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dc.contributor.authorLitvinenko, Alexander
dc.contributor.authorLogashenko, Dmitry
dc.contributor.authorTempone, Raul
dc.contributor.authorWittum, Gabriel
dc.contributor.authorKeyes, David E.
dc.date.accessioned2021-04-14T09:19:00Z
dc.date.available2021-04-14T09:19:00Z
dc.date.issued2019-11-18
dc.date.submitted2019-03-20
dc.identifier.citationLitvinenko, A., Logashenko, D., Tempone, R., Wittum, G., & Keyes, D. (2019). Efficient Simulations for Contamination of Groundwater Aquifers under Uncertainties. PAMM, 19(1). doi:10.1002/pamm.201900023
dc.identifier.issn1617-7061
dc.identifier.issn1617-7061
dc.identifier.doi10.1002/pamm.201900023
dc.identifier.urihttp://hdl.handle.net/10754/668757
dc.description.abstractAccurate 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 density-driven flow. We solve an Elder-like problem, where we use random fields to model the limited knowledge on the porosity and permeability. The uncertain solution, mass fraction, is approximated via low-cost generalized polynomial chaos expansion (gPCE). Parallelization is done in both the physical and parametric spaces.
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology (KAUST) and by the Alexan-der von Humboldt Foundation. We used the resources of the Supercomputing Laboratory at KAUST, under the development project k1051.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1002/pamm.201900023
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleEfficient Simulations for Contamination of Groundwater Aquifers under Uncertainties
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentOffice of the President
dc.contributor.departmentStochastic Numerics Research Group
dc.identifier.journalPAMM
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionRWTH Aachen Aachen Germany
dc.identifier.volume19
dc.identifier.issue1
kaust.personLogashenko, Dmitry
kaust.personTempone, Raul
kaust.personWittum, Gabriel
kaust.personKeyes, David E.
dc.date.accepted2019-04-02
refterms.dateFOA2021-04-14T09:20:19Z
kaust.acknowledged.supportUnitSupercomputing Laboratory at KAUST


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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.