Machine learning framework for analysis of transport through complex networks in porous, granular …
We present a data-driven framework to study the relationship between fluid flow at the macro-scale and the internal pore structure, across the micro- and meso-scales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network.…
Link to Full Article: Machine learning framework for analysis of transport through complex networks in porous, granular …
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