Articles | Volume 10, issue 6
https://doi.org/10.5194/wes-10-1033-2025
© Author(s) 2025. 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-10-1033-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jun 2025
Research article |
| 10 Jun 2025
| 10 Jun 2025
Load assessment of a wind farm considering negative and positive yaw misalignment for wake steering
National Renewable Energy Laboratory, Golden, CO, USA
Garrett Barter
National Renewable Energy Laboratory, Golden, CO, USA
Jason Jonkman
National Renewable Energy Laboratory, Golden, CO, USA
Rafael Mudafort
National Renewable Energy Laboratory, Golden, CO, USA
Christopher J. Bay
National Renewable Energy Laboratory, Golden, CO, USA
Kelsey Shaler
Shell International Exploration and Production, Houston, TX, USA
Jasper Kreeft
Shell Global Solutions International B.V., the Hague, the Netherlands
Related authors
Lucas Carmo, Jason Jonkman, and Regis Thedin
Wind Energ. Sci., 9, 1827–1847, https://doi.org/10.5194/wes-9-1827-2024, https://doi.org/10.5194/wes-9-1827-2024, 2024
Short summary
Short summary
As floating wind turbines progress to arrays with multiple units, it becomes important to understand how the wake of a floating turbine affects the performance of other units in the array. Due to the compliance of the floating substructure, the wake of a floating wind turbine may behave differently from that of a fixed turbine. In this work, we present an investigation of the mutual interaction between the motions of floating wind turbines and wakes.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Cory Frontin, Jeff Allen, Christopher J. Bay, Jared Thomas, Ethan Young, and Pietro Bortolotti
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-103, https://doi.org/10.5194/wes-2025-103, 2025
Preprint under review for WES
Short summary
Short summary
Wind farms produce energy and to do so have to occupy a non-trivial amount of space. Understanding how much energy a proposed wind farm will make (and at what cost) is technically challenging, especially when turbines are packed closely together. Plus, there's a key tradeoff in how much space a farm occupies and how cheap the energy it can produce might be: less space means more costly energy. This work shows an novel way to run computational simulations efficiently to understand that tradeoff.
Veronica Liverud Krathe, Jason Jonkman, and Erin Elizabeth Bachynski-Polić
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-92, https://doi.org/10.5194/wes-2025-92, 2025
Preprint under review for WES
Short summary
Short summary
This study looks into how changes in wind direction with height and drivetrain flexibility influence the behavior of large floating wind turbines. Using numerical simulations, it was found that these factors can significantly impact the lifetime of the turbines. These results suggest that standardized design methods may underestimate fatigue and that improved modeling could enhance turbine reliability as turbines continue to grow in size.
Marcus Becker, Maxime Lejeune, Philippe Chatelain, Dries Allaerts, Rafael Mudafort, and Jan-Willem van Wingerden
Wind Energ. Sci., 10, 1055–1075, https://doi.org/10.5194/wes-10-1055-2025, https://doi.org/10.5194/wes-10-1055-2025, 2025
Short summary
Short summary
Established turbine wake models are steady-state. This paper presents an open-source dynamic wake modeling framework that complements established steady-state wake models with dynamics. It is advantageous over steady-state wake models to describe wind farm power and energy over shorter periods. The model enables researchers to investigate the effectiveness of wind farm flow control strategies. This leads to a better utilization of wind farms and allows them to be used to their fullest extent.
Will Wiley, Jason Jonkman, and Amy Robertson
Wind Energ. Sci., 10, 941–970, https://doi.org/10.5194/wes-10-941-2025, https://doi.org/10.5194/wes-10-941-2025, 2025
Short summary
Short summary
Numerical models, used to assess loads on floating offshore wind turbines, require many input parameters to describe air and water conditions, system properties, and load calculations. All parameters have some possible range, due to uncertainty and/or variations with time. The selected values can have important effects on the uncertainty in the resulting loads. This work identifies the input parameters that have the most impact on ultimate and fatigue loads for extreme storm load cases.
