KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Extreme Computing Research Center
Office of the President
Permanent link to this recordhttp://hdl.handle.net/10754/660682
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AbstractAccurate modeling of contamination in subsurface flow and water aquifers is crucial for agriculture and environmental protection. Here, we demonstrate a parallel algorithm to quantify the propagation of 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 a two-dimensional Elder-like problem as a numerical benchmark, and we use random fields to model our limited knowledge on the porosity and permeability. We use the well-known low-cost generalized polynomial chaos (gPC) expansion surrogate model, where the gPC coefficients are computed by projection on sparse tensor grids. The numerical solver for the deterministic problem is based on the multigrid method and is run in parallel. Computation of high-dimensional integrals over the parametric space is done in parallel too.
CitationLitvinenko, A., Logashenko, D., Tempone, R., Wittum, G., & Keyes, D. (2021). Propagation of Uncertainties in Density-Driven Flow. Sparse Grids and Applications - Munich 2018, 101–126. https://doi.org/10.1007/978-3-030-81362-8_5
SponsorsThis 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 parallel supercomputer, developers of the ug4 simulation framework from Frankfurt University, two anonymous reviewers, and the associate editor for their careful reading and suggestions.
PublisherSpringer International Publishing
Conference/Event name5th Workshop on Sparse Grids and Applications, SGA 2018