Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1263-2022
© Author(s) 2022. 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-7-1263-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jun 2022
Research article |
| 21 Jun 2022
| 21 Jun 2022
Design, steady performance and wake characterization of a scaled wind turbine with pitch, torque and yaw actuation
Emmanouil M. Nanos
Wind Energy Institute, Technische Universität München,
Garching bei München, 85748, Germany
Wind Energy Institute, Technische Universität München,
Garching bei München, 85748, Germany
Filippo Campagnolo
Wind Energy Institute, Technische Universität München,
Garching bei München, 85748, Germany
Franz Mühle
Wind Energy Institute, Technische Universität München,
Garching bei München, 85748, Germany
Stefano Letizia
Center for Wind Energy, Mechanical Engineering, University of Texas
at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
G. Valerio Iungo
Center for Wind Energy, Mechanical Engineering, University of Texas
at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
Mario A. Rotea
Center for Wind Energy, Mechanical Engineering, University of Texas
at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
Related authors
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
Short summary
Short summary
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.
Raghavendra Krishnamurthy, Rob Newsom, Colleen Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna M. Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-29, https://doi.org/10.5194/wes-2024-29, 2024
Preprint under review for WES
Short summary
Short summary
The growth of wind farms in the central United States in the last decade has been staggering. This study looked at how wind farms affect the recovery of wind wakes – the disturbed air behind wind turbines. In places like the US Great Plains, phenomena such as low-level jets can form, changing how wind farms work. We studied how wind wakes recover under different weather conditions using real-world data, which is important for making wind energy more efficient and reliable.
Devesh Kumar and Mario Rotea
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-144, https://doi.org/10.5194/wes-2023-144, 2023
Preprint under review for WES
Short summary
Short summary
The performance of a wind turbine is affected by blade surface degradation due to wear and tear, dirt, bugs, icing. As blades degrade, optimal operating points such as the tip-speed ratio (TSR) can change. Re-tuning the TSR to its new optimal value can lead to recovery of energy losses under blade degradation. In this work, we utilize a real-time gradient-based algorithm to retune the TSR to its new unknown optimal value under blade degradation and demonstrate energy gains using simulations.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-134, https://doi.org/10.5194/wes-2023-134, 2023
Revised manuscript under review for WES
Short summary
Short summary
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 nerby met-mast and profiling lidar demonstrate the ability of the "rotor as a sensor" concept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-133, https://doi.org/10.5194/wes-2023-133, 2023
Revised manuscript under review for WES
Short summary
Short summary
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, even more importantly, of how the turbine is governed when it yaws. The model exhibits an excellent agreement with LES 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.
Franz Volker Mühle, Florian Marco Heckmeier, Filippo Campagnolo, and Christian Breitsamter
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-125, https://doi.org/10.5194/wes-2023-125, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Wind turbines influence each other and this 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
Short summary
Short summary
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
Short summary
Short summary
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.
Jenna Iori, Carlo Luigi Bottasso, and Michael Kenneth McWilliam
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-58, https://doi.org/10.5194/wes-2023-58, 2023
Revised manuscript accepted for WES
Short summary
Short summary
The controller of a wind turbine has an important role to regulate the power production and avoid failure of the structure. 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, higher and cheaper turbine tower can be built this way.
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Chengyu Wang, Filippo Campagnolo, Helena Canet, Daniel J. Barreiro, and Carlo L. Bottasso
Wind Energ. Sci., 6, 961–981, https://doi.org/10.5194/wes-6-961-2021, https://doi.org/10.5194/wes-6-961-2021, 2021
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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).
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2065–2093, https://doi.org/10.5194/amt-14-2065-2021, https://doi.org/10.5194/amt-14-2065-2021, 2021
Short summary
Short summary
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of velocity statistics is proposed. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, https://doi.org/10.5194/amt-14-2095-2021, 2021
Short summary
Short summary
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.
Matteo Puccioni and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 1457–1474, https://doi.org/10.5194/amt-14-1457-2021, https://doi.org/10.5194/amt-14-1457-2021, 2021
Short summary
Short summary
A procedure for correcting the turbulent-energy damping connected with spatial averaging of wind lidars is proposed. This effect of the lidar measuring process is modeled through a low-pass filter, whose order and cut-off frequency are estimated directly from the lidar data. The proposed procedure is first assessed through simultaneous and colocated lidar and sonic-anemometer measurements. Then it is applied to several datasets collected at sites with different terrain roughness.
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
Short summary
Short summary
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.
Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo
Wind Energ. Sci., 5, 1601–1622, https://doi.org/10.5194/wes-5-1601-2020, https://doi.org/10.5194/wes-5-1601-2020, 2020
Short summary
Short summary
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 5, 1537–1550, https://doi.org/10.5194/wes-5-1537-2020, https://doi.org/10.5194/wes-5-1537-2020, 2020
Short summary
Short summary
A new method is described to identify the aerodynamic characteristics of blade airfoils directly from operational data of the turbine. Improving on a previously published approach, the present method is based on a new maximum likelihood formulation that includes errors both in the outputs and the inputs. The method is demonstrated on the identification of the polars of small-scale turbines for wind tunnel testing.
Filippo Campagnolo, Robin Weber, Johannes Schreiber, and Carlo L. Bottasso
Wind Energ. Sci., 5, 1273–1295, https://doi.org/10.5194/wes-5-1273-2020, https://doi.org/10.5194/wes-5-1273-2020, 2020
Short summary
Short summary
The performance of an open-loop wake-steering controller is investigated with a new wind tunnel experiment. Three scaled wind turbines are placed on a large turntable and exposed to a turbulent inflow, resulting in dynamically varying wake interactions. The study highlights the importance of using a robust formulation and plant flow models of appropriate fidelity and the existence of possible margins for improvement by the use of dynamic controllers.
Johannes Schreiber, Carlo L. Bottasso, and Marta Bertelè
Wind Energ. Sci., 5, 867–884, https://doi.org/10.5194/wes-5-867-2020, https://doi.org/10.5194/wes-5-867-2020, 2020
Short summary
Short summary
This paper validates a method to estimate the vertical wind shear and detect the presence and location of an impinging wake with field data. Shear and wake awareness have multiple uses, from turbine and farm control to monitoring and forecasting.
Results indicate a very good correlation between the estimated vertical shear and the one measured by a met mast and a remarkable ability to locate and track the motion of an impinging wake on an affected rotor.
Johannes Schreiber, Carlo L. Bottasso, Bastian Salbert, and Filippo Campagnolo
Wind Energ. Sci., 5, 647–673, https://doi.org/10.5194/wes-5-647-2020, https://doi.org/10.5194/wes-5-647-2020, 2020
Short summary
Short summary
The paper describes a new method that uses standard historical operational data and reconstructs the flow at the rotor disk of each turbine in a wind farm. The method is based on a baseline wind farm flow and wake model, augmented with error terms that are
learnedfrom operational data using an ad hoc system identification approach. Both wind tunnel experiments and real data from a wind farm at a complex terrain site are used to show the capabilities of the new method.
Joeri Alexis Frederik, Robin Weber, Stefano Cacciola, Filippo Campagnolo, Alessandro Croce, Carlo Bottasso, and Jan-Willem van Wingerden
Wind Energ. Sci., 5, 245–257, https://doi.org/10.5194/wes-5-245-2020, https://doi.org/10.5194/wes-5-245-2020, 2020
Short summary
Short summary
The interaction between wind turbines in a wind farm through their wakes is a widely studied research area. Until recently, research was focused on finding constant turbine inputs that optimize the performance of the wind farm. However, recent studies have shown that time-varying, dynamic inputs might be more beneficial. In this paper, the validity of this approach is further investigated by implementing it in scaled wind tunnel experiments and assessing load effects, showing promising results.
Johannes Schreiber, Amr Balbaa, and Carlo L. Bottasso
Wind Energ. Sci., 5, 237–244, https://doi.org/10.5194/wes-5-237-2020, https://doi.org/10.5194/wes-5-237-2020, 2020
Short summary
Short summary
An analytical wake model with a double-Gaussian velocity distribution is used to improve on a similar formulation by Keane et al (2016). The choice of a double-Gaussian shape function is motivated by the behavior of the near-wake region that is observed in numerical simulations and experimental measurements. The model is calibrated and validated using large eddy simulations replicating scaled wind turbine experiments, yielding improved results with respect to a classical single-Gaussian profile.
Pietro Bortolotti, Helena Canet, Carlo L. Bottasso, and Jaikumar Loganathan
Wind Energ. Sci., 4, 397–406, https://doi.org/10.5194/wes-4-397-2019, https://doi.org/10.5194/wes-4-397-2019, 2019
Short summary
Short summary
The paper studies the effects of uncertainties in aeroservoelastic
wind turbine models. Uncertainties are associated with the wind
inflow characteristics and the blade surface state, and they are propagated
by means of two non-intrusive methods throughout the
aeroservoelastic model of a large conceptual offshore wind
turbine. Results are compared with a brute-force extensive Monte
Carlo sampling to assess the convergence characteristics of the
non-intrusive approaches.
