Articles | Volume 8, issue 7
https://doi.org/10.5194/wes-8-1071-2023
© Author(s) 2023. 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-8-1071-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
11 Jul 2023
Review article |
| 11 Jul 2023
| 11 Jul 2023
Grand challenges in the design, manufacture, and operation of future wind turbine systems
National Renewable Energy Laboratory, Golden, CO 80401, USA
Carlo L. Bottasso
Wind Energy Institute, Technical University of Munich, 85748 Garching bei München, Germany
Lance Manuel
Department of Civil, Architectural and Environmental Engineering, University of Texas, Austin, TX 78712, USA
Jonathan Naughton
Department of Mechanical Engineering and Wind Energy Research Center, University of Wyoming, Laramie, WY 82071-2000, USA
Lucy Pao
Electrical, Computer, and Energy Engineering, University of Colorado, and Renewable and Sustainable Energy Institute, Boulder, CO 80309-0425, USA
Joshua Paquette
Sandia National Laboratories, Albuquerque, NM 87185, USA
Amy Robertson
National Renewable Energy Laboratory, Golden, CO 80401, USA
Michael Robinson
National Renewable Energy Laboratory, Golden, CO 80401, USA
Shreyas Ananthan
Siemens Gamesa Renewable Energy, Inc., Orlando, FL 32826, USA
Thanasis Barlas
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, 4000, Denmark
Alessandro Bianchini
Department of Industrial Engineering, Università degli Studi di Firenze, 50139 Florence, Italy
Henrik Bredmose
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, 4000, Denmark
Sergio González Horcas
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, 4000, Denmark
Jonathan Keller
National Renewable Energy Laboratory, Golden, CO 80401, USA
Helge Aagaard Madsen
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, 4000, Denmark
James Manwell
Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003, USA
Patrick Moriarty
National Renewable Energy Laboratory, Golden, CO 80401, USA
Stephen Nolet
TPI Composites, Warren, RI 02885, USA
Jennifer Rinker
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, 4000, Denmark
Related authors
Shadan Mozafari, Paul Veers, Jennifer Rinker, and Katherine Dykes
Wind Energ. Sci., 9, 799–820, https://doi.org/10.5194/wes-9-799-2024, https://doi.org/10.5194/wes-9-799-2024, 2024
Short summary
Short summary
Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Peter Graf, Katherine Dykes, Rick Damiani, Jason Jonkman, and Paul Veers
Wind Energ. Sci., 3, 475–487, https://doi.org/10.5194/wes-3-475-2018, https://doi.org/10.5194/wes-3-475-2018, 2018
Short summary
Short summary
Current approaches to wind turbine extreme load estimation are insufficient to routinely and reliably make required estimates over 50-year return periods. Our work hybridizes the two main approaches and casts the problem as stochastic optimization. However, the extreme variability in turbine response implies even an optimal sampling strategy needs unrealistic computing resources. We therefore conclude that further improvement requires better understanding of the underlying causes of loads.
Francesco Papi, Jason Jonkman, Amy Robertson, and Alessandro Bianchini
Wind Energ. Sci., 9, 1069–1088, https://doi.org/10.5194/wes-9-1069-2024, https://doi.org/10.5194/wes-9-1069-2024, 2024
Short summary
Short summary
Blade element momentum (BEM) theory is the backbone of many industry-standard aerodynamic models. However, the analysis of floating offshore wind turbines (FOWTs) introduces new challenges, which could put BEM models to the test. This study systematically compares four aerodynamic models, ranging from BEM to computational fluid dynamics, in an attempt to shed light on the unsteady aerodynamic phenomena that are at stake in FOWTs and whether BEM is able to model them appropriately.
Roger Bergua, Will Wiley, Amy Robertson, Jason Jonkman, Cédric Brun, Jean-Philippe Pineau, Quan Qian, Wen Maoshi, Alec Beardsell, Joshua Cutler, Fabio Pierella, Christian Anker Hansen, Wei Shi, Jie Fu, Lehan Hu, Prokopios Vlachogiannis, Christophe Peyrard, Christopher Simon Wright, Dallán Friel, Øyvind Waage Hanssen-Bauer, Carlos Renan dos Santos, Eelco Frickel, Hafizul Islam, Arjen Koop, Zhiqiang Hu, Jihuai Yang, Tristan Quideau, Violette Harnois, Kelsey Shaler, Stefan Netzband, Daniel Alarcón, Pau Trubat, Aengus Connolly, Seán B. Leen, and Oisín Conway
Wind Energ. Sci., 9, 1025–1051, https://doi.org/10.5194/wes-9-1025-2024, https://doi.org/10.5194/wes-9-1025-2024, 2024
Short summary
Short summary
This paper provides a comparison for a floating offshore wind turbine between the motion and loading estimated by numerical models and measurements. The floating support structure is a novel design that includes a counterweight to provide floating stability to the system. The comparison between numerical models and the measurements includes system motion, tower loads, mooring line loads, and loading within the floating support structure.
Francesco Papi, Giancarlo Troise, Robert Behrens de Luna, Joseph Saverin, Sebastian Perez-Becker, David Marten, Marie-Laure Ducasse, and Alessandro Bianchini
Wind Energ. Sci., 9, 981–1004, https://doi.org/10.5194/wes-9-981-2024, https://doi.org/10.5194/wes-9-981-2024, 2024
Short summary
Short summary
Wind turbines need to be simulated for thousands of hours to estimate design loads. Mid-fidelity numerical models are typically used for this task to strike a balance between computational cost and accuracy. The considerable displacements of floating wind turbines may be a challenge for some of these models. This paper enhances comprehension of how modeling theories affect floating wind turbine loads by comparing three codes across three turbines, simulated in a real environment.
Shadan Mozafari, Paul Veers, Jennifer Rinker, and Katherine Dykes
Wind Energ. Sci., 9, 799–820, https://doi.org/10.5194/wes-9-799-2024, https://doi.org/10.5194/wes-9-799-2024, 2024
Short summary
Short summary
Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
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.
Pier Francesco Melani, Omar Sherif Mohamed, Stefano Cioni, Francesco Balduzzi, and Alessandro Bianchini
Wind Energ. Sci., 9, 601–622, https://doi.org/10.5194/wes-9-601-2024, https://doi.org/10.5194/wes-9-601-2024, 2024
Short summary
Short summary
The actuator line method (ALM) is a powerful tool for wind turbine simulation but struggles to resolve tip effects. The reason is still unclear. To investigate this, we use advanced angle of attack sampling and vortex tracking techniques to analyze the flow around a NACA0018 finite wing, simulated with ALM and blade-resolved computational fluid dynamics. Results show that the ALM can account for tip effects if the correct angle of attack sampling and force projection strategies are adopted.
Robert Behrens de Luna, Sebastian Perez-Becker, Joseph Saverin, David Marten, Francesco Papi, Marie-Laure Ducasse, Félicien Bonnefoy, Alessandro Bianchini, and Christian-Oliver Paschereit
Wind Energ. Sci., 9, 623–649, https://doi.org/10.5194/wes-9-623-2024, https://doi.org/10.5194/wes-9-623-2024, 2024
Short summary
Short summary
A novel hydrodynamic module of QBlade is validated on three floating offshore wind turbine concepts with experiments and two widely used simulation tools. Further, a recently proposed method to enhance the prediction of slowly varying drift forces is adopted and tested in varying met-ocean conditions. The hydrodynamic capability of QBlade matches the current state of the art and demonstrates significant improvement regarding the prediction of slowly varying drift forces with the enhanced model.
Kaylie L. Roach, Matthew A. Lackner, and James F. Manwell
Wind Energ. Sci., 8, 1873–1891, https://doi.org/10.5194/wes-8-1873-2023, https://doi.org/10.5194/wes-8-1873-2023, 2023
Short summary
Short summary
This paper presents an upscaling methodology for floating offshore wind turbine platforms using two case studies. The offshore wind turbine industry is trending towards fewer, larger offshore wind turbines within a farm, which is motivated by the per unit cost of a wind farm (including installation, interconnection, and maintenance costs). The results show the platform steel mass to be favorable with upscaling.
Helge Aagaard Madsen
Wind Energ. Sci., 8, 1853–1872, https://doi.org/10.5194/wes-8-1853-2023, https://doi.org/10.5194/wes-8-1853-2023, 2023
Short summary
Short summary
We present a linear analytical solution for a two-dimensional (2-D) actuator disc (AD) for a plane disc, a yawed disc and a coned disc. Comparisons of the 2-D model with three-dimensional computational fluid dynamics (CFD) AD simulations for a circular yawed disc and with an axis-symmetric CFD simulation of a coned disc show good correlation for the normal velocity component of the disc. This indicates that the 2-D AD model could form the basis for a consistent, simple new rotor induction model.
