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Digital Twin for the Prediction of Extreme Loads on a Wave Energy Conversion System

Author

Listed:
  • Eirini Katsidoniotaki

    (Renewable Energy Unit, RISE—Research Institutes of Sweden, P.O. Box 857, SE-501 15 Boras, Sweden
    Centre of Natural Hazards and Disaster Science (CNDS), Villavägen 16, SE-752 36 Uppsala, Sweden)

  • Foivos Psarommatis

    (SIRIUS, Department of Informatics, University of Oslo, Gaustadalleen 23 B, 0373 Oslo, Norway)

  • Malin Göteman

    (Renewable Energy Unit, RISE—Research Institutes of Sweden, P.O. Box 857, SE-501 15 Boras, Sweden
    Centre of Natural Hazards and Disaster Science (CNDS), Villavägen 16, SE-752 36 Uppsala, Sweden)

Abstract

Wave energy is a renewable energy source with the potential to contribute to the global electricity demand, but a remaining challenge is the survivability of the wave energy converters in harsh offshore conditions. To understand the system dynamics and improve the reliability, experimental and numerical studies are usually conducted. However, these processes are costly and time-consuming. A statistical model able to provide equivalent results is a promising approach. In this study, the digital twin of the CFD solution is developed and implemented for the prediction of the force in the mooring system of a point-absorber wave energy converter during extreme wave conditions. The results show that the digital twin can predict the mooring force with 90.36% average accuracy. Moreover, the digital twin needs only a few seconds to provide the solution, while the CFD code requires up to several days. By creating a digital analog of a wave energy converter and showing that it is able to predict the load in critical components during extreme wave conditions, this work constitutes an innovative approach in the wave energy field.

Suggested Citation

  • Eirini Katsidoniotaki & Foivos Psarommatis & Malin Göteman, 2022. "Digital Twin for the Prediction of Extreme Loads on a Wave Energy Conversion System," Energies, MDPI, vol. 15(15), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5464-:d:873960
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    References listed on IDEAS

    as
    1. Josh Davidson & John V. Ringwood, 2017. "Mathematical Modelling of Mooring Systems for Wave Energy Converters—A Review," Energies, MDPI, vol. 10(5), pages 1-46, May.
    2. Simon Thomas & Mikael Eriksson & Malin Göteman & Martyn Hann & Jan Isberg & Jens Engström, 2018. "Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays," Energies, MDPI, vol. 11(11), pages 1-16, November.
    3. Christian Windt & Josh Davidson & John V. Ringwood, 2020. "Investigation of Turbulence Modeling for Point-Absorber-Type Wave Energy Converters," Energies, MDPI, vol. 14(1), pages 1-18, December.
    4. Windt, Christian & Davidson, Josh & Ransley, Edward J. & Greaves, Deborah & Jakobsen, Morten & Kramer, Morten & Ringwood, John V., 2020. "Validation of a CFD-based numerical wave tank model for the power production assessment of the wavestar ocean wave energy converter," Renewable Energy, Elsevier, vol. 146(C), pages 2499-2516.
    5. Dawid Augustyn & Martin D. Ulriksen & John D. Sørensen, 2021. "Reliability Updating of Offshore Wind Substructures by Use of Digital Twin Information," Energies, MDPI, vol. 14(18), pages 1-23, September.
    6. Foivos Psarommatis & Gökan May & Paul-Arthur Dreyfus & Dimitris Kiritsis, 2020. "Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 1-17, January.
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    Cited by:

    1. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

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