Articles | Volume 6, issue 3
https://doi.org/10.5194/wes-6-961-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-961-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jun 2021
Research article |
| 30 Jun 2021
| 30 Jun 2021
How realistic are the wakes of scaled wind turbine models?
Chengyu Wang
Wind Energy Institute, Technische Universität München,
85748 Garching bei München, Germany
Filippo Campagnolo
Wind Energy Institute, Technische Universität München,
85748 Garching bei München, Germany
Helena Canet
Wind Energy Institute, Technische Universität München,
85748 Garching bei München, Germany
Daniel J. Barreiro
Wind Energy Institute, Technische Universität München,
85748 Garching bei München, Germany
Wind Energy Institute, Technische Universität München,
85748 Garching bei München, Germany
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Nicola Bodini, Patrick Moriarty, Regis Thedin, Paula Doubrawa, Cristina Archer, Myra Blaylock, Carlo Bottasso, Bruno Carmo, Lawrence Cheung, Camille Dubreuil, Rogier Floors, Thomas Herges, Daniel Houck, Ali Kanjari, Colleen M. Kaul, Christopher Kelley, Ru LI, Julie K. Lundquist, Desirae Major, Anh Kiet Nguyen, Mike Optis, Luan R. C. Parada, Alfredo Peña, Julian Quick, David Ricarte, William C. Radünz, Raj K. Rai, Oscar Garcia Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-34, https://doi.org/10.5194/wes-2026-34, 2026
Preprint under review for WES
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Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more efficient wind farms.
Andreas Vad, Adrien Guilloré, Abhinav Anand, Vasilis Pettas, Anik H. Shah, Ion Lizarraga-Saenz, Maria Aparicio-Sanchez, Irene Eguinoa, Nicolau Conti Gost, Iasonas Tsaklis, Ariane Frère, Koen W. Hermans, Joseph K. Kamau, Nikolay Dimitrov, Tuhfe Göçmen, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-45, https://doi.org/10.5194/wes-2026-45, 2026
Preprint under review for WES
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A modular, computationally efficient framework for wind‑farm response modeling is presented. It combines an engineering wake model with surrogate models trained on extensive aeroelastic simulations generated using a novel method for synthetic waked and clean inflows. The wind‑farm‑agnostic framework supports multiple turbine types and layouts, enabling accurate, low‑cost predictions for design, operation, and control.
Carlo L. Bottasso, Sandrine Aubrun, Nicolaos A. Cutululis, Julia Gottschall, Athanasios Kolios, Jakob Mann, and Paul Veers
Wind Energ. Sci., 11, 347–348, https://doi.org/10.5194/wes-11-347-2026, https://doi.org/10.5194/wes-11-347-2026, 2026
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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, Davide Bortolin, Filippo Campagnolo, Franz V. Muehle, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-254, https://doi.org/10.5194/wes-2025-254, 2025
Revised manuscript under review for WES
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This work presents the scaled experimental validation of an active power control (APC) algorithm that improves wind-farm power-tracking accuracy during turbulent wind lulls. Tests in a large low-blockage wind tunnel, including dynamic wind-direction changes, show that the method outperforms three reference APC strategies, especially under high power demand, while keeping structural fatigue low.
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.
Abhinav Anand and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-101, https://doi.org/10.5194/wes-2025-101, 2025
Revised manuscript under review for WES
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We formulate a controller for wind turbines that has three main characteristics. First, it optimizes profit by balancing revenue from power generation with cost. Second, cost includes the effects of cyclic fatigue that, departing from most of the existing literature on control, is rigorously accounted for by an exact cycle counting on receding horizons. Third, it uses a model capable of learning and improving its performance based on measured or synthetic data.
Andre Thommessen, Abhinav Anand, Christoph M. Hackl, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-72, https://doi.org/10.5194/wes-2025-72, 2025
Revised manuscript accepted for WES
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We present a method to forecast inertia that accounts for wake effects in a wind farm. The approach is based on mapping forecasted site conditions to each single wind turbine in the farm through a wake model. The resulting inflow conditions are used to predict the inertia that the wind farm can provide to the grid, taking the wind turbine control strategies and operational limits into account.
