Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil
Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estima...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Romain, Leroux [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Experiments in fluids - Berlin : Springer, 1983, 55(2014), 4 vom: 01. Apr. |
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| Übergeordnetes Werk: |
volume:55 ; year:2014 ; number:4 ; day:01 ; month:04 |
| Links: |
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| DOI / URN: |
10.1007/s00348-014-1699-3 |
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| Katalog-ID: |
SPR004380827 |
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| 100 | 1 | |a Romain, Leroux |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil |
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| 520 | |a Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. | ||
| 650 | 4 | |a Kalman Filter |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Proper Orthogonal Decomposition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Reconstruction Error |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Projection Mode |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Proper Orthogonal Decomposition Mode |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Chatellier, Ludovic |4 aut | |
| 700 | 1 | |a David, Laurent |4 aut | |
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10.1007/s00348-014-1699-3 doi (DE-627)SPR004380827 (SPR)s00348-014-1699-3-e DE-627 ger DE-627 rakwb eng Romain, Leroux verfasserin aut Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. Kalman Filter (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Projection Mode (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Chatellier, Ludovic aut David, Laurent aut Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 4 vom: 01. Apr. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:4 day:01 month:04 https://dx.doi.org/10.1007/s00348-014-1699-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2014 4 01 04 |
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10.1007/s00348-014-1699-3 doi (DE-627)SPR004380827 (SPR)s00348-014-1699-3-e DE-627 ger DE-627 rakwb eng Romain, Leroux verfasserin aut Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. Kalman Filter (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Projection Mode (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Chatellier, Ludovic aut David, Laurent aut Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 4 vom: 01. Apr. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:4 day:01 month:04 https://dx.doi.org/10.1007/s00348-014-1699-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2014 4 01 04 |
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10.1007/s00348-014-1699-3 doi (DE-627)SPR004380827 (SPR)s00348-014-1699-3-e DE-627 ger DE-627 rakwb eng Romain, Leroux verfasserin aut Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. Kalman Filter (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Projection Mode (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Chatellier, Ludovic aut David, Laurent aut Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 4 vom: 01. Apr. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:4 day:01 month:04 https://dx.doi.org/10.1007/s00348-014-1699-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2014 4 01 04 |
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10.1007/s00348-014-1699-3 doi (DE-627)SPR004380827 (SPR)s00348-014-1699-3-e DE-627 ger DE-627 rakwb eng Romain, Leroux verfasserin aut Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. Kalman Filter (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Projection Mode (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Chatellier, Ludovic aut David, Laurent aut Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 4 vom: 01. Apr. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:4 day:01 month:04 https://dx.doi.org/10.1007/s00348-014-1699-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2014 4 01 04 |
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10.1007/s00348-014-1699-3 doi (DE-627)SPR004380827 (SPR)s00348-014-1699-3-e DE-627 ger DE-627 rakwb eng Romain, Leroux verfasserin aut Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. Kalman Filter (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Projection Mode (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Chatellier, Ludovic aut David, Laurent aut Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 4 vom: 01. Apr. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:4 day:01 month:04 https://dx.doi.org/10.1007/s00348-014-1699-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2014 4 01 04 |
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Romain, Leroux @@aut@@ Chatellier, Ludovic @@aut@@ David, Laurent @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR004380827</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230328162558.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201001s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00348-014-1699-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR004380827</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00348-014-1699-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Romain, Leroux</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. 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Romain, Leroux |
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Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil Kalman Filter (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Projection Mode (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 |
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bayesian inference applied to spatio-temporal reconstruction of flows around a naca0012 airfoil |
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Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil |
| abstract |
Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. © Springer-Verlag Berlin Heidelberg 2014 |
| abstractGer |
Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. © Springer-Verlag Berlin Heidelberg 2014 |
| abstract_unstemmed |
Abstract In this paper, we shall investigate sequential data assimilation techniques to improve the stability of reduced-order models for fluid flows. The reduced-order model used relies on a Galerkin projection of Navier–Stokes equations on proper orthogonal decomposition (POD) basis vectors estimated from snapshots of the flow fields obtained with time-resolved particle image velocimetry (TR-PIV) measurements. The coefficients of the dynamical system are given through a least-squares regression technique applied to the experimental data and lead to a low-order model which is known to diverge, or damp, rapidly in time if left uncontrolled. In this context, a sequential data assimilation method based on a Bayesian approach is proposed. In this formalism, reduced-order models (ROMs) are modeled with discrete time from the hidden Markov processes. Given the whole trajectories of the POD temporal modes, the state of ROM coefficients initially provided by noisy PIV measurements are re-estimated from a Kalman filtering of the sequential data. Results are obtained for the flow around a NACA0012 airfoil at Reynolds numbers of 1000 and 2000 and angles of attack of %$10^{\circ },15^{\circ },20^{\circ }%$ and %$30^{\circ }%$. © Springer-Verlag Berlin Heidelberg 2014 |
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| title_short |
Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil |
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https://dx.doi.org/10.1007/s00348-014-1699-3 |
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Chatellier, Ludovic David, Laurent |
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10.1007/s00348-014-1699-3 |
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| score |
7.401354 |