James Cutler, Christopher Bay, and Andrew Ning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-172, https://doi.org/10.5194/wes-2024-172, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This study compares two methods for modeling wakes from tilted wind turbines. An optimized analytical model improves accuracy but is limited by assumptions about wake shape. In contrast, a deep learning approach captures complex wake patterns without these constraints, matching high-fidelity simulations with minimal computational effort. The comparison highlights the potential of deep learning to transform wake modeling, offering greater accuracy and efficiency for wind energy optimization.
Katarzyna Patryniak, Maurizio Collu, Jason Jonkman, Matthew Hall, Garrett Barter, Daniel Zalkind, and Andrea Coraddu
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-167, https://doi.org/10.5194/wes-2024-167, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This paper studies the Instantaneous Centre of Rotation (ICR) of Floating Offshore Wind Turbines (FOWTs). We present a method for computing the ICR and examine the correlations between the external loading, design features, ICR statistics, motions, and loads. We demonstrate how to apply the new insights to successfully modify the designs of the spar and semisubmersible FOWTs to reduce the loads in the moorings, the tower, and the blades, improving the ultimate strength and fatigue properties.
Lucas Carmo, Jason Jonkman, and Regis Thedin
Wind Energ. Sci., 9, 1827–1847, https://doi.org/10.5194/wes-9-1827-2024, https://doi.org/10.5194/wes-9-1827-2024, 2024
Short summary
Short summary
As floating wind turbines progress to arrays with multiple units, it becomes important to understand how the wake of a floating turbine affects the performance of other units in the array. Due to the compliance of the floating substructure, the wake of a floating wind turbine may behave differently from that of a fixed turbine. In this work, we present an investigation of the mutual interaction between the motions of floating wind turbines and wakes.
Kenneth Brown, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024, https://doi.org/10.5194/wes-9-1791-2024, 2024
Short summary
Short summary
This paper presents a study of the popular wind turbine design tool OpenFAST. We compare simulation results to measurements obtained from a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate turbulent flow fields through several techniques. We show that successful validation of the tool is not strongly dependent on the inflow generation technique used for mean quantities of interest. The type of inflow assimilation method has a larger effect on fatigue quantities.
Kelsey Shaler, Eliot Quon, Hristo Ivanov, and Jason Jonkman
Wind Energ. Sci., 9, 1451–1463, https://doi.org/10.5194/wes-9-1451-2024, https://doi.org/10.5194/wes-9-1451-2024, 2024
Short summary
Short summary
This paper presents a three-way verification and validation between an engineering-fidelity model, a high-fidelity model, and measured data for the wind farm structural response and wake dynamics during an evolving stable boundary layer of a small wind farm, generally with good agreement.
Francesco Papi, Jason Jonkman, Amy Robertson, and Alessandro Bianchini
Wind Energ. Sci., 9, 1069–1088, https://doi.org/10.5194/wes-9-1069-2024, https://doi.org/10.5194/wes-9-1069-2024, 2024
Short summary
Short summary
Blade element momentum (BEM) theory is the backbone of many industry-standard aerodynamic models. However, the analysis of floating offshore wind turbines (FOWTs) introduces new challenges, which could put BEM models to the test. This study systematically compares four aerodynamic models, ranging from BEM to computational fluid dynamics, in an attempt to shed light on the unsteady aerodynamic phenomena that are at stake in FOWTs and whether BEM is able to model them appropriately.
