Analysis of the blade aeroelastic effect on the floating offshore wind turbine wake in a focusing wave with a hybrid model

Abstract

With the recent trend toward larger floating offshore wind turbines (FOWT), the influence of blade deformation becomes more obvious. Modelling this fully coupled fluid-structure interaction (FSI) system, especially at extreme sea level with large-scale motion of the entire system, is an essential challenge. A hybrid numerical model integrating the potential-viscous flow model (qaleFOAM), the unstable actuator line method (UALM), the Legendre spectral finite element model (BeamDyn), and the Lumped Mass Mooring Model (MoorDyn) is employed in the current research. This model is capable of effortlessly and accurately reflecting the blade aeroelastic effect on the dynamics of a FOWT system under the circumstances of a focused wave and uniform wind. The result reveals that the aeroelastic impacts have been identified to not only increase the fatigue damage on the turbine blades but also affect the wake field distribution. Based on the temporal and spatial distribution of the velocity fields, it is found that the coupled effects of the large wave elevation and the FOWT high-frequency motions will disturb the velocity in the wake region. The slight variation of the wind speed in the wake region will be exacerbated by about 0.21% to 18.6%, and it transforms the shape of the wake region when the blade aeroelastic effect participates in the coupled effect. This phenomenon may further affect the downstream FOWT system features through the upstream FOWT wake field.

KEYWORDS:

• FOWT
• hybrid model
• aeroelastic response
• fatigue damage
• wake field distribution

Acknowledgement

This research work was funded by the National Key Research and Development Program of China (No.2020YFB1506701), the National Natural Science Foundation of China (Nos. 51739001; 51879051); Natural Science Foundation of Heilongjiang Province in China (LH2020E071), Open Fund of Zhejiang Provincial Key Laboratory of Wind Power Technology (ZOE2020007). Ziying Yu thanks Qingwei Ma and Shiqiang Yan at City, University of London for the support in software qaleFoam during his study.

Data availability

The data that supports the findings of this study are available within the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research work was funded by the National Key Research and Development Program of China (No. 2020YFB1506701), the National Natural Science Foundation of China (Nos. 51739001; 51879051); Natural Science Foundation of Heilongjiang Province in China (LH2020E071), Open Fund of Zhejiang Provincial Key Laboratory of Wind Power Technology (ZOE2020007).