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...
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Autor*in:	Mentzelopoulos, Andreas P. [verfasserIn] del Águila Ferrandis, José [verfasserIn] Rudy, Samuel [verfasserIn] Sapsis, Themistoklis [verfasserIn] Triantafyllou, Michael S. [verfasserIn] Fan, Dixia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	VIV Vortex induced vibrations Flexible body VIV Flexible cylinder Riser Marine riser Catenary riser SCR Optimization Learning Parametric hydrodynamic coefficient database

Übergeordnetes Werk:	Enthalten in: Ocean engineering - Amsterdam [u.a.] : Elsevier Science, 1970, 266
Übergeordnetes Werk:	volume:266

DOI / URN:	10.1016/j.oceaneng.2022.112833

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
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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
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912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
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912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
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912			\|a GBV_ILN_4313
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912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 50.92 \|j Meerestechnik
951			\|a AR
952			\|d 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
language	English
source	Enthalten in Ocean engineering 266 volume:266
sourceStr	Enthalten in Ocean engineering 266 volume:266
format_phy_str_mv	Article
bklname	Meerestechnik
institution	findex.gbv.de
topic_facet	VIV Vortex induced vibrations Flexible body VIV Flexible cylinder Riser Marine riser Catenary riser SCR Optimization Learning Parametric hydrodynamic coefficient database
dewey-raw	690
isfreeaccess_bool	false
container_title	Ocean engineering
authorswithroles_txt_mv	Mentzelopoulos, Andreas P. @@aut@@ del Águila Ferrandis, José @@aut@@ Rudy, Samuel @@aut@@ Sapsis, Themistoklis @@aut@@ Triantafyllou, Michael S. @@aut@@ Fan, Dixia @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	30658977X
dewey-sort	3690
id	ELV008884102
language_de	englisch
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author	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
authorStr	Mentzelopoulos, Andreas P.
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topic_title	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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title_full	Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors
author_sort	Mentzelopoulos, Andreas P.
journal	Ocean engineering
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author_browse	Mentzelopoulos, Andreas P. del Águila Ferrandis, José Rudy, Samuel Sapsis, Themistoklis Triantafyllou, Michael S. Fan, Dixia
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author-letter	Mentzelopoulos, Andreas P.
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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.
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title_short	Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors
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author2	del Águila Ferrandis, José Rudy, Samuel Sapsis, Themistoklis Triantafyllou, Michael S. Fan, Dixia
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up_date	2024-07-06T21:14:13.770Z
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Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors

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