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Estimation of nonlinear forces acting on floating bodies using machine learning
RISE Research Institutes of Sweden, Safety and Transport, Maritime department. Aalborg University, Denmark.ORCID iD: 0000-0001-6934-634x
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0003-3272-4145
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0001-8577-6745
Sigma Energy & Marine, Sweden.
2023 (English)In: Advances in the Analysis and Design of Marine Structures / [ed] J. W. Ringsberg, C. Guedes Soares, CRC Press, 2023, p. 63-72Chapter in book (Other academic)
Abstract [en]

Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using e.g. Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modelling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactory predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.

Place, publisher, year, edition, pages
CRC Press, 2023. p. 63-72
National Category
Marine Engineering
Identifiers
URN: urn:nbn:se:ri:diva-72114DOI: 10.1201/9781003399759ISBN: 9781003399759 (electronic)OAI: oai:DiVA.org:ri-72114DiVA, id: diva2:1842078
Funder
Swedish Energy Agency, 50196-1Available from: 2024-03-02 Created: 2024-03-02 Last updated: 2024-03-08Bibliographically approved

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Eskilsson, ClaesPashami, SepidehHolst, Anders

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CiteExportLink to record
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  • apa
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  • nn-NB
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Output format
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  • asciidoc
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