Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors
Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of syste...
Ausführliche Beschreibung
Autor*in: |
Mentzelopoulos, Andreas P. [verfasserIn] del Águila Ferrandis, José [verfasserIn] Rudy, Samuel [verfasserIn] Sapsis, Themistoklis [verfasserIn] Triantafyllou, Michael S. [verfasserIn] Fan, Dixia [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Ocean engineering - Amsterdam [u.a.] : Elsevier Science, 1970, 266 |
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Übergeordnetes Werk: |
volume:266 |
DOI / URN: |
10.1016/j.oceaneng.2022.112833 |
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Katalog-ID: |
ELV008884102 |
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100 | 1 | |a Mentzelopoulos, Andreas P. |e verfasserin |0 (orcid)0000-0002-0543-4807 |4 aut | |
245 | 1 | 0 | |a Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors |
264 | 1 | |c 2022 | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
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520 | |a Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. | ||
650 | 4 | |a VIV | |
650 | 4 | |a Vortex induced vibrations | |
650 | 4 | |a Flexible body VIV | |
650 | 4 | |a Flexible cylinder | |
650 | 4 | |a Riser | |
650 | 4 | |a Marine riser | |
650 | 4 | |a Catenary riser | |
650 | 4 | |a SCR | |
650 | 4 | |a Optimization | |
650 | 4 | |a Learning | |
650 | 4 | |a Parametric hydrodynamic coefficient database | |
700 | 1 | |a del Águila Ferrandis, José |e verfasserin |4 aut | |
700 | 1 | |a Rudy, Samuel |e verfasserin |4 aut | |
700 | 1 | |a Sapsis, Themistoklis |e verfasserin |0 (orcid)0000-0003-0302-0691 |4 aut | |
700 | 1 | |a Triantafyllou, Michael S. |e verfasserin |4 aut | |
700 | 1 | |a Fan, Dixia |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Ocean engineering |d Amsterdam [u.a.] : Elsevier Science, 1970 |g 266 |h Online-Ressource |w (DE-627)30658977X |w (DE-600)1498543-3 |w (DE-576)259484164 |x 0029-8018 |7 nnns |
773 | 1 | 8 | |g volume:266 |
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allfields |
10.1016/j.oceaneng.2022.112833 doi (DE-627)ELV008884102 (ELSEVIER)S0029-8018(22)02116-3 DE-627 ger DE-627 rda eng 690 DE-600 50.92 bkl Mentzelopoulos, Andreas P. verfasserin (orcid)0000-0002-0543-4807 aut Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. VIV Vortex induced vibrations Flexible body VIV Flexible cylinder Riser Marine riser Catenary riser SCR Optimization Learning Parametric hydrodynamic coefficient database del Águila Ferrandis, José verfasserin aut Rudy, Samuel verfasserin aut Sapsis, Themistoklis verfasserin (orcid)0000-0003-0302-0691 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 266 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.92 Meerestechnik AR 266 |
spelling |
10.1016/j.oceaneng.2022.112833 doi (DE-627)ELV008884102 (ELSEVIER)S0029-8018(22)02116-3 DE-627 ger DE-627 rda eng 690 DE-600 50.92 bkl Mentzelopoulos, Andreas P. verfasserin (orcid)0000-0002-0543-4807 aut Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. VIV Vortex induced vibrations Flexible body VIV Flexible cylinder Riser Marine riser Catenary riser SCR Optimization Learning Parametric hydrodynamic coefficient database del Águila Ferrandis, José verfasserin aut Rudy, Samuel verfasserin aut Sapsis, Themistoklis verfasserin (orcid)0000-0003-0302-0691 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 266 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.92 Meerestechnik AR 266 |
allfields_unstemmed |
10.1016/j.oceaneng.2022.112833 doi (DE-627)ELV008884102 (ELSEVIER)S0029-8018(22)02116-3 DE-627 ger DE-627 rda eng 690 DE-600 50.92 bkl Mentzelopoulos, Andreas P. verfasserin (orcid)0000-0002-0543-4807 aut Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. VIV Vortex induced vibrations Flexible body VIV Flexible cylinder Riser Marine riser Catenary riser SCR Optimization Learning Parametric hydrodynamic coefficient database del Águila Ferrandis, José verfasserin aut Rudy, Samuel verfasserin aut Sapsis, Themistoklis verfasserin (orcid)0000-0003-0302-0691 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 266 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.92 Meerestechnik AR 266 |
allfieldsGer |
10.1016/j.oceaneng.2022.112833 doi (DE-627)ELV008884102 (ELSEVIER)S0029-8018(22)02116-3 DE-627 ger DE-627 rda eng 690 DE-600 50.92 bkl Mentzelopoulos, Andreas P. verfasserin (orcid)0000-0002-0543-4807 aut Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. VIV Vortex induced vibrations Flexible body VIV Flexible cylinder Riser Marine riser Catenary riser SCR Optimization Learning Parametric hydrodynamic coefficient database del Águila Ferrandis, José verfasserin aut Rudy, Samuel verfasserin aut Sapsis, Themistoklis verfasserin (orcid)0000-0003-0302-0691 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 266 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.92 Meerestechnik AR 266 |
allfieldsSound |
10.1016/j.oceaneng.2022.112833 doi (DE-627)ELV008884102 (ELSEVIER)S0029-8018(22)02116-3 DE-627 ger DE-627 rda eng 690 DE-600 50.92 bkl Mentzelopoulos, Andreas P. verfasserin (orcid)0000-0002-0543-4807 aut Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. VIV Vortex induced vibrations Flexible body VIV Flexible cylinder Riser Marine riser Catenary riser SCR Optimization Learning Parametric hydrodynamic coefficient database del Águila Ferrandis, José verfasserin aut Rudy, Samuel verfasserin aut Sapsis, Themistoklis verfasserin (orcid)0000-0003-0302-0691 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 266 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:266 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.92 Meerestechnik AR 266 |
