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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Discussion papers
https://doi.org/10.5194/wes-2019-99
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-99
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 13 Jan 2020

Submitted as: research article | 13 Jan 2020

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).

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

Frédéric Blondel and Marie Cathelain Frédéric Blondel and Marie Cathelain
  • IFP Énergies nouvelles, 1 & 4 Avenue du Bois Préau, 92862 Rueil-Malmaison, France

Abstract. A new analytical wind turbine wake model, based on a super-Gaussian shape function, is presented. The super-Gaussian function evolves from a nearly top-hat shape in the near wake to a Gaussian shape in the far wake, which is consistent with observations and measurements made on wind turbine wakes. Using such a shape function allows to recover the mass and momentum conservation that is violated when applying a near-wake regularization function to the expression of the maximum velocity deficit of the Gaussian wake model. After a brief introduction of the theoretical aspects, an easy-to-implement model with a limited number of parameters is derived. The super-Gaussian model predictions are compared to wind tunnel measurements, full-scale measurements and a LES simulation, showing a good agreement and an improvement compared with predictions based on the Gaussian model.

Frédéric Blondel and Marie Cathelain
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Status: open (until 24 Feb 2020)
Status: open (until 24 Feb 2020)
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Frédéric Blondel and Marie Cathelain
Frédéric Blondel and Marie Cathelain
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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.
Analytical wind turbine wake models are of high interest for wind farm designers: they provide...
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