Abhinav Anand, Robert Braunbehrens, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-67, https://doi.org/10.5194/wes-2025-67, 2025
Revised manuscript under review for WES
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We present a new method for wind farm control, based on the optimization of an economic cost function that accounts for revenue from power production and cost due to operation and maintenance. The new formulation also includes constraints to ensure a desired lifetime duration. The application to relevant scenarios shows consistently improved profit when compared to alternative formulations from the recent literature.
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
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
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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The controller of a wind turbine has an important role in regulating power production and avoiding structural failure. However, it is often designed after the rest of the turbine, and thus its potential is not fully exploited. An alternative is to design the structure and the controller simultaneously. This work develops a method to identify if a given turbine design can benefit from this new simultaneous design process. For example, a higher and cheaper turbine tower can be built this way.
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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Wind turbines influence each other, and these wake effects limit the power production of downstream turbines. Controlling turbines collectively and not individually can limit such effects. We experimentally investigate a control strategy increasing mixing in the wake. We want to see the potential of this so-called Helix control for power optimization and understand the flow physics. Our study shows that the control technique leads to clearly faster wake recovery and thus higher power production.
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
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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
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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.
Helena Canet, Stefan Loew, and Carlo L. Bottasso
Wind Energ. Sci., 6, 1325–1340, https://doi.org/10.5194/wes-6-1325-2021, https://doi.org/10.5194/wes-6-1325-2021, 2021
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Lidar-assisted control (LAC) is used to redesign the rotor and tower of three turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save
mass, increase hub height, or extend lifetime. The first two strategies yield reductions in the cost of energy only for the tower of the largest machine, while more interesting benefits are obtained for lifetime extension.
Marta Bertelè, Carlo L. Bottasso, and Johannes Schreiber
Wind Energ. Sci., 6, 759–775, https://doi.org/10.5194/wes-6-759-2021, https://doi.org/10.5194/wes-6-759-2021, 2021
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A previously published wind sensing method is applied to an experimental dataset obtained from a 3.5 MW turbine and a nearby hub-tall met mast. The method uses blade load harmonics to estimate rotor-equivalent shears and wind directions at the rotor disk. Results indicate the good quality of the estimated shear, both in terms of 10 min averages and of resolved time histories, and a reasonable accuracy in the estimation of the yaw misalignment.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
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The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Cited articles
Abkara, M., and Porté-Agel, F.: Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study, Phys. Fluids, 27, 035104
https://doi.org/10.1063/1.4913695, 2015. a
ANSYS: Fluent 2019, available at:
https://www.ansys.com/products/fluids/ansys-fluent, last access: 18 December 2019. a
Bartl, J. and Sætran, L. R: Experimental testing of axial induction based control strategies for wake control and wind farm optimization, J. Phys.: Conf. Ser., 753, 032035, https://doi.org/10.1088/1742-6596/753/3/032035, 2016. 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.: A new miniature wind turbine for wind tunnel experiments. Part I: Design and performance, Energies, 10, 908, https://doi.org/10.3390/en10070908, 2017. a