Roger Bergua, Will Wiley, Amy Robertson, Jason Jonkman, Cédric Brun, Jean-Philippe Pineau, Quan Qian, Wen Maoshi, Alec Beardsell, Joshua Cutler, Fabio Pierella, Christian Anker Hansen, Wei Shi, Jie Fu, Lehan Hu, Prokopios Vlachogiannis, Christophe Peyrard, Christopher Simon Wright, Dallán Friel, Øyvind Waage Hanssen-Bauer, Carlos Renan dos Santos, Eelco Frickel, Hafizul Islam, Arjen Koop, Zhiqiang Hu, Jihuai Yang, Tristan Quideau, Violette Harnois, Kelsey Shaler, Stefan Netzband, Daniel Alarcón, Pau Trubat, Aengus Connolly, Seán B. Leen, and Oisín Conway
Wind Energ. Sci., 9, 1025–1051, https://doi.org/10.5194/wes-9-1025-2024, https://doi.org/10.5194/wes-9-1025-2024, 2024
Short summary
Short summary
This paper provides a comparison for a floating offshore wind turbine between the motion and loading estimated by numerical models and measurements. The floating support structure is a novel design that includes a counterweight to provide floating stability to the system. The comparison between numerical models and the measurements includes system motion, tower loads, mooring line loads, and loading within the floating support structure.
Emmanuel Branlard, Jason Jonkman, Cameron Brown, and Jiatian Zhang
Wind Energ. Sci., 9, 1–24, https://doi.org/10.5194/wes-9-1-2024, https://doi.org/10.5194/wes-9-1-2024, 2024
Short summary
Short summary
In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The article present methods to obtain reduced-order models of floating wind turbines. The models are used to form a digital twin which combines measurements from the TetraSpar prototype (a full-scale floating offshore wind turbine) to estimate signals that are not typically measured.
Stefano Cioni, Francesco Papi, Leonardo Pagamonci, Alessandro Bianchini, Néstor Ramos-García, Georg Pirrung, Rémi Corniglion, Anaïs Lovera, Josean Galván, Ronan Boisard, Alessandro Fontanella, Paolo Schito, Alberto Zasso, Marco Belloli, Andrea Sanvito, Giacomo Persico, Lijun Zhang, Ye Li, Yarong Zhou, Simone Mancini, Koen Boorsma, Ricardo Amaral, Axelle Viré, Christian W. Schulz, Stefan Netzband, Rodrigo Soto-Valle, David Marten, Raquel Martín-San-Román, Pau Trubat, Climent Molins, Roger Bergua, Emmanuel Branlard, Jason Jonkman, and Amy Robertson
Wind Energ. Sci., 8, 1659–1691, https://doi.org/10.5194/wes-8-1659-2023, https://doi.org/10.5194/wes-8-1659-2023, 2023
Short summary
Short summary
Simulations of different fidelities made by the participants of the OC6 project Phase III are compared to wind tunnel wake measurements on a floating wind turbine. Results in the near wake confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the reduced frequency exceeds 0.5. In the far wake, the impact of platform motion is overestimated by simulations and even seems to be oriented to the generation of a wake less prone to dissipation.
Will Wiley, Jason Jonkman, Amy Robertson, and Kelsey Shaler
Wind Energ. Sci., 8, 1575–1595, https://doi.org/10.5194/wes-8-1575-2023, https://doi.org/10.5194/wes-8-1575-2023, 2023
Short summary
Short summary
A sensitivity analysis determined the modeling parameters for an operating floating offshore wind turbine with the biggest impact on the ultimate and fatigue loads. The loads were the most sensitive to the standard deviation of the wind speed. Ultimate and fatigue mooring loads were highly sensitive to the current speed; only the fatigue mooring loads were sensitive to wave parameters. The largest platform rotation was the most sensitive to the platform horizontal center of gravity.
Paula Doubrawa, Kelsey Shaler, and Jason Jonkman
Wind Energ. Sci., 8, 1475–1493, https://doi.org/10.5194/wes-8-1475-2023, https://doi.org/10.5194/wes-8-1475-2023, 2023
Short summary
Short summary
Wind turbines are designed to withstand any wind conditions they might encounter. This includes high-turbulence flow fields found within wind farms due to the presence of the wind turbines themselves. The international standard allows for two ways to account for wind farm turbulence in the design process. We compared both ways and found large differences between them. To avoid overdesign and enable a site-specific design, we suggest moving towards validated, higher-fidelity simulation tools.
