trends of the coefficient matrix, which was composed of the SH coefficients of all the particles involved in the analysis. Finally, a successful modeling of a particle crushing event was achieved by replacing the particle, which was judged by the crushing criterion to undergo crushing, with a few sub-particles chosen randomly from a specific fragment template constructed using the micro-computed tomography (micro-CT) data.
KW - Discrete element method
KW - One-dimensional compression
KW - Particle fragmentation scheme
KW - Principal component analysis
KW - Statistical particle reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85122507684&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85122507684&origin=recordpage
U2 - 10.1139/cgj-2020-0308
DO - 10.1139/cgj-2020-0308
M3 - RGC 21 - Publication in refereed journal
VL - 59
SP - 144
EP - 157
JO - Canadian Geotechnical Journal
JF - Canadian Geotechnical Journal
SN - 0008-3674
IS - 1
ER -