Predictions of first passage times in sparse discrete fracture networks using graphbased reductions
Abstract
Here, we present a graphbased methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic threedimensional 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. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.
 Authors:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Publication Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1374351
 Alternate Identifier(s):
 OSTI ID: 1369102
 Report Number(s):
 LAUR1722022
Journal ID: ISSN 24700045; PLEEE8
 Grant/Contract Number:
 AC5206NA25396; 20150763PRD4; 20170103DR
 Resource Type:
 Journal Article: Accepted Manuscript
 Journal Name:
 Physical Review E
 Additional Journal Information:
 Journal Volume: 96; Journal Issue: 1; Journal ID: ISSN 24700045
 Publisher:
 American Physical Society (APS)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; 54 ENVIRONMENTAL SCIENCES; Earth Sciences; discrete fracture networks, graph theory, shortest paths, flow and transport
Citation Formats
Hyman, Jeffrey De'Haven, Hagberg, Aric Arild, MohdYusof, Jamaludin, Srinivasan, Gowri, and Viswanathan, Hari S. Predictions of first passage times in sparse discrete fracture networks using graphbased reductions. United States: N. p., 2017.
Web. doi:10.1103/PhysRevE.96.013304.
Hyman, Jeffrey De'Haven, Hagberg, Aric Arild, MohdYusof, Jamaludin, Srinivasan, Gowri, & Viswanathan, Hari S. Predictions of first passage times in sparse discrete fracture networks using graphbased reductions. United States. doi:10.1103/PhysRevE.96.013304.
Hyman, Jeffrey De'Haven, Hagberg, Aric Arild, MohdYusof, Jamaludin, Srinivasan, Gowri, and Viswanathan, Hari S. Mon .
"Predictions of first passage times in sparse discrete fracture networks using graphbased reductions". United States.
doi:10.1103/PhysRevE.96.013304.
@article{osti_1374351,
title = {Predictions of first passage times in sparse discrete fracture networks using graphbased reductions},
author = {Hyman, Jeffrey De'Haven and Hagberg, Aric Arild and MohdYusof, Jamaludin and Srinivasan, Gowri and Viswanathan, Hari S.},
abstractNote = {Here, we present a graphbased methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic threedimensional 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. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.},
doi = {10.1103/PhysRevE.96.013304},
journal = {Physical Review E},
number = 1,
volume = 96,
place = {United States},
year = {Mon Jul 10 00:00:00 EDT 2017},
month = {Mon Jul 10 00:00:00 EDT 2017}
}

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