Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability

Joost H. van der Linden, Guillermo A. Narsilio, and Antoinette Tordesillas
Phys. Rev. E 94, 022904 – Published 17 August 2016

Abstract

We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro- and mesoscales, 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. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.

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  • Received 28 April 2016
  • Revised 7 July 2016

DOI:https://doi.org/10.1103/PhysRevE.94.022904

©2016 American Physical Society

Physics Subject Headings (PhySH)

Polymers & Soft Matter

Authors & Affiliations

Joost H. van der Linden and Guillermo A. Narsilio

  • Department of Infrastructure Engineering, The University of Melbourne, Australia

Antoinette Tordesillas*

  • School of Mathematics and Statistics, School of Earth Sciences, The University of Melbourne, Australia

  • *atordesi@unimelb.edu.au

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Issue

Vol. 94, Iss. 2 — August 2016

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