Articles | Volume 6, issue 2
https://doi.org/10.5194/wes-6-555-2021
© 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-6-555-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2021
Research article |
| 22 Apr 2021
| 22 Apr 2021
The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows
Luis A. Martínez-Tossas
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, CO, USA
Jennifer King
National Renewable Energy Laboratory, Golden, CO, USA
Eliot Quon
National Renewable Energy Laboratory, Golden, CO, USA
Christopher J. Bay
National Renewable Energy Laboratory, Golden, CO, USA
Rafael Mudafort
National Renewable Energy Laboratory, Golden, CO, USA
Nicholas Hamilton
National Renewable Energy Laboratory, Golden, CO, USA
Michael F. Howland
Graduate Aerospace Laboratories (GALCIT), California Institute of Technology, Pasadena, CA 91125, USA
Paul A. Fleming
National Renewable Energy Laboratory, Golden, CO, USA
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35 citations as recorded by crossref.
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- Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
- Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
- Evolution of eddy viscosity in the wake of a wind turbine R. Scott et al. 10.5194/wes-8-449-2023
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- Investigating Turbulent Dynamics in the Nocturnal Boundary Layer: A Large Eddy Simulation Study of the South Fork Wind Farm A. Ayouche et al. 10.1088/1742-6596/3016/1/012048
- Wake turbulence modeling in stratified atmospheric flows using a novel k−ℓ model K. Klemmer & M. Howland 10.1063/5.0249278
- Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data R. Li et al. 10.1016/j.energy.2022.124845
- Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models S. Anagnostopoulos et al. 10.1016/j.renene.2023.119293
- Structural motion control of waked floating offshore wind farms H. del Pozo Gonzalez et al. 10.1016/j.oceaneng.2024.116709
- Modelling the induction, thrust and power of a yaw-misaligned actuator disk K. Heck et al. 10.1017/jfm.2023.129
- The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi 10.1115/1.4052775
- Multicluster Distributed Optimization Strategy for Turbine Wake Environment Z. Yu et al. 10.1002/aisy.202400884
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
- On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al. 10.5194/wes-9-1547-2024
- Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
- A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
- A Tutorial on the Control of Floating Offshore Wind Turbines: Stability Challenges and Opportunities for Power Capture D. Stockhouse et al. 10.1109/MCS.2024.3433208
- Faster wind farm AEP calculations with CFD using a generalized wind turbine model M. van der Laan et al. 10.1088/1742-6596/2265/2/022030
- Load assessment of a wind farm considering negative and positive yaw misalignment for wake steering R. Thedin et al. 10.5194/wes-10-1033-2025
- Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. 10.1038/s41560-022-01085-8
- Modification of wind turbine wakes by large-scale, convective atmospheric boundary layer structures L. Cheung et al. 10.1063/5.0211722
- FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. 10.5194/wes-7-1137-2022
- Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares J. Liew et al. 10.1002/we.2852
- A data-driven machine learning approach for yaw control applications of wind farms C. Santoni et al. 10.1016/j.taml.2023.100471
- A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines E. Mahmoodi et al. 10.3390/en16155710
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- Theory and verification of a new 3D RANS wake model P. Bradstock & W. Schlez 10.5194/wes-5-1425-2020
- Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. 10.1063/5.0023746
- Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation M. Howland & J. Dabiri 10.3390/en14010052
Latest update: 10 Aug 2025
Short summary
In this paper a three-dimensional steady-state solver for flow through a wind farm is developed and validated. The computational cost of the solver is on the order of seconds for large wind farms. The model is validated using high-fidelity simulations and SCADA.
In this paper a three-dimensional steady-state solver for flow through a wind farm is developed...
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