Articles | Volume 11, issue 10
https://doi.org/10.5194/wes-11-4005-2026
© Author(s) 2026. 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-11-4005-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
26 Oct 2026
Data description article |
| 26 Oct 2026
| 26 Oct 2026
Experimental wind tunnel dataset of wake and turbine measurements for wind farm control
Filippo Campagnolo
Wind Energy Institute, Technische Universität München, Garching bei München, 85748, Germany
Hamit Doruk Aktan
Wind Energy Institute, Technische Universität München, Garching bei München, 85748, Germany
Davide Bortolin
Wind Energy Institute, Technische Universität München, Garching bei München, 85748, Germany
Simone Tamaro
Wind Energy Institute, Technische Universität München, Garching bei München, 85748, Germany
Franz V. Mühle
Wind Energy Institute, Technische Universität München, Garching bei München, 85748, Germany
Wind Energy Institute, Technische Universität München, Garching bei München, 85748, Germany
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Accurate wind turbine load prediction is essential for wind farm design, optimization, and control. This study addresses both the design and implementation of location-agnostic, control-oriented load surrogate models. A key finding is that a finite level of physical complexity is sufficient for a general model to achieve converged performance. The results provide practical guidelines for reliable, computationally efficient fatigue load prediction across different implementation environments.
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As wind turbines grow to unprecedented sizes, ensuring their reliability requires advanced testing. We reviewed the current state of wind tunnel experiments, examining how scaled physical models are evaluated for wind flow, flexibility, and noise. We conclude that while laboratory testing remains essential, it is most powerful when integrated with computer simulations and real-world data. This combined approach will ultimately drive the design of more efficient and sustainable wind energy.
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Carlo L. Bottasso, Sandrine Aubrun, Nicolaos A. Cutululis, Julia Gottschall, Athanasios Kolios, Jakob Mann, and Paul Veers
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This editorial celebrates the 10th anniversary of Wind Energy Science, reflecting on a decade of rapid scientific progress and the journal’s role in advancing fundamental, interdisciplinary research. It highlights key developments in wind energy, the importance of open science and academia–industry collaboration, and emerging challenges such as data sharing and artificial intelligence. Above all, it honors the research community that has shaped the journal and looks ahead to the next decade.
Hadi Hoghooghi and Carlo L. Bottasso
Wind Energ. Sci., 11, 373–393, https://doi.org/10.5194/wes-11-373-2026, https://doi.org/10.5194/wes-11-373-2026, 2026
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We formulate and demonstrate a new digital shadow (i.e., a virtual copy) for wind turbines. The digital shadow is designed in order to be capable of mirroring the response of the machine even in complex inflow conditions. Results from field measurements illustrate the ability of the shadow to estimate loads with good accuracy, even with minimal tuning.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 10, 2705–2728, https://doi.org/10.5194/wes-10-2705-2025, https://doi.org/10.5194/wes-10-2705-2025, 2025
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We proposed a new method for active power control that uniquely combines induction control with wake steering to maximize power tracking margins. Our methodology results in significantly improved robustness against wind fluctuations and fatigue loading when compared to the state of the art.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1547–1575, https://doi.org/10.5194/wes-9-1547-2024, https://doi.org/10.5194/wes-9-1547-2024, 2024
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We develop a new simple model to predict power losses incurred by a wind turbine when it yaws out of the wind. The model reveals the effects of a number of rotor design parameters and how the turbine is governed when it yaws. The model exhibits an excellent agreement with large eddy simulations and wind tunnel measurements. We showcase the capabilities of the model by deriving the power-optimal yaw strategy for a single turbine and for a cluster of wake-interacting turbines.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
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A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
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Jenna Iori, Carlo Luigi Bottasso, and Michael Kenneth McWilliam
Wind Energ. Sci., 9, 1289–1304, https://doi.org/10.5194/wes-9-1289-2024, https://doi.org/10.5194/wes-9-1289-2024, 2024
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Franz V. Mühle, Florian M. Heckmeier, Filippo Campagnolo, and Christian Breitsamter
Wind Energ. Sci., 9, 1251–1271, https://doi.org/10.5194/wes-9-1251-2024, https://doi.org/10.5194/wes-9-1251-2024, 2024
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Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
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Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
Helena Canet, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci., 8, 1029–1047, https://doi.org/10.5194/wes-8-1029-2023, https://doi.org/10.5194/wes-8-1029-2023, 2023
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We propose a new approach to design that aims at optimal trade-offs between economic and environmental goals. New environmental metrics are defined, which quantify impacts in terms of CO2-equivalent emissions produced by the turbine over its entire life cycle. For some typical onshore installations in Germany, results indicate that a 1 % increase in the cost of energy can buy about a 5 % decrease in environmental impacts: a small loss for the individual can lead to larger gains for society.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
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The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
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).
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
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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.
Emmanouil M. Nanos, Carlo L. Bottasso, Simone Tamaro, Dimitris I. Manolas, and Vasilis A. Riziotis
Wind Energ. Sci., 7, 1641–1660, https://doi.org/10.5194/wes-7-1641-2022, https://doi.org/10.5194/wes-7-1641-2022, 2022
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A novel way of wind farm control is presented where the wake is deflected vertically to reduce interactions with downstream turbines. This is achieved by moving ballast in a floating offshore platform in order to pitch the support structure and thereby tilt the wind turbine rotor disk. The study considers the effects of this new form of wake control on the aerodynamics of the steering and wake-affected turbines, on the structure, and on the ballast motion system.
Stefan Loew and Carlo L. Bottasso
Wind Energ. Sci., 7, 1605–1625, https://doi.org/10.5194/wes-7-1605-2022, https://doi.org/10.5194/wes-7-1605-2022, 2022
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This publication presents methods to improve the awareness and control of material fatigue for wind turbines. This is achieved by enhancing a sophisticated control algorithm which utilizes wind prediction information from a laser measurement device. The simulation results indicate that the novel algorithm significantly improves the economic performance of a wind turbine. This benefit is particularly high for situations when the prediction quality is low or the prediction time frame is short.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
Short summary
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
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Short summary
This paper presents an open-access wind-tunnel dataset of actuated, scaled wind turbines with time-resolved measurements of loads, actuator commands, inflow conditions, and wakes. It covers multiple wake-control strategies (yaw steering, derating, Helix, dynamic yaw, Pulse, pitch control). Combined with numerical models, the dataset enables benchmarking, controller analysis, and validation of aeroelastic and wake models for wind farm control research.
This paper presents an open-access wind-tunnel dataset of actuated, scaled wind turbines with...
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