Stefano Cioni, Francesco Papi, Leonardo Pagamonci, Alessandro Bianchini, Néstor Ramos-García, Georg Pirrung, Rémi Corniglion, Anaïs Lovera, Josean Galván, Ronan Boisard, Alessandro Fontanella, Paolo Schito, Alberto Zasso, Marco Belloli, Andrea Sanvito, Giacomo Persico, Lijun Zhang, Ye Li, Yarong Zhou, Simone Mancini, Koen Boorsma, Ricardo Amaral, Axelle Viré, Christian W. Schulz, Stefan Netzband, Rodrigo Soto-Valle, David Marten, Raquel Martín-San-Román, Pau Trubat, Climent Molins, Roger Bergua, Emmanuel Branlard, Jason Jonkman, and Amy Robertson
Wind Energ. Sci., 8, 1659–1691, https://doi.org/10.5194/wes-8-1659-2023, https://doi.org/10.5194/wes-8-1659-2023, 2023
Short summary
Short summary
Simulations of different fidelities made by the participants of the OC6 project Phase III are compared to wind tunnel wake measurements on a floating wind turbine. Results in the near wake confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the reduced frequency exceeds 0.5. In the far wake, the impact of platform motion is overestimated by simulations and even seems to be oriented to the generation of a wake less prone to dissipation.
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 accepted 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.
Will Wiley, Jason Jonkman, Amy Robertson, and Kelsey Shaler
Wind Energ. Sci., 8, 1575–1595, https://doi.org/10.5194/wes-8-1575-2023, https://doi.org/10.5194/wes-8-1575-2023, 2023
Short summary
Short summary
A sensitivity analysis determined the modeling parameters for an operating floating offshore wind turbine with the biggest impact on the ultimate and fatigue loads. The loads were the most sensitive to the standard deviation of the wind speed. Ultimate and fatigue mooring loads were highly sensitive to the current speed; only the fatigue mooring loads were sensitive to wave parameters. The largest platform rotation was the most sensitive to the platform horizontal center of gravity.
Andrea Gamberini, Thanasis Barlas, Alejandro Gomez Gonzalez, and Helge Aagaard Madsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-63, https://doi.org/10.5194/wes-2023-63, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Movable surfaces on wind turbine (WT) blades, called active flaps, can reduce the cost of wind energy. However, they still need extensive testing. This study shows that the computer model used to design a WT with flaps aligns well with measurements obtained from a 3 month test on a commercial WT featuring a prototype flap. Particularly during flap actuation, there were minimal differences between simulated and measured data. These findings assure the reliability of WT designs incorporating flaps
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.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Roger Bergua, Amy Robertson, Jason Jonkman, Emmanuel Branlard, Alessandro Fontanella, Marco Belloli, Paolo Schito, Alberto Zasso, Giacomo Persico, Andrea Sanvito, Ervin Amet, Cédric Brun, Guillén Campaña-Alonso, Raquel Martín-San-Román, Ruolin Cai, Jifeng Cai, Quan Qian, Wen Maoshi, Alec Beardsell, Georg Pirrung, Néstor Ramos-García, Wei Shi, Jie Fu, Rémi Corniglion, Anaïs Lovera, Josean Galván, Tor Anders Nygaard, Carlos Renan dos Santos, Philippe Gilbert, Pierre-Antoine Joulin, Frédéric Blondel, Eelco Frickel, Peng Chen, Zhiqiang Hu, Ronan Boisard, Kutay Yilmazlar, Alessandro Croce, Violette Harnois, Lijun Zhang, Ye Li, Ander Aristondo, Iñigo Mendikoa Alonso, Simone Mancini, Koen Boorsma, Feike Savenije, David Marten, Rodrigo Soto-Valle, Christian W. Schulz, Stefan Netzband, Alessandro Bianchini, Francesco Papi, Stefano Cioni, Pau Trubat, Daniel Alarcon, Climent Molins, Marion Cormier, Konstantin Brüker, Thorsten Lutz, Qing Xiao, Zhongsheng Deng, Florence Haudin, and Akhilesh Goveas
Wind Energ. Sci., 8, 465–485, https://doi.org/10.5194/wes-8-465-2023, https://doi.org/10.5194/wes-8-465-2023, 2023
Short summary
Short summary
This work examines if the motion experienced by an offshore floating wind turbine can significantly affect the rotor performance. It was observed that the system motion results in variations in the load, but these variations are not critical, and the current simulation tools capture the physics properly. Interestingly, variations in the rotor speed or the blade pitch angle can have a larger impact than the system motion itself.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
Short summary
Short summary
Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Koen Boorsma, Gerard Schepers, Helge Aagard Madsen, Georg Pirrung, Niels Sørensen, Galih Bangga, Manfred Imiela, Christian Grinderslev, Alexander Meyer Forsting, Wen Zhong Shen, Alessandro Croce, Stefano Cacciola, Alois Peter Schaffarczyk, Brandon Lobo, Frederic Blondel, Philippe Gilbert, Ronan Boisard, Leo Höning, Luca Greco, Claudio Testa, Emmanuel Branlard, Jason Jonkman, and Ganesh Vijayakumar
Wind Energ. Sci., 8, 211–230, https://doi.org/10.5194/wes-8-211-2023, https://doi.org/10.5194/wes-8-211-2023, 2023
Short summary
Short summary
Within the framework of the fourth phase of the International Energy Agency's (IEA) Wind Task 29, a large comparison exercise between measurements and aeroelastic simulations has been carried out. Results were obtained from more than 19 simulation tools of various fidelity, originating from 12 institutes and compared to state-of-the-art field measurements. The result is a unique insight into the current status and accuracy of rotor aerodynamic modeling.
Kelsey Shaler, Amy N. Robertson, and Jason Jonkman
Wind Energ. Sci., 8, 25–40, https://doi.org/10.5194/wes-8-25-2023, https://doi.org/10.5194/wes-8-25-2023, 2023
Short summary
Short summary
This work evaluates which wind-inflow- and wake-related parameters have the greatest influence on fatigue and ultimate loads for turbines in a small wind farm. Twenty-eight parameters were screened using an elementary effects approach to identify the parameters that lead to the largest variation in these loads of each turbine. The findings show the increased importance of non-streamwise wind components and wake parameters in fatigue and ultimate load sensitivity of downstream turbines.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
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).
Christian Grinderslev, Niels Nørmark Sørensen, Georg Raimund Pirrung, and Sergio González Horcas
Wind Energ. Sci., 7, 2201–2213, https://doi.org/10.5194/wes-7-2201-2022, https://doi.org/10.5194/wes-7-2201-2022, 2022
Short summary
Short summary
As wind turbines increase in size, the risk of flow-induced instabilities increases. This study investigates the phenomenon of vortex-induced vibrations (VIVs) on a large 10 MW wind turbine blade using two high-fidelity methods. It is found that VIVs can occur with multiple equilibrium states for the same flow case, showing an dependence on the initial conditions. This means that a blade which is stable in a flow can become unstable if, e.g., a turbine operation provokes an initial vibration.
Alessandro Bianchini, Galih Bangga, Ian Baring-Gould, Alessandro Croce, José Ignacio Cruz, Rick Damiani, Gareth Erfort, Carlos Simao Ferreira, David Infield, Christian Navid Nayeri, George Pechlivanoglou, Mark Runacres, Gerard Schepers, Brent Summerville, David Wood, and Alice Orrell
Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, https://doi.org/10.5194/wes-7-2003-2022, 2022
Short summary
Short summary
The paper is part of the Grand Challenges Papers for Wind Energy. It provides a status of small wind turbine technology in terms of technical maturity, diffusion, and cost. Then, five grand challenges that are thought to be key to fostering the development of the technology are proposed. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Improvement areas are highlighted, within which 10 key enabling actions are finally proposed to the wind community.
Thanasis Barlas, Georg Raimund Pirrung, Néstor Ramos-García, Sergio González Horcas, Ang Li, and Helge Aagaard Madsen
Wind Energ. Sci., 7, 1957–1973, https://doi.org/10.5194/wes-7-1957-2022, https://doi.org/10.5194/wes-7-1957-2022, 2022
Short summary
Short summary
An aeroelastically optimized curved wind turbine blade tip is designed, manufactured, and tested on a novel outdoor rotating rig facility at the Risø campus of the Technical University of Denmark. Detailed aerodynamic measurements for various atmospheric conditions and results are compared to a series of in-house aeroelastic tools with a range of fidelities in aerodynamic modeling. The comparison highlights details in the ability of the codes to predict the performance of such a curved tip.
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.
