Articles | Volume 10, issue 6
https://doi.org/10.5194/wes-10-1055-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-10-1055-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Jun 2025
Research article |
| 11 Jun 2025
| 11 Jun 2025
A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
Maxime Lejeune
CORRESPONDING AUTHOR
Thermodynamics and Fluid Mechanics, Institute of Mechanics, Materials and Civil Engineering, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Philippe Chatelain
Thermodynamics and Fluid Mechanics, Institute of Mechanics, Materials and Civil Engineering, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Dries Allaerts
Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
deceased
Rafael Mudafort
National Renewable Energies Laboratory, Golden, CO, United States of America
Jan-Willem van Wingerden
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands
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Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
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Wind turbine wakes negatively affect wind farm performance as they impinge on downstream rotors. Wake steering reduces these losses by redirecting wakes using yaw misalignment of the upstream rotor. We develop a novel control strategy based on model predictions to implement wake steering under time-varying conditions. The controller is tested in a high-fidelity simulation environment and improves wind farm power output compared to a state-of-the-art reference controller.
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In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
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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.
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Revised manuscript accepted for WES
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Publication in WES not foreseen
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Livia Brandetti, Sebastiaan Paul Mulders, Yichao Liu, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1553–1573, https://doi.org/10.5194/wes-8-1553-2023, https://doi.org/10.5194/wes-8-1553-2023, 2023
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This research presents the additional benefits of applying an advanced combined wind speed estimator and tip-speed ratio tracking (WSE–TSR) controller compared to the baseline Kω2. Using a frequency-domain framework and an optimal calibration procedure, the WSE–TSR tracking control scheme shows a more flexible trade-off between conflicting objectives: power maximisation and load minimisation. Therefore, implementing this controller on large-scale wind turbines will facilitate their operation.
Daniel van den Berg, Delphine de Tavernier, and Jan-Willem van Wingerden
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Wind turbines placed in farms interact with their wake, lowering the power production of the wind farm. This can be mitigated using so-called wake mixing techniques. This work investigates the coupling between the pulse wake mixing technique and the motion of floating wind turbines using the pulse. Frequency response experiments and time domain simulations show that extra movement is undesired and that the
optimalexcitation frequency is heavily platform dependent.
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
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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.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
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We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Marcus Becker, Bastian Ritter, Bart Doekemeijer, Daan van der Hoek, Ulrich Konigorski, Dries Allaerts, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, https://doi.org/10.5194/wes-7-2163-2022, 2022
Short summary
Short summary
In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
Short summary
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Daan van der Hoek, Joeri Frederik, Ming Huang, Fulvio Scarano, Carlos Simao Ferreira, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 1305–1320, https://doi.org/10.5194/wes-7-1305-2022, https://doi.org/10.5194/wes-7-1305-2022, 2022
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The paper presents a wind tunnel experiment where dynamic induction control was implemented on a small-scale turbine. By periodically changing the pitch angle of the blades, the low-velocity turbine wake is perturbed, and hence it recovers at a faster rate. Small particles were released in the flow and subsequently recorded with a set of high-speed cameras. This allowed us to reconstruct the flow behind the turbine and investigate the effect of dynamic induction control on the wake.
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
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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.
Yichao Liu, Riccardo Ferrari, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 523–537, https://doi.org/10.5194/wes-7-523-2022, https://doi.org/10.5194/wes-7-523-2022, 2022
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The objective of the paper is to develop a data-driven output-constrained individual pitch control approach, which will not only mitigate the blade loads but also reduce the pitch activities. This is achieved by only reducing the blade loads violating a user-defined bound, which leads to an economically viable load control strategy. The proposed control strategy shows promising results of load reduction in the wake-rotor overlapping and turbulent sheared wind conditions.