Andrew P. J. Stanley, Christopher J. Bay, and Paul Fleming
Wind Energ. Sci., 8, 1341–1350, https://doi.org/10.5194/wes-8-1341-2023, https://doi.org/10.5194/wes-8-1341-2023, 2023
Short summary
Short summary
Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as a function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Jared J. Thomas, Nicholas F. Baker, Paul Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John Jasa, Christopher Bay, Federico Tilli, David Bieniek, Nick Robinson, Andrew P. J. Stanley, Wesley Holt, and Andrew Ning
Wind Energ. Sci., 8, 865–891, https://doi.org/10.5194/wes-8-865-2023, https://doi.org/10.5194/wes-8-865-2023, 2023
Short summary
Short summary
This work compares eight optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. Optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely spaced and roughly on a grid in the interior.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Roger Bergua, Amy Robertson, Jason Jonkman, Emmanuel Branlard, Alessandro Fontanella, Marco Belloli, Paolo Schito, Alberto Zasso, Giacomo Persico, Andrea Sanvito, Ervin Amet, Cédric Brun, Guillén Campaña-Alonso, Raquel Martín-San-Román, Ruolin Cai, Jifeng Cai, Quan Qian, Wen Maoshi, Alec Beardsell, Georg Pirrung, Néstor Ramos-García, Wei Shi, Jie Fu, Rémi Corniglion, Anaïs Lovera, Josean Galván, Tor Anders Nygaard, Carlos Renan dos Santos, Philippe Gilbert, Pierre-Antoine Joulin, Frédéric Blondel, Eelco Frickel, Peng Chen, Zhiqiang Hu, Ronan Boisard, Kutay Yilmazlar, Alessandro Croce, Violette Harnois, Lijun Zhang, Ye Li, Ander Aristondo, Iñigo Mendikoa Alonso, Simone Mancini, Koen Boorsma, Feike Savenije, David Marten, Rodrigo Soto-Valle, Christian W. Schulz, Stefan Netzband, Alessandro Bianchini, Francesco Papi, Stefano Cioni, Pau Trubat, Daniel Alarcon, Climent Molins, Marion Cormier, Konstantin Brüker, Thorsten Lutz, Qing Xiao, Zhongsheng Deng, Florence Haudin, and Akhilesh Goveas
Wind Energ. Sci., 8, 465–485, https://doi.org/10.5194/wes-8-465-2023, https://doi.org/10.5194/wes-8-465-2023, 2023
Short summary
Short summary
This work examines if the motion experienced by an offshore floating wind turbine can significantly affect the rotor performance. It was observed that the system motion results in variations in the load, but these variations are not critical, and the current simulation tools capture the physics properly. Interestingly, variations in the rotor speed or the blade pitch angle can have a larger impact than the system motion itself.
Kelsey Shaler, Benjamin Anderson, Luis A. Martínez-Tossas, Emmanuel Branlard, and Nick Johnson
Wind Energ. Sci., 8, 383–399, https://doi.org/10.5194/wes-8-383-2023, https://doi.org/10.5194/wes-8-383-2023, 2023
Short summary
Short summary
Free-vortex wake (OLAF) and low-fidelity blade-element momentum (BEM) structural results are compared to high-fidelity simulation results for a flexible downwind turbine for varying inflow conditions. Overall, OLAF results were more consistent than BEM results when compared to SOWFA results under challenging inflow conditions. Differences between OLAF and BEM results were dominated by yaw misalignment angle, with varying shear exponent and turbulence intensity causing more subtle differences.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
Short summary
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.
