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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Jun 2020

22 Jun 2020

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This preprint is currently under review for the journal WES.

Optimal tuning of engineering wake models through LiDAR measurements

Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo Lu Zhan et al.
  • Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, Richardson, Texas, USA

Abstract. Engineering wake models provide the invaluable advantage to predict wind turbine wakes, power capture, and, in turn, annual energy production for an entire wind farm with very low computational costs compared to higher-fidelity numerical tools. However, wake and power predictions obtained with engineering wake models can be not sufficiently accurate for wind-farm optimization problems due to the ad-hoc tuning of the model parameters, which are typically strongly dependent on the characteristics of the site and power plant under investigation. In this paper, LiDAR measurements collected for individual turbine wakes to evolve over a flat terrain are leveraged to perform optimal tuning of the parameters of four widely-used engineering wake models. The average wake velocity fields, used as a reference for the optimization problem, are obtained through a cluster analysis of LiDAR measurements performed under a broad range of turbine operative conditions, namely rotor thrust coefficients, and incoming wind characteristics, namely turbulence intensity at hub height. The sensitivity analysis of the optimally-tuned model parameters and the respective physical interpretation are presented. The performance of the optimally-tuned engineering wake models is discussed, while the results suggest that the optimally-tuned Bastankhah and Ainslie wake models provide very good predictions of wind turbine wakes. Specifically, the Bastankhah wake model should be tuned only for the far-wake region, namely where the wake velocity field can be well-approximated with a Gaussian profile in the radial direction. In contrast, the Ainslie model provides the advantage of using as input an arbitrary near-wake velocity profile, which can be obtained through other wake models, higher-fidelity tools, or experimental data. The good prediction capabilities of the Ainslie model indicate that the mixing-length model is a simple, yet efficient, turbulence closure to capture effects of incoming wind and wake-generated turbulence on the wake downstream evolution and predictions of turbine power yield.

Lu Zhan et al.

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Lu Zhan et al.

Lu Zhan et al.


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Latest update: 13 Aug 2020
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Short summary
The manuscript deals with the optimization of parameters of widely-used wake models based on LiDAR measurements. A discussion on the accuracy achieved with the optimized models is provided together with their physical implications.
The manuscript deals with the optimization of parameters of widely-used wake models based on...