Mads H. Aa. Madsen, Frederik Zahle, Sergio González Horcas, Thanasis K. Barlas, and Niels N. Sørensen
Wind Energ. Sci., 7, 1471–1501, https://doi.org/10.5194/wes-7-1471-2022, https://doi.org/10.5194/wes-7-1471-2022, 2022
Short summary
Short summary
This work presents a shape optimization framework based on computational fluid dynamics. The design framework is used to optimize wind turbine blade tips for maximum power increase while avoiding that extra loading is incurred. The final results are shown to align well with related literature. The resulting tip shape could be mounted on already installed wind turbines as a sleeve-like solution or be conceived as part of a modular blade with tips designed for site-specific conditions.
Ang Li, Mac Gaunaa, Georg Raimund Pirrung, Alexander Meyer Forsting, and Sergio González Horcas
Wind Energ. Sci., 7, 1341–1365, https://doi.org/10.5194/wes-7-1341-2022, https://doi.org/10.5194/wes-7-1341-2022, 2022
Short summary
Short summary
A consistent method of using two-dimensional airfoil data when using generalized lifting-line methods for the aerodynamic load calculation of non-planar horizontal-axis wind turbines is described. The important conclusions from the unsteady two-dimensional airfoil aerodynamics are highlighted. The impact of using a simplified approach instead of using the full model on the prediction of the aerodynamic performance of non-planar rotors is shown numerically for different aerodynamic models.
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.
Edward Hart, Adam Stock, George Elderfield, Robin Elliott, James Brasseur, Jonathan Keller, Yi Guo, and Wooyong Song
Wind Energ. Sci., 7, 1209–1226, https://doi.org/10.5194/wes-7-1209-2022, https://doi.org/10.5194/wes-7-1209-2022, 2022
Short summary
Short summary
We consider characteristics and drivers of loads experienced by wind turbine main bearings using simplified models of hub and main-bearing configurations. Influences of deterministic wind characteristics are investigated for 5, 7.5, and 10 MW turbine models. Load response to gusts and wind direction changes are also considered. Cubic load scaling is observed, veer is identified as an important driver of load fluctuations, and strong links between control and main-bearing load response are shown.
Jörg Alber, Marinos Manolesos, Guido Weinzierl-Dlugosch, Johannes Fischer, Alexander Schönmeier, Christian Navid Nayeri, Christian Oliver Paschereit, Joachim Twele, Jens Fortmann, Pier Francesco Melani, and Alessandro Bianchini
Wind Energ. Sci., 7, 943–965, https://doi.org/10.5194/wes-7-943-2022, https://doi.org/10.5194/wes-7-943-2022, 2022
Short summary
Short summary
This paper investigates the potentials and the limitations of mini Gurney flaps and their combination with vortex generators for improved rotor blade performance of wind turbines. These small passive add-ons are installed in order to increase the annual energy production by mitigating the effects of both early separation toward the root region and surface erosion toward the tip region of the blade. As such, this study contributes to the reliable and long-term generation of renewable energy.
Yong Su Jung, Ganesh Vijayakumar, Shreyas Ananthan, and James Baeder
Wind Energ. Sci., 7, 603–622, https://doi.org/10.5194/wes-7-603-2022, https://doi.org/10.5194/wes-7-603-2022, 2022
Short summary
Short summary
In RANS CFD, the eN-based method showed its superiority over local correlation-based transition models (LCTMs) coupled with the SST turbulence model for predicting transition behavior at high-Reynolds-number flows (3–15 million). We evaluated the performance of two LCTMs coupled with the SA turbulence model. As a result, the SA-based two-equation transition model showed a comparable performance with the eN-based method and better glide ratio (L/D) predictions than the SST-based model.
Rodrigo Soto-Valle, Stefano Cioni, Sirko Bartholomay, Marinos Manolesos, Christian Navid Nayeri, Alessandro Bianchini, and Christian Oliver Paschereit
Wind Energ. Sci., 7, 585–602, https://doi.org/10.5194/wes-7-585-2022, https://doi.org/10.5194/wes-7-585-2022, 2022
Short summary
Short summary
This paper compares different vortex identification methods to evaluate their suitability to study the tip vortices of a wind turbine. The assessment is done through experimental data from the wake of a wind turbine model. Results show comparability in some aspects as well as significant differences, providing evidence to justify further comparisons. Therefore, this study proves that the selection of the most suitable postprocessing methods of tip vortex data is pivotal to ensure robust results.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
Short summary
Short summary
This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps.
Ang Li, Georg Raimund Pirrung, Mac Gaunaa, Helge Aagaard Madsen, and Sergio González Horcas
Wind Energ. Sci., 7, 129–160, https://doi.org/10.5194/wes-7-129-2022, https://doi.org/10.5194/wes-7-129-2022, 2022
Short summary
Short summary
An engineering aerodynamic model for the swept horizontal-axis wind turbine blades is proposed. It uses a combination of analytical results and engineering approximations. The performance of the model is comparable with heavier high-fidelity models but has similarly low computational cost as currently used low-fidelity models. The model could be used for an efficient and accurate load calculation of swept wind turbine blades and could eventually be integrated in a design optimization framework.
Ang Li, Mac Gaunaa, Georg Raimund Pirrung, and Sergio González Horcas
Wind Energ. Sci., 7, 75–104, https://doi.org/10.5194/wes-7-75-2022, https://doi.org/10.5194/wes-7-75-2022, 2022
Short summary
Short summary
An engineering aerodynamic model for non-planar horizontal-axis wind turbines is proposed. The performance of the model is comparable with high-fidelity models but has similarly low computational cost as currently used low-fidelity models, which do not have the capability to model non-planar rotors. The developed model could be used for an efficient and accurate load calculation of non-planar wind turbines and eventually be integrated in a design optimization framework.
Nikhar J. Abbas, Daniel S. Zalkind, Lucy Pao, and Alan Wright
Wind Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, https://doi.org/10.5194/wes-7-53-2022, 2022
Short summary
Short summary
The publication of the Reference Open-Source Controller (ROSCO) provides a controller and generic controller tuning process to the wind energy research community that can perform comparably or better than existing reference wind turbine controllers and includes features that are consistent with industry standards. Notably, ROSCO provides the first known open-source controller with features that specifically address floating offshore wind turbine control.
Ernesto Camarena, Evan Anderson, Josh Paquette, Pietro Bortolotti, Roland Feil, and Nick Johnson
Wind Energ. Sci., 7, 19–35, https://doi.org/10.5194/wes-7-19-2022, https://doi.org/10.5194/wes-7-19-2022, 2022
Short summary
Short summary
The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway to further increasing the size of land-based wind turbines.
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.
Thanasis Barlas, Georg Raimund Pirrung, Néstor Ramos-García, Sergio González Horcas, Robert Flemming Mikkelsen, Anders Smærup Olsen, and Mac Gaunaa
Wind Energ. Sci., 6, 1311–1324, https://doi.org/10.5194/wes-6-1311-2021, https://doi.org/10.5194/wes-6-1311-2021, 2021
Short summary
Short summary
Curved blade tips can potentially have a significant impact on wind turbine performance and loads. A swept tip shape optimized for wind turbine applications is tested in a wind tunnel. A range of numerical aerodynamic simulation tools with various levels of fidelity are compared. We show that all numerical tools except for the simplest blade element momentum based are in good agreement with the measurements, suggesting the required level of model fidelity necessary for the design of such tips.
Pietro Bortolotti, Nick Johnson, Nikhar J. Abbas, Evan Anderson, Ernesto Camarena, and Joshua Paquette
Wind Energ. Sci., 6, 1277–1290, https://doi.org/10.5194/wes-6-1277-2021, https://doi.org/10.5194/wes-6-1277-2021, 2021
Short summary
Short summary
The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway for further increasing the size of land-based wind turbines.
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.
Mohammad Youssef Mahfouz, Climent Molins, Pau Trubat, Sergio Hernández, Fernando Vigara, Antonio Pegalajar-Jurado, Henrik Bredmose, and Mohammad Salari
Wind Energ. Sci., 6, 867–883, https://doi.org/10.5194/wes-6-867-2021, https://doi.org/10.5194/wes-6-867-2021, 2021
Short summary
Short summary
This paper introduces the numerical models of two 15 MW floating offshore wind turbines (FOWTs) WindCrete and Activefloat. WindCrete is a spar floating platform designed by Universitat Politècnica de Catalunya, while Activefloat is a semi-submersible platform designed by Esteyco. The floaters are designed within the Horizon 2020 project COREWIND. Later in the paper, the responses of both models to wind and second-order waves are analysed with an emphasis on the effect of second-order waves.
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.
Christian Grinderslev, Niels Nørmark Sørensen, Sergio González Horcas, Niels Troldborg, and Frederik Zahle
Wind Energ. Sci., 6, 627–643, https://doi.org/10.5194/wes-6-627-2021, https://doi.org/10.5194/wes-6-627-2021, 2021
Short summary
Short summary
This study investigates aero-elasticity of wind turbines present in the turbulent and chaotic wind flow of the lower atmosphere, using fluid–structure interaction simulations. This method combines structural response computations with high-fidelity modeling of the turbulent wind flow, using a novel turbulence model which combines the capabilities of large-eddy simulations for atmospheric flows with improved delayed detached eddy simulations for the separated flow near the rotor.
