Articles | Volume 3, issue 1
https://doi.org/10.5194/wes-3-329-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-329-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Wind tunnel experiments on wind turbine wakes in yaw: effects of inflow turbulence and shear
Jan Bartl
CORRESPONDING AUTHOR
Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway
Franz Mühle
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
Jannik Schottler
ForWind, Institute of Physics, University of Oldenburg, Oldenburg, Germany
Lars Sætran
Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway
Joachim Peinke
ForWind, Institute of Physics, University of Oldenburg, Oldenburg, Germany
Fraunhofer IWES, Oldenburg, Germany
Muyiwa Adaramola
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
Michael Hölling
ForWind, Institute of Physics, University of Oldenburg, Oldenburg, Germany
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65 citations as recorded by crossref.
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- On the wake deflection of vertical axis wind turbines by pitched blades M. Huang et al. 10.1002/we.2803
- FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
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- Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
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- Towards a better understanding of yawed turbine wake for efficient wake steering in tidal arrays P. Modali et al. 10.1016/j.renene.2021.05.152
- On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al. 10.5194/wes-9-1547-2024
- Effect of low-level jet on turbine aerodynamic blade loading using large-eddy simulations S. Gadde et al. 10.1088/1742-6596/1934/1/012001
- Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research H. Peng et al. 10.3390/su15108333
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- Parametric dependencies of the yawed wind‐turbine wake development E. Kleusberg et al. 10.1002/we.2395
- Turbine Wake Deflection Measurement in a Wind Tunnel with a Lidar WindScanner P. Hulsman et al. 10.1088/1742-6596/1452/1/012007
- Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. 10.1088/1742-6596/2265/3/032074
- Temporal Up-Sampling of Planar Long-Range Doppler LiDAR Wind Speed Measurements Using Space-Time Conversion H. Beck & M. Kühn 10.3390/rs11070867
- Characterizing porous disk wakes in different turbulent inflow conditions with higher-order statistics M. Vinnes et al. 10.1007/s00348-022-03565-9
- A yawed wake model to predict the velocity distribution of curled wake cross-section for wind turbines Q. Yang et al. 10.1016/j.oceaneng.2024.116911
- The Influence of Yaw Misalignment on Turbine Power Output Fluctuations and Unsteady Aerodynamic Loads within Wind Farms E. Aju et al. 10.2139/ssrn.4194363
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
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- A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
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- The aerodynamics of the curled wake: a simplified model in view of flow control L. Martínez-Tossas et al. 10.5194/wes-4-127-2019
- Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model D. Wei et al. 10.3390/en14154494
- Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al. 10.1017/jfm.2021.237
- Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data L. Wang et al. 10.1016/j.energy.2024.130401
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- Brief communication: A double-Gaussian wake model J. Schreiber et al. 10.5194/wes-5-237-2020
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- Experimental study on the characteristics of wind turbine wake field considering yaw conditions X. Zhao et al. 10.1002/ese3.987
- Wind farm power optimization via yaw angle control: A wind tunnel study M. Bastankhah & F. Porté-Agel 10.1063/1.5077038
- Large Eddy Simulation of Yawed Wind Turbine Wake Deformation H. Kim & S. Lee 10.3390/en15176125
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- Three-dimensional yaw wake model development with validations from wind tunnel experiments R. He et al. 10.1016/j.energy.2023.128402
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
- Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. 10.5194/wes-5-309-2020
- Wind turbine wake influence on the mixing of relative humidity quantified through wind tunnel experiments M. Obligado et al. 10.1063/5.0039090
- A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
- Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow M. Mohammadi et al. 10.3390/en15239135
1 citations as recorded by crossref.
Latest update: 23 Nov 2024
Short summary
Wake steering by yawing a wind turbine offers great potential to increase the wind farm power production. A model scale experiment in a controlled wind tunnel environment has been performed to map the wake flow's complex velocity distribution for different inflow conditions. A non-uniform sheared inflow was observed to affect the wake flow only insignificantly. The level of turbulent velocity fluctuations in the inflow, however, influenced the wake's velocity distribution to a higher degree.
Wake steering by yawing a wind turbine offers great potential to increase the wind farm power...
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