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dc.contributor.authorGarcia, D.
dc.contributor.authorPardo, D.
dc.contributor.authorDalcin, L.
dc.contributor.authorCalo, V.M.
dc.date.accessioned2018-08-21T12:24:16Z
dc.date.available2018-08-21T12:24:16Z
dc.date.issued2018-06-15
dc.identifier.issn0045-7825
dc.identifier.urihttp://hdl.handle.net/20.500.11824/840
dc.description.abstractStarting from a highly continuous Isogeometric Analysis (IGA) discretization, refined Isogeometric Analysis (rIGA) introduces $C^0$ hyperplanes that act as separators for the direct LU factorization solver. As a result, the total computational cost required to solve the corresponding system of equations using a direct LU factorization solver dramatically reduces (up to a factor of 55). At the same time, rIGA enriches the IGA spaces, thus improving the best approximation error. In this work, we extend the complexity analysis of rIGA to the case of iterative solvers. We build an iterative solver as follows: we first construct the Schur complements using a direct solver over small subdomains (macro-elements). We then assemble those Schur complements into a global skeleton system. Subsequently, we solve this system iteratively using Conjugate Gradients (CG) with an incomplete LU (ILU) preconditioner. For a 2D Poisson model problem with a structured mesh and a uniform polynomial degree of approximation, rIGA achieves moderate savings with respect to IGA in terms of the number of Floating Point Operations (FLOPs) and computational time (in seconds) required to solve the resulting system of linear equations. For instance, for a mesh with four million elements and polynomial degree $p=3$, the iterative solver is approximately $2.6$ times faster (in time) when applied to the rIGA system than to the IGA one. These savings occur because the skeleton rIGA system contains fewer non-zero entries than the IGA one. The opposite situation occurs for 3D problems, and as a result, 3D rIGA discretizations provide no gains with respect to their IGA counterparts when considering iterative solvers.en_US
dc.description.sponsorshipDavid Pardo has received funding from the Project of the Spanish Ministry of Economy and Competitiveness with reference MTM2016-76329-R (AEI/FEDER, EU), and MTM2016-81697-ERC/AEI, the BCAM “Severo Ocho” accreditation of excellence SEV-2013-0323, and the Basque Government through the BERC 2014-2017 program and the Consolidated Research Group Grant IT649-13 on “Mathematical Modeling, Simulation, and Industrial Applica- tions (M2SI)”. This publication was also made possible in part by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 644602, the CSIRO Professorial Chair in Computational Geoscience at Curtin University, the Deep Earth Imaging Enterprise Future Science Platforms of the Commonwealth Scientific Industrial Research Organisation, CSIRO, of Australia, the Mega-grant of the Russian Federation Government (N14.Y26.31.0013) and the Curtin Institute for Computation. The J. Tinsley Oden Faculty Fellowship Research Program at the Institute for Computational Engineering and Sciences (ICES) of the University of Texas at Austin has partially supported the visits of VMC to ICES. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paperen_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectIsogeometric Analysis (IGA)en_US
dc.subjectFinite Element Analysis (FEA)en_US
dc.subjectrefined Isogeometric Analysis (rIGA)en_US
dc.subjectsolver-based discretizationen_US
dc.subjectiterative solversen_US
dc.subjectConjugate gradienten_US
dc.subjectIncomplete LU factorizationen_US
dc.subjectk-refinementen_US
dc.titleRefined Isogeometric Analysis for a Preconditioned Conjugate Gradient Solveren_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doihttps://doi.org/10.1016/j.cma.2018.02.006
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S004578251830077Xen_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/644202en_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.relation.projectIDES/1PE/MTM2016-76329-Ren_US
dc.relation.projectIDES/1PE/MTM2016-81697-ERCen_US
dc.relation.projectIDEUS/BERC/BERC.2014-2017en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleComputer Methods in Applied Mechanics and Engineeringen_US


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