Pietro Bortolotti, Abhinav Kapila, and Carlo L. Bottasso
Wind Energ. Sci., 4, 115–125, https://doi.org/10.5194/wes-4-115-2019, https://doi.org/10.5194/wes-4-115-2019, 2019
Short summary
Short summary
The paper compares upwind and downwind three-bladed configurations
for a 10 MW wind turbine in terms of power and loads. For the
downwind case, the study also considers a load-aligned solution
with active coning. Results indicate that downwind solutions are
slightly more advantageous than upwind ones, although improvements
are small. Additionally, pre-alignment is difficult to achieve in
practice, and the active coning solution is associated with very
significant engineering challenges.
Jiangang Wang, Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 4, 71–88, https://doi.org/10.5194/wes-4-71-2019, https://doi.org/10.5194/wes-4-71-2019, 2019
Short summary
Short summary
This paper describes an LES approach for the simulation of wind
turbines and their wakes. The simulation model is used to
develop a complete digital copy of experiments performed with
scaled wind turbines in a boundary layer wind tunnel, including the
passive generation of a sheared turbulent flow. Numerical results
are compared with experimental measurements, with a good overall
matching between the two.
Marta Bertelè, Carlo L. Bottasso, and Stefano Cacciola
Wind Energ. Sci., 4, 89–97, https://doi.org/10.5194/wes-4-89-2019, https://doi.org/10.5194/wes-4-89-2019, 2019
Short summary
Short summary
This paper describes a new formulation for estimating the wind
inflow at the rotor disk, based on measurements of the blade loads.
The new method improves on previous formulations by exploiting the
rotational symmetry of the problem. Experimental results obtained
with an aeroelastically scaled model in a boundary layer wind
tunnel are used for validating the proposed approach.
Marta Bertelè, Carlo L. Bottasso, and Stefano Cacciola
Wind Energ. Sci., 3, 791–803, https://doi.org/10.5194/wes-3-791-2018, https://doi.org/10.5194/wes-3-791-2018, 2018
Short summary
Short summary
This work presents a new fully automated method to correct for
pitch misalignment imbalances of wind turbine rotors. The method
has minimal requirements, as it only assumes the availability of a
sensor of sufficient accuracy and bandwidth to detect the 1P
harmonic to the desired precision and the ability to command the
pitch setting of each blade independently from the others.
Extensive numerical simulations are used to demonstrate the new
procedure.
Jiangang Wang, Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-47, https://doi.org/10.5194/wes-2018-47, 2018
Revised manuscript has not been submitted
Short summary
Short summary
This paper describes a Scale Adaptive Simulation (SAS) approach for
the numerical simulation of wind turbines and their wakes. The SAS
formulation is found to be about one order of magnitude faster than
a classical LES approach. The simulation models are compared to
each other and with experimental measurements obtained with scaled
wind turbines in a boundary layer wind tunnel.
Marta Bertelè, Carlo L. Bottasso, Stefano Cacciola, Fabiano Daher Adegas, and Sara Delport
Wind Energ. Sci., 2, 615–640, https://doi.org/10.5194/wes-2-615-2017, https://doi.org/10.5194/wes-2-615-2017, 2017
Short summary
Short summary
The rotor of a wind turbine is used to determine some important parameters of the wind, including the direction of the wind vector relative to the rotor disk and horizontal and vertical shears. The method works by using measurements provided by existing onboard load sensors. The observed wind characteristics can be used to implement advanced features in smart wind turbine and wind farm controllers.
Marijn Floris van Dooren, Filippo Campagnolo, Mikael Sjöholm, Nikolas Angelou, Torben Mikkelsen, and Martin Kühn
Wind Energ. Sci., 2, 329–341, https://doi.org/10.5194/wes-2-329-2017, https://doi.org/10.5194/wes-2-329-2017, 2017
Short summary
Short summary
We conducted measurements in a wind tunnel with the remote sensing technique lidar to map the flow around a row of three model wind turbines. Two lidars were positioned near the wind tunnel walls to measure the two-dimensional wind vector over a defined scanning line or area without influencing the flow itself. A comparison of the lidar measurements with a hot-wire probe and a thorough uncertainty analysis confirmed the usefulness of lidar technology for such flow measurements in a wind tunnel.
