Wind Energy Science
Wind Energy Science
WES
Articles | Volume 5, issue 3
Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-1225-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-5-1225-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 3
Research article
 | 
28 Sep 2020
Research article |  | 28 Sep 2020

An alternative form of the super-Gaussian wind turbine wake model

Frédéric Blondel and Marie Cathelain
Authors' note: since the publication of this model, a revised set of parameters reported to offer improved accuracy has been proposed in Blondel (2023). We strongly advise using that updated parameter set.

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Cited articles

Aubrun, S., Loyer, S., Hancock, P., and Hayden, P.: Wind turbine wake properties: Comparison between a non-rotating simplified wind turbine model and a rotating model, J. Wind Eng. Ind. Aerod., 120, 1–8, https://doi.org/10.1016/j.jweia.2013.06.007, 2013. a, b, c, d
Bartl, J. and Sætran, L.: Blind test comparison of the performance and wake flow between two in-line wind turbines exposed to different turbulent inflow conditions, Wind Energ. Sci., 2, 55–76, https://doi.org/10.5194/wes-2-55-2017, 2017. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energ., 70, 116–123, https://doi.org/10.1016/j.renene.2014.01.002, 2014. a, b, c, d, e, f, g
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind turbine wakes in yawed conditions, Zenodo, https://doi.org/10.5281/zenodo.3607598, 2020. a
Boorsma, K., Schepers, J., Gomez-Iradi, S., Herraez, I., Lutz, T., Weihing, P., Oggiano, L., Pirrung, G., Madsen, H., Shen, W., Rahimi, H., and Schaffarczyk, P.: Final Report of IEA Wind Task 29 Mexnext (Phase 3), Tech. Rep. ECN-E–18-003, ECN Wind Energy, available at: https://publications.ecn.nl/WIN/0/ECN-E--18-003, last access: 18 November 2019. a
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Short summary
Analytical wind turbine wake models are of high interest for wind farm designers: they provide an estimation of wake losses for a given layout at a low computational cost. Consequently they are heavily used for wind farm design and power production evaluation. While most analytical models focus on far-wake characteristics, we propose an approach that is able to represent both near- and far-wake velocity deficit, enabling the simulation of closely packed wind farms.
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