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Mentzelopoulos, Andreas P. @@aut@@ del Águila Ferrandis, José @@aut@@ Rudy, Samuel @@aut@@ Sapsis, Themistoklis @@aut@@ Triantafyllou, Michael S. @@aut@@ Fan, Dixia @@aut@@ |
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Mentzelopoulos, Andreas P. |
spellingShingle |
Mentzelopoulos, Andreas P. ddc 690 bkl 50.92 misc VIV misc Vortex induced vibrations misc Flexible body VIV misc Flexible cylinder misc Riser misc Marine riser misc Catenary riser misc SCR misc Optimization misc Learning misc Parametric hydrodynamic coefficient database Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors |
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Mentzelopoulos, Andreas P. |
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690 DE-600 50.92 bkl Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors VIV Vortex induced vibrations Flexible body VIV Flexible cylinder Riser Marine riser Catenary riser SCR Optimization Learning Parametric hydrodynamic coefficient database |
topic |
ddc 690 bkl 50.92 misc VIV misc Vortex induced vibrations misc Flexible body VIV misc Flexible cylinder misc Riser misc Marine riser misc Catenary riser misc SCR misc Optimization misc Learning misc Parametric hydrodynamic coefficient database |
topic_unstemmed |
ddc 690 bkl 50.92 misc VIV misc Vortex induced vibrations misc Flexible body VIV misc Flexible cylinder misc Riser misc Marine riser misc Catenary riser misc SCR misc Optimization misc Learning misc Parametric hydrodynamic coefficient database |
topic_browse |
ddc 690 bkl 50.92 misc VIV misc Vortex induced vibrations misc Flexible body VIV misc Flexible cylinder misc Riser misc Marine riser misc Catenary riser misc SCR misc Optimization misc Learning misc Parametric hydrodynamic coefficient database |
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title |
Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors |
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(DE-627)ELV008884102 (ELSEVIER)S0029-8018(22)02116-3 |
title_full |
Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors |
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Mentzelopoulos, Andreas P. |
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Ocean engineering |
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2022 |
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Mentzelopoulos, Andreas P. del Águila Ferrandis, José Rudy, Samuel Sapsis, Themistoklis Triantafyllou, Michael S. Fan, Dixia |
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266 |
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690 DE-600 50.92 bkl |
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Elektronische Aufsätze |
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Mentzelopoulos, Andreas P. |
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10.1016/j.oceaneng.2022.112833 |
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title_sort |
data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors |
title_auth |
Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors |
abstract |
Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. |
abstractGer |
Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. |
abstract_unstemmed |
Semi-empirical models are currently the state-of-the-art industry tool for flexible cylinder vortex induced vibrations (VIV) fatigue prediction. Accurate prediction of the structural response relies entirely on the accuracy of the acquired hydrodynamic coefficient database. The construction of systematic hydrodynamic coefficient databases from rigid cylinder forced vibration experiments can be time-consuming for simple cases and intractable for multi-parametric cases. An alternative approach has been implemented in this work to improve the flexible cylinder VIV prediction by machine-learning optimal parametric hydrodynamic databases using physical experimental measurements. The methodology is applied to a straight riser in uniform flow and extended to non-straight riser configurations and non-uniform incoming flow profiles. Moreover, database inference is extended to using direct sparse sensor measurements along the structure. Specifically, a 19-dimensional parametric hydrodynamic coefficient database is obtained for: (i) straight riser in uniform flow (using sparse strain measurements), (ii) straight riser in sheared flow, and (iii) catenary riser in uniform flow with a 60 deg incidence angle between the catenary plane and the incoming flow stream. The predicted amplitude and frequency responses, using the extracted databases, are compared with observed experimental results. |
collection_details |
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title_short |
Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors |
remote_bool |
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author2 |
del Águila Ferrandis, José Rudy, Samuel Sapsis, Themistoklis Triantafyllou, Michael S. Fan, Dixia |
author2Str |
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doi_str |
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up_date |
2024-07-06T21:14:13.770Z |
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|
score |
7.397687 |