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CORDIS - Wyniki badań wspieranych przez UE
CORDIS

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

CORDIS oferuje możliwość skorzystania z odnośników do publicznie dostępnych publikacji i rezultatów projektów realizowanych w ramach programów ramowych HORYZONT.

Odnośniki do rezultatów i publikacji związanych z poszczególnymi projektami 7PR, a także odnośniki do niektórych konkretnych kategorii wyników, takich jak zbiory danych i oprogramowanie, są dynamicznie pobierane z systemu OpenAIRE .

Rezultaty

Validation and testing of radar wave sensing on FLOATGEN data (odnośnik otworzy się w nowym oknie)

This deliverable will describe the ability of predicting hydrodynamic excitation forces through radar-scanning of the incoming wave field. The methods through which measured incoming wave heights and platform motions can be correlated will be described. The methodology will be applied to an open-sea test case and results will be discussed herein.

Repositioning control solution capable of relocating a floating turbine within a wind farm (odnośnik otworzy się w nowym oknie)

This deliverable can be subdivided into two main parts: 1. Control development to move a floating turbine within a certain space: This focuses on designing a control solution that can track the turbine to a desired position using available actuators. This part solely focuses on the downstream turbine and how it should be controlled to achieve the desired increase in energy capture TUD) 2. Cooperative control between two floating turbines: This will cover how the controller developed in Part 1 can be best used when the incoming wake is dynamically changing over time. The majority of the work will be carried out by TUD and will be supported by TUB primarily through the use of Matlab Simulink and QBlade simulations. QBlade can be used for finding linear models for control development. This is done using system identification experiments. QBlade will also be used as a platform to test linear models in a higher fidelity simulation environment.

Novel generator design and performance (odnośnik otworzy się w nowym oknie)

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 (odnośnik otworzy się w nowym oknie)

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.

High fidelity structural modelling for identification of critical strain points (odnośnik otworzy się w nowym oknie)

This report details functionality and application of a specific tool dedicated to the automatic generation of three-dimensional tubular joint meshes, taking into account geometrical characteristics for a finite element analysis.

An Interface of HAWC2 to the Framework for Floating Offshore Turbine Design Optimization WEIS (odnośnik otworzy się w nowym oknie)

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 (odnośnik otworzy się w nowym oknie)

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.

Publikacje

A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data (odnośnik otworzy się w nowym oknie)

Autorzy: Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli
Opublikowane w: Journal of Marine Science and Engineering, Numer 12, 2024, ISSN 2077-1312
Wydawca: MDPI AG
DOI: 10.3390/JMSE12091609

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