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A hybrid linear potential flow - machine learning model for enhanced prediction of WEC performance
Dep. of the Built Environment, Aalborg University.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: Proceedings of the 15th European Wave and Tidal Energy Conference, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Linear potential flow (LPF) models remain the tools-of-the trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (vis- cous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF-model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.

Place, publisher, year, edition, pages
2023.
Keywords [en]
Linear potential flow, machine learning, recurrent neural network, floating bodies, wave energy
National Category
Marine Engineering
Identifiers
URN: urn:nbn:se:ri:diva-72107DOI: 10.36688/ewtec-2023-321OAI: oai:DiVA.org:ri-72107DiVA, id: diva2:1842073
Conference
The 15th European Wave and Tidal Energy 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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