Unai Gutierrez Santiago, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 505–521, https://doi.org/10.5194/wes-7-505-2022, https://doi.org/10.5194/wes-7-505-2022, 2022
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The gearbox is one of the main contributors to the overall cost of wind energy, and it is acknowledged that we still do not fully understand its loading. The study presented in this paper develops a new alternative method to measure input rotor torque in wind turbine gearboxes, overcoming the drawbacks related to measuring on a rotating shaft. The method presented in this paper could make measuring gearbox torque more cost-effective, which would facilitate its adoption in serial wind turbines.
Aemilius A. W. van Vondelen, Sachin T. Navalkar, Alexandros Iliopoulos, Daan C. van der Hoek, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 161–184, https://doi.org/10.5194/wes-7-161-2022, https://doi.org/10.5194/wes-7-161-2022, 2022
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The damping of an offshore wind turbine is a difficult physical quantity to predict, although it plays a major role in a cost-effective turbine design. This paper presents a review of all approaches that can be used for damping estimation directly from operational wind turbine data. As each use case is different, a novel suitability table is presented to enable the user to choose the most appropriate approach for the given availability and characteristics of measurement data.
Alessandro Fontanella, Mees Al, Jan-Willem van Wingerden, and Marco Belloli
Wind Energ. Sci., 6, 885–901, https://doi.org/10.5194/wes-6-885-2021, https://doi.org/10.5194/wes-6-885-2021, 2021
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Floating wind is a key technology to harvest the abundant wind energy resource of deep waters. This research introduces a new way of controlling the wind turbine to better deal with the action of waves. The turbine is made aware of the incoming waves, and the information is exploited to enhance power production.
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
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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
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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
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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.
Bart M. Doekemeijer, Stefan Kern, Sivateja Maturu, Stoyan Kanev, Bastian Salbert, Johannes Schreiber, Filippo Campagnolo, Carlo L. Bottasso, Simone Schuler, Friedrich Wilts, Thomas Neumann, Giancarlo Potenza, Fabio Calabretta, Federico Fioretti, and Jan-Willem van Wingerden
Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, https://doi.org/10.5194/wes-6-159-2021, 2021
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This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
Cited articles
Abkar, M., Sørensen, J. N., and Porté-Agel, F.: An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes, Energies, 11, 1838, https://doi.org/10.3390/en11071838, 2018. a
Annoni, J., Bay, C., Johnson, K., Dall'Anese, E., Quon, E., Kemper, T., and Fleming, P.: Wind direction estimation using SCADA data with consensus-based optimization, Wind Energ. Sci., 4, 355–368, https://doi.org/10.5194/wes-4-355-2019, 2019. a
Bak, C., Zahle, F., Bitsche, R., Kim, T., Yde, A., Henriksen, L. C., Hansen, M. H., Blasques, J. P. A. A., Gaunaa, M., and Natarajan, A.: The DTU 10 MW Reference Wind Turbine, Danish Wind Power Research 2013, Fredericia, Denmark, 27 to 28 May 2013, https://orbit.dtu.dk/en/publications/the-dtu-10-mw-reference-wind-turbine (last access: 3 June 2025), 2013. a, b