Bisplinghoff, R. L. and Ashley, H.: Principles of Aeroelasticity, Dover
Publications, Mineola, New York, USA, 2002. a
Bottasso, C. L. and Campagnolo, F.: Wind Tunnel Testing of Wind Turbines and Farms, in: Handbook of Wind Energy Aerodynamics, edited by: Stoevesandt, B., Schepers, G., Fuglsang, P., and Yuping, S., Springer, Cham, https://doi.org/10.1007/978-3-030-05455-7_54-1, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
Bottasso, C. L., Campagnolo, F. and Petrović, V.: Wind tunnel testing of
scaled wind turbine models: Beyond aerodynamics, J. Wind Eng. Indust. Aerodynam., 127, 11–28, https://doi.org/10.1016/j.jweia.2014.01.009, 2014a. a, b, c
Bottasso, C. L., Cacciola, S. and Iriarte, X.: Calibration of wind turbine lifting line models from rotor loads, Wind Eng. Indust. Aerodyam., 124, 29–45, https://doi.org/10.1016/j.jweia.2013.11.003, 2014b. a
Campagnolo, F.: Wind tunnel testing of scaled wind turbine models: aerodynamics and beyond, PhD Thesis, Politecnico di Milano, Milano, 2013. a
Campagnolo, F., Bottasso, C. L., and Bettini, P.: Design, manufacturing and characterization of aero-elastically scaled wind turbine blades for testing active and passive load alleviation techniques within an ABL wind tunnel, J. Phys.: Conf. Ser., 524, 012061, https://doi.org/10.1088/1742-6596/524/1/012061, 2014. a
Campagnolo, F., Petrović, V., Schreiber, J., Nanos, E. M., Croce, A., and Bottasso, C. L.: Wind tunnel testing of a closed-loop wake deflection controller for wind farm power maximization, J. Phys.: Conf. Ser., 753, 032006, https://doi.org/10.1088/1742-6596/753/3/032006, 2016. a, b, c
Campagnolo, F., Weber, R., Schreiber, J., and Bottasso, C. L.: Wind tunnel testing of wake steering with dynamic wind direction changes, Wind Energ. Sci., 5, 1273–1295, https://doi.org/10.5194/wes-5-1273-2020, 2020. a, b, c
Chamorro, L. P. and Porté-Agel, F.: A wind-tunnel investigation of wind-turbine wakes: boundary-layer turbulence effects, Bound.-Lay. Meteorol., 132, 129–149, https://doi.org/10.1007/s10546-009-9380-8, 2009. a
Chamorro, L. P. and Porté-Agel, F.: Effects of thermal stability and
incoming boundary-layer flow characteristics on wind-turbine wakes: a
wind-tunnel study, Bound.-Lay. Meteorol., 136, 515–533, https://doi.org/10.1007/s10546-010-9512-1, 2010. a, b
Chamorro, L. P., Arndt, R. E. A., and Sotiropoulos, F.: Reynolds number
dependence of turbulence statistics in the wake of wind turbines, Wind
Energy, 15, 733–742, https://doi.org/10.1002/we.501, 2012. a
Chen, T. Y. and Liou, L. R.: Blockage corrections in wind tunnel tests of small horizontal-axis wind turbines, Exp. Therm. Fluid Sci., 35, 565–569,
https://doi.org/10.1016/j.expthermflusci.2010.12.005, 2011. a
Churchfield, M. J., Schreck, S., Martínez-Tossas, L. A., Meneveau, C., and Spalart, P. R.: An advanced actuator line method for wind energy applications and beyond, in: 35th Wind Energy Symposium, 9–13 January 2017, Grapevine, Texas, USA, p. 1998, 2017. a
Dowler J. L. and Schmitz, S.: A solution-based stall delay model for horizontal-axis wind turbines, Wind Energy, 18, 1793–1813,
https://doi.org/10.1002/we.1791, 2015. a
España, G., Aubrun, S., Loyer, S., and Devinant, P.: Spatial study of the wake meandering using modelled wind turbines in a wind tunnel, Wind Energy,
14, 923–937, https://doi.org/10.1002/we.515, 2011. a
Fleming, P., Annoni, J., Churchfield, M., Martínez-Tossas, L. A., Gruchalla, K., Lawson, M., and Moriarty, P.: A simulation study demonstrating the importance of large-scale trailing vortices in wake steering, Wind Energ.
Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, 2018. a
Fleming, P. A., Pieter, M. O., Lee, S., Wingerden, J., Johnson, K., Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Evaluating techniques for redirecting turbine wakes using SOWFA, Renew. Energy, 70, 211–218, https://doi.org/10.1016/j.renene.2014.02.015, 2014. a
Frederik, J. A., Weber, R., Cacciola, S., Campagnolo, F., Croce, A., Bottasso, C. L., and van Wingerden, J. W.: Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments, Wind Energ. Sci., 5, 245–257, https://doi.org/10.5194/wes-5-245-2020, 2020a. a
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, 2020b. a
