A reduced order variational multiscale approach for turbulent flows
The purpose of this work is to present different reduced order model strategies starting from full order simulations stabilized using a residual-based variational multiscale (VMS) approach. The focus is on flows with moderately high Reynolds numbers. The reduced order models (ROMs) presented in this...
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Published in | Advances in computational mathematics Vol. 45; no. 5-6; pp. 2349 - 2368 |
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Main Authors | , , , |
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Language | English |
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01.12.2019
Springer Nature B.V |
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ISSN | 1019-7168 1572-9044 |
DOI | 10.1007/s10444-019-09712-x |
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Abstract | The purpose of this work is to present different reduced order model strategies starting from full order simulations stabilized using a residual-based variational multiscale (VMS) approach. The focus is on flows with moderately high Reynolds numbers. The reduced order models (ROMs) presented in this manuscript are based on a POD-Galerkin approach. Two different reduced order models are presented, which differ on the stabilization used during the Galerkin projection. In the first case, the VMS stabilization method is used at both the full order and the reduced order levels. In the second case, the VMS stabilization is used only at the full order level, while the projection of the standard Navier-Stokes equations is performed instead at the reduced order level. The former method is denoted as
consistent ROM
, while the latter is named
non-consistent ROM
, in order to underline the different choices made at the two levels. Particular attention is also devoted to the role of inf-sup stabilization by means of supremizers in ROMs based on a VMS formulation. Finally, the developed methods are tested on a numerical benchmark. |
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AbstractList | The purpose of this work is to present different reduced order model strategies starting from full order simulations stabilized using a residual-based variational multiscale (VMS) approach. The focus is on flows with moderately high Reynolds numbers. The reduced order models (ROMs) presented in this manuscript are based on a POD-Galerkin approach. Two different reduced order models are presented, which differ on the stabilization used during the Galerkin projection. In the first case, the VMS stabilization method is used at both the full order and the reduced order levels. In the second case, the VMS stabilization is used only at the full order level, while the projection of the standard Navier-Stokes equations is performed instead at the reduced order level. The former method is denoted as
consistent ROM
, while the latter is named
non-consistent ROM
, in order to underline the different choices made at the two levels. Particular attention is also devoted to the role of inf-sup stabilization by means of supremizers in ROMs based on a VMS formulation. Finally, the developed methods are tested on a numerical benchmark. The purpose of this work is to present different reduced order model strategies starting from full order simulations stabilized using a residual-based variational multiscale (VMS) approach. The focus is on flows with moderately high Reynolds numbers. The reduced order models (ROMs) presented in this manuscript are based on a POD-Galerkin approach. Two different reduced order models are presented, which differ on the stabilization used during the Galerkin projection. In the first case, the VMS stabilization method is used at both the full order and the reduced order levels. In the second case, the VMS stabilization is used only at the full order level, while the projection of the standard Navier-Stokes equations is performed instead at the reduced order level. The former method is denoted as consistent ROM, while the latter is named non-consistent ROM, in order to underline the different choices made at the two levels. Particular attention is also devoted to the role of inf-sup stabilization by means of supremizers in ROMs based on a VMS formulation. Finally, the developed methods are tested on a numerical benchmark. |
