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...

Full description

Saved in:
Bibliographic Details
Published inAdvances in computational mathematics Vol. 45; no. 5-6; pp. 2349 - 2368
Main Authors Stabile, Giovanni, Ballarin, Francesco, Zuccarino, Giacomo, Rozza, Gianluigi
Format Journal Article
LanguageEnglish
Published New York Springer US 01.12.2019
Springer Nature B.V
Subjects
Online AccessGet full text
ISSN1019-7168
1572-9044
DOI10.1007/s10444-019-09712-x

Cover

Loading…
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.
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
BookMark eNp9kE1LAzEURYNUsK3-AVcB16P5mEwmy1LUCgU3ug6ZfOiU6aQmGa3_3rQjCC66yoN3z8vlzMCk970F4BqjW4wQv4sYlWVZICwKJDgmxf4MTDHjpBB5McnzYcVxVV-AWYwbhJCoOJuC5QIGawZtDfTB2AA_VWhVan2vOrgdutRGrToL1W4XvNLv0PkA0xCaobN9gq7zX_ESnDvVRXv1-87B68P9y3JVrJ8fn5aLdaEpFqlwVDNiGoOcco2pjSjLxhCnaue0sYYTppqKK6orkQOsYqVzTDPBdK2cdhWdg5vxbq7yMdiY5MYPIReNklBKaEUJ4zlFxpQOPsZgndyFdqvCt8RIHmTJUZbMRuRRltxnqP4H6TYdNaSg2u40Skc05n_6Nxv-Wp2gfgAL9IOk
CitedBy_id crossref_primary_10_1016_j_jcp_2024_113058
crossref_primary_10_3390_fluids5010039
crossref_primary_10_1063_5_0010315
crossref_primary_10_1016_j_camwa_2023_09_039
crossref_primary_10_1002_nme_6942
crossref_primary_10_1016_j_jcp_2021_110260
crossref_primary_10_1007_s10444_021_09870_x
crossref_primary_10_1016_j_jcp_2020_109513
crossref_primary_10_1002_gamm_202100007
crossref_primary_10_1007_s11831_024_10197_1
crossref_primary_10_1016_j_cma_2021_114102
crossref_primary_10_1016_j_apm_2020_07_029
crossref_primary_10_1016_j_jcp_2023_112356
crossref_primary_10_1007_s10915_022_02019_y
crossref_primary_10_1016_j_camwa_2022_07_017
crossref_primary_10_1016_j_compfluid_2020_104477
crossref_primary_10_1016_j_compfluid_2022_105604
crossref_primary_10_1063_5_0061577
crossref_primary_10_1098_rspa_2022_0392
crossref_primary_10_1007_s10444_024_10209_5
crossref_primary_10_1016_j_cma_2020_112991
crossref_primary_10_1016_j_jcp_2021_110742
crossref_primary_10_1016_j_cma_2023_116232
crossref_primary_10_3934_acse_2023008
crossref_primary_10_1002_nme_6525
crossref_primary_10_1016_j_cma_2024_117041
crossref_primary_10_1016_j_anucene_2020_108056
crossref_primary_10_1016_j_cma_2024_117161
crossref_primary_10_3390_fluids6080298
crossref_primary_10_1016_j_camwa_2019_06_026
crossref_primary_10_1016_j_camwa_2024_05_004
crossref_primary_10_1016_j_mechrescom_2020_103599
crossref_primary_10_1137_20M1341866
crossref_primary_10_1016_j_cma_2021_113956
