Articles | Volume 8, issue 3
https://doi.org/10.5194/wes-8-401-2023
© Author(s) 2023. 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-8-401-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Mar 2023
Research article |
| 24 Mar 2023
| 24 Mar 2023
Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model
Christopher J. Bay
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Paul Fleming
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Bart Doekemeijer
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Jennifer King
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Matt Churchfield
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Rafael Mudafort
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
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Cited
18 citations as recorded by crossref.
- Scalable SCADA-Based Calibration for Analytical Wake Models Across an Offshore Cluster D. Binsbergen et al. 10.1088/1742-6596/2745/1/012014
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. 10.5194/wes-9-1507-2024
- Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning C. Santoni et al. 10.1063/5.0213321
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model Z. Zhang & P. Huang 10.1016/j.renene.2023.119418
- A Comparative Analysis of Actuator-Based Turbine Structure Parametrizations for High-Fidelity Modeling of Utility-Scale Wind Turbines under Neutral Atmospheric Conditions C. Santoni et al. 10.3390/en17030753
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- Benchmarking engineering wake models for farm-to-farm interactions L. Vollmer et al. 10.1088/1742-6596/2767/9/092095
- Multi-model approach for wind resource assessment B. Sengers et al. 10.1088/1742-6596/2767/9/092024
- Comparison of steady-state analytical wake models implemented in wind farm analysis software R. Mudafort et al. 10.1088/1742-6596/2767/5/052066
- Modeling of Blockage and Wake Effect: Comparison with Field data P. Maheshwari et al. 10.1088/1742-6596/2767/9/092021
- A momentum-conserving wake superposition method for wind-farm flows under pressure gradient B. Du et al. 10.1017/jfm.2024.761
- Ranking multi-fidelity model performances in reproducing internal and external wake impacts at neighbouring offshore wind farms S. Freitas et al. 10.1088/1742-6596/2767/9/092045
- A fast-running physics-based wake model for a semi-infinite wind farm M. Bastankhah et al. 10.1017/jfm.2024.282
- Wind pattern clustering of high frequent field measurements for dynamic wind farm flow control M. Becker et al. 10.1088/1742-6596/2767/3/032028
- Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model F. Blondel 10.5194/wes-8-141-2023
- Evolution of eddy viscosity in the wake of a wind turbine R. Scott et al. 10.5194/wes-8-449-2023
- Model predictive control of wakes for wind farm power tracking A. Sterle et al. 10.1088/1742-6596/2767/3/032005
15 citations as recorded by crossref.
- Scalable SCADA-Based Calibration for Analytical Wake Models Across an Offshore Cluster D. Binsbergen et al. 10.1088/1742-6596/2745/1/012014
- Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. 10.5194/wes-9-1507-2024
- Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning C. Santoni et al. 10.1063/5.0213321
- Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
- Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model Z. Zhang & P. Huang 10.1016/j.renene.2023.119418
- A Comparative Analysis of Actuator-Based Turbine Structure Parametrizations for High-Fidelity Modeling of Utility-Scale Wind Turbines under Neutral Atmospheric Conditions C. Santoni et al. 10.3390/en17030753
- Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
- Benchmarking engineering wake models for farm-to-farm interactions L. Vollmer et al. 10.1088/1742-6596/2767/9/092095
- Multi-model approach for wind resource assessment B. Sengers et al. 10.1088/1742-6596/2767/9/092024
- Comparison of steady-state analytical wake models implemented in wind farm analysis software R. Mudafort et al. 10.1088/1742-6596/2767/5/052066
- Modeling of Blockage and Wake Effect: Comparison with Field data P. Maheshwari et al. 10.1088/1742-6596/2767/9/092021
- A momentum-conserving wake superposition method for wind-farm flows under pressure gradient B. Du et al. 10.1017/jfm.2024.761
- Ranking multi-fidelity model performances in reproducing internal and external wake impacts at neighbouring offshore wind farms S. Freitas et al. 10.1088/1742-6596/2767/9/092045
- A fast-running physics-based wake model for a semi-infinite wind farm M. Bastankhah et al. 10.1017/jfm.2024.282
- Wind pattern clustering of high frequent field measurements for dynamic wind farm flow control M. Becker et al. 10.1088/1742-6596/2767/3/032028
3 citations as recorded by crossref.
- Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model F. Blondel 10.5194/wes-8-141-2023
- Evolution of eddy viscosity in the wake of a wind turbine R. Scott et al. 10.5194/wes-8-449-2023
- Model predictive control of wakes for wind farm power tracking A. Sterle et al. 10.1088/1742-6596/2767/3/032005
Latest update: 16 Nov 2024
Short summary
This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
This paper introduces the cumulative-curl wake model that allows for the fast and accurate...
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