A DEM study on the effect of inherent variability of assemblies of spherical particles

Leak, J.; Barreto, D.

Abstract

This study presents DEM simulations of assemblies of spheres using periodic boundaries to assess the effect of inherent sample variability. DEM specimens with different particle size distributions (PSDs) were randomly generated, and then isotropically compressed prior to undrained (constant volume) triaxial shearing. DEM specimens with variable numbers of spheres between 500 and 10000 were generated for each PSD. Sets of simulations therefore differed only in terms of the initial, random positions of individual particles. Both macro-and micro-scale variables are then considered to assess the effect of inherent variability on the numerical results. Results indicate that all variables are significantly affected by inherent variability, however the range of variation generally decreased with an increase in specimen size. These results are some of the very first to consider the effects of sample variability that may also be intrinsically present in physical laboratory experiments on granular materials. They also highlight the importance of selecting DEM specimens of adequate size whilst considering the potential (random) variability of their mechanical behaviour.

Citation

Leak, J., & Barreto, D. (2023, June). A DEM study on the effect of inherent variability of assemblies of spherical particles. Presented at NUMGE 2023: 10th European Conference on Numerical Methods in Geotechnical Engineering, London

• Presentation Conference Type: Conference Paper (published)
• Conference Name: NUMGE 2023: 10th European Conference on Numerical Methods in Geotechnical Engineering
• Start Date: Jun 26, 2023
• End Date: Jun 28, 2023
• Acceptance Date: Feb 5, 2023
• Online Publication Date: Jun 28, 2023
• Publication Date: Jun 28, 2023
• Deposit Date: Aug 16, 2023
• Publicly Available Date: Aug 16, 2023
• Book Title: Proceedings 10th NUMGE 2023: 10th European Conference on Numerical Methods in Geotechnical Engineering
• DOI: https://doi.org/10.53243/NUMGE2023-388
• Keywords: DEM; Variability; Specimen Generation; PSD
• Related Public URLs: https://www.issmge.org/publications/online-library