Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method

This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for ne...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xu, Lihua [verfasserIn] Wang, Jiasong [verfasserIn] Triantafyllou, Michael S. [verfasserIn] Fan, Dixia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Vortex-induced vibrations POD-DeepONet Data assimilation Multi-fidelity prediction

Übergeordnetes Werk:	Enthalten in: Marine structures - Amsterdam [u.a.] : Elsevier Science, 1988, 93
Übergeordnetes Werk:	volume:93

DOI / URN:	10.1016/j.marstruc.2023.103539

Katalog-ID:	ELV065991230

Internformat


LEADER	01000naa a22002652 4500
001	ELV065991230
003	DE-627
005	20231205093003.0
007	cr uuu---uuuuu
008	231205s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.marstruc.2023.103539 \|2 doi
035			\|a (DE-627)ELV065991230
035			\|a (ELSEVIER)S0951-8339(23)00172-7
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 380 \|q VZ
084			\|a 50.92 \|2 bkl
084			\|a 56.30 \|2 bkl
100	1		\|a Xu, Lihua \|e verfasserin \|4 aut
245	1	0	\|a Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method
264		1	\|c 2023
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering.
650		4	\|a Vortex-induced vibrations
650		4	\|a POD-DeepONet
650		4	\|a Data assimilation
650		4	\|a Multi-fidelity prediction
700	1		\|a Wang, Jiasong \|e verfasserin \|0 (orcid)0000-0001-7854-7821 \|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 Marine structures \|d Amsterdam [u.a.] : Elsevier Science, 1988 \|g 93 \|h Online-Ressource \|w (DE-627)308449363 \|w (DE-600)1502454-4 \|w (DE-576)259484296 \|7 nnns
773	1	8	\|g volume:93
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
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
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
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_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
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_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 50.92 \|j Meerestechnik \|q VZ
936	b	k	\|a 56.30 \|j Wasserbau \|q VZ
951			\|a AR
952			\|d 93

