Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks

This work aims at obtaining Artificial Neural Networks (ANNs) to assess the seakeeping of ships navigating with forward speed. The targets of these ANNs are the Froude–Krylov and wave diffraction-radiation loads needed to compute the ship’s Response Amplitude Operators (RAOs). This research presents...

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Detalhes bibliográficos
Autores: Romero-Tello, Pablo, Gutiérrez-Romero, José Enrique, Serván-Camas, Borja
Tipo de documento: artigo
Data de publicação:2025
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2117/440425
Acesso em linha:https://hdl.handle.net/2117/440425
https://dx.doi.org/10.1007/s40722-025-00395-9
Access Level:Acceso aberto
Palavra-chave:Machine learning
Hydrodynamic loads prediction
Artificial neural networks
Response amplitude operator
Seakeeping
Àrees temàtiques de la UPC::Nàutica::Enginyeria naval
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spelling Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networksRomero-Tello, PabloGutiérrez-Romero, José EnriqueServán-Camas, BorjaMachine learningHydrodynamic loads predictionArtificial neural networksResponse amplitude operatorSeakeepingÀrees temàtiques de la UPC::Nàutica::Enginyeria navalThis work aims at obtaining Artificial Neural Networks (ANNs) to assess the seakeeping of ships navigating with forward speed. The targets of these ANNs are the Froude–Krylov and wave diffraction-radiation loads needed to compute the ship’s Response Amplitude Operators (RAOs). This research presents a methodology for obtaining the optimal ANN architecture, generating the ship database used for training, and data treatment to enable the prediction of the targets. The dataset is generated with a tridimensional potential code used to solve the wave diffraction-radiation problem using the Boundary Element Method (BEM) for different wave headings and a range of Froude numbers. To assess the developed tool, six assessment ships not included within the training database are used to compare the ANNs predictions against BEM results. The results show deviations of less than 3% compared to BEM for RAO curves. Moreover, RAO curves exhibit high adjustment compared with BEM results for different encounter wave frequencies. Furthermore, ANN’s computational times show a speedup of ×3750 respect to BEM computations.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was funded by the research project “Mathematical Optimization For a More Efficient, Safer and Decarbonized Maritime Transport—Ayudas Fundación BBVA a Proyectos de Investigación Científica 2021”.Peer ReviewedSpringer Nature20252025-08-0120252025-08-14journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/440425https://dx.doi.org/10.1007/s40722-025-00395-9reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4404252026-05-27T15:37:01Z
dc.title.none.fl_str_mv Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks
title Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks
spellingShingle Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks
Romero-Tello, Pablo
Machine learning
Hydrodynamic loads prediction
Artificial neural networks
Response amplitude operator
Seakeeping
Àrees temàtiques de la UPC::Nàutica::Enginyeria naval
title_short Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks
title_full Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks
title_fullStr Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks
title_full_unstemmed Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks
title_sort Predicting seakeeping of conventional monohull vessels with forward speed using artificial neural networks
dc.creator.none.fl_str_mv Romero-Tello, Pablo
Gutiérrez-Romero, José Enrique
Serván-Camas, Borja
author Romero-Tello, Pablo
author_facet Romero-Tello, Pablo
Gutiérrez-Romero, José Enrique
Serván-Camas, Borja
author_role author
author2 Gutiérrez-Romero, José Enrique
Serván-Camas, Borja
author2_role author
author
dc.subject.none.fl_str_mv Machine learning
Hydrodynamic loads prediction
Artificial neural networks
Response amplitude operator
Seakeeping
Àrees temàtiques de la UPC::Nàutica::Enginyeria naval
topic Machine learning
Hydrodynamic loads prediction
Artificial neural networks
Response amplitude operator
Seakeeping
Àrees temàtiques de la UPC::Nàutica::Enginyeria naval
description This work aims at obtaining Artificial Neural Networks (ANNs) to assess the seakeeping of ships navigating with forward speed. The targets of these ANNs are the Froude–Krylov and wave diffraction-radiation loads needed to compute the ship’s Response Amplitude Operators (RAOs). This research presents a methodology for obtaining the optimal ANN architecture, generating the ship database used for training, and data treatment to enable the prediction of the targets. The dataset is generated with a tridimensional potential code used to solve the wave diffraction-radiation problem using the Boundary Element Method (BEM) for different wave headings and a range of Froude numbers. To assess the developed tool, six assessment ships not included within the training database are used to compare the ANNs predictions against BEM results. The results show deviations of less than 3% compared to BEM for RAO curves. Moreover, RAO curves exhibit high adjustment compared with BEM results for different encounter wave frequencies. Furthermore, ANN’s computational times show a speedup of ×3750 respect to BEM computations.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-08-01
2025
2025-08-14
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/440425
https://dx.doi.org/10.1007/s40722-025-00395-9
url https://hdl.handle.net/2117/440425
https://dx.doi.org/10.1007/s40722-025-00395-9
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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