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Title [sv]
Förbättrad överlevnadsgrad och tillförlitlighet för mekaniska vågkraftsdelsystem
Title [en]
Improved reliability and survivability of mechanical wave energy subsystems
Abstract [sv]
Projektet syftar till att öka överlevnadsförmågan för vågkraftverk och därmed minska behovet av dyra experiment och kostnader för underhåll och reparationer till havs. Det ska göras genom att mäta och beräkna vågkrafter på mekaniska vågkraftskomponenter och analysera hur dessa påverkar systemens överlevnadsförmåga. Osäkerheter kommer att kvantifieras och risken för haverier i olika vågförhållanden och för olika komponent- och designval fastställs. CFD- modelleringsprogrammet som används kommer att utökas med inställningar inom projektet och verifieras.
Abstract [en]
The gathered experience of several decades of wave energy research and development indicates that the largest challenge is how to guarantee the survivability of the wave energy converters in offshore conditions. The project will measure and compute wave loads on mechanical wave energy components and analyze the impact on the survivability of the systems. Uncertainties will be quantified and the risk for failures in different wave conditions and for different choices of components and design determined. With reliable data and models for survivability, the need for expensive experimental test schemes is reduced and the offshore maintenance and repair costs minimized. The project group consists of eminent researchers from five leading Swedish and international research institutions, and a reference group of key companies warrants that existing knowledge in the sector is integrated in the project and that the results are communicated and can be utilized directly by the stakeholders.
Publications (1 of 1) Show all publications
Shahroozi, Z., Göteman, M. & Engström, J. (2023). A Neural Network Approach To Minimize Line Forces In The Survivability Of The Point-Absorber Wave Energy Converters. In: Proceedings of ASME 2023 42nd International Conference on Ocean, Offshore & Arctic Engineering (OMAE2023): . Paper presented at International Conference on Ocean, Offshore & Arctic Engineering (OMAE), 11-16 June, 2023, Melbourne, Australia. ASME Press, 8, Article ID OMAE2023-102422.
Open this publication in new window or tab >>A Neural Network Approach To Minimize Line Forces In The Survivability Of The Point-Absorber Wave Energy Converters
2023 (English)In: Proceedings of ASME 2023 42nd International Conference on Ocean, Offshore & Arctic Engineering (OMAE2023), ASME Press, 2023, Vol. 8, article id OMAE2023-102422Conference paper, Published paper (Refereed)
Abstract [en]

One strategy for the survivability of wave energy converters(WECs) is to minimize the extreme forces on the structure by adjusting the system damping. In this paper, a neural network model is developed to predict the peak line force for a 1:30 scaled point-absorber WEC with a linear friction-damping power take-off (PTO). The algorithm trains over the wave tank experimental data and enables an update of the system damping based on the system state (i.e. position, velocity, and acceleration) and information on the incoming waves for the extreme sea states. The results show that the deep neural network (DNN) developed here is relatively fast and able to predict the peak line forces with a correlation of 88% when compared to the true (experimental)data. Then, the optimum damping for survivability purposes is found by minimizing the peak line force. It is shown that the optimum damping varies depending on the system state in each zero up-crossing episode.

Place, publisher, year, edition, pages
ASME Press, 2023
National Category
Control Engineering Marine Engineering Ocean and River Engineering
Identifiers
urn:nbn:se:uu:diva-506611 (URN)10.1115/OMAE2023-102422 (DOI)001216330300065 ()978-0-7918-8690-8 (ISBN)
Conference
International Conference on Ocean, Offshore & Arctic Engineering (OMAE), 11-16 June, 2023, Melbourne, Australia
Funder
Swedish Energy Agency, 47264-1Swedish Research Council, 2020-03634StandUpÅForsk (Ångpanneföreningen's Foundation for Research and Development)
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2024-06-12Bibliographically approved
Principal InvestigatorGöteman, Malin
Coordinating organisation
Uppsala University
Funder
Period
2019-04-01 - 2022-03-31
National Category
Energy Engineering
Identifiers
DiVA, id: project:7039Project, id: P47264-1_Energi

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