Articles | Volume 6, issue 5
https://doi.org/10.5194/wes-6-1117-2021
© 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-6-1117-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Nikolay Dimitrov
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Alfredo Peña
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Thomas Herges
Wind Energy Technologies, Sandia National Laboratories, Albuquerque, New Mexico 87123, USA
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Cited
16 citations as recorded by crossref.
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 1: Large-eddy-simulation study E. Jézéquel et al. https://doi.org/10.5194/wes-9-97-2024
- Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition A. Kidambi Sekar et al. https://doi.org/10.3390/rs14112681
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. https://doi.org/10.1016/j.rser.2024.114279
- Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number P. Brugger et al. https://doi.org/10.5194/wes-9-1363-2024
- A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines E. Mahmoodi et al. https://doi.org/10.3390/en16155710
- A multi-fidelity approach for wind farm simulations and comparison with field data W. Yu et al. https://doi.org/10.1088/1742-6596/2767/5/052039
- A reduced-order model for the near wake dynamics of a wind turbine: Model development and uncertainty quantification A. Qatramez & D. Foti https://doi.org/10.1063/5.0071789
- Machine learning-based Bi-objective optimization of offshore turbine sets considering yaw control J. Li et al. https://doi.org/10.1016/j.marstruc.2026.104111
- Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control R. He et al. https://doi.org/10.1016/j.apenergy.2023.120878
- Influence of atmospheric thermal stability on wake meandering of a large-scale wind turbine Y. Wang et al. https://doi.org/10.1063/5.0297434
- Wind turbine power curve modelling under wake conditions using measurements from a spinner-mounted lidar A. Sebastiani et al. https://doi.org/10.1016/j.apenergy.2024.122985
- Analysis of wake model characteristics based on wind field experiments and numerical simulations D. Miao et al. https://doi.org/10.1016/j.jweia.2026.106374
- Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling E. Jézéquel et al. https://doi.org/10.5194/wes-9-119-2024
- Estimation of near wake turbulence characteristics with dual WindScanner lidar measurements S. Uluocak et al. https://doi.org/10.1088/1742-6596/3224/3/032094
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. https://doi.org/10.3389/fmech.2023.1261017
16 citations as recorded by crossref.
- Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
- Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 1: Large-eddy-simulation study E. Jézéquel et al. https://doi.org/10.5194/wes-9-97-2024
- Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition A. Kidambi Sekar et al. https://doi.org/10.3390/rs14112681
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. https://doi.org/10.1016/j.rser.2024.114279
- Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number P. Brugger et al. https://doi.org/10.5194/wes-9-1363-2024
- A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines E. Mahmoodi et al. https://doi.org/10.3390/en16155710
- A multi-fidelity approach for wind farm simulations and comparison with field data W. Yu et al. https://doi.org/10.1088/1742-6596/2767/5/052039
- A reduced-order model for the near wake dynamics of a wind turbine: Model development and uncertainty quantification A. Qatramez & D. Foti https://doi.org/10.1063/5.0071789
- Machine learning-based Bi-objective optimization of offshore turbine sets considering yaw control J. Li et al. https://doi.org/10.1016/j.marstruc.2026.104111
- Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control R. He et al. https://doi.org/10.1016/j.apenergy.2023.120878
- Influence of atmospheric thermal stability on wake meandering of a large-scale wind turbine Y. Wang et al. https://doi.org/10.1063/5.0297434
- Wind turbine power curve modelling under wake conditions using measurements from a spinner-mounted lidar A. Sebastiani et al. https://doi.org/10.1016/j.apenergy.2024.122985
- Analysis of wake model characteristics based on wind field experiments and numerical simulations D. Miao et al. https://doi.org/10.1016/j.jweia.2026.106374
- Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling E. Jézéquel et al. https://doi.org/10.5194/wes-9-119-2024
- Estimation of near wake turbulence characteristics with dual WindScanner lidar measurements S. Uluocak et al. https://doi.org/10.1088/1742-6596/3224/3/032094
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. https://doi.org/10.3389/fmech.2023.1261017
Saved (final revised paper)
Latest update: 01 Jun 2026
Short summary
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using...
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