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, b
Bastine, D., Witha, B., Wächter, M., and Peinke, J.: Towards a Simplified DynamicWake Model Using POD Analysis, Energies, 8, 895–920, https://doi.org/10.3390/en8020895, 2015. a
Bay, C. J., Fleming, P., Doekemeijer, B., King, J., Churchfield, M., and Mudafort, R.: Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model, Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, 2023. a
Becker, M. and Lejeune, M.: Data belonging to the publication: A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies, 4TU.ResearchData [data set] https://doi.org/10.4121/29C209FA-F2A4-456D-9353-67CF81BE1AAA, 2024a. a
Becker, M. and Lejeune, M.: OFF framework, code underlying the publication A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies, Version 2, 4TU.ResearchData [code], https://doi.org/10.4121/331f86fe-5acb-4a60-99cd-7f8f0135c200, 2024. a
Becker, M., Allaerts, D., and van Wingerden, J.-W.: Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn, Energies, 15, 8589, https://doi.org/10.3390/en15228589, 2022a. a, b
Becker, M., Allaerts, D., and van Wingerden, J. W.: FLORIDyn – A Dynamic and Flexible Framework for Real-Time Wind Farm Control, J. Phys. Conf. Ser., 2265, 032103, https://doi.org/10.1088/1742-6596/2265/3/032103, 2022b. a, b, c
Becker, M., Ritter, B., Doekemeijer, B., van der Hoek, D., Konigorski, U., Allaerts, D., and van Wingerden, J.-W.: The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake, Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, 2022c. a
Braunbehrens, R., Schreiber, J., and Bottasso, C. L.: Application of an Open-Loop Dynamic Wake Model with High-Frequency SCADA Data, J. Phys. Conf. Ser., 2265, 022031, https://doi.org/10.1088/1742-6596/2265/2/022031, 2022. a
Braunbehrens, R., Tamaro, S., and Bottasso, C. L.: Towards the Multi-Scale Kalman Filtering of Dynamic Wake Models: Observing Turbulent Fluctuations and Wake Meandering, J. Phys. Conf. Ser., 2505, 012044, https://doi.org/10.1088/1742-6596/2505/1/012044, 2023. a
Chatelain, P., Backaert, S., Winckelmans, G., and Kern, S.: Large Eddy Simulation of Wind Turbine Wakes, Flow Turbul. Combust., 91, 587–605, https://doi.org/10.1007/s10494-013-9474-8, 2013. a
Churchfield, M. J., Lee, S., Michalakes, J., and Moriarty, P. J.: A Numerical Study of the Effects of Atmospheric and Wake Turbulence on Wind Turbine Dynamics, J. Turbul., 13, N14, https://doi.org/10.1080/14685248.2012.668191, 2012. a, b
Ciri, U., Rotea, M. A., and Leonardi, S.: Model-Free Control of Wind Farms: A Comparative Study between Individual and Coordinated Extremum Seeking, Renew. Energ., 113, 1033–1045, https://doi.org/10.1016/j.renene.2017.06.065, 2017. a
Coquelet, M., Bricteux, L., Moens, M., and Chatelain, P.: A Reinforcement-Learning Approach for Individual Pitch Control, Wind Energy, 25, 1343–1362, https://doi.org/10.1002/we.2734, 2022. a
Costanzo, G. and Brindley, G.: Wind Energy in Europe – 2023 Statistics and the Outlook for 2024–2023, Tech. rep., Wind Europe, https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2023-statistics-and-the-outlook-for-2024-2030/ (last access: 28 May 2025), 2024. a
Di Cave, J., Braunbehrens, R., Krause, J., Guilloré, A., and Bottasso, C. L.: Closed-Loop Coupling of a Dynamic Wake Model with a Wind Inflow Estimator, J. Phys. Conf. Ser., 2767, 032034, https://doi.org/10.1088/1742-6596/2767/3/032034, 2024. a
Doekemeijer, B. M., van der Hoek, D., and van Wingerden, J.: Closed-Loop Model-Based Wind Farm Control Using FLORIS under Time-Varying Inflow Conditions, Renew. Energ., 156, 719–730, https://doi.org/10.1016/j.renene.2020.04.007, 2020. a
