Articles | Volume 6, issue 3
https://doi.org/10.5194/wes-6-737-2021
© Author(s) 2021. 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-6-737-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 May 2021
Research article |
| 28 May 2021
| 28 May 2021
Design and analysis of a wake model for spatially heterogeneous flow
Alayna Farrell
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, CO, 80401, USA
Jennifer King
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, CO, 80401, USA
Caroline Draxl
National Renewable Energy Laboratory, Golden, CO, 80401, USA
Rafael Mudafort
National Renewable Energy Laboratory, Golden, CO, 80401, USA
Nicholas Hamilton
National Renewable Energy Laboratory, Golden, CO, 80401, USA
Christopher J. Bay
National Renewable Energy Laboratory, Golden, CO, 80401, USA
Paul Fleming
National Renewable Energy Laboratory, Golden, CO, 80401, USA
Eric Simley
National Renewable Energy Laboratory, Golden, CO, 80401, USA
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Cited
22 citations as recorded by crossref.
- An Analytical Model for Wind Turbine Wakes under Pressure Gradient A. Dar & F. Porté-Agel 10.3390/en15155345
- Parameters estimation of a steady-state wind farm wake model implemented in OpenFAST A. Cioffi et al. 10.1177/0309524X221117820
- A dynamic model of wind turbine yaw for active farm control G. Starke et al. 10.1002/we.2884
- 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
- Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- An experimental and analytical study of wind turbine wakes under pressure gradient A. Dar et al. 10.1063/5.0145043
- Large-eddy simulation and analytical modeling study of the wake of a wind turbine behind an abrupt rough-to-smooth surface roughness transition N. Kethavath et al. 10.1063/5.0129022
- The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake M. Becker et al. 10.5194/wes-7-2163-2022
- An investigation of spatial wind direction variability and its consideration in engineering models A. von Brandis et al. 10.5194/wes-8-589-2023
- Evaluation of the topology anisotropy effect on wake development over complex terrain based on a novel method and verified by LiDAR measurements X. Zongyuan et al. 10.1016/j.enconman.2024.119154
- A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies M. Becker et al. 10.5194/wes-10-1055-2025
- Wake effect parameter calibration with large-scale field operational data using stochastic optimization P. Jain et al. 10.1016/j.apenergy.2023.121426
- The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects S. Stipa et al. 10.5194/wes-9-1123-2024
- A momentum-conserving wake superposition method for wind-farm flows under pressure gradient B. Du et al. 10.1017/jfm.2024.761
- Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. 10.1109/TCST.2024.3362518
- Wind farm control technologies: from classical control to reinforcement learning H. Dong et al. 10.1088/2516-1083/ac6cc1
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields L. Lanzilao & J. Meyers 10.1002/we.2669
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. 10.5194/wes-8-691-2023
- Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings M. Sinner et al. 10.1063/5.0039899
- Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
20 citations as recorded by crossref.
- An Analytical Model for Wind Turbine Wakes under Pressure Gradient A. Dar & F. Porté-Agel 10.3390/en15155345
- Parameters estimation of a steady-state wind farm wake model implemented in OpenFAST A. Cioffi et al. 10.1177/0309524X221117820
- A dynamic model of wind turbine yaw for active farm control G. Starke et al. 10.1002/we.2884
- 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
- Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- An experimental and analytical study of wind turbine wakes under pressure gradient A. Dar et al. 10.1063/5.0145043
- Large-eddy simulation and analytical modeling study of the wake of a wind turbine behind an abrupt rough-to-smooth surface roughness transition N. Kethavath et al. 10.1063/5.0129022
- The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake M. Becker et al. 10.5194/wes-7-2163-2022
- An investigation of spatial wind direction variability and its consideration in engineering models A. von Brandis et al. 10.5194/wes-8-589-2023
- Evaluation of the topology anisotropy effect on wake development over complex terrain based on a novel method and verified by LiDAR measurements X. Zongyuan et al. 10.1016/j.enconman.2024.119154
- A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies M. Becker et al. 10.5194/wes-10-1055-2025
- Wake effect parameter calibration with large-scale field operational data using stochastic optimization P. Jain et al. 10.1016/j.apenergy.2023.121426
- The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects S. Stipa et al. 10.5194/wes-9-1123-2024
- A momentum-conserving wake superposition method for wind-farm flows under pressure gradient B. Du et al. 10.1017/jfm.2024.761
- Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. 10.1109/TCST.2024.3362518
- Wind farm control technologies: from classical control to reinforcement learning H. Dong et al. 10.1088/2516-1083/ac6cc1
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields L. Lanzilao & J. Meyers 10.1002/we.2669
- The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data R. Braunbehrens et al. 10.5194/wes-8-691-2023
2 citations as recorded by crossref.
Latest update: 30 Jun 2025
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Most current wind turbine wake models struggle to accurately simulate spatially variant wind...
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