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CORDIS - Forschungsergebnisse der EU
CORDIS

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

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Novel generator design and performance (öffnet in neuem Fenster)

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 (öffnet in neuem Fenster)

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.

An Interface of HAWC2 to the Framework for Floating Offshore Turbine Design Optimization WEIS (öffnet in neuem Fenster)

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 (öffnet in neuem Fenster)

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.

Veröffentlichungen

A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data (öffnet in neuem Fenster)

Autoren: Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli
Veröffentlicht in: Journal of Marine Science and Engineering, Ausgabe 12, 2024, ISSN 2077-1312
Herausgeber: MDPI AG
DOI: 10.3390/JMSE12091609

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