Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2065-2021
© 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-14-2065-2021
© 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 1: Theoretical framework
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
19 citations as recorded by crossref.
- 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
- Design of the American Wake Experiment (AWAKEN) field campaign M. Debnath et al. 10.1088/1742-6596/2265/2/022058
- Error analysis of low-fidelity models for wake steering based on field measurements S. Letizia et al. 10.1088/1742-6596/2767/4/042029
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- Data-driven wind turbine wake modeling via probabilistic machine learning S. Ashwin Renganathan et al. 10.1007/s00521-021-06799-6
- Wind Farm Wakes and Farm-to-Farm Interactions: Lidar and Wind Tunnel Tests W. Ahmed et al. 10.1088/1742-6596/2767/9/092105
- LiDAR Measurements to Investigate Farm-to-Farm Interactions at the AWAKEN Experiment M. Puccioni et al. 10.1088/1742-6596/2505/1/012045
- 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
- Validation of near‐shore wind measurements using a dual scanning light detection and ranging system S. Shimada et al. 10.1002/we.2757
- 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
- Blockage and speedup in the proximity of an onshore wind farm: A scanning wind LiDAR experiment M. Puccioni et al. 10.1063/5.0157937
- An international benchmark for wind plant wakes from the American WAKE ExperimeNt (AWAKEN) N. Bodini et al. 10.1088/1742-6596/2767/9/092034
- 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
- 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 S. Letizia et al. 10.5194/amt-14-2095-2021
- 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
- Profiling wind LiDAR measurements to quantify blockage for onshore wind turbines C. Moss et al. 10.1002/we.2877
- Optimal tuning of engineering wake models through lidar measurements L. Zhan et al. 10.5194/wes-5-1601-2020
18 citations as recorded by crossref.
- 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
- Design of the American Wake Experiment (AWAKEN) field campaign M. Debnath et al. 10.1088/1742-6596/2265/2/022058
- Error analysis of low-fidelity models for wake steering based on field measurements S. Letizia et al. 10.1088/1742-6596/2767/4/042029
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- Data-driven wind turbine wake modeling via probabilistic machine learning S. Ashwin Renganathan et al. 10.1007/s00521-021-06799-6
- Wind Farm Wakes and Farm-to-Farm Interactions: Lidar and Wind Tunnel Tests W. Ahmed et al. 10.1088/1742-6596/2767/9/092105
- LiDAR Measurements to Investigate Farm-to-Farm Interactions at the AWAKEN Experiment M. Puccioni et al. 10.1088/1742-6596/2505/1/012045
- 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
- Validation of near‐shore wind measurements using a dual scanning light detection and ranging system S. Shimada et al. 10.1002/we.2757
- 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
- Blockage and speedup in the proximity of an onshore wind farm: A scanning wind LiDAR experiment M. Puccioni et al. 10.1063/5.0157937
- An international benchmark for wind plant wakes from the American WAKE ExperimeNt (AWAKEN) N. Bodini et al. 10.1088/1742-6596/2767/9/092034
- 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
- 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 S. Letizia et al. 10.5194/amt-14-2095-2021
- 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
- Profiling wind LiDAR measurements to quantify blockage for onshore wind turbines C. Moss et al. 10.1002/we.2877
1 citations as recorded by crossref.
Latest update: 23 Nov 2024
Short summary
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of velocity statistics is proposed. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field.
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and...