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
Farrell, A., King, J., Draxl, C., Mudafort, R., Hamilton, N., Bay, C. J., Fleming, P., and Simley, E.: Design and analysis of a wake model for spatially heterogeneous flow, Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, 2021. 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., Stanley, A. P. J., Bay, C. J., King, J., Simley, E., Doekemeijer, B. M., and Mudafort, R.: Serial-Refine Method for Fast Wake-Steering Yaw Optimization, J. Phys. Conf. Ser., 2265, 032109, https://doi.org/10.1088/1742-6596/2265/3/032109, 2022. a
Foloppe, B., Munters, W., Buckingham, S., Vandevelde, L., and van Beeck, J.: Development of a Dynamic Wake Model Accounting for Wake Advection Delays and Mesoscale Wind Transients, J. Phys. Conf. Ser., 2265, 022055, https://doi.org/10.1088/1742-6596/2265/2/022055, 2022. a, b
Frederik, J. A., Doekemeijer, B. M., Mulders, S. P., and van Wingerden, J.-W.: The Helix Approach: Using Dynamic Individual Pitch Control to Enhance Wake Mixing in Wind Farms, Wind Energy, 23, 1739–1751, https://doi.org/10.1002/we.2513, 2020. a
Froude, R. E.: On the Part Played in Propulsion by Differences of Fluid Pressure, Transactions of the Institution of Naval Architects, 30, 390, 1889. a
Gebraad, P. M. O. and van Wingerden, J. W.: A Control-Oriented Dynamic Model for Wakes in Wind Plants, J. Phys. Conf. Ser., 524, 012186, https://doi.org/10.1088/1742-6596/524/1/012186, 2014. a, b
Gebraad, P. M. O., Fleming, P. A., and van Wingerden, J. W.: Wind Turbine Wake Estimation and Control Using FLORIDyn, a Control-Oriented Dynamic Wind Plant Model, American Control Conference (ACC), Chicago, Illinois, United States of America, 1–3 July 2015, https://doi.org/10.1109/ACC.2015.7170978, 2015. a
Gutknecht, J., Becker, M., Muscari, C., Lutz, T., and Wingerden, J.-W. V.: Scaling DMD Modes for Modeling Dynamic Induction Control Wakes in Various Wind Speeds, in: 2023 IEEE Conference on Control Technology and Applications (CCTA), IEEE, Bridgetown, Barbados, 16–18 August 2023, 574–580, ISBN 9798350335446, https://doi.org/10.1109/CCTA54093.2023.10252400, 2023. a
Heck, K., Johlas, H., and Howland, M.: Modelling the Induction, Thrust and Power of a Yaw-Misaligned Actuator Disk, J. Fluid Mech., 959, A9, https://doi.org/10.1017/jfm.2023.129, 2023. a
Howland, M. F., Johlas, H. M., Quesada, J. B., Pena Martinez, J. J., Zhong, W., and Larranaga, F. P.: On the Impact of the Yaw Update Frequency and Wind Direction Forecasting on Open-Loop Wake Steering Control, in: 2022 American Control Conference (ACC), IEEE, Atlanta, GA, USA, 8–10 June 2022, 4218–4223, ISBN 978-1-66545-196-3, https://doi.org/10.23919/ACC53348.2022.9867443, 2022. a
Hulsman, P., Howland, M., Göçmen, T., and Petrović, V.: Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel, J. Phys. Conf. Ser., 2767, 032049, https://doi.org/10.1088/1742-6596/2767/3/032049, 2024. a
Jensen, N. O.: A Note on Wind Generator Interaction, Riso-M, No. 2411, https://backend.orbit.dtu.dk/ws/portalfiles/portal/55857682/ris_m_2411.pdf (last accessed: 28 May 2025), 1983. a
Jonkman, J., Doubrawa, P., Hamilton, N., Annoni, J., and Fleming, P.: Validation of FAST.Farm Against Large-Eddy Simulations, J. Phys. Conf. Ser., 1037, 062005, https://doi.org/10.1088/1742-6596/1037/6/062005, 2018. 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 Wind Energy Symposium, American Institute of Aeronautics and Astronautics, Grapevine, Texas, 9–13 January 2017, ISBN 978-1-62410-456-5, https://doi.org/10.2514/6.2017-0454, 2017. a