Mithu Debnath, Giacomo Valerio Iungo, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Scott Gunter, Julie K. Lundquist, John L. Schroeder, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 1215–1227, https://doi.org/10.5194/amt-10-1215-2017, https://doi.org/10.5194/amt-10-1215-2017, 2017
Short summary
Short summary
The XPIA experiment was conducted in 2015 at the Boulder Atmospheric Observatory to estimate capabilities of various remote-sensing techniques for the characterization of complex atmospheric flows. Among different tests, XPIA provided the unique opportunity to perform simultaneous virtual towers with Ka-band radars and scanning Doppler wind lidars. Wind speed and wind direction were assessed against lidar profilers and sonic anemometer data, highlighting a good accuracy of the data retrieved.
Mithu Debnath, G. Valerio Iungo, Ryan Ashton, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Julie K. Lundquist, William J. Shaw, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, https://doi.org/10.5194/amt-10-431-2017, 2017
Short summary
Short summary
Triple RHI scans were performed with three simultaneous scanning Doppler wind lidars and assessed with lidar profiler and sonic anemometer data. This test is part of the XPIA experiment. The scan strategy consists in two lidars performing co-planar RHI scans, while a third lidar measures the transversal velocity component. The results show that horizontal velocity and wind direction are measured with good accuracy, while the vertical velocity is typically measured with a significant error.
Katherine McCaffrey, Paul T. Quelet, Aditya Choukulkar, James M. Wilczak, Daniel E. Wolfe, Steven P. Oncley, W. Alan Brewer, Mithu Debnath, Ryan Ashton, G. Valerio Iungo, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 393–407, https://doi.org/10.5194/amt-10-393-2017, https://doi.org/10.5194/amt-10-393-2017, 2017
Short summary
Short summary
During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) field campaign, the wake and flow distortion from a 300-meter meteorological tower was identified using pairs of sonic anemometers mounted on opposite sides of the tower, as well as profiling and scanning lidars. Wind speed deficits up to 50% and TKE increases of 2 orders of magnitude were observed at wind directions in the wake, along with wind direction differences (flow deflection) outside of the wake.
Aditya Choukulkar, W. Alan Brewer, Scott P. Sandberg, Ann Weickmann, Timothy A. Bonin, R. Michael Hardesty, Julie K. Lundquist, Ruben Delgado, G. Valerio Iungo, Ryan Ashton, Mithu Debnath, Laura Bianco, James M. Wilczak, Steven Oncley, and Daniel Wolfe
Atmos. Meas. Tech., 10, 247–264, https://doi.org/10.5194/amt-10-247-2017, https://doi.org/10.5194/amt-10-247-2017, 2017
Short summary
Short summary
This paper discusses trade-offs among various wind measurement strategies using scanning Doppler lidars. It is found that the trade-off exists between being able to make highly precise point measurements versus covering large spatial extents. The highest measurement precision is achieved when multiple lidar systems make wind measurements at one point in space, while highest spatial coverage is achieved through using single lidar scanning measurements and using complex retrieval techniques.
Carlo L. Bottasso, Alessandro Croce, Federico Gualdoni, Pierluigi Montinari, and Carlo E. D. Riboldi
Wind Energ. Sci., 1, 297–310, https://doi.org/10.5194/wes-1-297-2016, https://doi.org/10.5194/wes-1-297-2016, 2016
Short summary
Short summary
The paper discusses different concepts for reducing loads on wind turbines using movable blade tips. Passive and semi-passive tip solutions move freely in response to air load fluctuations, while in the active case an actuator drives the tip motion in response to load measurements. The various solutions are compared with a standard blade and with each other in terms of their ability to reduce both fatigue and extreme loads.
Riccardo Riva, Stefano Cacciola, and Carlo Luigi Bottasso
Wind Energ. Sci., 1, 177–203, https://doi.org/10.5194/wes-1-177-2016, https://doi.org/10.5194/wes-1-177-2016, 2016
Short summary
Short summary
This paper presents a method to assess the stability of a wind turbine. The proposed approach uses the recorded time history of the system response and fits to it a periodic reduced-order model that can handle stochastic disturbances. Stability is computed by using Floquet theory on the reduced-order model. Since the method only uses response data, it is applicable to any simulation model as well as to experimental test data. The method is compared to the well-known operational modal analysis.