Author | Stabile, Giovanni Zuccarino, Giacomo Ballarin, Francesco Rozza, Gianluigi |
Author_xml | – sequence: 1 givenname: Giovanni surname: Stabile fullname: Stabile, Giovanni organization: mathLab, Mathematics Area, SISSA – sequence: 2 givenname: Francesco orcidid: 0000-0001-6460-3538 surname: Ballarin fullname: Ballarin, Francesco email: fballarin@sissa.it organization: mathLab, Mathematics Area, SISSA – sequence: 3 givenname: Giacomo surname: Zuccarino fullname: Zuccarino, Giacomo organization: mathLab, Mathematics Area, SISSA – sequence: 4 givenname: Gianluigi surname: Rozza fullname: Rozza, Gianluigi organization: mathLab, Mathematics Area, SISSA |
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Cites_doi | 10.1016/j.jcp.2013.02.028 10.1002/9781119176817.ecm2117 10.1016/j.camwa.2020.03.019 10.1016/0377-0427(96)00025-8 10.1002/fld.2456 10.1016/j.cma.2016.08.006 10.1016/j.jcp.2008.09.024 10.1016/j.cma.2014.11.023 10.1007/978-3-642-23099-8 10.1007/978-3-642-36519-5 10.1002/num.21835 10.1016/j.cma.2014.02.005 10.1016/j.crma.2004.08.006 10.1016/j.cma.2006.09.005 10.1006/jcph.1998.5943 10.1007/978-3-319-58786-8 10.1016/j.cma.2015.01.020 10.1007/978-3-319-22470-1 10.1137/17M1118233 10.1016/j.cma.2018.12.040 10.1007/s00162-009-0112-y 10.1016/j.jcp.2020.109513 10.1063/1.868433 10.1016/0045-7825(92)90102-P 10.1016/j.compfluid.2018.01.035 10.1016/j.compfluid.2004.11.006 10.1016/j.cma.2012.04.015 10.1137/110854084 10.1016/j.cma.2009.05.017 10.1515/caim-2017-0011 10.1016/j.jcp.2013.12.004 10.1016/0045-7825(95)00844-9 10.1016/S0045-7825(98)00079-6 10.1016/j.physd.2005.02.006 10.1016/j.cma.2007.07.016 10.1002/0470091355.ecm051 10.1063/1.857881 10.1016/j.compfluid.2015.05.011 10.1002/nme.4772 10.1137/0910047 10.1007/978-3-319-15431-2 10.1090/S0025-5718-2013-02683-X 10.1007/s001620050119 |
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References | HughesTJRMultiscale phenomena: green’s functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methodsComput. Methods Appl. Mech. Eng.19951271-4387401136538110.1016/0045-7825(95)00844-9 Ali, S.: Stabilized reduced basis methods for parametrized steady Stokes and Navier-Stokes equations. Submitted 2019 (2019) Chacón RebolloTGómez MármolMRubinoSNumerical analysis of a finite element projection-based VMS turbulence model with wall lawsComput. Methods Appl. Mech. Eng.2015285379405331266910.1016/j.cma.2014.11.023 DavidsonPTurbulence: an introduction for scientists and engineers2004New YorkOxford University Press1061.76001 FortiDDedèLSemi-implicit BDF time discretization of the Navier–Stokes equations with VMS-LES modeling in a high performance computing frameworkComput. Fluids2015117168182336103610.1016/j.compfluid.2015.05.011 PacciariniPRozzaGStabilized reduced basis method for parametrized advection-diffusion PDEsComput. Methods Appl. Mech. Eng.2014274118319680410.1016/j.cma.2014.02.005 Hughes, T.J.R., Scovazzi, G., Franca, L.P.: Multiscale and stabilized methods, vol. 3 (Chapter 4), Wiley Online Library (2004) KaratzasENStabileGNouveauLScovazziGRozzaGA reduced basis approach for PDEs on parametrized geometries based on the shifted boundary finite element method and application to a stokes flowComput. Methods Appl. Mech. Eng.2019347568587390174110.1016/j.cma.2018.12.040 Benner, P., Ohlberger, M., Patera, A.T., Rozza, G., Urban, K.: Model reduction of parametrized systems, vol. 1st ed. 2017, MS&A series, no. Vol. 17, Springer (2017) StabileGHijaziSMolaALorenziSRozzaGPOD-Galerkin reduced order methods for CFD using finite Volume Discretisation: vortex shedding around a circular cylinderCommunication in Applied Industrial Mathematics201781210236373929510.1515/caim-2017-0011 Logg, A., Mardal, K.