Comparative analysis of porosity coarse-graining techniques for discrete element simulations of dense particulate systems

M Kalderon and E Smith and C O'Sullivan, COMPUTATIONAL PARTICLE MECHANICS, 9, 199-219 (2022).

DOI: 10.1007/s40571-021-00402-4

The discrete element method (DEM) is a well-established approach to study granular materials in numerous fields of application; each granular particle is modelled individually to predict the overall behaviour. This behaviour can be then extracted by averaging, or coarse graining, the sample using a suitable method. The choice of appropriate coarse-graining method entails a compromise between accuracy and computational cost, especially in the large-scale simulations typically required by industry. A number of coarse-graining methods have been proposed in the literature, and these are reviewed and categorized in this work. Within this contribution, two novel porosity coarse-graining strategies are proposed including a voxel method where a secondary dense grid of "pixel cells" is implemented adopting a binary logic for the coarse graining and a hybrid method where both analytical formulas and pixels are utilized. The proposed methods are compared with four coarse- graining schemes that have been documented in the literature, including the particle centroid method, an analytical method, a method which solves the diffusion equation and an approach which employs averaging using kernels. The novel methods are validated for problems in both two and three dimensions through comparison with the "accurate" analytical method. It is shown that, once validated, both the proposed schemes can approximate the exact solutions quite accurately; however, there is a high computational cost associated with the voxel method. The accuracy of both methods can be adjusted allowing the user to decide between accuracy and computational time. A detailed comparison is then presented for all six schemes considering "accuracy", "smoothness" and "computational cost". Optimal parameters are obtained for all six methods, and recommendations for coarse-graining DEM samples are discussed.

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