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Different approaches to the development of reduced-order models for NS equations

hal.structure.identifierModeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
dc.contributor.authorBERGMANN, Michel
hal.structure.identifierModeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
dc.contributor.authorFERRERO, Andrea
hal.structure.identifierModeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
dc.contributor.authorIOLLO, Angelo
dc.date.accessioned2024-04-04T03:12:39Z
dc.date.available2024-04-04T03:12:39Z
dc.date.issued1998
dc.date.conference2016-09-26
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193886
dc.description.abstractEnIn this contribution we explore some numerical alternatives to derive efficient and robust low-order models of the Navier–Stokes equations. The first considered approach is based on an hybrid CFD-ROM approach in which the ROM is used to define the boundary conditions of a CFD simulation. This makes possible to reduce significantly the size of the domain studied by the CFD solver. An alternative approach, based on residual minimization, is presented in the following. We start from the fact that classical Galerkin or Petrov-Galerkin approaches for ROM can be derived in the context of a residual minimization method similar to variational multi scale modelling , VMS [1]. Based on this, we introduce a residual minimization scheme that directly includes VMS stabilizing terms in the low-order model as proposed in [2], [3]. Here, however, the unknowns of the minimization problem are the union of the coefficients of a modal representation of the solution and of the physical unknowns at certain collocation points [4]. The modal representation is typically based on empirical eigenfunctions obtained bye proper-orthogonal decomposition, whereas the residual at collocation points are obtained by an adaptive discretization. Examples relative to model problems and moderately complex flows will be presented. [1] Hugues TJR, Feijoo G, Mazzei L, Quincy JB. The variational multiscale method—a paradigm for computational mechanics. Computer Methods in Applied Mechanics and Engineering 1998; 166:3–24.[2] Bergmann M, Bruneau C, Iollo A. Enablers for robust pod models. Journal of Computational Physics 2009; 228(2):516–538.[3] Weller J., Lombardi E., Bergmann, Iollo A. Numerical methods for low-order modeling of fluid flows based on POD Int. J. Numer. Meth. Fluids 2009.[4] Buffoni, M., Telib, H., & Iollo, A. (2009). Iterative methods for model reduction by domain decomposition. Computers & Fluids, 38(6), 1160–1167. http://doi.org/10.1016/j.compfluid.2008.11.008
dc.language.isoen
dc.title.enDifferent approaches to the development of reduced-order models for NS equations
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halSciences de l'ingénieur [physics]/Mécanique [physics.med-ph]/Mécanique des fluides [physics.class-ph]
dc.description.sponsorshipEuropeAeroelastic Gust Modelling
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleALOP Workshop: Reduced Order Models in Optimization
bordeaux.countryDE
bordeaux.conference.cityTrier
bordeaux.peerReviewedoui
hal.identifierhal-01405487
hal.version1
hal.invitedoui
hal.proceedingsnon
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01405487v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=1998&rft.au=BERGMANN,%20Michel&FERRERO,%20Andrea&IOLLO,%20Angelo&rft.genre=unknown


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