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.
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: 04 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...