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).
Thanasis Barlas, Néstor Ramos-García, Georg Raimund Pirrung, and Sergio González Horcas
Wind Energ. Sci., 6, 491–504, https://doi.org/10.5194/wes-6-491-2021, https://doi.org/10.5194/wes-6-491-2021, 2021
Short summary
Short summary
A method to design advanced tip extensions for modern wind turbine blades is presented in this work. The resulting design concept has high potential in terms of actual implementation in a real rotor upscaling with a potential business case in reducing the cost of energy produced by future large wind turbine rotors.
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.
Alejandro Gomez Gonzalez, Peder B. Enevoldsen, Athanasios Barlas, and Helge A. Madsen
Wind Energ. Sci., 6, 33–43, https://doi.org/10.5194/wes-6-33-2021, https://doi.org/10.5194/wes-6-33-2021, 2021
Short summary
Short summary
This work describes a series of tests of active flaps on a 4 MW wind turbine. The measurements were performed between October 2017 and June 2019 using two different active flap configurations on a blade of the turbine, showing a potential to manipulate the loading of the turbine between 5 % and 10 %. This project is performed with the aim of demonstrating a technology with the potential of reducing the levelized cost of energy for wind power.
Daniel S. Zalkind, Emiliano Dall'Anese, and Lucy Y. Pao
Wind Energ. Sci., 5, 1579–1600, https://doi.org/10.5194/wes-5-1579-2020, https://doi.org/10.5194/wes-5-1579-2020, 2020
Short summary
Short summary
New wind turbine designs require updated control parameters, which should be optimal in terms of the performance measures that drive hardware design. We show how a zeroth-order optimization algorithm can randomly generate control parameters, use simulation results to estimate the gradient of the parameter space, and find an optimal set of those parameters. We then apply this automatic controller tuning procedure to three problems in wind turbine control.
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.
Özge Sinem Özçakmak, Helge Aagaard Madsen, Niels Nørmark Sørensen, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1487–1505, https://doi.org/10.5194/wes-5-1487-2020, https://doi.org/10.5194/wes-5-1487-2020, 2020
Short summary
Short summary
Accurate prediction of the laminar-turbulent transition process is critical for design and prediction tools to be used in the industrial design process, particularly for the high Reynolds numbers experienced by modern wind turbines. Laminar-turbulent transition behavior of a wind turbine blade section is investigated in this study by means of field experiments and 3-D computational fluid dynamics (CFD) rotor simulations.
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.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
Short summary
Short summary
A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, https://doi.org/10.5194/wes-5-945-2020, 2020
Short summary
Short summary
This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
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.
Sebastian Perez-Becker, Francesco Papi, Joseph Saverin, David Marten, Alessandro Bianchini, and Christian Oliver Paschereit
Wind Energ. Sci., 5, 721–743, https://doi.org/10.5194/wes-5-721-2020, https://doi.org/10.5194/wes-5-721-2020, 2020
Short summary
Short summary
Aeroelastic design load calculations play a key role in determining the design loads of the different wind turbine components. This study compares load estimations from calculations using a Blade Element Momentum aerodynamic model with estimations from calculations using a higher-order Lifting-Line Free Vortex Wake aerodynamic model. The paper finds and explains the differences in fatigue and extreme turbine loads for power production simulations that cover a wide range of turbulent wind speeds.
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.
Christian Grinderslev, Federico Belloni, Sergio González Horcas, and Niels Nørmark Sørensen
Wind Energ. Sci., 5, 543–560, https://doi.org/10.5194/wes-5-543-2020, https://doi.org/10.5194/wes-5-543-2020, 2020
Short summary
Short summary
This study focuses on coupled computational fluid and structural dynamics simulations of a dynamic structural test of a wind turbine blade, as performed in laboratories. It is found that drag coefficients used for simulations, when planning fatigue tests, underestimate air resistance to the dynamic motion that the blade undergoes during tests. If this is not corrected for, this can result in the forces applied to the blade actually being lower in reality during tests than what was planned.
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.
Helge Aagaard Madsen, Torben Juul Larsen, Georg Raimund Pirrung, Ang Li, and Frederik Zahle
Wind Energ. Sci., 5, 1–27, https://doi.org/10.5194/wes-5-1-2020, https://doi.org/10.5194/wes-5-1-2020, 2020
Short summary
Short summary
We show in the paper that the upscaling of turbines has led to new requirements in simulation of the unsteady aerodynamic forces by the engineering blade element momentum (BEM) model, originally developed for simulation of the aerodynamics of propellers and helicopters. We present a new implementation of the BEM model on a polar grid which can be characterized as an engineering actuator disc model. The aeroelastic load impact of the new BEM implementation is analyzed and quantified.
Róbert Ungurán, Vlaho Petrović, Lucy Y. Pao, and Martin Kühn
Wind Energ. Sci., 4, 677–692, https://doi.org/10.5194/wes-4-677-2019, https://doi.org/10.5194/wes-4-677-2019, 2019
Short summary
Short summary
A novel lidar-based sensory system for wind turbine control is proposed. The main contributions are the parametrization method of the novel measurement system, the identification of possible sources of measurement uncertainty, and their modelling. Although not the focus of the submitted paper, the mentioned contributions represent essential building blocks for robust feedback–feedforward wind turbine control development which could be used to improve wind turbine control strategies.
Daniel S. Zalkind, Gavin K. Ananda, Mayank Chetan, Dana P. Martin, Christopher J. Bay, Kathryn E. Johnson, Eric Loth, D. Todd Griffith, Michael S. Selig, and Lucy Y. Pao
Wind Energ. Sci., 4, 595–618, https://doi.org/10.5194/wes-4-595-2019, https://doi.org/10.5194/wes-4-595-2019, 2019
Short summary
Short summary
We present a model that both (1) reduces the computational effort involved in analyzing design trade-offs and (2) provides a qualitative understanding of the root cause of fatigue and extreme structural loads for wind turbine components from the blades to the tower base. We use this model in conjunction with design loads from high-fidelity simulations to analyze and compare the trade-offs between power capture and structural loading for large rotor concepts.
Freddy J. Madsen, Antonio Pegalajar-Jurado, and Henrik Bredmose
Wind Energ. Sci., 4, 527–547, https://doi.org/10.5194/wes-4-527-2019, https://doi.org/10.5194/wes-4-527-2019, 2019
Short summary
Short summary
This paper presents a comparison study of the simplified model QuLAF (Quick Load Analysis of Floating wind turbines) and FAST for the planar version of various design load cases, in order to investigate how accurate results can be obtained from this simplified model.
The overall analysis shows that QuLAF is generally very good at estimating the bending moment at the tower base and the floater motions, whereas the nacelle acceleration is generally underpredicted.
Amy N. Robertson, Kelsey Shaler, Latha Sethuraman, and Jason Jonkman
Wind Energ. Sci., 4, 479–513, https://doi.org/10.5194/wes-4-479-2019, https://doi.org/10.5194/wes-4-479-2019, 2019
Short summary
Short summary
This paper identifies the most sensitive parameters for the load response of a 5 MW wind turbine. Two sets of parameters are examined: one set relating to the wind excitation characteristics and a second related to the physical properties of the wind turbine. The two sensitivity analyses are done separately, and the top most-sensitive parameters are identified for different load outputs throughout the structure. The findings will guide future validation campaigns and measurement needs.
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.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Gunner Christian Larsen
Wind Energ. Sci., 4, 303–323, https://doi.org/10.5194/wes-4-303-2019, https://doi.org/10.5194/wes-4-303-2019, 2019
Short summary
Short summary
In this paper, detailed inflow information extracted from measurements is used to improve the accuracy of simulated wind turbine fatigue loads. Inflow information from nearby met masts is utilised as well as information from a blade-mounted flow sensor in combination with a method to compensate for the disturbance to the flow caused by the presence of the wind turbine.
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
Short summary
Short summary
Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
Mehdi Vali, Vlaho Petrović, Gerald Steinfeld, Lucy Y. Pao, and Martin Kühn
Wind Energ. Sci., 4, 139–161, https://doi.org/10.5194/wes-4-139-2019, https://doi.org/10.5194/wes-4-139-2019, 2019
Short summary
Short summary
A new active power control (APC) approach is investigated to simultaneously reduce the wake-induced power tracking errors and structural fatigue loads of individual turbines within a wind farm. The non-unique solution of the APC problem with respect to the distribution of the individual powers is exploited. The simple control architecture and practical measurement system make the proposed approach prominent for real-time control of large wind farms with turbulent flows and wakes.
