Developing the Next Generation of Environmentally-Friendly Floating Wind Farms with Innovative Technologies and Sustainable Solutions

Informations projet

FLOATFARM

N° de convention de subvention: 101136091

DOI

10.3030/101136091

Date de signature de la CE 17 Novembre 2023

Date de début 1 Janvier 2024

Date de fin 31 Decembre 2027

Financé au titre de

• Climate, Energy and Mobility

Coût total

€ 5 997 630,00

Contribution de l’UE

€ 5 997 630,00

Coordonné par

TECHNISCHE UNIVERSITAT BERLIN
Germany

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (2)

Novel generator design and performance

The preliminary design of an innovative lightweight generator design that leverages Hagnesia’s patented technology will be carried out. The design will be optimized based on the requirements of the project. The characteristics of the direct-drive generator will be described.

Validated wake model for QBlade capable of modeling turbine interactions

In this task, a new wake model will be developed to increase the fidelity of the wake model within QBlade without sacrificing computational efficiency. TUB, with the help of DTU, will develop this new wake model, which will be released as an open-source flow solver (M12). This solver will be coupled to QBlade in order to allow fast simulation of active wake excitation methods. The wake model will be validated against unsteady wake measurements using data gathered in wind tunnel experiments at the TU Delft.

Other (2)

An Interface of HAWC2 to the Framework for Floating Offshore Turbine Design Optimization WEIS

The source code of the interface between HAWC2 and WEIS will be released on GitHub (or similar) together with a hands-on examples on its application and inital demonstration cases.

An Interface of QBlade to the Framework for Floating Offshore Turbine Design Optimization WEIS

The source code of the interface between QBlade and WEIS will be released on GitHub (or similar) together with a hands-on examples on its application and inital demonstration cases.

Publications

Peer reviewed articles (1)

A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data

Auteurs: Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli
Publié dans: Journal of Marine Science and Engineering, Numéro 12, 2024, ISSN 2077-1312
Éditeur: MDPI AG
DOI: 10.3390/JMSE12091609