Koen Boorsma, Gerard Schepers, Helge Aagard Madsen, Georg Pirrung, Niels Sørensen, Galih Bangga, Manfred Imiela, Christian Grinderslev, Alexander Meyer Forsting, Wen Zhong Shen, Alessandro Croce, Stefano Cacciola, Alois Peter Schaffarczyk, Brandon Lobo, Frederic Blondel, Philippe Gilbert, Ronan Boisard, Leo Höning, Luca Greco, Claudio Testa, Emmanuel Branlard, Jason Jonkman, and Ganesh Vijayakumar
Wind Energ. Sci., 8, 211–230, https://doi.org/10.5194/wes-8-211-2023, https://doi.org/10.5194/wes-8-211-2023, 2023
Short summary
Short summary
Within the framework of the fourth phase of the International Energy Agency's (IEA) Wind Task 29, a large comparison exercise between measurements and aeroelastic simulations has been carried out. Results were obtained from more than 19 simulation tools of various fidelity, originating from 12 institutes and compared to state-of-the-art field measurements. The result is a unique insight into the current status and accuracy of rotor aerodynamic modeling.
Kelsey Shaler, Amy N. Robertson, and Jason Jonkman
Wind Energ. Sci., 8, 25–40, https://doi.org/10.5194/wes-8-25-2023, https://doi.org/10.5194/wes-8-25-2023, 2023
Short summary
Short summary
This work evaluates which wind-inflow- and wake-related parameters have the greatest influence on fatigue and ultimate loads for turbines in a small wind farm. Twenty-eight parameters were screened using an elementary effects approach to identify the parameters that lead to the largest variation in these loads of each turbine. The findings show the increased importance of non-streamwise wind components and wake parameters in fatigue and ultimate load sensitivity of downstream turbines.
Julian Quick, Ryan N. King, Garrett Barter, and Peter E. Hamlington
Wind Energ. Sci., 7, 1941–1955, https://doi.org/10.5194/wes-7-1941-2022, https://doi.org/10.5194/wes-7-1941-2022, 2022
Short summary
Short summary
Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of alignment with the incoming wind, thereby steering wakes away from downstream turbines. Trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present an optimization approach for efficiently exploring the trade-offs between power and loading during wake steering.
Michael J. LoCascio, Christopher J. Bay, Majid Bastankhah, Garrett E. Barter, Paul A. Fleming, and Luis A. Martínez-Tossas
Wind Energ. Sci., 7, 1137–1151, https://doi.org/10.5194/wes-7-1137-2022, https://doi.org/10.5194/wes-7-1137-2022, 2022
Short summary
Short summary
This work introduces the FLOW Estimation and Rose Superposition (FLOWERS) wind turbine wake model. This model analytically integrates the wake over wind directions to provide a time-averaged flow field. This new formulation is used to perform layout optimization. The FLOWERS model provides a smooth flow field over an entire wind plant at fraction of the computational cost of the standard numerical integration approach.
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022, https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Short summary
Using highly accurate simulations within a design cycle is prohibitively computationally expensive. We implement and present a multifidelity optimization method and showcase its efficacy using three different case studies. We examine aerodynamic blade design, turbine controls tuning, and a wind plant layout problem. In each case, the multifidelity method finds an optimal design that performs better than those obtained using simplified models but at a lower cost than high-fidelity optimization.
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022, https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
Short summary
In wind plants, turbines can be yawed to steer their wakes away from downstream turbines and achieve an increase in plant power. The yaw angles become expensive to solve for in large farms. This paper presents a new method to solve for the optimal turbine yaw angles in a wind plant. The yaw angles are defined as Boolean variables – each turbine is either yawed or nonyawed. With this formulation, most of the gains from wake steering can be reached with a large reduction in computational expense.
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022, https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary
Short summary
This paper summarizes efforts done to understand the impact of design parameter variations in the physical system (e.g., mass, stiffness, geometry, aerodynamic, and hydrodynamic coefficients) on the linearized system using OpenFAST in support of the development of the WEIS toolset to enable controls co-design of floating offshore wind turbines.
