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Hierarchical Approaches to Train Recurrent Neural Networks for Wave-Body Interaction Problems
RISE Research Institutes of Sweden, Safety and Transport, Maritime department.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: The Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023, Vol. 33, article id 307Conference paper, Published paper (Refereed)
Abstract [en]

We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modelling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from a LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduce errors that propagate through the hierarchical modelling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.

Place, publisher, year, edition, pages
2023. Vol. 33, article id 307
Keywords [en]
Wave-body interaction; hierarchical modelling; linear potential flow; hybrid modeling; machine learning; recurrent neural net- work.
National Category
Marine Engineering
Identifiers
URN: urn:nbn:se:ri:diva-72110OAI: oai:DiVA.org:ri-72110DiVA, id: diva2:1842076
Conference
The 33rd International Ocean and Polar Engineering Conference
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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