Fuglsang, P., Antoniou, I., Dahl, K. S., and Aagaard Madsen, H.: Wind tunnel tests of the FFA-W3-241, FFA-W3-301 and NACA 63-430 airfoils, Risoe-R No. 1041(EN), Forskningscenter Risoe, Risoe, 1998. a
Hassanzadeh, A., Naughton, J., Kelley, C. L., and Maniaci, D. C.: Wind turbine blade design for subscale testing, J. Phys.: Conf. Ser., 753, 022048, https://doi.org/10.1088/1742-6596/753/2/022048, 2016. a
Jasak, H.: OpenFOAM: open source CFD in research and industry, Int. J. Nav. Arch. Ocean, 1, 89–94, https://doi.org/10.2478/IJNAOE-2013-0011, 2009. a
Jasak, H. and Rigler, D.: Finite volume immersed boundary method for turbulent flow simulations, in: 9th OpenFOAM Workshop, 23–26 June 2014, Zagreb, Hrvatska, 2014. a
Jasak, H., Weller, H., and Gosman, A.: High resolution NVD differencing scheme for arbitrarily unstructured meshes, Int. J. Numer. Meth. Fluids, 31, 431–449, https://doi.org/10.1002/(SICI)1097-0363(19990930)31:2<431::AID-FLD884>3.0.CO;2-T, 1999. a
Kelly, M.: From standard wind measurements to spectral characterization: turbulence length scale and distribution, Wind Energ. Sci., 3, 533–543,
https://doi.org/10.5194/wes-3-533-2018, 2018. a
Kröger, L., Frederik, J., van Wingerden, J.-W., Peinke, J., and Hölling, M.: Generation of user defined turbulent inflow conditions by an active grid for validation experiments, J. Phys.: Conf. Ser., 1037, 052002, https://doi.org/10.1088/1742-6596/1037/5/052002, 2018. a
Lignarolo, L. E. M., Ragni, D., Ferreira, C. J., and Van Bussel, G. J. W.: Experimental comparison of a wind-turbine and of an actuator-disc near wake, J. Renew. Sustain. Energ., 8, 023301, https://doi.org/10.1063/1.4941926, 2016. a
Lissaman, P. B. S.: Low-Reynolds-number airfoils, Annu. Rev. Fluid Mech., 15, 223–239, https://doi.org/10.1146/annurev.fl.15.010183.001255, 1983. a
Martínez-Tossas, L. A., Churchfield, M. J. and Leonardi, S.: Large eddy simulations of the flow past wind turbines: actuator line and disk modeling, Wind Energy, 18, 1047–1060, https://doi.org/10.1002/we.1747, 2015. a
Meinhart, C. D., Wereley, S. T., and Santiago, J. G.: PIV measurements of a microchannel flow, Exp. Fluids., 27, 414–419, https://doi.org/10.1007/s003480050366, 1999. a
Mittal, R. and Iaccarino, G.: Immersed boundary methods, Annu. Rev. Fluid Mech., 37, 239–261, https://doi.org/10.1146/annurev.fluid.37.061903.175743, 2005. a
Okulov, V. L. and Sørensen, J. N.: Stability of helical tip vortices in a
rotor far wake, J. Fluid Mech. 576, 1–25, https://doi.org/10.1017/S0022112006004228, 2007. a
Pitt, D. M., and Peters, D. A.: Theoretical prediction of dynamic-inflow derivatives, Vertica, 5, 21–34, 1981. a
Selig, M.: Low Reynolds number airfoil design lecture notes, in: VKI Lecture Series, available at: https://m-selig.ae.illinois.edu/pubs/Selig-2003-VKI-LRN-Airfoil-Design-Lecture-Series.pdf
(last access: 7 June 2021), November 2003. a
Selig, M. and McGranahan, B.: Wind tunnel aerodynamic tests of six airfoils for use on small wind turbines, J. Sol. Energ. Eng., 126, 986–1001, https://doi.org/10.1115/1.1793208, 2004. a
Shipley, D. E., Miller, M. S., and Robinson, M. C.: Dynamic stall occurrence on a horizontal axis wind turbine blade, in: CONF-950116-8, National Renewable Energy Lab., Golden, CO, USA, 1995. a
Smagorinsky, J.: General circulation experiments with the primitive equations: I. The basic experiment, Mon. Weather Rev., 91, 99–164, https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963. a
Snel, H., Houwink, R., and Bosschers, J.: Sectional prediction of lift coefficients on rotating wind turbine blades in stall, Netherlands Energy Research Foundation, Petten, the Netherlands, 1994. a
Sørensen, J. N.: Instability of helical tip vortices in rotor wakes, J. Fluid Mech., 682, 1–4, https://doi.org/10.1017/jfm.2011.277, 2011. a
Tian, W., Ozbay, A. and Hu, H.: An experimental investigation on the wake
interferences among wind turbines sited in aligned and staggered wind farms, Wind Energy, 21, 100–114, https://doi.org/10.1002/we.2147, 2018.
a
Troldborg, N., Sørensen, J. N., and Mikkelsen, R.: Actuator line simulation of wake of wind turbine operating in turbulent inflow, J. Phys.: Conf. Ser., 75, 032031, https://doi.org/10.1088/1742-6596/75/1/012063, 2007. a
van der Laan, M. P. and Sørensen, N. N.: Why the Coriolis force turns a
wind farm wake to the right in the Northern Hemisphere, J. Phys.: Conf.