-A., Wells, G.: Automated solution of differential equations by the finite element method: the Fenics book, vol. 84, Springer Science & Business Media (2012) CarlbergKFarhatCCortialJAmsallemDThe GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flowsJ. Comput. Phys.2013242623647306205110.1016/j.jcp.2013.02.028 ItoKRavindranSSA reduced-order method for simulation and control of fluid flowsJ. Comput. Phys.19981432403425163117610.1006/jcph.1998.5943 Karatzas, E.N., Ballarin, F., Rozza, G.: Projection-based reduced order models for a cut finite element method in parametrized domains. Submitted - arXiv:1901.03846 (2019) WillcoxKUnsteady flow sensing and estimation via the gappy proper orthogonal decompositionComput. Fluids200635220822610.1016/j.compfluid.2004.11.006 BazilevsYCaloVMCottrellJAHughesTJRRealiAScovazziGVariational multiscale residual-based turbulence modeling for large Eddy simulation of incompressible flowsComput. Methods Appl. Mech. Eng.20071971-4173201236147510.1016/j.cma.2007.07.016 MasudAScovazziGA heterogeneous multiscale modeling framework for hierarchical systems of partial differential equationsInt. J. Numer. Methods Fluids2011651-32842279044610.1002/fld.2456 Ali, S., Ballarin, F., Rozza, G.: Reduced basis stabilization for the unsteady stokes and Navier-Stokes equations. In: Preparation (2019) Karatzas, E.N., Stabile, G., Atallah, N., Rozza, G., Scovazzi, G.: A reduced order approach for the embedded shifted boundary FEM and a heat exchange system on parametrized geometries. Accepted - arXiv:1807.07753.pdf (2018) SirisupSKarniadakisGEStability and accuracy of periodic flow solutions obtained by a POD-penalty methodPhysica D: Nonlinear Phenomena20052023-4218237213238810.1016/j.physd.2005.02.006 CaiazzoAIliescuTJohnVSchyschlowaSA numerical investigation of velocity-pressure reduced order models for incompressible flowsJ. Comput. Phys.2014259598616314858510.1016/j.jcp.2013.12.004 GravemeierVGeeMWKronbichlerMWallWAAn algebraic variational multiscale–multigrid method for large eddy simulation of turbulent flowComput. Methods Appl. Mech. Eng.201019913-16853864258134810.1016/j.cma.2009.05.017 Chinesta, F., Huerta, A., Rozza, G., Willcox, K.: Model order reduction, encyclopedia of computational mechanics. Elsevier Editor, 2016 (2016) BrezziFBristeauM-OFrancaLPMalletMRogéGA relationship between stabilized finite element methods and the galerkin method with bubble functionsComput. Methods Appl. Mech. Eng.1992961117129115959210.1016/0045-7825(92)90102-P PopeSBTurbulent flows2001CambridgeCambridge University Press0966.76002 Hijazi, S., Stabile, G., Mola, A., Rozza, G.: Data-driven POD–Galerkin reduced order model for turbulent flows. In: Preparation (2019) BergmannMBruneauC-HIolloAEnablers for robust POD modelsJ. Comput. Phys.20092282516538247993410.1016/j.jcp.2008.09.024 Chacón RebolloTDelgado ÁvilaEGómez MármolMBallarinFRozzaGOn a certified Smagorinsky reduced basis turbulence modelSIAM J. Numer. Anal.201755630473067373338210.1137/17M1118233 DeaneAEKevrekidisIGKarniadakisGEOrszagLow-dimensional models for complex geometry flows: application to grooved channels and circular cylindersPhys. Fluids A19913102337235410.1063/1.857881 IliescuTVariational multiscale proper orthogonal decomposition: Navier-stokes equationsNumer. Methods Partial Differential Equations2014302641663316397910.1002/num.21835 HughesTJRStewartJRA space-time formulation for multiscale phenomenaJ. Comput. Appl. Math.1996741-2217229143037510.1016/0377-0427(96)00025-8 RBniCS - reduced order modelling in FEniCS, http://mathlab.sissa.it/rbnics (2015) IolloALanteriSApproximation of compressible flows by a reduced order model1998BerlinSpringer5560 NoackBREckelmannHA low-dimensional Galerkin method for the three-dimensional flow around a circular cylinderPhys. Fluids19946112414310.1063/1.868433 StabileGiovanniRozzaGianluigiFinite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equationsComputers & Fluids2018173273284384366910.1016/j.compfluid.2018.01.035 AkhtarINayfehAHRibbensCJOn the stability and extension of reduced-order Galerkin models in incompressible flowsTheor. Comput. Fluid Dyn.200923321323710.1007/s00162-009-0112-y RozzaGVeroyKOn the stability of the reduced basis method for Stokes equations in parametrized domainsComput. Methods Appl. Mech. Eng.2007196712441260228177710.1016/j.cma.2006.09.005 Sagaut, P.: Large Eddy simulation for incompressible flows. 