Literature DB >> 29347061

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions.

Jeffrey D Hyman1, Aric Hagberg2, Gowri Srinivasan2, Jamaludin Mohd-Yusof3, Hari Viswanathan1.   

Abstract

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

Entities:  

Year:  2017        PMID: 29347061     DOI: 10.1103/PhysRevE.96.013304

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  1 in total

1.  Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning.

Authors:  Gowri Srinivasan; Jeffrey D Hyman; David A Osthus; Bryan A Moore; Daniel O'Malley; Satish Karra; Esteban Rougier; Aric A Hagberg; Abigail Hunter; Hari S Viswanathan
Journal:  Sci Rep       Date:  2018-08-03       Impact factor: 4.379

  1 in total

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