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

Eirini Katsidoniotaki, Foivos Psarommatis and Malin Göteman
Additional contact information
Eirini Katsidoniotaki: Renewable Energy Unit, RISE—Research Institutes of Sweden, P.O. Box 857, SE-501 15 Boras, 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

Energies, 2022, vol. 15, issue 15, 1-24

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.

Keywords: digital twin; wave energy; extreme loads; CFD; survivability; design (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/15/5464/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/15/5464/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:15:p:5464-:d:873960

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().