3rd. Springer, Berlin (2006) IliescuTWangZVariational multiscale proper orthogonal decomposition: convection-dominated convection-diffusion-reaction equationsMath. Comput.20138228313571378304256710.1090/S0025-5718-2013-02683-X LorenziSCammiALuzziLRozzaGPOD-Galerkin method for finite volume approximation of Navier-Stokes and RANS equationsComput. Methods Appl. Mech. Eng.2016311151179356468510.1016/j.cma.2016.08.006 GernerA-LVeroyKCertified reduced basis methods for parametrized saddle point problemsSIAM J. Sci. Comput.2012345A2812A2836302372710.1137/110854084 PetersonJSThe reduced basis method for incompressible viscous flow calculationsSIAM J. Sci. Stat. Comput.1989104777786100074510.1137/0910047 WangZAkhtarIBorggaardJIliescuTProper orthogonal decomposition closure models for turbulent flows: a numerical comparisonComput. Methods Appl. Mech. Eng.2012237–2401026294283010.1016/j.cma.2012.04.015 BoffiDanieleBrezziFrancoFortinMichelMixed Finite Element Methods and Applications2013Berlin, HeidelbergSpringer Berlin Heidelberg10.1007/978-3-642-36519-5 HughesTJRFeijóoGRMazzeiLQuincyJ-BThe variational multiscale method—a paradigm for computational mechanicsComput. Methods Appl. Mech. Eng.19981661-2324166014110.1016/S0045-7825(98)00079-6 BarraultMMadayYNguyenNCPateraAAn ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equationsComptes Rendus Mathematique20043399667672210320810.1016/j.crma.2004.08.006 Codina, R., Badia, S., Baiges, J., Principe, J.: Variational multiscale methods in computational fluid dynamics, Encyclopedia of computational mechanics (2017) IolloALanteriSDésidériJ-AStability properties of POD–galerkin approximations for the compressible Navier–Stokes equationsTheor. Comput. Fluid Dyn.200013637739610.1007/s001620050119 QuarteroniAValliANumerical approximation of partial differential equations, vol. 232008BerlinSpringer1151.65339 GiereSIliescuTJohnVWellsDSUPG reduced order models for convection-dominated convection–diffusion–reaction equationsComput. Methods Appl. Mech. Eng.2015289454474332716210.1016/j.cma.2015.01.020 Hijazi, S., Ali, S., Stabile, G., Ballarin, F., Rozza, G.: The effort of increasing Reynolds number in projection-based reduced order methods: from laminar to turbulent flows. In: Press, FEF proc. (2017) Fick, L., Maday, Y., Patera, A., Taddei, T.: A reduced basis technique for long-time unsteady turbulent flows. Journal of Computational Physics (submitted) (2017) Quarteroni, A., Manzoni, A., Negri, A.: Reduced basis methods for partial differential equations. Springer International Publishing (2016) Hesthaven, J.S., Rozza, G., Stamm, B.: Certified reduced basis methods for parametrized partial differential equations. Springer International Publishing (2016) BallarinFManzoniAQuarteroniARozzaGSupremizer stabilization of POD-galerkin approximation of parametrized steady incompressible Navier–Stokes equationsInt. J. Numer. Methods Eng.2015102511361161334125010.1002/nme.4772 9712_CR1 EN Karatzas (9712_CR37) 2019; 347 G Stabile (9712_CR52) 2017; 8 9712_CR4 TJR Hughes (9712_CR30) 1996; 74 9712_CR3 9712_CR8 AE Deane (9712_CR19) 1991; 3 V Gravemeier (9712_CR24) 2010; 199 F Ballarin (9712_CR5) 2015; 102 G Rozza (9712_CR49) 2007; 196 9712_CR17 9712_CR16 K Willcox (9712_CR55) 2006; 35 9712_CR50 TJR Hughes (9712_CR27) 1995; 127 T Iliescu (9712_CR32) 2014; 30 9712_CR26 S Sirisup (9712_CR51) 2005; 202 K Carlberg (9712_CR13) 2013; 242 9712_CR25 9712_CR29 JS Peterson (9712_CR45) 1989; 10 I Akhtar (9712_CR2) 2009; 23 Z Wang (9712_CR54) 2012; 237–240 A Masud (9712_CR42) 2011; 65 T Chacón Rebollo (9712_CR15) 2015; 285 F Brezzi (9712_CR11) 1992; 96 9712_CR20 M Bergmann (9712_CR9) 2009; 228 D Forti (9712_CR21) 2015; 117 A Iollo (9712_CR34) 2000; 13 9712_CR36 S Giere (9712_CR23) 2015; 289 A Iollo (9712_CR33) 1998 T Iliescu (9712_CR31) 2013; 82 Giovanni Stabile (9712_CR53) 2018; 173 9712_CR39 A Caiazzo (9712_CR12) 2014; 259 9712_CR38 Y Bazilevs (9712_CR7) 2007; 197 T Chacón Rebollo (9712_CR14) 2017; 55 SB Pope (9712_CR46) 2001 S Lorenzi (9712_CR41) 2016; 311 K Ito (9712_CR35) 1998; 143 M Barrault (9712_CR6) 2004; 339 A-L Gerner (9712_CR22) 2012; 34 P Davidson (9712_CR18) 2004 A Quarteroni (9712_CR48) 2008 TJR Hughes (9712_CR28) 1998; 166 9712_CR47 9712_CR40 Daniele Boffi (9712_CR10) 2013 P Pacciarini (9712_CR44) 2014; 274 BR Noack (9712_CR43) 1994; 6 |
References_xml | – reference: PacciariniPRozzaGStabilized reduced basis method for parametrized advection-diffusion PDEsComput. Methods Appl. Mech. Eng.2014274118319680410.1016/j.cma.2014.02.005 – reference: Sagaut, P.: Large Eddy simulation for incompressible flows. 3rd. Springer, Berlin (2006) – reference: Ali, S., Ballarin, F., Rozza, G.: Reduced basis stabilization for the unsteady stokes and Navier-Stokes equations. In: Preparation (2019) – reference: Ali, S.: Stabilized reduced basis methods for parametrized steady Stokes and Navier-Stokes equations. Submitted 2019 (2019) – reference: Codina, R., Badia, S., Baiges, J., Principe, J.: Variational multiscale methods in computational fluid dynamics, Encyclopedia of computational mechanics (2017) – reference: Chinesta, F., Huerta, A., Rozza, G., Willcox, K.: Model order reduction, encyclopedia of computational mechanics. Elsevier Editor, 2016 (2016) – reference: MasudAScovazziGA heterogeneous multiscale modeling framework for hierarchical systems of partial differential equationsInt. J. Numer. Methods Fluids2011651-32842279044610.1002/fld.2456 – reference: KaratzasENStabileGNouveauLScovazziGRozzaGA reduced basis approach for PDEs on parametrized geometries based on the shifted boundary finite element method and application to a stokes flowComput. Methods Appl. Mech. Eng.2019347568587390174110.1016/j.cma.2018.12.040 – reference: IliescuTWangZVariational multiscale proper orthogonal decomposition: convection-dominated convection-diffusion-reaction equationsMath. Comput.20138228313571378304256710.1090/S0025-5718-2013-02683-X – reference: CarlbergKFarhatCCortialJAmsallemDThe GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flowsJ. Comput. Phys.2013242623647306205110.1016/j.jcp.2013.02.028 – reference: RBniCS - reduced order modelling in FEniCS, http://mathlab.sissa.it/rbnics (2015) – reference: Hijazi, S., Ali, S., Stabile, G., Ballarin, F., Rozza, G.: The effort of increasing Reynolds number in projection-based reduced order methods: from laminar to turbulent flows. In: Press, FEF proc. (2017) – reference: BazilevsYCaloVMCottrellJAHughesTJRRealiAScovazziGVariational multiscale residual-based turbulence modeling for large Eddy simulation of incompressible flowsComput. Methods Appl. Mech. Eng.20071971-4173201236147510.1016/j.cma.2007.07.016 – reference: IolloALanteriSApproximation of compressible flows by a reduced order model1998BerlinSpringer5560 – reference: Hijazi, S., Stabile, G., Mola, A., Rozza, G.: Data-driven POD–Galerkin reduced order model for turbulent flows. In: Preparation (2019) – reference: GernerA-LVeroyKCertified reduced basis methods for parametrized saddle point problemsSIAM J. Sci. Comput.2012345A2812A2836302372710.1137/110854084 – reference: Chacón RebolloTGómez MármolMRubinoSNumerical analysis of a finite element projection-based VMS turbulence model with wall lawsComput. Methods Appl. Mech. Eng.2015285379405331266910.1016/j.cma.2014.11.023 – reference: CaiazzoAIliescuTJohnVSchyschlowaSA numerical investigation of velocity-pressure reduced order models for incompressible flowsJ. Comput. Phys.2014259598616314858510.1016/j.jcp.2013.12.004 – reference: LorenziSCammiALuzziLRozzaGPOD-Galerkin method for finite volume approximation of Navier-Stokes and RANS equationsComput. Methods Appl. Mech. Eng.2016311151179356468510.1016/j.cma.2016.08.006 – reference: Logg, A., Mardal, K.