Emmanuel Branlard, Ian Brownstein, Benjamin Strom, Jason Jonkman, Scott Dana, and Edward Ian Baring-Gould
Wind Energ. Sci., 7, 455–467, https://doi.org/10.5194/wes-7-455-2022, https://doi.org/10.5194/wes-7-455-2022, 2022
Short summary
Short summary
In this work, we present an aerodynamic tool that can model an arbitrary collections of wings, blades, rotors, and towers. With these functionalities, the tool can be used to study and design advanced wind energy concepts, such as horizontal-axis wind turbines, vertical-axis wind turbines, kites, or multi-rotors. This article describes the key features of the tool and presents multiple applications. Field measurements of horizontal- and vertical-axis wind turbines are used for comparison.
Jared J. Thomas, Christopher J. Bay, Andrew P. J. Stanley, and Andrew Ning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-4, https://doi.org/10.5194/wes-2022-4, 2022
Revised manuscript not accepted
Short summary
Short summary
We wanted to determine if and how optimization algorithms may be exploiting inaccuracies in the simple models used for wind farm layout optimization. Comparing optimization results from a simple model to large-eddy simulations showed that even a simple model provides enough information for optimizers to find good layouts. However, varying the number of wind directions in the optimization showed that the wind resource discretization can negatively impact the optimization results.
Andrew P. J. Stanley, Jennifer King, Christopher Bay, and Andrew Ning
Wind Energ. Sci., 7, 433–454, https://doi.org/10.5194/wes-7-433-2022, https://doi.org/10.5194/wes-7-433-2022, 2022
Short summary
Short summary
In this paper, we present a computationally inexpensive model to calculate wind turbine blade fatigue caused by waking and partial waking. The model accounts for steady state on the blade, as well as wind turbulence. The model is fast enough to be used in wind farm layout optimization, which has not been possible with more expensive fatigue models in the past. The methods introduced in this paper will allow for farms with increased energy production that maintain turbine structural reliability.
Ethan Young, Jeffery Allen, John Jasa, Garrett Barter, and Ryan King
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-7, https://doi.org/10.5194/wes-2022-7, 2022
Preprint withdrawn
Short summary
Short summary
In this study, we present ways to measure the phenomenon of wind plant blockage, or the the velocity slowdown upstream from a farm, and carry out turbine layout optimizations to reduce this effect. We find that farm-wide measurements provide a better characterization of blockage compared to more localized measurements and that, in the absence of any constraint on total power output, layouts which minimize the effect of blockage are frequently characterized by streamwise alignment of turbines.
Matthias Kretschmer, Jason Jonkman, Vasilis Pettas, and Po Wen Cheng
Wind Energ. Sci., 6, 1247–1262, https://doi.org/10.5194/wes-6-1247-2021, https://doi.org/10.5194/wes-6-1247-2021, 2021
Short summary
Short summary
We perform a validation of the new simulation tool FAST.Farm for the prediction of power output and structural loads in single wake conditions with respect to measurement data from the offshore wind farm alpha ventus. With a new wake-added turbulence functionality added to FAST.Farm, good agreement between simulations and measurements is achieved for the considered quantities. We hereby give insights into load characteristics of an offshore wind turbine subjected to single wake conditions.
Andrew P. J. Stanley, Owen Roberts, Jennifer King, and Christopher J. Bay
Wind Energ. Sci., 6, 1143–1167, https://doi.org/10.5194/wes-6-1143-2021, https://doi.org/10.5194/wes-6-1143-2021, 2021
Short summary
Short summary
Wind farm layout optimization is an essential part of wind farm design. In this paper, we present different methods to determine the number of turbines in a wind farm, as well as their placement. Also in this paper we explore the effect that the objective function has on the wind farm design and found that wind farm layout is highly sensitive to the objective. The optimal number of turbines can vary greatly, from 15 to 54 for the cases in this paper, depending on the metric that is optimized.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Jennifer King, Paul Fleming, Ryan King, Luis A. Martínez-Tossas, Christopher J. Bay, Rafael Mudafort, and Eric Simley
Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, https://doi.org/10.5194/wes-6-701-2021, 2021
Short summary
Short summary
This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
Luis A. Martínez-Tossas, Jennifer King, Eliot Quon, Christopher J. Bay, Rafael Mudafort, Nicholas Hamilton, Michael F. Howland, and Paul A. Fleming
Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, https://doi.org/10.5194/wes-6-555-2021, 2021
Short summary
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.