crossref_primary_10_1002_fld_5184
crossref_primary_10_3390_fluids5010026
crossref_primary_10_1038_s41598_022_22598_y
crossref_primary_10_1016_j_camwa_2023_06_038
crossref_primary_10_1016_j_compfluid_2020_104615
crossref_primary_10_1016_j_camwa_2020_03_019
crossref_primary_10_1007_s10915_022_02032_1
crossref_primary_10_1016_j_jcp_2021_110802
crossref_primary_10_1016_j_compfluid_2020_104819
crossref_primary_10_1016_j_ces_2022_117795
crossref_primary_10_1002_nme_6321
crossref_primary_10_1016_j_jcp_2020_109681
crossref_primary_10_1103_PhysRevE_102_043302
crossref_primary_10_3389_fphy_2022_900064
crossref_primary_10_1137_19M1354819
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
ContentType Journal Article
Copyright Springer Science+Business Media, LLC, part of Springer Nature 2019
Copyright Springer Nature B.V. 2019
Copyright_xml – notice: Springer Science+Business Media, LLC, part of Springer Nature 2019
– notice: Copyright Springer Nature B.V. 2019
DBID AAYXX
CITATION
DOI 10.1007/s10444-019-09712-x
DatabaseName CrossRef
DatabaseTitle CrossRef
DatabaseTitleList

DeliveryMethod fulltext_linktorsrc
Discipline Applied Sciences
Mathematics
EISSN 1572-9044
EndPage 2368
ExternalDocumentID 10_1007_s10444_019_09712_x
GrantInformation_xml – fundername: INDAM GNCS
  grantid: year 2018
– fundername: European Research Council
  grantid: 681447
  funderid: https://doi.org/10.13039/501100000781
GroupedDBID -52
-59
-5G
-BR
-EM
-Y2
-~C
.4S
.86
.DC
.VR
06D
0R~
0VY
199
1N0
1SB
2.D
203
23M
28-
2J2
2JN
2JY
2KG
2KM
2LR
2P1
2VQ
2~H
30V
4.4
406
408
409
40D
40E
5GY
5QI
5VS
67Z
6NX
78A
8TC
8UJ
95-
95.
95~
96X
AAAVM
AABHQ
AACDK
AAHNG
AAIAL
AAJBT
AAJKR
AANZL
AARHV
AARTL
AASML
AATNV
AATVU
AAUYE
AAWCG
AAYIU
AAYQN
AAYTO
AAYZH
ABAKF
ABBBX
ABBXA
ABDZT
ABECU
ABFTV
ABHLI
ABHQN
ABJNI
ABJOX
ABKCH
ABKTR
ABLJU
ABMNI
ABMQK
ABNWP
ABQBU
ABQSL
ABSXP
ABTEG
ABTHY
ABTKH
ABTMW
ABULA
ABWNU
ABXPI
ACAOD
ACBXY
ACDTI
ACGFS
ACHSB
ACHXU
ACIWK
ACKNC
ACMDZ
ACMLO
ACOKC
ACOMO
ACPIV
ACSNA
ACZOJ
ADHHG
ADHIR
ADIMF
ADINQ
ADKNI
ADKPE
ADRFC
ADTPH
ADURQ
ADYFF
ADZKW
AEBTG
AEFIE
AEFQL
AEGAL
AEGNC
AEJHL
AEJRE
AEKMD
AEMSY
AENEX
AEOHA
AEPYU
AESKC
AETLH
AEVLU
AEXYK
AFBBN
AFEXP
AFGCZ
AFLOW
AFQWF
AFWTZ
AFZKB
AGAYW
AGDGC
AGGDS
AGJBK
AGMZJ
AGQEE
AGQMX
AGRTI
AGWIL
AGWZB
AGYKE
AHAVH
AHBYD
AHKAY
AHSBF
AHYZX
AIAKS
AIGIU
AIIXL
AILAN
AITGF
AJBLW
AJRNO
AJZVZ
ALMA_UNASSIGNED_HOLDINGS
ALWAN
AMKLP
AMXSW
AMYLF
AMYQR
AOCGG
ARCSS
ARMRJ
ASPBG
AVWKF
AXYYD
AYJHY
AZFZN
B-.