-A., Wells, G.: Automated solution of differential equations by the finite element method: the Fenics book, vol. 84, Springer Science & Business Media (2012) – reference: GravemeierVGeeMWKronbichlerMWallWAAn algebraic variational multiscale–multigrid method for large eddy simulation of turbulent flowComput. Methods Appl. Mech. Eng.201019913-16853864258134810.1016/j.cma.2009.05.017 – reference: Fick, L., Maday, Y., Patera, A., Taddei, T.: A reduced basis technique for long-time unsteady turbulent flows. Journal of Computational Physics (submitted) (2017) – reference: HughesTJRFeijóoGRMazzeiLQuincyJ-BThe variational multiscale method—a paradigm for computational mechanicsComput. Methods Appl. Mech. Eng.19981661-2324166014110.1016/S0045-7825(98)00079-6 – reference: BarraultMMadayYNguyenNCPateraAAn ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equationsComptes Rendus Mathematique20043399667672210320810.1016/j.crma.2004.08.006 – reference: Chacón RebolloTDelgado ÁvilaEGómez MármolMBallarinFRozzaGOn a certified Smagorinsky reduced basis turbulence modelSIAM J. Numer. Anal.201755630473067373338210.1137/17M1118233 – reference: Benner, P., Ohlberger, M., Patera, A.T., Rozza, G., Urban, K.: Model reduction of parametrized systems, vol. 1st ed. 2017, MS&A series, no. Vol. 17, Springer (2017) – reference: GiereSIliescuTJohnVWellsDSUPG reduced order models for convection-dominated convection–diffusion–reaction equationsComput. Methods Appl. Mech. Eng.2015289454474332716210.1016/j.cma.2015.01.020 – reference: PetersonJSThe reduced basis method for incompressible viscous flow calculationsSIAM J. Sci. Stat. Comput.1989104777786100074510.1137/0910047 – reference: SirisupSKarniadakisGEStability and accuracy of periodic flow solutions obtained by a POD-penalty methodPhysica D: Nonlinear Phenomena20052023-4218237213238810.1016/j.physd.2005.02.006 – reference: FortiDDedèLSemi-implicit BDF time discretization of the Navier–Stokes equations with VMS-LES modeling in a high performance computing frameworkComput. Fluids2015117168182336103610.1016/j.compfluid.2015.05.011 – reference: IliescuTVariational multiscale proper orthogonal decomposition: Navier-stokes equationsNumer. Methods Partial Differential Equations2014302641663316397910.1002/num.21835 – reference: QuarteroniAValliANumerical approximation of partial differential equations, vol. 232008BerlinSpringer1151.65339 – reference: Hughes, T.J.R., Scovazzi, G., Franca, L.P.: Multiscale and stabilized methods, vol. 3 (Chapter 4), Wiley Online Library (2004) – reference: PopeSBTurbulent flows2001CambridgeCambridge University Press0966.76002 – reference: Karatzas, E.N., Ballarin, F., Rozza, G.: Projection-based reduced order models for a cut finite element method in parametrized domains. Submitted - arXiv:1901.03846 (2019) – reference: DavidsonPTurbulence: an introduction for scientists and engineers2004New YorkOxford University Press1061.76001 – reference: NoackBREckelmannHA low-dimensional Galerkin method for the three-dimensional flow around a circular cylinderPhys. Fluids19946112414310.1063/1.868433 – reference: DeaneAEKevrekidisIGKarniadakisGEOrszagLow-dimensional models for complex geometry flows: application to grooved channels and circular cylindersPhys. Fluids A19913102337235410.1063/1.857881 – reference: WangZAkhtarIBorggaardJIliescuTProper orthogonal decomposition closure models for turbulent flows: a numerical comparisonComput. Methods Appl. Mech. Eng.2012237–2401026294283010.1016/j.cma.2012.04.015 – reference: Quarteroni, A., Manzoni, A., Negri, A.: Reduced basis methods for partial differential equations. Springer International Publishing (2016) – reference: StabileGHijaziSMolaALorenziSRozzaGPOD-Galerkin reduced order methods for CFD using finite Volume Discretisation: vortex shedding around a circular cylinderCommunication in Applied Industrial Mathematics201781210236373929510.1515/caim-2017-0011 – reference: HughesTJRStewartJRA space-time formulation for multiscale