Ser., 753, 032031, https://doi.org/10.1088/1742-6596/753/3/032031, 2016. a
van Dooren, M. F., Campagnolo, F., Sjöholm, M., Angelou, N., and Mikkelsen, T.: Demonstration and uncertainty analysis of synchronised scanning lidar measurements of 2-D velocity fields in a boundary-layer wind tunnel, Wind Energ. Sci., 2, 329–341, https://doi.org/10.5194/wes-2-329-2017, 2017. a
von Karman, T.: Über den Mechanismus des Widerstandes, den ein bewegter Körper in einer Flüssigkeit erfärt, 1. Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse, Gesellschaft der Wissenschaften zu Göttingen, Gesellschaft der Wissenschaften zu Göttingen, 509–517, 1911. a
Xiao, H., Duan, L., Sui, R., and Roesgen, T.: Experimental investigations of turbulent wake behind porous disks, in: 1st Marine Energy Technology Symposium, Washington, DC, USA, April 2013. a
Wang, C., Wang, J., Campagnolo, F., Carraón, D. B., and Bottasso, C. L.: Validation of large-eddy simulation of scaled waked wind turbines in different yaw misalignment conditions, J. Phys.: Conf. Ser., 1037, 062007,
https://doi.org/10.1088/1742-6596/1037/6/062007, 2018. a, b, c
Wang, C., Campagnolo, F., and Bottasso, C. L.: Identification of airfoil polars from uncertain experimental measurements, Wind Energ. Sci., 5, 1537–1550, https://doi.org/10.5194/wes-5-1537-2020, 2020a. a, b
Wang, C., Muñoz-Simón, A. Deskos, G., Laizet, S., Palacios, R., Campagnolo, F., and Bottasso, C. L.: Code-to-code-to-experiment validation of LES-ALM wind farm simulators, J. Phys.: Conf. Ser., 1618, 062041, https://doi.org/10.1088/1742-6596/1618/6/062041, 2020b. a
Wang, C., Campagnolo, F., Sharma, A., and Bottasso, C. L.: Effects of dynamic induction control on power and loads, by LES-ALM simulations and wind tunnel experiments, J. Phys.: Conf. Ser., 1618, 022036, https://doi.org/10.1088/1742-6596/1618/2/022036, 2020c. a, b
Whale, J., Papadopoulos, K. H., Anderson, C. G., Helmis, C. G., and Skyner, D. J.: A study of the near wake structure of a wind turbine comparing measurements from laboratory and full-scale experiments, Solar Energy, 56, 621–633, https://doi.org/10.1016/0038-092X(96)00019-9, 1996. a
WindEEE: The Wind Engineering, Energy and Environment Research Institute, available at: https://www.eng.uwo.ca/windeee/, last access: 13 October 2020. a
Winslow, J., Otsuka, H., Govindarajan, B., and Chopra, I.: Basic Understanding of Airfoil Characteristics at Low Reynolds Numbers, J. Aircraft, 55, 1050–1061, https://doi.org/10.2514/1.C034415, 2018.
a
Wu, Y. and Porté-Agel, F.: Large-eddy simulation of wind-turbine wakes: evaluation of turbine parametrisations, Bound.-Lay. Meteorol., 138, 345–366, https://doi.org/10.1007/s10546-010-9569-x, 2011.
a
Zhan, L., Letizia, S., and Iungo, G. V.: LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes, Wind Energy, 23, 501–527, https://doi.org/10.1002/we.2430, 2020. a
Short summary
This paper quantifies the fidelity of the wakes generated by a small (1 m diameter) scaled wind turbine model operated in a large boundary layer wind tunnel. A detailed scaling analysis accompanied by large-eddy simulations shows that these wakes are very realistic scaled versions of the ones generated by the parent full-scale wind turbine in the field.
This paper quantifies the fidelity of the wakes generated by a small (1 m diameter) scaled wind...
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