Project description
Revolutionising offshore wind turbine analysis and design
Recent estimates suggest that achieving the European Green Deal goals will require significant upscaling of offshore wind turbines. However, the current design methodologies and analyses have reached their limits, and a solution has not yet been found. To address this challenge, the ERC-funded DATA-DRIVEN OFFSHORE project aims to revolutionise aerohydroelastic simulations used in wind turbine research and development. Specifically, it will directly integrate experimental data into these simulations. This will enable unveiling the hidden information contained in the data that could help to overcome the current challenges in designing the new generation of offshore wind turbines.
Objective
A massive upscaling of offshore wind turbines is necessary to reach the goals of the European Green Deal. However, current methodologies for analysis and design are at their limits. One of the major bottlenecks is that it is so far not possible to directly integrate experimental data into aerohydroelastic simulations of offshore wind turbines. By and large, including these data into the aerohydroelastic analysis is indirectly accomplished through the offline adjustment of those parameters that define instances of existing models. Although such a practice can reasonably improve the short-time predictive capability, the underlying models remain unmodified. Thus, further physics available in the data remains inaccessible. In the numerical simulation context, this represents a main challenge to taking advantage of the experimental data in their entirety. In this context, DATA-DRIVEN OFFSHORE proposes to simultaneously integrate these highly-valuable data into aerohydroelastic simulations through data-driven computational mechanics. Such an approach is one of the most advanced computing frameworks, and relies on the reformulation of classical boundary and initial value problems in solid and fluid mechanics such that constitutive models, boundary conditions and/or applied loads are directly replaced by some form of experimental data. DATA-DRIVEN OFFSHORE will thus enable for the first-time investigation of the aerohydroelastic behavior of an offshore wind turbine relying truly on experimental data, capturing the hidden features that these contain. This will greatly improve the predictive capabilities with respect to existing models, allow the conception of less-conservative designs, and enable upscaling beyond 20 MW of rated power, increasing the efficiency while reducing the cost per unit of power produced. Thus, it will contribute to triggering a change of paradigm for future generations of offshore wind turbines.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energywind power
- social scienceseconomics and businesseconomicssustainable economy
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
5020 Bergen
Norway