Transferability of Meta-Model Configurations for Different Wind Turbine Types

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Original languageEnglish
Title of host publication Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering
Subtitle of host publicationOcean Renewable Energy
Number of pages10
VolumeVolume 8
ISBN (electronic)9780791885932
Publication statusPublished - 13 Oct 2022

Abstract

The dynamic responses analysis of floating offshore wind turbines (FOWTs) is very complex, which involves very strong nonlinear and coupling effects. This has also led to the current engineering application of FOWTs still facing critical gaps, especially in innovative design and dynamic performances prediction. Both academia and the wind industry are constantly exploring breakthroughs in terms of design and maintenance for FOWTs. The purpose of this paper is to apply a novel method, named SADA, which combines AI technology with numerical analysis methods, on full-scale measured data to optimize the platform motion prediction of the Hywind FOWT. The full-scale data used in this paper was collected by one of Hywind FOWTs in Scotland. The results show that the AI-Trained numerical model can predict the motions of Hywind supporting floater with higher accuracy. The tension of the fairlead has undergone a huge change although the deformation of the blades and the tower has been reduced. In addition, compared with the deformation of the tower and the blades, the changes in the mooring system are the most significant. In summary, the SADA method can bring an innovative vision for FOWTs full-scale measurement technology in the future.

Keywords

    DARwind, Hywind, SADA, floating offshore wind turbine, full-scale measurement

ASJC Scopus subject areas

Cite this

Transferability of Meta-Model Configurations for Different Wind Turbine Types. / Müller, Franziska; Hübler, Clemens; Rolfes, Raimund.
Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering: Ocean Renewable Energy. Vol. Volume 8 2022. V008T09A042.

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Müller, F, Hübler, C & Rolfes, R 2022, Transferability of Meta-Model Configurations for Different Wind Turbine Types. in Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering: Ocean Renewable Energy. vol. Volume 8, V008T09A042. https://doi.org/10.1115/OMAE2022-79698
Müller, F., Hübler, C., & Rolfes, R. (2022). Transferability of Meta-Model Configurations for Different Wind Turbine Types. In Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering: Ocean Renewable Energy (Vol. Volume 8). Article V008T09A042 https://doi.org/10.1115/OMAE2022-79698
Müller F, Hübler C, Rolfes R. Transferability of Meta-Model Configurations for Different Wind Turbine Types. In Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering: Ocean Renewable Energy. Vol. Volume 8. 2022. V008T09A042 doi: 10.1115/OMAE2022-79698
Müller, Franziska ; Hübler, Clemens ; Rolfes, Raimund. / Transferability of Meta-Model Configurations for Different Wind Turbine Types. Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering: Ocean Renewable Energy. Vol. Volume 8 2022.
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abstract = "The dynamic responses analysis of floating offshore wind turbines (FOWTs) is very complex, which involves very strong nonlinear and coupling effects. This has also led to the current engineering application of FOWTs still facing critical gaps, especially in innovative design and dynamic performances prediction. Both academia and the wind industry are constantly exploring breakthroughs in terms of design and maintenance for FOWTs. The purpose of this paper is to apply a novel method, named SADA, which combines AI technology with numerical analysis methods, on full-scale measured data to optimize the platform motion prediction of the Hywind FOWT. The full-scale data used in this paper was collected by one of Hywind FOWTs in Scotland. The results show that the AI-Trained numerical model can predict the motions of Hywind supporting floater with higher accuracy. The tension of the fairlead has undergone a huge change although the deformation of the blades and the tower has been reduced. In addition, compared with the deformation of the tower and the blades, the changes in the mooring system are the most significant. In summary, the SADA method can bring an innovative vision for FOWTs full-scale measurement technology in the future.",
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