Kanev, S.: Dynamic Wake Steering and Its Impact on Wind Farm Power Production and Yaw Actuator Duty, Renew. Energ., 146, 9–15, https://doi.org/10.1016/j.renene.2019.06.122, 2020. a
Kheirabadi, A. C. and Nagamune, R.: A Low-Fidelity Dynamic Wind Farm Model for Simulating Time-Varying Wind Conditions and Floating Platform Motion, Ocean Eng., 234, 109313, https://doi.org/10.1016/j.oceaneng.2021.109313, 2021. a
Kipke, V. and Sourkounis, C.: Three-Dimensional Dynamic Wake Model for Real-Time Wind Farm Simulation, in: 2024 32nd Mediterranean Conference on Control and Automation (MED), IEEE, Chania – Crete, Greece, 11–14 June 2024, 808–815, ISBN 9798350395440, https://doi.org/10.1109/MED61351.2024.10566140, 2024. a
Larsen, T. J., Larsen, G. C., Pedersen, M. M., Enevoldsen, K., and Madsen, H. A.: Validation of the Dynamic Wake Meander Model with Focus on Tower Loads, J. Phys. Conf. Ser., 854, 012027, https://doi.org/10.1088/1742-6596/854/1/012027, 2017. a
Lejeune, M., Moens, M., and Chatelain, P.: A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction, Frontiers in Energy Research, 10, 884068, https://doi.org/10.3389/fenrg.2022.884068, 2022. a, b, c, d
Lejeune, M., Frère, A., Moens, M., and Chatelain, P.: Are Steady-State Wake Models and Lookup Tables Sufficient to Design Profitable Wake Steering Strategies? A Large Eddy Simulation Investigation, J. Phys. Conf. Ser., 2767, 092075, https://doi.org/10.1088/1742-6596/2767/9/092075, 2024. a, b
Liew, J., Göçmen, T., Lio, A. W. H., and Larsen, G. Chr.: Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm, Wind Energ. Sci., 8, 1387–1402, https://doi.org/10.5194/wes-8-1387-2023, 2023. a
Lio, W. H., Larsen, G. C., and Thorsen, G. R.: Dynamic Wake Tracking Using a Cost-Effective LiDAR and Kalman Filtering: Design, Simulation and Full-Scale Validation, Renew. Energ., 172, 1073–1086, https://doi.org/10.1016/j.renene.2021.03.081, 2021. a
Madsen, H. A., Larsen, G. C., Larsen, T. J., Troldborg, N., and Mikkelsen, R.: Calibration and Validation of the Dynamic Wake Meandering Model for Implementation in an Aeroelastic Code, J. Sol. Energ.-T. ASME, 132, 041014, https://doi.org/10.1115/1.4002555, 2010. a
Marichal, Y., Visscher, I. D., Chatelain, P., and Winckelmans, G.: Towards Physics-Based Operational Modeling of the Unsteady Wind Turbine Response to Atmospheric and Wake-Induced Turbulence, J. Phys. Conf. Ser., 854, 012030, https://doi.org/10.1088/1742-6596/854/1/012030, 2017. a
Marten, D.: QBlade: A Modern Tool for the Aeroelastic Simulation of Wind Turbines, PhD thesis, TU Berlin, https://doi.org/10.14279/DEPOSITONCE-10646, 2020. a
Martinez, L., Leonardi, S., Churchfield, M., and Moriarty, P.: A Comparison of Actuator Disk and Actuator Line Wind Turbine Models and Best Practices for Their Use, in: 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, American Institute of Aeronautics and Astronautics, Nashville, Tennessee, 9–12 January 2012, ISBN 978-1-60086-936-5, https://doi.org/10.2514/6.2012-900, 2012. a
Miao, Y., Soltani, M. N., Hajizadeh, A., and Simani, S.: Artificial Neural Network-based Wake Steering Control under the Time-varying Inflow, in: 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), IEEE, Vallette, Malta, 1–4 July 2024, 1988–1993, ISBN 9798350373974, https://doi.org/10.1109/CoDIT62066.2024.10708147, 2024. a
Moens, M., Lejeune, M., and Chatelain, P.: An Advanced Farm Flow Estimator for the Real-Time Evaluation of the Potential Wind Power of a down-Regulated Wind Farm, J. Phys. Conf. Ser., 2767, 032044, https://doi.org/10.1088/1742-6596/2767/3/032044, 2024. a
