Assessment of direct numerical simulation data of turbulent boundary layers
Statistics obtained from seven different direct numerical simulations (DNSs) pertaining to a canonical turbulent boundary layer (TBL) under zero pressure gradient are compiled and compared. The considered data sets include a recent DNS of a TBL with the extended range of Reynolds numbers Reθ = 500–4...
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| Published in | Journal of fluid mechanics Vol. 659; pp. 116 - 126 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Cambridge, UK
Cambridge University Press
25.09.2010
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Statistics obtained from seven different direct numerical simulations (DNSs) pertaining to a canonical turbulent boundary layer (TBL) under zero pressure gradient are compiled and compared. The considered data sets include a recent DNS of a TBL with the extended range of Reynolds numbers Reθ = 500–4300. Although all the simulations relate to the same physical flow case, the approaches differ in the applied numerical method, grid resolution and distribution, inflow generation method, boundary conditions and box dimensions. The resulting comparison shows surprisingly large differences not only in both basic integral quantities such as the friction coefficient cf or the shape factor H12, but also in their predictions of mean and fluctuation profiles far into the sublayer. It is thus shown that the numerical simulation of TBLs is, mainly due to the spatial development of the flow, very sensitive to, e.g. proper inflow condition, sufficient settling length and appropriate box dimensions. Thus, a DNS has to be considered as a numerical experiment and should be the subject of the same scrutiny as experimental data. However, if a DNS is set up with the necessary care, it can provide a faithful tool to predict even such notoriously difficult flow cases with great accuracy. |
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| AbstractList | Statistics obtained from seven different direct numerical simulations (DNSs) pertaining to a canonical turbulent boundary layer (TBL) under zero pressure gradient are compiled and compared. The considered data sets include a recent DNS of a TBL with the extended range of Reynolds numbers Reθ = 500–4300. Although all the simulations relate to the same physical flow case, the approaches differ in the applied numerical method, grid resolution and distribution, inflow generation method, boundary conditions and box dimensions. The resulting comparison shows surprisingly large differences not only in both basic integral quantities such as the friction coefficient cf or the shape factor H12, but also in their predictions of mean and fluctuation profiles far into the sublayer. It is thus shown that the numerical simulation of TBLs is, mainly due to the spatial development of the flow, very sensitive to, e.g. proper inflow condition, sufficient settling length and appropriate box dimensions. Thus, a DNS has to be considered as a numerical experiment and should be the subject of the same scrutiny as experimental data. However, if a DNS is set up with the necessary care, it can provide a faithful tool to predict even such notoriously difficult flow cases with great accuracy. Statistics obtained from seven different direct numerical simulations (DNSs) pertaining to a canonical turbulent boundary layer (TBL) under zero pressure gradient are compiled and compared. The considered data sets include a recent DNS of a TBL with the extended range of Reynolds numbers Re θ = 500–4300. Although all the simulations relate to the same physical flow case, the approaches differ in the applied numerical method, grid resolution and distribution, inflow generation method, boundary conditions and box dimensions. The resulting comparison shows surprisingly large differences not only in both basic integral quantities such as the friction coefficient c f or the shape factor H 12 , but also in their predictions of mean and fluctuation profiles far into the sublayer. It is thus shown that the numerical simulation of TBLs is, mainly due to the spatial development of the flow, very sensitive to, e.g. proper inflow condition, sufficient settling length and appropriate box dimensions. Thus, a DNS has to be considered as a numerical experiment and should be the subject of the same scrutiny as experimental data. However, if a DNS is set up with the necessary care, it can provide a faithful tool to predict even such notoriously difficult flow cases with great accuracy. Statistics obtained from seven different direct numerical simulations (DNSs) pertaining to a canonical turbulent boundary layer (TBL) under zero pressure gradient are compiled and compared. The considered data sets include a recent DNS of a TBL with the extended range of Reynolds numbers Re[thetas] = 500-4300. Although all the simulations relate to the same physical flow case, the approaches differ in the applied numerical method, grid resolution and distribution, inflow generation method, boundary conditions and box dimensions. The resulting comparison shows surprisingly large differences not only in both basic integral quantities such as the friction coefficient cf or the shape factor H12, but also in their predictions of mean and fluctuation profiles far into the sublayer. It is thus shown that the numerical simulation of TBLs is, mainly due to the spatial development of the flow, very sensitive to, e.g. proper inflow condition, sufficient settling length and appropriate box