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    Propagation of Uncertainties in Density-Driven Flow

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    Type
    Preprint
    Authors
    Litvinenko, Alexander
    Logashenko, Dmitry
    Tempone, Raul cc
    Wittum, Gabriel
    Keyes, David E. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Applied Mathematics and Computational Science Program
    Extreme Computing Research Center
    Office of the President
    Date
    2019-05-06
    Permanent link to this record
    http://hdl.handle.net/10754/660682
    
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    Abstract
    Accurate modeling of contamination in subsurface flow and water aquifers is crucial for agriculture and environmental protection. Here, we demonstrate a parallel method to quantify the propagation of the uncertainty in the dispersal of pollution in subsurface flow. Specifically, we consider the density-driven flow and estimate how uncertainty from permeability and porosity propagates to the solution. We take an Elder-like problem as a numerical benchmark and we use random fields to model the limited knowledge on the porosity and permeability. We construct a low-cost generalized polynomial chaos expansion (gPC) surrogate model, where the gPC coefficients are computed by projection on sparse and full tensor grids. We parallelize both the numerical solver for the deterministic problem based on the multigrid method, and the quadrature over the parametric space
    Sponsors
    This work was supported by the King Abdullah University of Science and Technology (KAUST) and by the Alexander von Humboldt Foundation. We used the resources of the Supercomputing Laboratory at KAUST, under the development project k1051. We would like to thank the KAUST core lab for the assistance with Shaheen II supercomputer. We would also like to acknowledge developers of the ug4 simulation framework from Frankfurt University.
    Publisher
    arXiv
    arXiv
    1905.01770
    Additional Links
    https://arxiv.org/pdf/1905.01770
    Collections
    Preprints; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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