Pietro Bortolotti, Carlo L. Bottasso, and Alessandro Croce
Wind Energ. Sci., 1, 71–88, https://doi.org/10.5194/wes-1-71-2016, https://doi.org/10.5194/wes-1-71-2016, 2016
Short summary
Short summary
The paper presents a new method to conduct the holistic optimization of a wind turbine. The proposed approach allows one to define the rotor radius and tower height, while simultaneously performing the detailed sizing of rotor and tower. For the rotor, the procedures perform simultaneously the design both from the aerodynamic and structural points of view. The overall optimization seeks a minimum for the cost of energy, while accounting for a wide range of user-defined design constraints.
G. A. M. van Kuik, J. Peinke, R. Nijssen, D. Lekou, J. Mann, J. N. Sørensen, C. Ferreira, J. W. van Wingerden, D. Schlipf, P. Gebraad, H. Polinder, A. Abrahamsen, G. J. W. van Bussel, J. D. Sørensen, P. Tavner, C. L. Bottasso, M. Muskulus, D. Matha, H. J. Lindeboom, S. Degraer, O. Kramer, S. Lehnhoff, M. Sonnenschein, P. E. Sørensen, R. W. Künneke, P. E. Morthorst, and K. Skytte
Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, https://doi.org/10.5194/wes-1-1-2016, 2016
Cited articles
Ainslie, J. F.: Wake modelling and the prediction of turbulence properties,
in: Proceedings of the BWEA Wind Energy Conference, 19–21 March 1986, Cambridge, 115–120, 1986. a
Annoni, J., Gebraad, P. M. O., Scholbrock, A. K., Fleming, P. A., and van Wingerden, J.-W.: Analysis of axial-induction-based wind plant control using an engineering and a high-order wind plant model, Wind Energy, 19,
1135–1150, https://doi.org/10.1002/we.1891, 2016. a
Bachmann: Bachmann website, http://www.bachmann.info (last access: 10 June 2022), 2020. a
Bak, C., Zahle, F., Bitsche, R., Kim, T., Yde, A., Henriksen, L. C., Hansen,
M. H., Blasques, J. P., Gaunaa, M., and Natarajan, A.: The DTU 10-MW
Reference Wind Turbine, Danish Wind Power Research 2013, Technical University
of Denmark, DTU Wind Energy, https://backend.orbit.dtu.dk/ws/portalfiles/portal/55645274/The_DTU_10MW_Reference_Turbine_Christian_Bak.pdf (last access: 11 June 2022), 2013. a
Barlow, J. B., Rae, W. H., and Pope, A.: Low-speed wind tunnel testing, 3rd Edn., Wiley, ISBN 978-0-471-55774-6, 1999. a
Bartl, J., Mühle, F., and Saetran, L.: Wind tunnel study on power output and yaw moments for two yaw-controlled model wind turbines, Wind Energ. Sci.,
3, 489–502, https://doi.org/10.5194/wes-3-489-2018, 2018. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energy, 70, 116–123, https://doi.org/10.1016/j.renene.2014.01.002, 2014. a, b
Bastankhah, M. and Porté-Agel, F.: A wind-tunnel investigation of
wind-turbine wakes in yawed conditions, J. Phys.: Conf. Ser., 625, 012014, https://doi.org/10.1088/1742-6596/625/1/012014, 2015. 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
Bastankhah, M. and Porté-Agel, F.: A new miniature wind turbine for wind
tunnel experiments. Part II: wake structure and flow dynamics, Energies, 10,
923, https://doi.org/10.3390/en10070923, 2017a. a, b, c
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, 2017b.
