POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback
Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is ex...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Suzuki, Takao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
Proper Orthogonal Decomposition |
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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), 8 vom: 13. Aug. |
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| Übergeordnetes Werk: |
volume:55 ; year:2014 ; number:8 ; day:13 ; month:08 |
| Links: |
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| DOI / URN: |
10.1007/s00348-014-1798-1 |
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| Katalog-ID: |
SPR004381793 |
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| 245 | 1 | 0 | |a POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback |
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| 520 | |a Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. | ||
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| 650 | 4 | |a Dynamic Mode Decomposition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Galerkin Projection |7 (dpeaa)DE-He213 | |
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10.1007/s00348-014-1798-1 doi (DE-627)SPR004381793 (SPR)s00348-014-1798-1-e DE-627 ger DE-627 rakwb eng Suzuki, Takao verfasserin aut POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. Proper Orthogonal Decomposition (dpeaa)DE-He213 Feedback Gain (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Galerkin Projection (dpeaa)DE-He213 Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 8 vom: 13. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:8 day:13 month:08 https://dx.doi.org/10.1007/s00348-014-1798-1 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 8 13 08 |
| spelling |
10.1007/s00348-014-1798-1 doi (DE-627)SPR004381793 (SPR)s00348-014-1798-1-e DE-627 ger DE-627 rakwb eng Suzuki, Takao verfasserin aut POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. Proper Orthogonal Decomposition (dpeaa)DE-He213 Feedback Gain (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Galerkin Projection (dpeaa)DE-He213 Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 8 vom: 13. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:8 day:13 month:08 https://dx.doi.org/10.1007/s00348-014-1798-1 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 8 13 08 |
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10.1007/s00348-014-1798-1 doi (DE-627)SPR004381793 (SPR)s00348-014-1798-1-e DE-627 ger DE-627 rakwb eng Suzuki, Takao verfasserin aut POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. Proper Orthogonal Decomposition (dpeaa)DE-He213 Feedback Gain (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Galerkin Projection (dpeaa)DE-He213 Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 8 vom: 13. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:8 day:13 month:08 https://dx.doi.org/10.1007/s00348-014-1798-1 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 8 13 08 |
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10.1007/s00348-014-1798-1 doi (DE-627)SPR004381793 (SPR)s00348-014-1798-1-e DE-627 ger DE-627 rakwb eng Suzuki, Takao verfasserin aut POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. Proper Orthogonal Decomposition (dpeaa)DE-He213 Feedback Gain (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Galerkin Projection (dpeaa)DE-He213 Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 8 vom: 13. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:8 day:13 month:08 https://dx.doi.org/10.1007/s00348-014-1798-1 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 8 13 08 |
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10.1007/s00348-014-1798-1 doi (DE-627)SPR004381793 (SPR)s00348-014-1798-1-e DE-627 ger DE-627 rakwb eng Suzuki, Takao verfasserin aut POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. Proper Orthogonal Decomposition (dpeaa)DE-He213 Feedback Gain (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Galerkin Projection (dpeaa)DE-He213 Enthalten in Experiments in fluids Berlin : Springer, 1983 55(2014), 8 vom: 13. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:55 year:2014 number:8 day:13 month:08 https://dx.doi.org/10.1007/s00348-014-1798-1 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 8 13 08 |
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Suzuki, Takao |
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Suzuki, Takao misc Proper Orthogonal Decomposition misc Feedback Gain misc Proper Orthogonal Decomposition Mode misc Dynamic Mode Decomposition misc Galerkin Projection POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback |
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POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback Proper Orthogonal Decomposition (dpeaa)DE-He213 Feedback Gain (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Galerkin Projection (dpeaa)DE-He213 |
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pod-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved ptv feedback |
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POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback |
| abstract |
Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. © Springer-Verlag Berlin Heidelberg 2014 |
| abstractGer |
Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. © Springer-Verlag Berlin Heidelberg 2014 |
| abstract_unstemmed |
Abstract A data-driven system-identification technique is explored for proper orthogonal decomposition (POD)-based reduced-order unsteady simulation integrated with time-resolved particle-image-velocimetry/particle-tracking-velocimetry (PIV/PTV) feedback. Principal interaction pattern analysis is extended to calculate a nonlinear transfer function for the POD-mode evolution. Compared with the transfer function derived from the Galerkin projection of the Navier–Stokes equation, instability is suppressed in this approach by introducing a specific norm to be minimized. A feedback loop is implemented such that multiple POD modes obtained by the snapshot method can be stably tracked and assimilated into the PIV/PTV measurement over time. The proposed algorithm is demonstrated by solving a planar-jet problem at %$Re \approx 2{,}000%$. Suitable feedback gain is analyzed, and the capability for data assimilation is discussed. © Springer-Verlag Berlin Heidelberg 2014 |
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POD-based reduced-order hybrid simulation using the data-driven transfer function with time-resolved PTV feedback |
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https://dx.doi.org/10.1007/s00348-014-1798-1 |
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10.1007/s00348-014-1798-1 |
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2024-07-04T00:56:18.174Z |
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