the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Revealing inflow and wake conditions of a 6MW floating turbine
Jakob Mann
Camille Dubreuil-Boisclair
Abstract. We investigate the characteristics of the inflow and the wake of a 6MW floating wind turbine from the Hywind Scotland offshore wind farm, the world's first floating wind farm. We use two commercial nacelle-mounted lidars to measure the up- and downwind conditions, with a fixed and a scanning measuring geometry, respectively. In the analysis, the effect of the surge and sway motion of the nacelle on the lidar measuring location is taken into account. The upwind conditions are parameterised in terms of the mean horizontal wind vector at hub height, the shear and veer of the wind profile along the upper part of the rotor and the induction of the wind turbine rotor. The wake characteristics are studied in two narrow wind speed intervals 8.5–9.5 ms-1 and 12.5–13.5 ms-1, corresponding to below and above rotor rated speeds, respectively, and for turbulence intensity values between 3.3 %–6.4 %. The wake flow is measured by a wind lidar scanning in a horizontal plan position indicator mode, which reaches ten rotor diameters downwind. This study focuses on the downstream area between 3 and 8 rotor diameters. In this region, our observations show that the transverse profile of the wake can be adequately described by a self-similar wind speed deficit, that follows a Gaussian distribution. We find that even small variations (∼1 %–2 %) of the ambient turbulence intensity can result in an up to 10 % faster wake recovery. Furthermore, we do not observe any additional spread of the wake due to the motion of the floating wind turbine.
Nikolas Angelou et al.
Status: open (extended)
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RC1: 'Comment on wes-2023-37', Anonymous Referee #1, 20 Apr 2023
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The research topic is well presented in the introduction, and the abstract clearly states the study objectives. The article presents a new methodology that is well adapted to the study of the wake of floating offshore wind turbines. This approach consists in the deployment of wind LiDARs. The scientific results are very promising. The results are produced through the analysis of a new and valuable dataset, as a result of a rigorous experimental setup.
Despite the very promising dataset, the article struggles to clearly transmit the key messages of the article to the reader. The article’s structure is confusing amid mistakes, repetitions, and an unnecessary count of 22 figures, some of which are not presented or described in the text. Furthermore, the study does not comment and discuss the results at sufficient depths. For example, the authors seem to observe contradicting effects of the yaw on the center of the mean wake center (Sect. 4.4) and struggle to explain the seemingly absent correlation between shear, vear with the induction factor (Sect. 4.2) but do not sufficiently investigate possible explanations, and do not bring these issues to the discussion and conclusion. This is unfortunate, as these observations should be highlighted, so as to allow for improvement in future work.
In conclusion, I believe that this article should be published. However, major corrections are required in order to improve language, structure, and physical interpretations. I advise the authors to thoroughly read the article again to find possible typos, and provide a more concise and dynamic description of the work. Please find my comments and suggestions below.
Nikolas Angelou et al.
Nikolas Angelou et al.
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