Cited articles
Abbas, N. J., Zalkind, D. S., Pao, L., and Wright, A.: A reference open-source controller for fixed and floating offshore wind turbines, Wind Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, 2022. a
Adaramola, M. and Krogstad, P.-Å.: Experimental investigation of wake effects on wind turbine performance, Renew. Energy, 36, 2078–2086, https://doi.org/10.1016/j.renene.2011.01.024, 2011. a
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541, https://doi.org/10.1017/jfm.2016.595, 2016. a
Bastankhah, M. and Porté-Agel, F.: Wind farm power optimization via yaw angle control: A wind tunnel study, J. Renew. Sustain. Energ., 11, 023301, https://doi.org/10.1063/1.5077038, 2019. a, b
Branlard, E., Martínez-Tossas, L. A., and Jonkman, J.: A time-varying formulation of the curled wake model within the FAST.Farm framework, Wind Energy, 26, 44–63, 2023. a
Ciri, U., Rotea, M. A., and Leonardi, S.: Effect of the turbine scale on yaw control, Wind Energy, 21, 1395–1405, https://doi.org/10.1002/we.2262, 2018. a
Damiani, R., Dana, S., Annoni, J., Fleming, P., Roadman, J., van Dam, J., and Dykes, K.: Assessment of wind turbine component loads under yaw-offset conditions, Wind Energ. Sci., 3, 173–189, https://doi.org/10.5194/wes-3-173-2018, 2018. a, b, c
Doekemeijer, B. M., Kern, S., Maturu, S., Kanev, S., Salbert, B., Schreiber, J., Campagnolo, F., Bottasso, C. L., Schuler, S., Wilts, F., Neumann, T., Potenza, G., Calabretta, F., Fioretti, F., and van Wingerden, J.-W.: Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy, Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, 2021. a
Ennis, B. L., White, J. R., and Paquette, J. A.: Wind turbine blade load characterization under yaw offset at the SWiFT facility, J. Phys.: Conf. Ser., 1037, 052001, https://doi.org/10.1088/1742-6596/1037/5/052001, 2018. a
Fleming, P., Annoni, J., Shah, J. J., Wang, L., Ananthan, S., Zhang, Z., Hutchings, K., Wang, P., Chen, W., and Chen, L.: Field test of wake steering at an offshore wind farm, Wind Energ. Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017, 2017. a
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1, Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, 2019. a
Fleming, P., King, J., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Jager, D., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2, Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, 2020. a
Fleming, P. A., Gebraad, P. M., Lee, S., van Wingerden, J.-W., Johnson, K., Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Evaluating techniques for redirecting turbine wakes using SOWFA, Renew. Energy, 70, 211–218, 2014. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G. E., Abbas, N. J., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: IEA wind TCP task 37: definition of the IEA 15-megawatt offshore reference wind turbine, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO, USA, https://docs.nrel.gov/docs/fy20osti/75698.pdf (last access: 10 January 2024), 2020. a
Gebraad, P. M. O., Teeuwisse, F. W., van Wingerden, J. W., Fleming, P. A., Ruben, S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization through yaw control using a parametric model for wake effects – a CFD simulation study, Wind Energy, 19, 95–114, https://doi.org/10.1002/we.1822, 2016. a
Houck, D. R.: Review of wake management techniques for wind turbines, Wind Energy, 25, 195–220, https://doi.org/10.1002/we.2668, 2021. a
Hulsman, P., Wosnik, M., Petrović, V., Hölling, M., and Kühn, M.: Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow, Wind Energ. Science, 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, 2022. a