BA0
BAPOH
BBWZM
BDATZ
BGNMA
BSONS
CAG
COF
CS3
CSCUP
DDRTE
DL5
DNIVK
DPUIP
EBLON
EBS
EDO
EIOEI
EJD
ESBYG
FEDTE
FERAY
FFXSO
FIGPU
FINBP
FNLPD
FRRFC
FSGXE
FWDCC
GGCAI
GGRSB
GJIRD
GNWQR
GQ6
GQ7
GQ8
GXS
H13
HF~
HG5
HG6
HMJXF
HQYDN
HRMNR
HVGLF
HZ~
I09
IHE
IJ-
IKXTQ
ITM
IWAJR
IXC
IZIGR
IZQ
I~X
I~Z
J-C
J0Z
JBSCW
JCJTX
JZLTJ
KDC
KOV
KOW
LAK
LLZTM
M4Y
MA-
MK~
N2Q
N9A
NB0
NDZJH
NPVJJ
NQJWS
NU0
O9-
O93
O9G
O9I
O9J
OAM
OVD
P19
P2P
P9O
PF0
PT4
PT5
QOK
QOS
R4E
R89
R9I
RHV
RNI
RNS
ROL
RPX
RSV
RZC
RZE
RZK
S16
S1Z
S26
S27
S28
S3B
SAP
SCLPG
SCO
SDD
SDH
SDM
SHX
SISQX
SJYHP
SNE
SNPRN
SNX
SOHCF
SOJ
SPISZ
SRMVM
SSLCW
STPWE
SZN
T13
T16
TEORI
TSG
TSK
TSV
TUC
TUS
U2A
UG4
UOJIU
UTJUX
UZXMN
VC2
VFIZW
W23
W48
WK8
YLTOR
Z45
Z83
ZMTXR
ZWQNP
~EX
AAPKM
AAYXX
ABBRH
ABDBE
ABFSG
ACSTC
ADHKG
AEZWR
AFDZB
AFHIU
AFOHR
AGQPQ
AHPBZ
AHWEU
AIXLP
AMVHM
ATHPR
AYFIA
CITATION
ABRTQ
ID FETCH-LOGICAL-c319t-f3c52dbd0fafbd8d944bd2fa8ffcded725ab67a3c69afb5654ff5c595c8afcf63
IEDL.DBID U2A
ISSN 1019-7168
IngestDate Fri Jul 25 11:09:01 EDT 2025
Tue Jul 01 02:55:34 EDT 2025
Thu Apr 24 23:08:17 EDT 2025
Fri Feb 21 02:38:02 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 5-6
Keywords Variational multiscale
65N12
Reduced order methods
76F65
76D05
High Reynolds number flows
65N30
Navier-Stokes equations
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c319t-f3c52dbd0fafbd8d944bd2fa8ffcded725ab67a3c69afb5654ff5c595c8afcf63
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0001-6460-3538
PQID 2332363257
PQPubID 2043875
PageCount 20
ParticipantIDs proquest_journals_2332363257
crossref_primary_10_1007_s10444_019_09712_x
crossref_citationtrail_10_1007_s10444_019_09712_x
springer_journals_10_1007_s10444_019_09712_x
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2019-12-01
PublicationDateYYYYMMDD 2019-12-01
PublicationDate_xml – month: 12
  year: 2019
  text: 2019-12-01
  day: 01
PublicationDecade 2010
PublicationPlace New York
PublicationPlace_xml – name: New York
PublicationTitle Advances in computational mathematics
PublicationTitleAbbrev Adv Comput Math
PublicationYear 2019
Publisher Springer US
Springer Nature B.V
Publisher_xml – name: Springer US
– name: Springer Nature B.V
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. Methods Eng.
  doi: 10.1002/nme.4772
– ident: 9712_CR25
– start-page: 55
  volume-title: Approximation of compressible flows by a reduced order model
  year: 1998
  ident: 9712_CR33
– volume: 10
  start-page: 777
  issue: 4
  year: 1989
  ident: 9712_CR45
  publication-title: SIAM J. Sci. Stat. Comput.
  doi: 10.1137/0910047
– volume-title: Turbulent flows
  year: 2001
  ident: 9712_CR46
– ident: 9712_CR47
  doi: 10.1007/978-3-319-15431-2
– volume: 82
  start-page: 1357
  issue: 283
  year: 2013
  ident: 9712_CR31
  publication-title: Math. Comput.
  doi: 10.1090/S0025-5718-2013-02683-X
– volume: 13
  start-page: 377
  issue: 6
  year: 2000
  ident: 9712_CR34
  publication-title: Theor. Comput. Fluid Dyn.
  doi: 10.1007/s001620050119
SSID ssj0009675
Score 2.518592
Snippet The purpose of this work is to present different reduced order model strategies starting from full order simulations stabilized using a residual-based...