dimensions. Thus, a DNS has to be considered as a numerical experiment and should be the subject of the same scrutiny as experimental data. However, if a DNS is set up with the necessary care, it can provide a faithful tool to predict even such notoriously difficult flow cases with great accuracy. Statistics obtained from seven different direct numerical simulations (DNSs) pertaining to a canonical turbulent boundary layer (TBL) under zero pressure gradient are compiled and compared. The considered data sets include a recent DNS of a TBL with the extended range of Reynolds numbers Re-theta = 500-4300. Although all the simulations relate to the same physical flow case, the approaches differ in the applied numerical method, grid resolution and distribution, inflow generation method, boundary conditions and box dimensions. The resulting comparison shows surprisingly large differences not only in both basic integral quantities such as the friction coefficient c(f) or the shape factor II12, but also in their predictions of mean and fluctuation profiles far into the sublayer. It is thus shown that the numerical simulation of TBLs is, mainly due to the spatial development of the flow, very sensitive to, e. g. proper inflow condition, sufficient settling length and appropriate box dimensions. Thus, a DNS has to be considered as a numerical experiment and should be the subject of the same scrutiny as experimental data. However, if a DNS is set up with the necessary care, it can provide a faithful tool to predict even such notoriously difficult flow cases with great accuracy. Statistics obtained from seven different direct numerical simulations (DNSs) pertaining to a canonical turbulent boundary layer (TBL) under zero pressure gradient are compiled and compared. The considered data sets include a recent DNS of a TBL with the extended range of Reynolds numbers Reθ = 500-4300. Although all the simulations relate to the same physical flow case, the approaches differ in the applied numerical method, grid resolution and distribution, inflow generation method, boundary conditions and box dimensions. The resulting comparison shows surprisingly large differences not only in both basic integral quantities such as the friction coefficient cf or the shape factor H12, but also in their predictions of mean and fluctuation profiles far into the sublayer. It is thus shown that the numerical simulation of TBLs is, mainly due to the spatial development of the flow, very sensitive to, e.g. proper inflow condition, sufficient settling length and appropriate box dimensions. Thus, a DNS has to be considered as a numerical experiment and should be the subject of the same scrutiny as experimental data. However, if a DNS is set up with the necessary care, it can provide a faithful tool to predict even such notoriously difficult flow cases with great accuracy. [PUBLICATION ABSTRACT] |
| Author | SCHLATTER, PHILIPP ÖRLÜ, RAMIS |
| Author_xml | – sequence: 1 givenname: PHILIPP surname: SCHLATTER fullname: SCHLATTER, PHILIPP email: pschlatt@mech.kth.se organization: Linné Flow Centre, KTH Mechanics, SE-100 44 Stockholm, Sweden – sequence: 2 givenname: RAMIS surname: ÖRLÜ fullname: ÖRLÜ, RAMIS organization: Linné Flow Centre, KTH Mechanics, SE-100 44 Stockholm, Sweden |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23252639$$DView record in Pascal Francis https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-26680$$DView record from Swedish Publication Index |
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| Keywords | turbulent boundary layers turbulence simulation Turbulent flow Skin friction Digital simulation Modelling Velocity distribution Form factors Boundary layers Turbulence structure |
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| References | S0022112010003113_ref8 S0022112010003113_ref10 S0022112010003113_ref9 Schlatter (S0022112010003113_ref22) 2009; 54 S0022112010003113_ref4 S0022112010003113_ref14 Tsukahara (S0022112010003113_ref28) 2005 S0022112010003113_ref5 S0022112010003113_ref13 S0022112010003113_ref6 S0022112010003113_ref12 S0022112010003113_ref11 S0022112010003113_ref7 S0022112010003113_ref18 S0022112010003113_ref17 S0022112010003113_ref16 S0022112010003113_ref19 S0022112010003113_ref1 S0022112010003113_ref2 S0022112010003113_ref3 S0022112010003113_ref21 S0022112010003113_ref20 S0022112010003113_ref25 S0022112010003113_ref24 S0022112010003113_ref23 S0022112010003113_ref29 S0022112010003113_ref27 Smits (S0022112010003113_ref26) 1983; 27 Khujadze (S0022112010003113_ref15) 2007 |
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| Snippet | Statistics obtained from seven different direct numerical simulations (DNSs) pertaining to a canonical turbulent boundary layer (TBL) under zero pressure... |
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| SubjectTerms | Boundary conditions Boundary layer Boundary layer and shear turbulence Boundary layers Computer simulation Engineering mechanics Exact sciences and technology Fluid dynamics Fluid flow Fluid mechanics Friction Fundamental areas of phenomenology (including applications) Inflow Mathematical models Numerical analysis Physics Statistical analysis Strömningsmekanik TECHNOLOGY TEKNIKVETENSKAP Teknisk mekanik Turbulence turbulence simulation Turbulence simulation and modeling Turbulent boundary layer turbulent boundary layers Turbulent flow Turbulent flows, convection, and heat transfer |
| Title | Assessment of direct numerical simulation data of turbulent boundary layers |
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