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dc.contributor.authorLiu, Yang
dc.contributor.authorLi, Jingfa
dc.contributor.authorSun, Shuyu
dc.contributor.authorYu, Bo
dc.date.accessioned2019-12-18T06:02:11Z
dc.date.available2019-12-18T06:02:11Z
dc.date.issued2019-10-10
dc.identifier.urihttp://hdl.handle.net/10754/660649
dc.description.abstractThe subsurface flow is usually subject to uncertain porous media structures. In most cases, however, we only have partial knowledge about the porous media properties. A common approach is to model the uncertain parameters of porous media as random fields, then the statistical moments (e.g. expectation) of the Quantity of Interest(QoI) can be evaluated by the Monte Carlo method. In this study, we develop a full multigrid-multilevel Monte Carlo (FMG-MLMC) method to speed up the evaluation of random parameters effects on single-phase porous flows. In general, MLMC method applies a series of discretization with increasing resolution and computes the QoI on each of them. The effective variance reduction is the success of the method. We exploit the similar hierarchies of MLMC and multigrid methods and obtain the solution on coarse mesh $Q^c_l$ as a byproduct of the full multigrid solution on fine mesh $Q^f_l$ on each level $l$. In the cases considered in this work, the computational saving due to the coarse mesh samples saving is $20\%$ asymptotically. Besides, a comparison of Monte Carlo and Quasi-Monte Carlo (QMC) methods reveals a smaller estimator variance and a faster convergence rate of the latter approach in this study.
dc.description.sponsorshipThe authors gratefully acknowledge the support from the National Natural Science Foundation of China (Nos. 51874262, 51904031) and the Research Funding from King Abdullah University of Science and Technology (KAUST) through the grants BAS/1/1351-01-01.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1910.04727
dc.rightsArchived with thanks to arXiv
dc.titleA full multigrid multilevel Monte Carlo method for the single phase subsurface flow with random coefficients
dc.typePreprint
dc.contributor.departmentComputational Transport Phenomena Lab
dc.contributor.departmentComputational Transport Phenomena Laboratory, Division of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionSchool of Mechanical Engineering, Beijing Key Laboratory of Pipeline Critical Technology and Equipment for Deepwater Oil & Gas Development, Beijing Institute of Petrochemical Technology, Beijing 102617, China
dc.identifier.arxivid1910.04727
kaust.personLiu, Yang
kaust.personLi, Jingfa
kaust.personSun, Shuyu
kaust.grant.numberBAS/1/1351-01-01
refterms.dateFOA2019-12-18T06:02:48Z


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