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.
Jonathan Keller, Yi Guo, Zhiwei Zhang, and Doug Lucas
Wind Energ. Sci., 3, 947–960, https://doi.org/10.5194/wes-3-947-2018, https://doi.org/10.5194/wes-3-947-2018, 2018
Short summary
Short summary
The US Department of Energy's National Renewable Energy Laboratory (NREL) and industry partners successfully demonstrated a new gearbox design using preloaded tapered roller bearings in the planetary section. The new gearbox design demonstrated improved planetary load-sharing characteristics in the presence of rotor pitch and yaw moments, resulting in a predicted gearbox lifetime that is 3.5 times greater than the previous conventional design with cylindrical roller bearings.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
Short summary
Short summary
Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
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.
Bart M. Doekemeijer, Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 749–765, https://doi.org/10.5194/wes-3-749-2018, https://doi.org/10.5194/wes-3-749-2018, 2018
Short summary
Short summary
Most wind farm control algorithms in the literature rely on a simplified mathematical model that requires constant calibration to the current conditions. This paper provides such an estimation algorithm for a dynamic model capturing the turbine power production and flow field at hub height. Performance was demonstrated in high-fidelity simulations for two-turbine and nine-turbine farms, accurately estimating the ambient conditions and wind field inside the farms at a low computational cost.
Antonio Pegalajar-Jurado, Michael Borg, and Henrik Bredmose
Wind Energ. Sci., 3, 693–712, https://doi.org/10.5194/wes-3-693-2018, https://doi.org/10.5194/wes-3-693-2018, 2018
Short summary
Short summary
This paper presents a simplified numerical model to quickly predict motion and loads of floating offshore wind turbines. Hydrodynamic, aerodynamic and mooring loads are extracted from higher-fidelity numerical tools. Without calibration, the model can predict with good accuracy the motions of the system in real wind and wave conditions. Loads at the tower base are estimated with errors between 0.2 % and 11.3 %. The model can simulate between 1300 and 2700 times faster than real time.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
Short summary
Short summary
This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Georg Raimund Pirrung and Helge Aagaard Madsen
Wind Energ. Sci., 3, 545–551, https://doi.org/10.5194/wes-3-545-2018, https://doi.org/10.5194/wes-3-545-2018, 2018
Short summary
Short summary
A wind turbine sees an overshoot in loading after a step change in pitch angle because the wake takes some time to reach a new equilibrium. The time constants of this dynamic inflow effect are expected to decrease significantly towards the blade tip. This radial dependency has not been found to the expected extent in previous analyses of force measurements from the NASA Ames Phase VI experiment. In the present article the findings from the experiment are explained based on a simple vortex model.
Peter Graf, Katherine Dykes, Rick Damiani, Jason Jonkman, and Paul Veers
Wind Energ. Sci., 3, 475–487, https://doi.org/10.5194/wes-3-475-2018, https://doi.org/10.5194/wes-3-475-2018, 2018
Short summary
Short summary
Current approaches to wind turbine extreme load estimation are insufficient to routinely and reliably make required estimates over 50-year return periods. Our work hybridizes the two main approaches and casts the problem as stochastic optimization. However, the extreme variability in turbine response implies even an optimal sampling strategy needs unrealistic computing resources. We therefore conclude that further improvement requires better understanding of the underlying causes of loads.
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.
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, https://doi.org/10.5194/wes-3-243-2018, 2018
Short summary
Short summary
This paper investigates the role of flow structures in wind farm control through yaw misalignment. A pair of counter-rotating vortices is shown to be important in deforming the shape of the wake. Further, we demonstrate that the vortex structures created in wake steering can enable a greater change power generation than currently modeled in control-oriented models. We propose that wind farm controllers can be made more effective if designed to take advantage of these effects.
Michael K. McWilliam, Thanasis K. Barlas, Helge A. Madsen, and Frederik Zahle
Wind Energ. Sci., 3, 231–241, https://doi.org/10.5194/wes-3-231-2018, https://doi.org/10.5194/wes-3-231-2018, 2018
Short summary
Short summary
Maximizing wind energy production is challenging because the winds are always changing. Design optimization was used to explore how flaps can give rotor design engineers greater ability to adapt the rotor for different conditions. For rotors designed for peak efficiency (i.e. older designs) the flap adds 0.5 % improvement in energy production. However, for modern designs that optimize both the performance and the structure, the flap can provide a 1 % improvement.
Dana Martin, Kathryn Johnson, Christopher Bay, Daniel Zalkind, Lucy Pao, Meghan Kaminski, and Eric Loth
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-27, https://doi.org/10.5194/wes-2018-27, 2018
Revised manuscript not accepted
Short summary
Short summary
The paper provides an account of the synthesis of a Linear Parameter Varying (LPV) controller and its improved performance as applied to a down-wind, two bladed, per-aligned rotor. The analysis of controller performance during a turbulent inflow with a mean wind speed of 4 m/s show increased performance in terms of better tip speed ratio tracking and reduced fatigue damage to various turbine components. The results provide a basis of LPV control and its ability to increase turbine lifetime.
Patrick Hawbecker, Sukanta Basu, and Lance Manuel
Wind Energ. Sci., 3, 203–219, https://doi.org/10.5194/wes-3-203-2018, https://doi.org/10.5194/wes-3-203-2018, 2018
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Søren Juhl Andersen
Wind Energ. Sci., 3, 121–138, https://doi.org/10.5194/wes-3-121-2018, https://doi.org/10.5194/wes-3-121-2018, 2018
Short summary
Short summary
The wind speed measured by a flow sensor mounted on the blade of a wind turbine is disturbed by the turbine. This paper presents a method to obtain the free turbulence inflow by compensating for this disturbance.
The method is tested using numerical simulations and can be used to extract inflow information for accurate aeroelastic load simulations.
Signe Schløer, Laura Garcia Castillo, Morten Fejerskov, Emanuel Stroescu, and Henrik Bredmose
Wind Energ. Sci., 3, 57–73, https://doi.org/10.5194/wes-3-57-2018, https://doi.org/10.5194/wes-3-57-2018, 2018
Short summary
Short summary
A model for quick load analysis is presented. This is a fast model for the calculation of dynamic loads of an offshore wind turbine tower and foundation. The model is compared to the state-of-the-art aeroelastic code. In general, there is good similarity between the two models. This indicates that in the early stage of the design phase a simple dynamic model can be used to make a preliminary design of the foundation and wind turbine tower.
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.
Georg R. Pirrung, Helge A. Madsen, and Scott Schreck
Wind Energ. Sci., 2, 521–532, https://doi.org/10.5194/wes-2-521-2017, https://doi.org/10.5194/wes-2-521-2017, 2017
Short summary
Short summary
Current fast aeroelastic wind turbine codes suitable for certification lack an induction model for standstill conditions. A near-wake model for wind turbines in operation is extended to cover these conditions. The model is validated in aerodynamic simulations of the NREL/NASA Ames Phase VI rotor. Good agreement with the experiments has been obtained in attached flow and beginning separation. Aeroelastic simulations of the DTU 10 MW turbine in standstill indicate a minor impact of the model.
Mads M. Pedersen, Torben J. Larsen, Helge Aa. Madsen, and Gunner Chr. Larsen
Wind Energ. Sci., 2, 547–567, https://doi.org/10.5194/wes-2-547-2017, https://doi.org/10.5194/wes-2-547-2017, 2017
Short summary
Short summary
This paper presents an alternative method to evaluate power performance and loads on wind turbines using a blade-mounted flow sensor. A high correlation is found between the wind speed measured at the blades and the power/loads, and simulations indicate that it is possible to reduce the time required for power and load assessment considerably. This result, however, cannot be confirmed from the full-scale measurement study due to practical circumstances.
Paul Fleming, Jennifer Annoni, Jigar J. Shah, Linpeng Wang, Shreyas Ananthan, Zhijun Zhang, Kyle Hutchings, Peng Wang, Weiguo Chen, and Lin Chen
Wind Energ. Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017, https://doi.org/10.5194/wes-2-229-2017, 2017
Short summary
Short summary
In this paper, a field test of wake-steering control is presented. In the campaign, an array of turbines within an operating commercial offshore wind farm have the normal yaw controller modified to implement wake steering according to a yaw control strategy. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture.
Georg Pirrung, Vasilis Riziotis, Helge Madsen, Morten Hansen, and Taeseong Kim
Wind Energ. Sci., 2, 15–33, https://doi.org/10.5194/wes-2-15-2017, https://doi.org/10.5194/wes-2-15-2017, 2017
Short summary
Short summary
The certification process of a wind turbine requires simulations of a coupled structural and aerodynamic wind turbine model in many different external conditions. Due to the large number of load cases, the complexity of the aerodynamics models has to be limited. In this paper, a simplified vortex method based aerodynamics model is described. It is shown that this model, which is fast enough for use in a certification context, can produce results similar to those of a more complex vortex model.