SourceID proquest
crossref
springer
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 2349
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
URI https://link.springer.com/article/10.1007/s10444-019-09712-x
https://www.proquest.com/docview/2332363257
Volume 45
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEG4MXPTgAzWiSHrwpptA2y3dIxCQaOAkCZ4229eJgGFB_flOS5dVoyae9rCzc5jpzHyznX5F6KZlXY5LdKS4JBEgcAipRJjIMKKFZS1jlGsUxxM-mrKHWTwLh8LyYtq92JL0mfrTYTfG3MSEG_HptEkEyLEau94dVvGUdEuqXe7pddtOEroBEY7K_KzjazkqMea3bVFfbYbH6DDARNzd-vUE7ZlFDR0FyIhDQOY1dDDe0a7mp6jfxStHxQoinlITv0InHP72YT86mINLDC6IxDEgVgw1R25c7cF2vnzLz9B0OHjqj6JwS0KkIHzWkaUqJlrqls2s1EInjElNbCasVdroDokzyTsZVTwBAcBvzNpYxUmsRGaV5fQcVRbLhblAmEtpoMUBdZZDI6MAG1IlpUhkJpQQto7ahbFSFSjE3U0W87QkP3YGTsHAqTdw-l5Ht7tvXrYEGn9KNwofpCGY8pRQSiinkFzq6K7wS_n6d22X_xO_QvvELQ0_rNJAlfVqY64BcqxlE1W7w15v4p73z4-Dpl9xHwel0j4
linkProvider Springer Nature
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEG6MHtSDD9SIovbgTTeBtlu6R0IkqMAJEm7N9nUiYFhQf77T0gU1auJ5Z3uY6cx80858Rei27nyMy0yiuSIJIHBwqUzYxDJihGN1a7UvFPsD3h2xp3E6jkNhRdntXl5Jhkj9adiNMd8x4Vt8mg2SAHLcATAgfCPXiLQ2VLs80Os2vCRUAyKOyvy8xtd0tMGY365FQ7bpHKGDCBNxa2XXY7RlpxV0GCEjjg5ZVNB-f027WpygdgvPPRUriARKTfwKlXA87cOhdbAAk1hcEoljQKwYco5a-tyD3WT2VpyiUedh2O4m8ZWERIP7LBJHdUqMMnWXO2WEyRhThrhcOKeNNU2S5oo3c6p5BgKA35hzqU6zVIvcacfpGdqezqb2HGGulIUSB5ZzHAoZDdiQaqVEpnKhhXBV1CiVJXWkEPcvWUzkhvzYK1iCgmVQsHyvorv1Py8rAo0_pWulDWR0pkISSgnlFIJLFd2Xdtl8_n21i_-J36Dd7rDfk73HwfMl2iN-m4TGlRraXsyX9grgx0Jdh932AeiQ0iE
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDI4QSAgOPAaIwYAcuEG1NW2z9DgNpvHYxIFJu1XN6zR109oBPx8nS9eBAIlz3Ui1Y_tzY39B6LqlTYyLpScoJx4gcHCpmClPhUQyHbaUEqZQHAxpfxQ-jqPx2hS_7XYvjySXMw2GpSkrmjOpm2uDb2FouidMu0_bJx6gyC0Ix77Z1yPSqWh3qaXa9Y0kVAbMjc38vMbX1FThzW9HpDbz9A7QnoOMuLO08SHaUFkN7Tv4iJ1z5jW0O1hRsOZHqNvBc0PLCiKWXhO_QVXs_vxh20aYg3kULknFMaBXDPmHL0wewnoyfc-P0ah3_9rte-7GBE_AtxeeDkREJJctnWoumYzDkEuiU6a1kEq2SZRy2k4DQWMQACwXah2JKI4ES7XQNDhBm9k0U6cIU84VlDuwnKZQ1AjAiYHgnMU8ZYIxXUd-qaxEODpxc6vFJKmIkI2CE1BwYhWcfNTRzeqd2ZJM40_pRmmDxDlWnpAgIAENINDU0W1pl-rx76ud_U_8Cm2_3PWS54fh0znaIWaX2B6WBtos5gt1AUik4Jd2s30CG3nWXQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+reduced+order+variational+multiscale+approach+for+turbulent+flows&rft.jtitle=Advances+in+computational+mathematics&rft.au=Stabile%2C+Giovanni&rft.au=Ballarin%2C+Francesco&rft.au=Zuccarino%2C+Giacomo&rft.au=Rozza%2C+Gianluigi&rft.date=2019-12-01&rft.pub=Springer+Nature+B.V&rft.issn=1019-7168&rft.eissn=1572-9044&rft.volume=45&rft.issue=5&rft.spage=2349&rft.epage=2368&rft_id=info:doi/10.1007%2Fs10444-019-09712-x&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1019-7168&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1019-7168&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1019-7168&client=summon