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dc.contributor.authorTaghavi, Mahmood
dc.contributor.authorPerera, Lokukaluge Prasad Channa
dc.date.accessioned2024-02-26T12:47:57Z
dc.date.available2024-02-26T12:47:57Z
dc.date.issued2024-02-24
dc.description.abstractDue to the growing rate of energy consumption, it is necessary to develop frameworks for enhancing ship energy efficiency. This paper proposes a solution for this issue by introducing a digital twin framework for quantifying ship performance. For this purpose, extensive low-level clustering is performed using Gaussian Mixture Models (GMM) with the Expectation Maximization algorithm on a dataset of a selected vessel to detect the vessel’s most frequent operating regions. Then, a regression analysis is performed in each operating region, to identify their shapes using Singular Value Decomposition (SVD). The results of SVD make the basis for model development in digital twin applications. For this reason, a low-level clustering is performed so that a more accurate model can be developed in future. Moreover, based on the resulting cluster analysis, an energy efficiency index is devel oped, and the energy efficiency of each cluster has been evaluated to identify the most efficient operating condition. Hence, the main contribution of this research is to develop a digital twin framework of a marine engine which can be utilized for green ship operations. The same contribution can facilitate the shipping industry to meet the International Maritime Organization energy efficiency requirements.en_US
dc.identifier.citationTaghavi, Perera. Advanced data cluster analyses in digital twin development for marine engines towards ship performance quantification. Ocean Engineering. 2024en_US
dc.identifier.cristinIDFRIDAID 2249475
dc.identifier.doi10.1016/j.oceaneng.2024.117098
dc.identifier.issn0029-8018
dc.identifier.issn1873-5258
dc.identifier.urihttps://hdl.handle.net/10037/33040
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalOcean Engineering
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/857840/EU/Next generation short-sea ship dual-fuel engine and propulsion retrofit technologies/SeaTech/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleAdvanced data cluster analyses in digital twin development for marine engines towards ship performance quantificationen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)