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...
| Autores: | , , |
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| 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 |
| Resumo: | 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. |
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