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

Informazioni relative al progetto

FLOATFARM

ID dell’accordo di sovvenzione: 101136091

DOI

10.3030/101136091

Data della firma CE 17 Novembre 2023

Data di avvio 1 Gennaio 2024

Data di completamento 31 Dicembre 2027

Finanziato da

• Climate, Energy and Mobility

Costo totale

€ 5 997 630,00

Contributo UE

€ 5 997 630,00

Coordinato da

TECHNISCHE UNIVERSITAT BERLIN
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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.

Pubblicazioni

Peer reviewed articles (1)

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

Autori: Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli
Pubblicato in: Journal of Marine Science and Engineering, Numero 12, 2024, ISSN 2077-1312
Editore: MDPI AG
DOI: 10.3390/JMSE12091609