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
Related subject area
Thematic area: Wind technologies | Topic: Design concepts and methods for plants, turbines, and components
Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses
Gradient-based wind farm layout optimization with inclusion and exclusion zones
A novel techno-economical layout optimization tool for floating wind farm design
Hybrid-Lambda: a low-specific-rating rotor concept for offshore wind turbines
Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout
Identification of electro-mechanical interactions in wind turbines
Nonlinear vibration characteristics of virtual mass systems for wind turbine blade fatigue testing
Extreme wind turbine response extrapolation with the Gaussian mixture model
The effect of site-specific wind conditions and individual pitch control on wear of blade bearings
A neighborhood search integer programming approach for wind farm layout optimization
Validation of aeroelastic dynamic model of Active Trailing Edge Flap system tested on a 4.3 MW wind turbine
Enabling control co-design of the next generation of wind power plants
Offshore wind farm optimisation: a comparison of performance between regular and irregular wind turbine layouts
A data-driven reduced-order model for rotor optimization
How to identify design optimization problems that can be improved with a control co-design approach ?
Computational fluid dynamics (CFD) modeling of actual eroded wind turbine blades
Grand Challenges: wind energy research needs for a global energy transition
Current status and grand challenges for small wind turbine technology
CFD-based curved tip shape design for wind turbine blades
Impacts of wind field characteristics and non-steady deterministic wind events on time-varying main-bearing loads
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 697–719, https://doi.org/10.5194/wes-9-697-2024, https://doi.org/10.5194/wes-9-697-2024, 2024
Short summary
Short summary
Wind farms at sea are becoming more densely clustered, which means that next to individual wind turbines interfering with each other in a single wind farm also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and are also highly sensitive to the spacing between the wind farms.
Javier Criado Risco, Rafael Valotta Rodrigues, Mikkel Friis-Møller, Julian Quick, Mads Mølgaard Pedersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 585–600, https://doi.org/10.5194/wes-9-585-2024, https://doi.org/10.5194/wes-9-585-2024, 2024
Short summary
Short summary
Wind energy developers frequently have to face some spatial restrictions at the time of designing a new wind farm due to different reasons, such as the existence of protected natural areas around the wind farm location, fishing routes, and the presence of buildings. Wind farm design has to account for these restricted areas, but sometimes this is not straightforward to achieve. We have developed a methodology that allows for different inclusion and exclusion areas in the optimization framework.
Amalia Ida Hietanen, Thor Heine Snedker, Katherine Dykes, and Ilmas Bayati
Wind Energ. Sci., 9, 417–438, https://doi.org/10.5194/wes-9-417-2024, https://doi.org/10.5194/wes-9-417-2024, 2024
Short summary
Short summary
The layout of a floating offshore wind farm was optimized to maximize the relative net present value (NPV). By modeling power generation, losses, inter-array cables, anchors and operational costs, an increase of EUR 34.5 million in relative NPV compared to grid-based layouts was achieved. A sensitivity analysis was conducted to examine the impact of economic factors, providing valuable insights. This study contributes to enhancing the efficiency and cost-effectiveness of floating wind farms.
Daniel Ribnitzky, Frederik Berger, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 9, 359–383, https://doi.org/10.5194/wes-9-359-2024, https://doi.org/10.5194/wes-9-359-2024, 2024
Short summary
Short summary
This paper provides an innovative blade design methodology for offshore wind turbines with very large rotors compared to their rated power, which are tailored for an increased power feed-in at low wind speeds. Rather than designing the blade for a single optimized operational point, we include the application of peak shaving in the design process and introduce a design for two tip speed ratios. We describe how enlargement of the rotor diameter can be realized to improve the value of wind power.
Rafael Valotta Rodrigues, Mads Mølgaard Pedersen, Jens Peter Schøler, Julian Quick, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 321–341, https://doi.org/10.5194/wes-9-321-2024, https://doi.org/10.5194/wes-9-321-2024, 2024
Short summary
Short summary
The use of wind energy has been growing over the last few decades, and further increase is predicted. As the wind energy industry is starting to consider larger wind farms, the existing numerical methods for analysis of small and medium wind farms need to be improved. In this article, we have explored different strategies to tackle the problem in a feasible and timely way. The final product is a set of recommendations when carrying out trade-off analysis on large wind farms.
Fiona Dominique Lüdecke, Martin Schmid, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-13, https://doi.org/10.5194/wes-2024-13, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Large direct drive wind turbines, with a multi-megawatt power rating, face design challenges. Moving towards a more system-oriented design approach could potentially reduce mass and costs. Exploiting the full design space, though, may invoke interaction mechanisms, which have been neglected in the past. Based on coupled simulations, this work derives a better understanding of the electro-mechanical interaction mechanisms and identifies potential for design relevance.
Aiguo Zhou, Jinlei Shi, Tao Dong, Yi Ma, and Zhenhui Weng
Wind Energ. Sci., 9, 49–64, https://doi.org/10.5194/wes-9-49-2024, https://doi.org/10.5194/wes-9-49-2024, 2024
Short summary
Short summary
This paper explores the nonlinear influence of the virtual mass mechanism on the test system in blade biaxial tests. The blade theory and simulation model are established to reveal the nonlinear amplitude–frequency characteristics of the blade-virtual-mass system. Increasing the amplitude of the blade or decreasing the seesaw length will lower the resonance frequency and load of the system. The virtual mass also affects the blade biaxial trajectory.
Xiaodong Zhang and Nikolay Dimitrov
Wind Energ. Sci., 8, 1613–1623, https://doi.org/10.5194/wes-8-1613-2023, https://doi.org/10.5194/wes-8-1613-2023, 2023
Short summary
Short summary
Wind turbine extreme response estimation based on statistical extrapolation necessitates using a small number of simulations to calculate a low exceedance probability. This is a challenging task especially if we require small prediction error. We propose the use of a Gaussian mixture model as it is capable of estimating a low exceedance probability with minor bias error, even with limited simulation data, having flexibility in modeling the distributions of varying response variables.
Arne Bartschat, Karsten Behnke, and Matthias Stammler
Wind Energ. Sci., 8, 1495–1510, https://doi.org/10.5194/wes-8-1495-2023, https://doi.org/10.5194/wes-8-1495-2023, 2023
Short summary
Short summary
Blade bearings are among the most stressed and challenging components of a wind turbine. Experimental investigations using different test rigs and real-size blade bearings have been able to show that rather short time intervals of only several hours of turbine operation can cause wear damage on the raceways of blade bearings. The proposed methods can be used to assess wear-critical operation conditions and to validate control strategies as well as lubricants for the application.
Juan-Andrés Pérez-Rúa, Mathias Stolpe, and Nicolaos Antonio Cutululis
Wind Energ. Sci., 8, 1453–1473, https://doi.org/10.5194/wes-8-1453-2023, https://doi.org/10.5194/wes-8-1453-2023, 2023
Short summary
Short summary
With the challenges of ensuring secure energy supplies and meeting climate targets, wind energy is on course to become the cornerstone of decarbonized energy systems. This work proposes a new method to optimize wind farms by means of smartly placing wind turbines within a given project area, leading to more green-energy generation. This method performs satisfactorily compared to state-of-the-art approaches in terms of the resultant annual energy production and other high-level metrics.
Andrea Gamberini, Thanasis Barlas, Alejandro Gomez Gonzalez, and Helge Aagaard Madsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-63, https://doi.org/10.5194/wes-2023-63, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Movable surfaces on wind turbine (WT) blades, called active flaps, can reduce the cost of wind energy. However, they still need extensive testing. This study shows that the computer model used to design a WT with flaps aligns well with measurements obtained from a 3 month test on a commercial WT featuring a prototype flap. Particularly during flap actuation, there were minimal differences between simulated and measured data. These findings assure the reliability of WT designs incorporating flaps
Andrew P. J. Stanley, Christopher J. Bay, and Paul Fleming
Wind Energ. Sci., 8, 1341–1350, https://doi.org/10.5194/wes-8-1341-2023, https://doi.org/10.5194/wes-8-1341-2023, 2023
Short summary
Short summary
Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as a function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.
Maaike Sickler, Bart Ummels, Michiel Zaaijer, Roland Schmehl, and Katherine Dykes
Wind Energ. Sci., 8, 1225–1233, https://doi.org/10.5194/wes-8-1225-2023, https://doi.org/10.5194/wes-8-1225-2023, 2023
Short summary
Short summary
This paper investigates the effect of wind farm layout on the performance of offshore wind farms. A regular farm layout is compared to optimised irregular layouts. The irregular layouts have higher annual energy production, and the power production is less sensitive to wind direction. However, turbine towers require thicker walls to counteract increased fatigue due to increased turbulence levels in the farm. The study shows that layout optimisation can be used to maintain high-yield performance.