a
Bastankhah, M. and Porté-Agel, F.: Wind tunnel study of the wind turbine
interaction with a boundary-layer flow: Upwind region, turbine performance,
and wake region, Phys. Fluids, 29, 065105, https://doi.org/10.1063/1.4984078, 2017c. a, b, c, d
Bertelè, M., Bottasso, C. L., and Schreiber, J.: Wind inflow observation from load harmonics: initial steps towards a field validation, Wind Energ. Sci., 6, 759–775, https://doi.org/10.5194/wes-6-759-2021, 2021. a
Bluteau, C. E., Jones, N. L., and Ivey, G. N.: Estimating turbulent kinetic
energy dissipation using the inertial subrange method in environmental flows,
Limnol. Oceanogr. Meth., 9, 302–321, https://doi.org/10.4319/lom.2011.9.302, 2011. a
Bottasso, C. L. and Campagnolo, F.: Wind tunnel testing of wind turbines and
farms, in: Handbook of Wind Energy Aerodynamics, 1st Edn., edited by: Stoevesandt, B., Schepers, G., Fuglsang, P., and Sun, Y., Springer,
ISBN 10 3030313069, ISBN 13 978-3030313067, 2022. a, b, c, d, e, f, g, h, i, j, k, l, m
Bottasso, C., Campagnolo, F., and Croce, A.: Multi-disciplinary constrained
optimization of wind turbines, Multibody Syst. Dynam., 27, 21–53,
https://doi.org/10.1007/s11044-011-9271-x, 2012. a
Bottasso, C. L., Cacciola, S., and Iriarte, X.: Calibration of wind turbine lifting line models from rotor loads, J. Wind Eng. Indust. Aerodynam., 124, 29–45, https://doi.org/10.1016/j.jweia.2013.11.003, 2014a. 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 a ABL wind tunnel,
J. Phys.: Conf. Ser., 524, 012061, 2014. a
Campagnolo, F., Petrović, V., Bottasso, C. L., and Croce, A.: Wind
tunnel testing of wake control strategies, in: Proceedings of the American
Control Conference, vol. 2016 July, Institute of Electrical and
Electronics Engineers Inc., 513–518, https://doi.org/10.1109/ACC.2016.7524965, 2016a. a
Canet, H., Bortolotti, P., and Bottasso, C. L.: On the scaling of wind turbine rotors, Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, 2021. a, b, c, d
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, b
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
Chamorro, L. P., Arndt, R., 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
Champagne, F. H.: The fine-scale structure of the turbulent velocity field,
J. Fluid Mech., 86, 67–108, https://doi.org/10.1017/S0022112078001019, 1978. 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
Cheng, W. C. and Porté-Agel, F.: A simple physically-based model for
wind-turbine wake growth in a turbulent boundary layer, Bound.-Lay. Meteorol., 169, 1–10, https://doi.org/10.1007/s10546-018-0366-2, 2018. a
Crespo, A. and Hernández, J.: Turbulence characteristics in wind-turbine
wakes, J. Wind Eng. Indust. Aerodynam., 61, 71–85, https://doi.org/10.1016/0167-6105(95)00033-X, 1996. a, b, c
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
Drela, M.: XFOIL Subsonic Airfoil Development System, MIT Aero & Astro, Boston, Massachusetts, https://web.mit.edu/drela/Public/web/xfoil/, last access: 10 June 2022. a
Durst, F.: Fluid mechanics – An introduction to the theory of fluid flows,
Springer, ISBN 13 978-3540713425, 2008. a
Fairall, C. and Larsen, S. E.: Inertial-dissipation methods and turbulent
fluxes at the air-ocean interface, Bound.-Lay. Meteorol., 34, 287–301,
https://doi.org/10.1007/BF00122383, 1986. a
Frederik, J. A., Weber, R., Cacciola, S., Campagnolo, F., Croce, A., Bottasso, C., 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, 2020. a, b, c
Hamilton, N., Kang, H., Meneveau, C. C., and Cal, R. B.: Statistical analysis of kinetic energy entrainment in a model wind turbine array boundary layer, J. Renew. Sustain. Energ., 4, 063105, https://doi.org/10.1007/978-3-030-25253-3_61, 2012. a
Heckmeier, F. M., Iglesias, D., and Breitsamter, C.: Unsteady multi-hole probe measurements of the near wake of a circular cylinder at sub-critical Reynolds numbers, Note. Numer. Fluid Mech. Multidisciplin. Design, 142, 643–652, https://doi.org/10.1007/978-3-030-25253-3_61, 2019. a