phenomenaJ. Comput. Appl. Math.1996741-2217229143037510.1016/0377-0427(96)00025-8 – reference: BoffiDanieleBrezziFrancoFortinMichelMixed Finite Element Methods and Applications2013Berlin, HeidelbergSpringer Berlin Heidelberg10.1007/978-3-642-36519-5 – reference: Karatzas, E.N., Stabile, G., Atallah, N., Rozza, G., Scovazzi, G.: A reduced order approach for the embedded shifted boundary FEM and a heat exchange system on parametrized geometries. Accepted - arXiv:1807.07753.pdf (2018) – reference: AkhtarINayfehAHRibbensCJOn the stability and extension of reduced-order Galerkin models in incompressible flowsTheor. Comput. Fluid Dyn.200923321323710.1007/s00162-009-0112-y – reference: RozzaGVeroyKOn the stability of the reduced basis method for Stokes equations in parametrized domainsComput. Methods Appl. Mech. Eng.2007196712441260228177710.1016/j.cma.2006.09.005 – reference: WillcoxKUnsteady flow sensing and estimation via the gappy proper orthogonal decompositionComput. Fluids200635220822610.1016/j.compfluid.2004.11.006 – reference: StabileGiovanniRozzaGianluigiFinite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equationsComputers & Fluids2018173273284384366910.1016/j.compfluid.2018.01.035 – reference: BergmannMBruneauC-HIolloAEnablers for robust POD modelsJ. Comput. Phys.20092282516538247993410.1016/j.jcp.2008.09.024 – reference: BrezziFBristeauM-OFrancaLPMalletMRogéGA relationship between stabilized finite element methods and the galerkin method with bubble functionsComput. Methods Appl. Mech. Eng.1992961117129115959210.1016/0045-7825(92)90102-P – reference: IolloALanteriSDésidériJ-AStability properties of POD–galerkin approximations for the compressible Navier–Stokes equationsTheor. Comput. Fluid Dyn.200013637739610.1007/s001620050119 – reference: Hesthaven, J.S., Rozza, G., Stamm, B.: Certified reduced basis methods for parametrized partial differential equations. Springer International Publishing (2016) – reference: HughesTJRMultiscale phenomena: green’s functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methodsComput. Methods Appl. Mech. Eng.19951271-4387401136538110.1016/0045-7825(95)00844-9 – reference: ItoKRavindranSSA reduced-order method for simulation and control of fluid flowsJ. Comput. Phys.19981432403425163117610.1006/jcph.1998.5943 – reference: BallarinFManzoniAQuarteroniARozzaGSupremizer stabilization of POD-galerkin approximation of parametrized steady incompressible Navier–Stokes equationsInt. J. Numer. Methods Eng.2015102511361161334125010.1002/nme.4772 – volume-title: Turbulence: an introduction for scientists and engineers year: 2004 ident: 9712_CR18 – volume: 242 start-page: 623 year: 2013 ident: 9712_CR13 publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2013.02.028 – ident: 9712_CR17 doi: 10.1002/9781119176817.ecm2117 – ident: 9712_CR39 – ident: 9712_CR16 – ident: 9712_CR50 – ident: 9712_CR3 doi: 10.1016/j.camwa.2020.03.019 – volume: 74 start-page: 217 issue: 1-2 year: 1996 ident: 9712_CR30 publication-title: J. Comput. Appl. Math. doi: 10.1016/0377-0427(96)00025-8 – volume: 65 start-page: 28 issue: 1-3 year: 2011 ident: 9712_CR42 publication-title: Int. J. Numer. Methods Fluids doi: 10.1002/fld.2456 – volume: 311 start-page: 151 year: 2016 ident: 9712_CR41 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2016.08.006 – volume: 228 start-page: 516 issue: 2 year: 2009 ident: 9712_CR9 publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2008.09.024 – volume: 285 start-page: 379 year: 2015 ident: 9712_CR15 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2014.11.023 – ident: 9712_CR40 doi: 10.1007/978-3-642-23099-8 – volume-title: Mixed Finite Element Methods and Applications year: 2013 ident: 9712_CR10 doi: 10.1007/978-3-642-36519-5 – volume: 30 start-page: 641 issue: 2 year: 2014 ident: 9712_CR32 publication-title: Numer. Methods Partial Differential Equations doi: 10.1002/num.21835 – volume: 274 start-page: 1 year: 2014 ident: 9712_CR44 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2014.02.005 – ident: 9712_CR38 – volume: 339 start-page: 667 issue: 9 year: 2004 ident: 9712_CR6 publication-title: Comptes Rendus Mathematique doi: 10.1016/j.crma.2004.08.006 – volume: 196 start-page: 1244 issue: 7 year: 2007 ident: 9712_CR49 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2006.09.005 – volume: 143 start-page: 403 issue: 2 year: 1998 ident: 9712_CR35 publication-title: J. Comput. Phys. doi: 10.1006/jcph.1998.5943 – volume-title: Numerical approximation of partial differential equations, vol. 23 year: 2008 ident: 9712_CR48 – ident: 9712_CR8 doi: 10.1007/978-3-319-58786-8 – ident: 9712_CR1 – volume: 289 start-page: 454 year: 2015 ident: 9712_CR23 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2015.01.020 – ident: 9712_CR36 doi: 10.1007/978-3-319-22470-1 – volume: 55 start-page: 3047 issue: 6 year: 2017 ident: 9712_CR14 publication-title: SIAM J. Numer. Anal. doi: 10.1137/17M1118233 – volume: 347 start-page: 568 year: 2019 ident: 9712_CR37 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2018.12.040 – ident: 9712_CR4 doi: 10.1016/j.camwa.2020.03.019 – volume: 23 start-page: 213 issue: 3 year: 2009 ident: 9712_CR2 publication-title: Theor. Comput. Fluid Dyn. doi: 10.1007/s00162-009-0112-y – ident: 9712_CR26 doi: 10.1016/j.jcp.2020.109513 – volume: 6 start-page: 124 issue: 1 year: 1994 ident: 9712_CR43 publication-title: Phys. Fluids doi: 10.1063/1.868433 – volume: 96 start-page: 117 issue: 1 year: 1992 ident: 9712_CR11 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/0045-7825(92)90102-P – volume: 173 start-page: 273 year: 2018 ident: 9712_CR53 publication-title: Computers & Fluids doi: 10.1016/j.compfluid.2018.01.035 – volume: 35 start-page: 208 issue: 2 year: 2006 ident: 9712_CR55 publication-title: Comput. Fluids doi: 10.1016/j.compfluid.2004.11.006 – volume: 237–240 start-page: 10 year: 2012 ident: 9712_CR54 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2012.04.015 – volume: 34 start-page: A2812 issue: 5 year: 2012 ident: 9712_CR22 publication-title: SIAM J. Sci. Comput. doi: 10.1137/110854084 – volume: 199 start-page: 853 issue: 13-16 year: 2010 ident: 9712_CR24 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2009.05.017 – volume: 8 start-page: 210 issue: 1 year: 2017 ident: 9712_CR52 publication-title: Communication in Applied Industrial Mathematics doi: 10.1515/caim-2017-0011 – ident: 9712_CR20 – volume: 259 start-page: 598 year: 2014 ident: 9712_CR12 publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2013.12.004 – volume: 127 start-page: 387 issue: 1-4 year: 1995 ident: 9712_CR27 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/0045-7825(95)00844-9 – volume: 166 start-page: 3 issue: 1-2 year: 1998 ident: 9712_CR28 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/S0045-7825(98)00079-6 – volume: 202 start-page: 218 issue: 3-4 year: 2005 ident: 9712_CR51 publication-title: Physica D: Nonlinear Phenomena doi: 10.1016/j.physd.2005.02.006 – volume: 197 start-page: 173 issue: 1-4 year: 2007 ident: 9712_CR7 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2007.07.016 – ident: 9712_CR29 doi: 10.1002/0470091355.ecm051 – volume: 3 start-page: 2337 issue: 10 year: 1991 ident: 9712_CR19 publication-title: Phys. Fluids A doi: 10.1063/1.857881 – volume: 117 start-page: 168 year: 2015 ident: 9712_CR21 publication-title: Comput. Fluids doi: 10.1016/j.compfluid.2015.05.011 – volume: 102 start-page: 1136 issue: 5 year: 2015 ident: 9712_CR5 publication-title: Int. J. Numer. 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SubjectTerms | Computational fluid dynamics Computational mathematics Computational Mathematics and Numerical Analysis Computational Science and Engineering Computer simulation Galerkin method Mathematical and Computational Biology Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Model reduction of parametrized Systems Multiscale analysis Reduced order models Stabilization Visualization |
Title | A reduced order variational multiscale approach for turbulent flows |
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