CONTEXT

Within the context of floating wind development, there will be at least 10 operational floating wind turbines in French waters by 2023. They will provide a valuable learning center to achieve operational excellence and design optimisation in floating wind sector which is crucial to drive the levelised cost of energy down. Being able to im-plement lessons learnt from field experiments into their own digital twin system would allow industry to be fully prepared when farm deployments come along.

OBJECTIVE

To develop and test a fatigue monitoring system for floater and mooring lines of a floating wind turbine

SCIENTIFIC AND TECHNICAL CONTENT

• Review of the structural health methodology usefull for fatigue life of floater parts
• At sea testing of functionalities thanks to the deployment of sensors on a real offshore wind turbine
• Assimilation of data from field observations in the floating wind turbine digital twin with a machine learning methodology to improve integration between sensors (wave, wind, motions, structural health monitoring) and numerical models of floating wind turbine
• Web platform developmment
• Construction of the digital twin: sensors ca-libration and deployment, numerical tool development, default detection, analysis of outputs, meta-model learning assessment, multi-level analysis

RESOURCES

DIONYSOS project sheet (PDF)

PARTNERS AND FUNDING

This project is led by France Energies Marines.

The total budhet of this project is €1,302K.

This project receives funding from France Energies Marines and its members and partners, as well as French State funding managed by the French National Research Agency under the France 2030 investment plan. This project is financially supported by Pôle Mer Bretagne Atlantique.

Photo credit: Yohann Boutin