Howard, K., Hu, L., and Chamorro, L. P.: Characterizing the response of a wind turbine model under complex inflow conditions, Wind Energy, 18, 729–743, https://doi.org/10.1002/we.1724, 2015. a
Hu, H., Yang, Z., and Sarkar, P.: Dynamic wind loads and wake characteristics
of a wind turbine model in an atmospheric boundary layer wind, Exp. Fluids,
52, 1277–1294, https://doi.org/10.1007/s00348-011-1253-5, 2012. a
Iungo, G. V., Viola, F., Camarri, S., Porté-Agel, F., and Gallaire, F.:
Linear stability analysis of wind turbine wakes performed on wind tunnel
measurements, J. Fluid Mech., 737, 499–526, https://doi.org/10.1017/jfm.2013.569, 2013. a
Jonkman, J. and Jonkman, B.: FAST 8, Tech. rep., NREL,
https://nwtc.nrel.gov/FAST8 (last access: 10 June 2022), 2018. a
Kelley, C. L., Maniaci, D. C., and Resor, B. R.: Scaled Aerodynamic Wind
Turbine Design for Wake Similarity, in: 34th Wind Energy Symposium, AIAA SciTech Forum, American Institute of Aeronautics and Astronautics,
http://arc.aiaa.org/doi/10.2514/6.2016-1521 (last access: 10 June 2022), 2016. a
Lanfazame, R., Mauro, S., and Messina, M.: Numerical and experimental analysis of micro HAWTs designed for wind tunnel applications, Int. J. Energ. Environ. Eng., 7, 199–210, https://doi.org/10.1007/s40095-016-0202-8, 2016. a
Lundquist, J. K. and Bariteau, L.: Dissipation of turbulence in the wake of a
wind turbine, Bound.-Lay. Meteorol., 154, 229–241, https://doi.org/10.1007/s10546-014-9978-3, 2015. a, b, c
Mathworks: MATLAB version 9.7.0.1216025 (R2019b) Update 1, The Mathworks,
Inc., Natick, Massachusetts, https://www.mathworks.com (last access:
10 June 2022), 2019. a
McAuliffe, B. and Larose, G.: Reynolds-number and surface-modeling
sensitivities for experimental simulation of flow over complex topography, J. Wind Eng. Indust. Aerodynam., 104-106, 603–613, https://doi.org/10.1016/j.jweia.2012.03.016, 2012. a
Medici, D.: Experimental studies of wind turbine wakes – power optimization and meandering, mechanics, KTH – Royal Institute of Technology, Stockholm, http://www.diva-portal.org/smash/record.jsf?pid=diva2:14563&dswid=6871 (last access: 11 June 2022), 2006. a
Mühle, F., Campagnolo, F., Llobell, J., and Bottasso, C. L.: Design and
testing of a model-scale yaw mechanism for an experimental wind turbine
model, J. Phys.: Conf. Ser., 2265, 022094, https://doi.org/10.1088/1742-6596/2265/2/022094, 2022. a
Munters, W. and Meyers, J.: Towards practical dynamic induction control of
wind farms: analysis of optimally controlled wind-farm boundary layers and
sinusoidal induction control of first-row turbines, Wind Energ. Sci., 3,
409–425, https://doi.org/10.5194/wes-3-409-2018, 2018. a
Nanos, E., Yilmazlar, K., Zanotti, A., Croce, A., and Bottasso, C.: Wind tunnel testing of a wind turbine in complex terrain, J. Phys.: Conf. Ser., 1618, 032041, https://doi.org/10.1088/1742-6596/1618/3/032041, 2020. a
Niayifar, A. and Porté-Agel, F.: A new analytical model for wind farm power prediction, J. Phys.: Conf. Ser., 625, 012039, https://doi.org/10.1088/1742-6596/625/1/012039, 2015. a
Pedersen, T. F.: On wind turbine power performance measurements at inclined
airflow, Wind Energy, 7, 163–176, https://doi.org/10.1002/we.112, 2004. a
Perry, A. E. and Morrison, G. L.: A study of the constant-temperature hot-wire anemometer, J. Fluid Mech., 47, 577–599, https://doi.org/10.1017/S0022112071001241, 1971. a
Piper, M. D.: The effects of a frontal passage on fine-scale nocturnal boundary layer turbulence, PhD thesis, University of Colorado, Boulder, https://ui.adsabs.harvard.edu/abs/2001PhDT........26P/abstract (last access: 11 June 2022), 2001. a
Quarton, D. and Ainslie, J. F.: Turbulence in wind turbine wakes, Wind Eng., 14, 15–23, 1990. a
Sarlak, H., Nishino, T., Martínez-Tossas, L. A., Meneveau, C., and
Sørensen, J. N.: Assessment of blockage effects on the wake characteristics and power of wind turbines, Renew. Energy, 93, 340–352, https://doi.org/10.1016/j.renene.2016.01.101, 2016. a, b
