Articles | Volume 5, issue 4
https://doi.org/10.5194/wes-5-1601-2020
© Author(s) 2020. 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-5-1601-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Nov 2020
Research article |
| 19 Nov 2020
| 19 Nov 2020
Optimal tuning of engineering wake models through lidar measurements
Lu Zhan
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, Richardson, Texas, USA
Stefano Letizia
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, Richardson, Texas, USA
Giacomo Valerio Iungo
CORRESPONDING AUTHOR
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, Richardson, Texas, USA
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Cited
29 citations as recorded by crossref.
- Sensitivity of Wake Modelling Setups L. Kemme et al. 10.1088/1742-6596/2265/2/022007
- Near-wake measurements and simulations of a floating wind turbine using a four-beam nacelle-based lidar U. Özinan et al. 10.1088/1742-6596/2767/9/092100
- Wake effect parameter calibration with large-scale field operational data using stochastic optimization P. Jain et al. 10.1016/j.apenergy.2023.121426
- Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics D. Conti et al. 10.5194/wes-6-1117-2021
- Analytical solution for the cumulative wake of wind turbines in wind farms M. Bastankhah et al. 10.1017/jfm.2020.1037
- Quantification of wind turbine energy loss due to leading‐edge erosion through infrared‐camera imaging, numerical simulations, and assessment against SCADA and meteorological data K. Panthi & G. Iungo 10.1002/we.2798
- Gaussian FLOWERS: Wind-rose-based analytical integration of Gaussian wake model for extremely fast AEP estimation C. Whittaker et al. 10.1063/5.0245886
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- Effects of wind shear and thrust coefficient on the induction zone of a porous disk: A wind tunnel study W. Ahmed & G. Iungo 10.1002/we.2910
- A novel analytical wake model for mountain wind farms considering variable surface roughness and wake effects of near-middle region B. Wang et al. 10.1016/j.renene.2025.122442
- Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements G. Iungo et al. 10.1063/5.0070094
- A Review of Optimization Technologies for Large-Scale Wind Farm Planning With Practical and Prospective Concerns T. Zuo et al. 10.1109/TII.2022.3217282
- Blockage and speedup in the proximity of an onshore wind farm: A scanning wind LiDAR experiment M. Puccioni et al. 10.1063/5.0157937
- Profiling wind LiDAR measurements to quantify blockage for onshore wind turbines C. Moss et al. 10.1002/we.2877
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Effects of the thrust force induced by wind turbine rotors on the incoming wind field: A wind LiDAR experiment S. Letizia et al. 10.1088/1742-6596/2265/2/022033
- Data-driven wind turbine wake modeling via probabilistic machine learning S. Ashwin Renganathan et al. 10.1007/s00521-021-06799-6
- Cumulative Interactions between the Global Blockage and Wake Effects as Observed by an Engineering Model and Large-Eddy Simulations B. Cañadillas et al. 10.3390/en16072949
- Augmenting insights from wind turbine data through data-driven approaches C. Moss et al. 10.1016/j.apenergy.2024.124116
- A diffusion-based wind turbine wake model K. Ali et al. 10.1017/jfm.2024.1077
- An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer G. Narasimhan et al. 10.1063/5.0251305
- A wake prediction framework based on the MOST Gaussian wake model and a deep learning approach M. Wang et al. 10.1016/j.jweia.2024.105952
- A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
- Influence of incoming turbulent scales on the wind turbine wake: A large-eddy simulation study D. Vahidi & F. Porté-Agel 10.1063/5.0222372
- Wind turbine wake influence on the mixing of relative humidity quantified through wind tunnel experiments M. Obligado et al. 10.1063/5.0039090
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- A comparison between scanning LiDAR and scada-based hyperparameter tuning of analytical wake models P. Daems et al. 10.1088/1742-6596/3025/1/012002
- Spectral correction of turbulent energy damping on wind lidar measurements due to spatial averaging M. Puccioni & G. Iungo 10.5194/amt-14-1457-2021
- A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction M. Lejeune et al. 10.3389/fenrg.2022.884068
28 citations as recorded by crossref.
- Sensitivity of Wake Modelling Setups L. Kemme et al. 10.1088/1742-6596/2265/2/022007
- Near-wake measurements and simulations of a floating wind turbine using a four-beam nacelle-based lidar U. Özinan et al. 10.1088/1742-6596/2767/9/092100
- Wake effect parameter calibration with large-scale field operational data using stochastic optimization P. Jain et al. 10.1016/j.apenergy.2023.121426
- Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics D. Conti et al. 10.5194/wes-6-1117-2021
- Analytical solution for the cumulative wake of wind turbines in wind farms M. Bastankhah et al. 10.1017/jfm.2020.1037
- Quantification of wind turbine energy loss due to leading‐edge erosion through infrared‐camera imaging, numerical simulations, and assessment against SCADA and meteorological data K. Panthi & G. Iungo 10.1002/we.2798
- Gaussian FLOWERS: Wind-rose-based analytical integration of Gaussian wake model for extremely fast AEP estimation C. Whittaker et al. 10.1063/5.0245886
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- Effects of wind shear and thrust coefficient on the induction zone of a porous disk: A wind tunnel study W. Ahmed & G. Iungo 10.1002/we.2910
- A novel analytical wake model for mountain wind farms considering variable surface roughness and wake effects of near-middle region B. Wang et al. 10.1016/j.renene.2025.122442
- Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements G. Iungo et al. 10.1063/5.0070094
- A Review of Optimization Technologies for Large-Scale Wind Farm Planning With Practical and Prospective Concerns T. Zuo et al. 10.1109/TII.2022.3217282
- Blockage and speedup in the proximity of an onshore wind farm: A scanning wind LiDAR experiment M. Puccioni et al. 10.1063/5.0157937
- Profiling wind LiDAR measurements to quantify blockage for onshore wind turbines C. Moss et al. 10.1002/we.2877
- Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
- Effects of the thrust force induced by wind turbine rotors on the incoming wind field: A wind LiDAR experiment S. Letizia et al. 10.1088/1742-6596/2265/2/022033
- Data-driven wind turbine wake modeling via probabilistic machine learning S. Ashwin Renganathan et al. 10.1007/s00521-021-06799-6
- Cumulative Interactions between the Global Blockage and Wake Effects as Observed by an Engineering Model and Large-Eddy Simulations B. Cañadillas et al. 10.3390/en16072949
- Augmenting insights from wind turbine data through data-driven approaches C. Moss et al. 10.1016/j.apenergy.2024.124116
- A diffusion-based wind turbine wake model K. Ali et al. 10.1017/jfm.2024.1077
- An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer G. Narasimhan et al. 10.1063/5.0251305
- A wake prediction framework based on the MOST Gaussian wake model and a deep learning approach M. Wang et al. 10.1016/j.jweia.2024.105952
- A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
- Influence of incoming turbulent scales on the wind turbine wake: A large-eddy simulation study D. Vahidi & F. Porté-Agel 10.1063/5.0222372
- Wind turbine wake influence on the mixing of relative humidity quantified through wind tunnel experiments M. Obligado et al. 10.1063/5.0039090
- Wind farm yaw control set-point optimization under model parameter uncertainty M. Howland 10.1063/5.0051071
- A comparison between scanning LiDAR and scada-based hyperparameter tuning of analytical wake models P. Daems et al. 10.1088/1742-6596/3025/1/012002
- Spectral correction of turbulent energy damping on wind lidar measurements due to spatial averaging M. Puccioni & G. Iungo 10.5194/amt-14-1457-2021
Latest update: 02 Jul 2025
Short summary
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning...
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