Nicholas Peters, Christopher Silva, and John Ekaterinaris
Wind Energ. Sci., 8, 1201–1223, https://doi.org/10.5194/wes-8-1201-2023, https://doi.org/10.5194/wes-8-1201-2023, 2023
Short summary
Short summary
Wind turbines have increasingly been leveraged as a viable approach for obtaining renewable energy. As such, it is essential that engineers have a high-fidelity, low-cost approach to modeling rotor load distributions. In this study, such an approach is proposed. This modeling approach was shown to make high-fidelity predictions at a low computational cost for rotor distributed-pressure loads as rotor geometry varied, allowing for an optimization of the rotor to be completed.
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.
Kisorthman Vimalakanthan, Harald van der Mijle Meijer, Iana Bakhmet, and Gerard Schepers
Wind Energ. Sci., 8, 41–69, https://doi.org/10.5194/wes-8-41-2023, https://doi.org/10.5194/wes-8-41-2023, 2023
Short summary
Short summary
Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge is required to optimize blade maintenance. Providing accurate modeling tools is therefore essential. This novel study assesses CFD approaches for modeling high-resolution scanned LE surfaces from an actual blade with LEE damages.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Alessandro Bianchini, Galih Bangga, Ian Baring-Gould, Alessandro Croce, José Ignacio Cruz, Rick Damiani, Gareth Erfort, Carlos Simao Ferreira, David Infield, Christian Navid Nayeri, George Pechlivanoglou, Mark Runacres, Gerard Schepers, Brent Summerville, David Wood, and Alice Orrell
Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, https://doi.org/10.5194/wes-7-2003-2022, 2022
Short summary
Short summary
The paper is part of the Grand Challenges Papers for Wind Energy. It provides a status of small wind turbine technology in terms of technical maturity, diffusion, and cost. Then, five grand challenges that are thought to be key to fostering the development of the technology are proposed. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Improvement areas are highlighted, within which 10 key enabling actions are finally proposed to the wind community.
Mads H. Aa. Madsen, Frederik Zahle, Sergio González Horcas, Thanasis K. Barlas, and Niels N. Sørensen
Wind Energ. Sci., 7, 1471–1501, https://doi.org/10.5194/wes-7-1471-2022, https://doi.org/10.5194/wes-7-1471-2022, 2022
Short summary
Short summary
This work presents a shape optimization framework based on computational fluid dynamics. The design framework is used to optimize wind turbine blade tips for maximum power increase while avoiding that extra loading is incurred. The final results are shown to align well with related literature. The resulting tip shape could be mounted on already installed wind turbines as a sleeve-like solution or be conceived as part of a modular blade with tips designed for site-specific conditions.
Edward Hart, Adam Stock, George Elderfield, Robin Elliott, James Brasseur, Jonathan Keller, Yi Guo, and Wooyong Song
Wind Energ. Sci., 7, 1209–1226, https://doi.org/10.5194/wes-7-1209-2022, https://doi.org/10.5194/wes-7-1209-2022, 2022
Short summary
Short summary
We consider characteristics and drivers of loads experienced by wind turbine main bearings using simplified models of hub and main-bearing configurations. Influences of deterministic wind characteristics are investigated for 5, 7.5, and 10 MW turbine models. Load response to gusts and wind direction changes are also considered. Cubic load scaling is observed, veer is identified as an important driver of load fluctuations, and strong links between control and main-bearing load response are shown.
Cited articles
Abbas, N. J., Zalkind, D. S., Pao, L., and Wright, A.: A Reference Open-Source Controller for Fixed and Floating Offshore Wind Turbines, Wind
Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, 2022.
Advanced Research Projects Agency – Energy (ARPA-E): Aerodynamic Turbines
Lighter and Afloat with Nautical Technologies and Integrated Servo-control
(ATLANTIS), https://arpa-e.energy.gov/technologies/programs/atlantis (last access: December 2020), 2019.
Aho, J., Buckspan, A., Laks, J., Fleming, P., Jeong, Y., Dunne, F., Churchfield, M., Pao, L., and Johnson, K.: A Tutorial of Wind Turbine
Control for Supporting Grid Frequency through Active Power Control, in:
Proceedings of the American Control Conference, 27–29 June 2012, Montreal, Canada, 3120–3131, https://doi.org/10.1109/ACC.2012.6315180, 2012.
Aho, J., Buckspan, A., Pao, L., and Fleming, P.: An Active Power Control
System for Wind Turbines Capable of Primary and Secondary Frequency Control
for Supporting Grid Reliability, in: Proceedings of AIAA Aerospace Sciences
Meeting, 7–10 January 2013, Grapevine, Texas, Global Wind Energy Council,
2013-0456, https://doi.org/10.2514/6.2013-456, 2013a.
Aho, J., A. D. Buckspan, F. Dunne, and L. Y. Pao: Controlling Wind Energy
for Utility Grid Reliability, ASME Dynamic Systems and Control Magazine,
1, 4–12 September 2013; also selected for inclusion in Mech. Eng., 135, S4–S12, https://doi.org/10.1115/1.2013-SEP-4, 2013b.
Aho, J., Fleming, P. and Pao, L. Y.: Active Power Control of Wind Turbines
for Ancillary Services: A Comparison of Pitch and Torque Control Methodologies, in: Proceedings of the American Control Conference, 6–8 July 2016, Boston, Massachusetts, 1407–1412, https://doi.org/10.1109/ACC.2016.7525114, 2016.
Alexander, F., Almgren, A., Bell, J., Bhattacharjee, A., Chen, J., Colella, P., Daniel, D., DeSlippe, J., Diachin, L., Draeger, E., Dubey, A., Dunning, T., Evans, T., Foster, I., Francois, M., Germann, T., Gordon, M., Habib, S., Halappanavar, M., Hamilton, S., Hart, W., Huang, Z., Hungerford, A., Kasen, D., Kent, P. R. C., Kolev, T., Kothe, D. B., Kronfeld, A., Luo, Y., Mackenzie, P., McCallen, D., Messer, B., Mniszewski, S., Oehmen, C., Perazzo, A., Perez, D., Richards, D., Rider, W. J., Rieben, R., Roche, K., Siegel, A., Sprague, M., Steefel, C., Stevens, R., Syamlal, M., Taylor, M., Turner, J., Vay, J.-L., Voter, A. F., Windus, T. L., and Yelick, K.: Exascale applications: skin in the game, Philos. T. Roy. Soc. A, 378, 20190056, https://doi.org/10.1098/rsta.2019.0056, 2020.
Ali, N., Cortina, G., Hamilton, N., Calaf, M., and Cal, R. B.: Turbulence
characteristics of a thermally stratified wind turbine array boundary layer
via proper orthogonal decomposition, J. Fluid Mech., 828, 175–195,
https://doi.org/10.1017/jfm.2017.492, 2017.
Allaerts, D. and Meyers, J.: Boundary-layer development and gravity waves in
conventionally neutral wind farms, J. Fluid Mech., 814, 95–130, https://doi.org/10.1017/jfm.2017.11, 2017.
Allison, J. T. and Herber, D. R.: Multidisciplinary Design Optimization of
Dynamic Engineering Systems, AIAA J., 52, 691–710, https://doi.org/10.2514/6.2013-1462, 2013.
Allison, J. T., Guo, T., and Han, Z.: Co-design of an Active Suspension Using Simultaneous Dynamic Optimization, J. Mech. Des., 136, 081003–081017, https://doi.org/10.1115/1.4027335, 2014.
Alves Dias, P., Bobba, S., Carrara, S., and Plazzotta, B.: The role of rare
earth elements in wind energy and electric mobility, EUR 30488 EN,
Publication Office of the European Union, Luxembourg, https://doi.org/10.2760/303258,
2020.
American Institute of Aeronautics and Astronautics: Guide for the
Verification and Validation of Computational Fluid Dynamics Simulations,
AIAA G-077-1998, https://doi.org/10.2514/4.472855, 1998.
Ananthan, S., Vijayakumar, G., and Yellapantula, S.: A DNN surrogate unsteady
aerodynamic model for wind turbine loads calculations, J. Phys.: Conf. Ser.,
1618, 052060, https://doi.org/10.1088/1742-6596/1618/5/052060, 2020.
Baidya Roy, S. B. and Traiteur, J. J.: Impacts of wind farms on surface air
temperatures, P. Natl. Acad. Sci. USA, 107, 17899–17904, https://doi.org/10.1073/pnas.1000493107, 2010.