Indexfelder

author_variant	l x lx j w jw m s t ms mst d f df
matchkey_str	xulihuawangjiasongtriantafylloumichaelsf:2023----:rdcinomliclvreidcdirtosaeoautfdlt
hierarchy_sort_str	2023
bklnumber	50.92 56.30
publishDate	2023
allfields	10.1016/j.marstruc.2023.103539 doi (DE-627)ELV065991230 (ELSEVIER)S0951-8339(23)00172-7 DE-627 ger DE-627 rda eng 380 VZ 50.92 bkl 56.30 bkl Xu, Lihua verfasserin aut Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering. Vortex-induced vibrations POD-DeepONet Data assimilation Multi-fidelity prediction Wang, Jiasong verfasserin (orcid)0000-0001-7854-7821 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Marine structures Amsterdam [u.a.] : Elsevier Science, 1988 93 Online-Ressource (DE-627)308449363 (DE-600)1502454-4 (DE-576)259484296 nnns volume:93 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ 56.30 Wasserbau VZ AR 93
spelling	10.1016/j.marstruc.2023.103539 doi (DE-627)ELV065991230 (ELSEVIER)S0951-8339(23)00172-7 DE-627 ger DE-627 rda eng 380 VZ 50.92 bkl 56.30 bkl Xu, Lihua verfasserin aut Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering. Vortex-induced vibrations POD-DeepONet Data assimilation Multi-fidelity prediction Wang, Jiasong verfasserin (orcid)0000-0001-7854-7821 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Marine structures Amsterdam [u.a.] : Elsevier Science, 1988 93 Online-Ressource (DE-627)308449363 (DE-600)1502454-4 (DE-576)259484296 nnns volume:93 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ 56.30 Wasserbau VZ AR 93
allfields_unstemmed	10.1016/j.marstruc.2023.103539 doi (DE-627)ELV065991230 (ELSEVIER)S0951-8339(23)00172-7 DE-627 ger DE-627 rda eng 380 VZ 50.92 bkl 56.30 bkl Xu, Lihua verfasserin aut Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering. Vortex-induced vibrations POD-DeepONet Data assimilation Multi-fidelity prediction Wang, Jiasong verfasserin (orcid)0000-0001-7854-7821 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Marine structures Amsterdam [u.a.] : Elsevier Science, 1988 93 Online-Ressource (DE-627)308449363 (DE-600)1502454-4 (DE-576)259484296 nnns volume:93 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ 56.30 Wasserbau VZ AR 93
allfieldsGer	10.1016/j.marstruc.2023.103539 doi (DE-627)ELV065991230 (ELSEVIER)S0951-8339(23)00172-7 DE-627 ger DE-627 rda eng 380 VZ 50.92 bkl 56.30 bkl Xu, Lihua verfasserin aut Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering. Vortex-induced vibrations POD-DeepONet Data assimilation Multi-fidelity prediction Wang, Jiasong verfasserin (orcid)0000-0001-7854-7821 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Marine structures Amsterdam [u.a.] : Elsevier Science, 1988 93 Online-Ressource (DE-627)308449363 (DE-600)1502454-4 (DE-576)259484296 nnns volume:93 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ 56.30 Wasserbau VZ AR 93
allfieldsSound	10.1016/j.marstruc.2023.103539 doi (DE-627)ELV065991230 (ELSEVIER)S0951-8339(23)00172-7 DE-627 ger DE-627 rda eng 380 VZ 50.92 bkl 56.30 bkl Xu, Lihua verfasserin aut Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering. Vortex-induced vibrations POD-DeepONet Data assimilation Multi-fidelity prediction Wang, Jiasong verfasserin (orcid)0000-0001-7854-7821 aut Triantafyllou, Michael S. verfasserin aut Fan, Dixia verfasserin aut Enthalten in Marine structures Amsterdam [u.a.] : Elsevier Science, 1988 93 Online-Ressource (DE-627)308449363 (DE-600)1502454-4 (DE-576)259484296 nnns volume:93 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ 56.30 Wasserbau VZ AR 93
language	English
source	Enthalten in Marine structures 93 volume:93
sourceStr	Enthalten in Marine structures 93 volume:93
format_phy_str_mv	Article
bklname	Meerestechnik Wasserbau
institution	findex.gbv.de
topic_facet	Vortex-induced vibrations POD-DeepONet Data assimilation Multi-fidelity prediction
dewey-raw	380
isfreeaccess_bool	false
container_title	Marine structures
authorswithroles_txt_mv	Xu, Lihua @@aut@@ Wang, Jiasong @@aut@@ Triantafyllou, Michael S. @@aut@@ Fan, Dixia @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	308449363
dewey-sort	3380
id	ELV065991230
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">ELV065991230</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205093003.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231205s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.marstruc.2023.103539</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV065991230</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0951-8339(23)00172-7</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">380</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.92</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">56.30</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xu, Lihua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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="520" ind1=" " ind2=" "><subfield code="a">This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vortex-induced vibrations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">POD-DeepONet</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data assimilation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-fidelity prediction</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jiasong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-7854-7821</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Triantafyllou, Michael S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fan, Dixia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Marine structures</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1988</subfield><subfield code="g">93</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)308449363</subfield><subfield code="w">(DE-600)1502454-4</subfield><subfield code="w">(DE-576)259484296</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:93</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.92</subfield><subfield code="j">Meerestechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">56.30</subfield><subfield code="j">Wasserbau</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">93</subfield></datafield></record></collection>
author	Xu, Lihua
spellingShingle	Xu, Lihua ddc 380 bkl 50.92 bkl 56.30 misc Vortex-induced vibrations misc POD-DeepONet misc Data assimilation misc Multi-fidelity prediction Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method
authorStr	Xu, Lihua
ppnlink_with_tag_str_mv	@@773@@(DE-627)308449363
format	electronic Article
dewey-ones	380 - Commerce, communications & transportation
delete_txt_mv	keep
author_role	aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
topic_title	380 VZ 50.92 bkl 56.30 bkl Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method Vortex-induced vibrations POD-DeepONet Data assimilation Multi-fidelity prediction
topic	ddc 380 bkl 50.92 bkl 56.30 misc Vortex-induced vibrations misc POD-DeepONet misc Data assimilation misc Multi-fidelity prediction
topic_unstemmed	ddc 380 bkl 50.92 bkl 56.30 misc Vortex-induced vibrations misc POD-DeepONet misc Data assimilation misc Multi-fidelity prediction
topic_browse	ddc 380 bkl 50.92 bkl 56.30 misc Vortex-induced vibrations misc POD-DeepONet misc Data assimilation misc Multi-fidelity prediction
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Marine structures
hierarchy_parent_id	308449363
dewey-tens	380 - Commerce, communications & transportation
hierarchy_top_title	Marine structures
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)308449363 (DE-600)1502454-4 (DE-576)259484296
title	Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method
ctrlnum	(DE-627)ELV065991230 (ELSEVIER)S0951-8339(23)00172-7
title_full	Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method
author_sort	Xu, Lihua
journal	Marine structures
journalStr	Marine structures
lang_code	eng
isOA_bool	false
dewey-hundreds	300 - Social sciences
recordtype	marc
publishDateSort	2023
contenttype_str_mv	zzz
author_browse	Xu, Lihua Wang, Jiasong Triantafyllou, Michael S. Fan, Dixia
container_volume	93
class	380 VZ 50.92 bkl 56.30 bkl
format_se	Elektronische Aufsätze
author-letter	Xu, Lihua
doi_str_mv	10.1016/j.marstruc.2023.103539
normlink	(ORCID)0000-0001-7854-7821
normlink_prefix_str_mv	(orcid)0000-0001-7854-7821
dewey-full	380
author2-role	verfasserin
title_sort	predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method
title_auth	Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method
abstract	This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering.
abstractGer	This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering.
abstract_unstemmed	This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering.
collection_details	GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700
title_short	Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method
remote_bool	true
author2	Wang, Jiasong Triantafyllou, Michael S. Fan, Dixia
author2Str	Wang, Jiasong Triantafyllou, Michael S. Fan, Dixia
ppnlink	308449363
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.marstruc.2023.103539
up_date	2024-07-07T00:57:16.479Z
_version_	1803879801402949632
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">ELV065991230</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205093003.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231205s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.marstruc.2023.103539</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV065991230</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0951-8339(23)00172-7</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">380</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.92</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">56.30</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xu, Lihua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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="520" ind1=" " ind2=" "><subfield code="a">This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vortex-induced vibrations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">POD-DeepONet</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data assimilation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-fidelity prediction</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jiasong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-7854-7821</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Triantafyllou, Michael S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fan, Dixia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Marine structures</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1988</subfield><subfield code="g">93</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)308449363</subfield><subfield code="w">(DE-600)1502454-4</subfield><subfield code="w">(DE-576)259484296</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:93</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.92</subfield><subfield code="j">Meerestechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">56.30</subfield><subfield code="j">Wasserbau</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">93</subfield></datafield></record></collection>
score	7.401019

Nicht das Richtige dabei?

Schreiben Sie uns!

Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?