Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2095-2021
© 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/amt-14-2095-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Mar 2021
Research article |
| 16 Mar 2021
| 16 Mar 2021
LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 2: Applications to lidar measurements of wind turbine wakes
Stefano Letizia
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
Lu Zhan
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
Giacomo Valerio Iungo
CORRESPONDING AUTHOR
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
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Cited
16 citations as recorded by crossref.
- Comparison of horizontal wind speed and direction measurements from dual-Doppler radar and profiling lidars A. Vöhringer et al. 10.1088/1742-6596/2767/9/092101
- Identification of the energy contributions associated with wall-attached eddies and very-large-scale motions in the near-neutral atmospheric surface layer through wind LiDAR measurements M. Puccioni et al. 10.1017/jfm.2022.1080
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
- Numerical study of the effect of tip-speed ratio on hydrokinetic turbine wake recovery O. El Fajri et al. 10.1016/j.renene.2021.10.030
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework S. Letizia et al. 10.5194/amt-14-2065-2021
- LiDAR Measurements to Investigate Farm-to-Farm Interactions at the AWAKEN Experiment M. Puccioni et al. 10.1088/1742-6596/2505/1/012045
- 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
- 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
- Holistic scan optimization of nacelle-mounted lidars for inflow and wake characterization at the RAAW and AWAKEN field campaigns S. Letizia et al. 10.1088/1742-6596/2505/1/012048
- 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
- Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks J. Zhang & X. Zhao 10.1088/1742-6596/2767/9/092017
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
- Coupling wind LiDAR fixed and volumetric scans for enhanced characterization of wind turbulence and flow three‐dimensionality M. Puccioni et al. 10.1002/we.2865
- Optimal tuning of engineering wake models through lidar measurements L. Zhan et al. 10.5194/wes-5-1601-2020
15 citations as recorded by crossref.
- Comparison of horizontal wind speed and direction measurements from dual-Doppler radar and profiling lidars A. Vöhringer et al. 10.1088/1742-6596/2767/9/092101
- Identification of the energy contributions associated with wall-attached eddies and very-large-scale motions in the near-neutral atmospheric surface layer through wind LiDAR measurements M. Puccioni et al. 10.1017/jfm.2022.1080
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
- Numerical study of the effect of tip-speed ratio on hydrokinetic turbine wake recovery O. El Fajri et al. 10.1016/j.renene.2021.10.030
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework S. Letizia et al. 10.5194/amt-14-2065-2021
- LiDAR Measurements to Investigate Farm-to-Farm Interactions at the AWAKEN Experiment M. Puccioni et al. 10.1088/1742-6596/2505/1/012045
- 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
- 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
- Holistic scan optimization of nacelle-mounted lidars for inflow and wake characterization at the RAAW and AWAKEN field campaigns S. Letizia et al. 10.1088/1742-6596/2505/1/012048
- 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
- Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks J. Zhang & X. Zhao 10.1088/1742-6596/2767/9/092017
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
- Coupling wind LiDAR fixed and volumetric scans for enhanced characterization of wind turbulence and flow three‐dimensionality M. Puccioni et al. 10.1002/we.2865
1 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in...