Schepers, J. G.: EU projects in German Dutch Wind Tunnel, DNW, Netherlands
Energy Research Foundation, https://www.osti.gov/etdeweb/biblio/20138387 (last access: 10 June 2022), 2001. a
Schottler, J., Holling, A., Peinke, J., and Holling, M.: Design and
implementation of a controllable model wind turbine for experimental studies,
J. Phys.: Conf.e Ser., 753, 506–541, https://doi.org/10.1088/1742-6596/753/7/072030, 2016. a
Schreiber, J., Nanos, E. M., Campagnolo, F., and Bottasso, C. L.: Verification and Calibration of a Reduced Order Wind Farm Model by Wind Tunnel Experiments, J. Phys.: Conf. Ser., 854, 012041,
https://doi.org/10.1088/1742-6596/854/1/012041, 2017a. a
Schreiber, J., Nanos, E. M., Campagnolo, F., and Bottasso, C. L.: Verification and Calibration of a Reduced Order Wind Farm Model by Wind Tunnel Experiments, J. Phys.: Conf. Ser., 854, 012041, https://doi.org/10.1088/1742-6596/854/1/012041, 2017b. a, b
Schreiber, J., Cacciola, S., and Bottasso, C.: Local wind speed estimation,
with application to wake impingement detection, Renew. Energy, 116, 155–168, 2018. a
Schreiber, J., Balbaa, A., and Bottasso, C.: Brief communication: A
double-Gaussian wake model, Wind Energ. Sci., 5, 237–244,
https://doi.org/10.5194/wes-5-237-2020, 2020a. a
Schreiber, J., Bottasso, C. L., and Bertelè, M.: Field testing of a local wind inflow estimator and wake detector, Wind Energ. Sci., 5, 867–884, https://doi.org/10.5194/wes-5-867-2020, 2020b. a
Selig, M. S. and McGranahan, B. D.: Wind tunnel aerodynamic tests of six
airfoils for use on small wind turbines, J. Sol. Energ. Eng., 126, 4, https://doi.org/10.1115/1.1793208, 2004. a
Shamsoddin, S. and Porté-Agel, F.: A large-eddy simulation study of
vertical axis wind turbine wakes in the atmospheric boundary layer, Energies,
9, 366, https://doi.org/10.3390/en9050366, 2016. a
Smalikho, I. N., Banakh, V. A., Pichugina, Y. L., Brewer, W., Banta, R. M.,
Lundquist, J., and Kelley, N.: Lidar investigation of atmosphere effect on a
wind turbine wake, J. Atmos. Ocean Tech., 30, 2554–2570,
https://doi.org/10.1175/JTECH-D-12-00108.1, 2013. a
Sreenivasan, K.: On the universality of Kolomogorov constant, Phys. Fluids, 7, 2778, https://doi.org/10.1063/1.868656, 1995. a
Vermeer, L. J., Sørensen, J. N., and Crespo, A.: Wind turbine wake
aerodynamics, Prog. Aerosp. Sci., 39, 467–510, https://doi.org/10.1016/S0376-0421(03)00078-2, 2003. a
Viola, F., Iungo, G. V., Camarri, S., Porté-Agel, F., and Gallaire, F.:
Prediction of the hub vortex instability in a wind turbine wake: Stability
analysis with eddy-viscosity models calibrated on wind tunnel data, J. Fluid Mech., 750, R1, https://doi.org/10.1017/jfm.2014.263, 2014. a
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, 2020. a, b
Wang, J., Foley, S., Nanos, E. M., Yu, T., Campagnolo, F., Bottasso, C. L.,
Zanotti, A., and Croce, A.: Numerical and Experimental Study of Wake
Redirection Techniques in a Boundary Layer Wind Tunnel, J. Phys.: Conf. Ser., 854, 012048, https://doi.org/10.1088/1742-6596/854/1/012048, 2017. a
Wang, J., Wang, C., Campagnolo, F., and Bottasso, C. L.: Wake behavior and control: comparison of LES simulations and wind tunnel measurements, Wind Energ. Sci., 4, 71–88, https://doi.org/10.5194/wes-4-71-2019, 2019.
a, b, c, d
Winslow, J., Otsuka, H., Govidarajan, B., and Chopra, I.: Basic understanding
of airfoil characteristics at low Reynolds numbers (104–105), J.
Aircraft, 55, 1–12, https://doi.org/10.2514/1.C034415, 2018. a, b
Yang, Z., Sarkar, P., and Hu, H.: An Experimental Investigation on the
Aeromechanic Performance and Wake Characteristics of a Wind Turbine Model
Subjected to Pitch Motions, in: 29th AIAA Applied Aerodynamics Conference, 4–8 January 2016, San Diego, California, USA, https://doi.org/10.2514/6.2016-1997, 2016. a
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.
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake...
Altmetrics
Final-revised paper
Preprint