Munters, W. and Meyers, J.: An Optimal Control Framework for Dynamic Induction Control of Wind Farms and Their Interaction with the Atmospheric Boundary Layer, Philos. T. Roy. Soc. A, 375, 20160100, https://doi.org/10.1098/rsta.2016.0100, 2017. a
Pedersen, M. M., Meyer Forsting, A., van der Laan, P., Riva, R., Alcayaga Romàn, L. A., Criado Risco, J., Friis-Møller, M., Quick, J., Schøler Christiansen, J. P., Valotta Rodrigues, R., Olsen, B. T., and Réthoré, P.-E.: PyWake 2.5.0: An Open-Source Wind Farm Simulation Tool, GitLab [code], https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake (last access: 28 May 2025), 2023. a, b
Quick, J., King, R. N., Barter, G., and Hamlington, P. E.: Multifidelity multiobjective optimization for wake-steering strategies, Wind Energ. Sci., 7, 1941–1955, https://doi.org/10.5194/wes-7-1941-2022, 2022. a
Rankine, W. J. M.: On the Mechanical Principles of the Action of Propellers, Transactions of the Institution of Naval Architects, 6, 13 pp., 1865. a
Schmidt, J., Vollmer, L., Dörenkämper, M., and Stoevesandt, B.: FOXES: Farm Optimization and eXtended yield Evaluation Software, Journal of Open Source Software, 8, 5464, https://doi.org/10.21105/joss.05464, 2023. a
Shapiro, C. R., Gayme, D. F., and Meneveau, C.: Filtered Actuator Disks: Theory and Application to Wind Turbine Models in Large Eddy Simulation, Wind Energy, 22, 1414–1420, https://doi.org/10.1002/we.2376, 2019. a
Simley, E., Fleming, P., and King, J.: Design and analysis of a wake steering controller with wind direction variability, Wind Energ. Sci., 5, 451–468, https://doi.org/10.5194/wes-5-451-2020, 2020. a
Starke, G. M., Meneveau, C., King, J. R., and Gayme, D. F.: A Dynamic Model of Wind Turbine Yaw for Active Farm Control, Wind Energy, 27, we.2884, https://doi.org/10.1002/we.2884, 2023. a
Sterle, A., Hans, C. A., and Raisch, J.: Model Predictive Control of Wakes for Wind Farm Power Tracking, J. Phys. Conf. Ser., 2767, 032005, https://doi.org/10.1088/1742-6596/2767/3/032005, 2024. a
Tamaro, S., Campagnolo, F., and Bottasso, C. L.: On the power and control of a misaligned rotor – beyond the cosine law, Wind Energ. Sci., 9, 1547–1575, https://doi.org/10.5194/wes-9-1547-2024, 2024. a, b, c
van den Broek, M. J., Sanderse, B., and Wingerden, J.-W. V.: Flow Modelling for Wind Farm Control: 2D vs. 3D, J. Phys. Conf. Ser., 2265, 032086, https://doi.org/10.1088/1742-6596/2265/3/032086, 2022. a
van den Broek, M. J., De Tavernier, D., Hulsman, P., van der Hoek, D., Sanderse, B., and van Wingerden, J.-W.: Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects, Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, 2023. a
van den Broek, M. J., Becker, M., Sanderse, B., and van Wingerden, J.-W.: Dynamic wind farm flow control using free-vortex wake models, Wind Energ. Sci., 9, 721–740, https://doi.org/10.5194/wes-9-721-2024, 2024. a, b
van der Hoek, D., Sinner, M., Simley, E., Pao, L., and van Wingerden, J.-W.: Estimation of the Ambient Wind Field From Wind Turbine Measurements Using Gaussian Process Regression, in: 2021 American Control Conference (ACC), IEEE, New Orleans, LA, USA, 25–28 May 2021, 558–563, ISBN 978-1-66544-197-1, https://doi.org/10.23919/ACC50511.2021.9483088, 2021. a
Zong, H. and Porté-Agel, F.: A Momentum-Conserving Wake Superposition Method for Wind Farm Power Prediction, J. Fluid Mech., 889, A8, https://doi.org/10.1017/jfm.2020.77, 2020. a, b
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.
Established turbine wake models are steady-state. This paper presents an open-source dynamic...
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