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Validation of a CFD model for wave energy system dynamics in extreme waves
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity. Centre of Natural Hazards and Disaster Science (CNDS).ORCID iD: 0000-0001-5096-3559
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity.ORCID iD: 0000-0002-1165-5569
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2023 (English)In: Ocean Engineering, ISSN 0029-8018, E-ISSN 1873-5258, Vol. 268, article id 113320Article in journal (Refereed) Published
Abstract [en]

The design of wave energy converters should rely on numerical models that are able to estimate accurately the dynamics and loads in extreme wave conditions. A high-fidelity CFD model of a 1:30 scale point-absorber is developed and validated on experimental data. This work constitutes beyond the state-of-the-art validation study as the system is subjected to 50-year return period waves. Additionally, a new methodology that addresses the well-known challenge in CFD codes of mesh deformation is successfully applied and validated. The CFD model is evaluated in different conditions: wave-only, free decay, and wave–structure interaction. The results show that the extreme waves and the experimental setup of the wave energy converter are simulated within an accuracy of 2%. The developed high-fidelity model is able to capture the motion of the system and the force in the mooring line under extreme waves with satisfactory accuracy. The deviation between the numerical and corresponding experimental RAOs is lower than 7% for waves with smaller steepness. In higher waves, the deviation increases up to 10% due to the inevitable wave reflections and complex dynamics. The pitch motion presents a larger deviation, however, the pitch is of secondary importance for a point-absorber wave energy converter.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 268, article id 113320
Keywords [en]
Extreme waves, CFD, OpenFOAM, Validation model, Wave energy, Point-absorber
National Category
Energy Engineering Marine Engineering
Identifiers
URN: urn:nbn:se:uu:diva-491092DOI: 10.1016/j.oceaneng.2022.113320ISI: 000905510100001OAI: oai:DiVA.org:uu-491092DiVA, id: diva2:1720100
Funder
Swedish Research Council, 2015-04657Swedish Energy Agency, 47264-1Swedish National Infrastructure for Computing (SNIC)Available from: 2022-12-16 Created: 2022-12-16 Last updated: 2024-04-04Bibliographically approved
In thesis
1. Offshore renewable energy systems: Quantification of extreme loads using computational methods
Open this publication in new window or tab >>Offshore renewable energy systems: Quantification of extreme loads using computational methods
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This Ph.D. thesis investigates the dynamic response of offshore energy systems in extreme waves. The use of offshore energy technologies, such as wave energy systems and offshore wind turbines, is crucial for transitioning to clean energy and mitigating the effects of climate change. However, to design reliable systems, it is important to understand their behavior in harsh environmental conditions.The first part of the thesis focuses on classical Computational Fluid Dynamics (CFD) simulations for modeling the response of structures in extreme waves. Breaking waves are numerically reproduced and the corresponding slamming loads are estimated, as well as the maximal forces on critical components such as the mooring system. The thesis addresses the challenge of computational mesh deformation, which can lead to numerical instability and failure in simulating extreme structural responses. Dynamic mesh techniques are implemented to overcome the limitations of classical techniques. Additionally, the thesis explores alternative approaches to representing a sea state, such as equivalent regular waves and focused waves, to reduce the computational cost of full sea state simulations. A mid-fidelity numerical model is also employed, with its accuracy verified against a high-fidelity solution.

The second part of the thesis advances the use of probabilistic machine learning to develop a surrogate model for the mapping between extreme waves and the corresponding forces on the structure. A Bayesian active learning method is employed to train the model with high prediction accuracy, especially in extreme events. The surrogate model is many orders of magnitude faster than classical modeling methods and enables efficient statistical quantification of the quantities of interest, such as loads in critical system components.Overall, this thesis provides a comprehensive examination of advanced computational methods for estimating the dynamic response of offshore energy systems in extreme waves and enables reliable and cost-effective design through the use of fast and accurate surrogate models.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2023. p. 125
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2233
Keywords
offshore systems, wave energy, CFD, extreme events, machine learning, Bayesian experimental design, active learning, surrogate modeling
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:uu:diva-492822 (URN)978-91-513-1701-4 (ISBN)
Public defence
2023-03-17, Häggsalen, Ångströmlaboratoriet. Lägerhyddsvägen 2, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2023-02-23 Created: 2023-01-28 Last updated: 2023-02-23

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Katsidoniotaki, EiriniShahroozi, ZahraEngström, JensGöteman, Malin

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