Journal article

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, Antoinette Tordesillas



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..

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Awarded by Australian Research Council

Awarded by U.S. Air Force

Awarded by U.S. Army Research Office

Funding Acknowledgements

The authors acknowledge the support of the Melbourne Energy Institute, the Australian Research Council (Grants No. FT140100227 and No. DP120104759), the U.S. Air Force (Grant No. AFOSR 15IOA059), and the U.S. Army Research Office (Grant No. W911NF-11-1-0175). We thank Dr. B. Rubinstein and Professor S. Matthai for the helpful discussions, and Dr. T. Aste for sharing the reference sample data.