Jiménez, Á., Crespo, A., and Migoya, E.: Application of a LES technique to characterize the wake deflection of a wind turbine in yaw, Wind Energy, 13, 559–572, https://doi.org/10.1002/we.380, 2010. a
Jonkman, J. M., Annoni, J., Hayman, G., Jonkman, B., and Purkayastha, A.: Development of FAST.Farm: A new multi-physics engineering tool for wind-farm design and analysis, in: 35th ind Energy Symposium, 9–13 January 2017, Grapevine, Texas, https://doi.org/10.2514/6.2017-0454, 2017. a
Kanev, S., Bot, E., and Giles, J.: Wind Farm Loads under Wake Redirection Control, Energies, 13, 4088, https://doi.org/10.3390/en13164088, 2020. a
Kelley, N. D. and Jonkman, B. J.: Overview of the TurbSim stochastic inflow turbulence simulator, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO, USA, https://docs.nrel.gov/docs/fy07osti/41137.pdf (last access: 10 January 2024), 2005. a
Kuhn, M., Henry de Frahan, M., Mohan, P., Deskos, G., Churchfield, M., Cheung, L., Sharma, A., Almgren, A., Ananthan, S., Brazell, M., Martínez-Tossas, L., Thedin, R., Rood, J., Sakievich, P., Vijayakumar, G., Zhang, W., and Sprague, M.: AMR-Wind: A Performance-Portable, High-Fidelity Flow Solver for Wind Farm Simulations. Wind Energy, 28, e70010, https://doi.org/10.1002/we.70010, 2025. a
Kuhn, M. B.: Exawind/amr-wind, GitHub [code], https://github.com/exawind/amr-wind (last access: 23 May 2025), 2025. a
Martínez-Tossas, L. A., King, J., Quon, E., Bay, C. J., Mudafort, R., Hamilton, N., Howland, M. F., and Fleming, P. A.: The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows, Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, 2021. a
Medici, D. and Alfredsson, P. H.: Measurements on a wind turbine wake: 3D effects and bluff body vortex shedding, Wind Energy, 9, 219–236, https://doi.org/10.1002/we.156, 2006. a
Platt, A.: OpenFAST/openfast, GitHub [code], https://github.com/OpenFAST/openfast (last access: 23 May 2025), 2025. a
Quon, E., Doubrawa, P., and Debnath, M.: Comparison of rotor wake identification and characterization methods for the analysis of wake dynamics and evolution, J. Phys.: Conf. Ser., 1452, 012070, https://doi.org/10.1088/1742-6596/1452/1/012070, 2020. a
Shaler, K. and Jonkman, J.: FAST.Farm development and validation of structural load prediction against large eddy simulations, Wind Energy, 24, 428–449, 2021. a
Simley, E., Fleming, P., Girard, N., Alloin, L., Godefroy, E., and Duc, T.: Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, 2021. a
Stanley, A. P. J., King, J., Bay, C., and Ning, A.: A model to calculate fatigue damage caused by partial waking during wind farm optimization, Wind Energ. Sci., 7, 433–454, https://doi.org/10.5194/wes-7-433-2022, 2022. a, b
Wagenaar, J., Machielse, L., and Schepers, J.: Controlling wind in ECN's scaled wind farm, in: Proc. Europe Premier Wind Energy Event, 16–19 April 2012, Copenhagen, Denmark, 685–694, 2012. a
Zalkind, D. S. and Pao, L. Y.: The fatigue loading effects of yaw control for wind plants, in: IEEE 2016 American Control Conference (ACC), 6–8 July 2016, Boston, MA, 537–542, https://doi.org/10.1109/ACC.2016.7524969, 2016. a
Short summary
We investigate asymmetries in terms of power performance and fatigue loading on a five-turbine wind farm subject to wake steering strategies. Both the yaw misalignment angle and the wind direction were varied from negative to positive. We highlight conditions in which fatigue loading is lower while still maintaining good power gains and show that a partial wake is the source of the asymmetries observed. We provide recommendations in terms of yaw misalignment angles for a given wind direction.
We investigate asymmetries in terms of power performance and fatigue loading on a five-turbine...
Altmetrics
Final-revised paper
Preprint