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dc.contributor.authorZhang, J
dc.contributor.authorZhao, X
dc.contributor.authorGreaves, Deborah
dc.contributor.authorJin, S
dc.date.accessioned2023-04-25T08:24:18Z
dc.date.available2023-04-25T08:24:18Z
dc.date.issued2023-07-01
dc.identifier.issn0306-2619
dc.identifier.issn1872-9118
dc.identifier.other121072
dc.identifier.urihttps://pearl.plymouth.ac.uk/handle/10026.1/20761
dc.description.abstract

Model identification for a hinged-raft wave energy converter (WEC) is investigated in this paper, based on wave tank experiments and deep operator learning. Different from previous works which all formulated this issue as a function approximation task, this work, for the first time, formulates it as an operator approximation task (which learns the mapping from a function space to another function space). As such, a continuous-time WEC model is identified from data, greatly expanding the horizon of data-based WEC modeling because previous works were limited to discrete-time model identification. The error accumulation for multi-step predictions in the discrete-time formulation is thus also addressed. The model is developed by first carrying out a set of wave tank experiments to generate the training data, and then the deep operator learning model, i.e. the DeepONet, is constructed and trained based on the experimental data. The validation study shows that the model captures the WEC dynamics accurately. A new set of experimental runs are further carried out and the results show that after training, the model can be used as a digital wave tank, an alternative to the expensive numerical and physical wave tanks, for accurate and real-time simulations of the WEC dynamics.

dc.format.extent121072-121072
dc.languageen
dc.publisherElsevier BV
dc.subjectData-based modeling
dc.subjectDeep learning
dc.subjectDeepONet
dc.subjectWave energy converter
dc.subjectWave tank experiment
dc.titleModeling of a hinged-raft wave energy converter via deep operator learning and wave tank experiments
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000982412200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume341
plymouth.publication-statusPublished
plymouth.journalApplied Energy
dc.identifier.doi10.1016/j.apenergy.2023.121072
plymouth.organisational-group|Plymouth
plymouth.organisational-group|Plymouth|Research Groups
plymouth.organisational-group|Plymouth|PRIMaRE Publications
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering|School of Engineering, Computing and Mathematics
plymouth.organisational-group|Plymouth|Research Groups|Marine Institute
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA
plymouth.organisational-group|Plymouth|Users by role
plymouth.organisational-group|Plymouth|Users by role|Academics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA|UoA12 Engineering
plymouth.organisational-group|Plymouth|Users by role|Researchers in ResearchFish submission
plymouth.organisational-group|Plymouth|Research Groups|COAST Engineering Research Group
dcterms.dateAccepted2023-04-01
dc.date.updated2023-04-25T08:24:08Z
dc.rights.embargodate2023-4-26
dc.identifier.eissn1872-9118
dc.rights.embargoperiodforever
rioxxterms.versionofrecord10.1016/j.apenergy.2023.121072


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