Banta, R., Newsom, R. K., Lundquist, J. K. Pichugina, Y. L., Coulter, R. L.,
and Mahrt, L.: Nocturnal Low-Level Jet Characteristics Over Kansas During
Cases-99, Bound.-Lay. Meteorol., 105, 221–252, https://doi.org/10.1023/A:1019992330866, 2002.
Barber, S., Deparday, J., Marykovskiy, Y., Chatzi, E., Abdallah, I., Duthé, G., Magno, M., Polonelli, T., Fischer, R., and Müller, H.:
Development of a wireless, non-intrusive, MEMS-based pressure and acoustic
measurement system for large-scale operating wind turbine blades, Wind
Energ. Sci., 7, 1383–1398, https://doi.org/10.5194/wes-7-1383-2022, 2022.
Barlas, T. K. and van Kuik, G. A: Review of state of the art in smart rotor
control research for wind turbines, Prog. Aerosp. Sci., 46, 1–27, https://doi.org/10.1016/j.paerosci.2009.08.002, 2010.
Barone, M., Paquette, J., Resor, B., and Manuel, L.: Decades of Wind Turbine
Load Simulation, in: 50th AIAA Aerospace Sciences Meeting, January 2012, Nashville, Tennessee, USA, AIAA 2012-1288, https://doi.org/10.2514/6.2012-1288, 2012.
Barter, G. E., Robertson, A., and Musial, W.: A systems engineering vision
for floating offshore wind cost optimization, Renewab. Energ. Focus, 34, 1–16, https://doi.org/10.1016/j.ref.2020.03.002, 2020.
Barthelmie, R. J. and Jensen, L. E.: Evaluation of wind farm efficiency and
wind turbine wakes at the Nysted offshore wind farm, Wind Energy, 13,
573–586, https://doi.org/10.1002/we.408, 2010.
Barthelmie, R. J., Hansen, K., Frandsen, S. T., Rathmann, O., Schepers, J.,
Schlez, W., Phillips, J., Rados, K., Zervos, A., Politis, E., and Chaviaropoulos, P. K.: Modelling and measuring flow and wind turbine wakes
in large wind farms offshore, Wind Energy, 12, 431–444, https://doi.org/10.1002/we.348, 2009.
Barthelmie, R. J., Hansen, K. S., and Pryor, S. C.: Meteorological Controls on Wind Turbine Wakes, Proc. IEEE, 101, 1010–1019, https://doi.org/10.1109/JPROC.2012.2204029, 2013.
Bastankhah, M. and Porté-Agel, F.: Wind farm power optimization via yaw
angle control: A wind tunnel study, J. Renew. Sustain. Energ., 11, 023301, https://doi.org/10.1063/1.5077038, 2019.
Bay, C., Annoni, J., Taylor, T., Pao, L., and Johnson, K.: Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication, in: Proceedings of the American Control Conference, 27–29 June 2018, Milwaukee, Wisconsin, 682–687, https://doi.org/10.23919/ACC.2018.8431764, 2018.
Berg, J., Bryant, J., LeBlanc, B., Maniaci, D. C., Naughton, B., Paquette,
J. A., Resor, B. R., White, J., and Kroeker, D.: Scaled Wind Farm Technology Facility Overview, in: 32nd ASME Wind Energy Symposium, American Institute of Aeronautics and Astronautics Inc., https://doi.org/10.2514/6.2014-1088, 2014.
Berg, J., Natarajan, A., Mann, J., and Patton, E. G.: Gaussian vs non-Gaussian turbulence: impact on wind turbine loads, Wind Energy, 19:
1975–1989, https://doi.org/10.1002/we.1963, 2016.
Berg, J. C. and Resor, B. R.: Numerical manufacturing and design tool (NuMAD V2.0) for wind turbine blades: user's guide, Tech. Rep., Sandia National Laboratories, SAND2012-7028, https://doi.org/10.2172/1051715, 2012.
Bleeg, J., Purcell, M., Ruisi, R., and Traiger, E.: Wind Farm Blockage and
the Consequences of Neglecting Its Impact on Energy Production, Energies, 11, 1609, https://doi.org/10.3390/en11061609, 2018.
Bodini, N., Lundquist, J. K., and Kirincich, A.: U.S. East Coast Lidar
Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric
Turbulence, Geophys. Res. Lett., 46, 5582–5591, https://doi.org/10.1029/2019GL082636, 2019.
Bolinger, M., Lantz, E., Wiser, R., Hoen, B., Rand, J., and Hammond, R.:
Opportunities for and challenges to further reductions in the “specific
power” rating of wind turbines installed in the United States, Wind
Eng., 45, 351–368, https://doi.org/10.1177/0309524X19901012, 2020.
Bonds, T.: Low Cost, High Volume, Carbon Fiber Precursor for Plasma
Oxidation, Institute for Advanced Composite Manufacturing Innovation, Knoxville, TN,
https://iacmi.org/wp-content/uploads/2022/02/IACMI-6.13-Final-Project-Report-1.24.22-AMO-Approved.pdf
(last access: 19 June 2023), 2020.
Boorsma, K., Schepers, J. G., Gomez-Iradi, S., Herráez, I., Lutz, T.,
Weihing, P., Oggiano, L., Pirrung, G., Madsen, H. A., Shen, W. Z., Rahimi, H., and Schaffarczyk, P.: Final Report of IEA Wind Task 29 Mexnext (Phase 3), Report No. ECN-E–18-003, International Energy Agency, Paris, France,
http://resolver.tudelft.nl/uuid:251f749d-41dc-4091-a6fa-08704eae2bab
(last access: 19 June 2023), 2018.
Borisade, F., Koch, C., Lemmer, F., Cheng, P. W., Campagnolo, F., and Matha,
D.: Validation of INNWIND.EU Scaled Model Tests of a Semisubmersible Floating Wind Turbine, Int. J. Offshore Polar Eng., 28, 54–64, 2018.
Bortolotti, P., Bottasso, C. L., and Croce, A.: Combined preliminary-detailed design of wind turbines, Wind Energ. Sci., 1, 71–88, https://doi.org/10.5194/wes-1-71-2016, 2016.
Bortolotti, P., Tarrés, H. C., Dykes, K., Merz, K., Sethuraman, L., Verelst, D., and Zahle, F.: IEA Wind TCP Task 37: Systems Engineering in Wind
Energy-WP2.1 Reference Wind Turbines, NREL – National Renewable Energy
Laboratory, Golden, CO, NREL/TP-5000-73492, https://doi.org/10.2172/1529216, 2019a.
Bortolotti, P., Canet, H., Bottasso, C. L., and Loganathan, J.: Performance
of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines, Wind Energ. Sci., 4, 397–406,
https://doi.org/10.5194/wes-4-397-2019, 2019b.
Bortolotti, Pietro, Bay, C., Barter, G., Gaertner, E., Dykes, K., McWilliam,
M., Friis-Moller, M., Molgaard Pedersen, M., and Zahle, F.: System Modeling
Frameworks for Wind Turbines and Plants: Review and Requirements Specifications, NREL – National Renewable Energy Laboratory, Golden, CO,
NREL/TP-5000-82621, https://doi.org/10.2172/1868328, 2022.
Bossanyi, E. A.: Individual Blade Pitch Control for Load Reduction, Wind
Energy, 6, 119–128, https://doi.org/10.1002/we.76, 2003.
Bottasso, C. L. and Campagnolo, F.: Wind Tunnel Testing of Wind Turbines and
Farms, Handbook of Wind Energy Aerodynamics, edited by: Stoevesandt, B.,
Schepers, G., Fuglsang, P., Sun, Y., Springer Nature, Cham,
https://doi.org/10.1007/978-3-030-05455-7_54-1, 2021.
Bottasso, C. L., Campagnolo, F., and Croce, A.: Multi-disciplinary constrained optimization of wind turbines, Multibod. Syst. Dynam., 27, 21–53, https://doi.org/10.1007/s11044-011-9271-x, 2012.
Bottasso, C. L., Campagnolo, F., Croce, A., and Tibaldi, C.: Optimization-based study of bend–twist coupled rotor blades for passive and
integrated passive/active load alleviation, Wind Energy, 16, 1149–1166, https://doi.org/10.1002/we.1543, 2013.
Bottasso, C. L., Pizzinelli, P., Riboldi, C. E. F., and Tasca, L.: LiDAR-enabled model predictive control of wind turbines with real-time
capabilities, Renew. Energy, 71, 442–452, https://doi.org/10.1016/j.renene.2014.05.041, 2014a.
Bottasso, C. L., Campagnolo, F., Croce, A., Dilli, S., Gualdoni, F., and Nielsen, M. B.: Structural optimization of wind turbine rotor blades by
multilevel sectional/multibody/3D-FEM analysis, Multibod. Syst. Dynam., 32,
87–116, https://doi.org/10.1007/s11044-013-9394-3, 2014b.
Bottasso, C. L., Croce, A., Sartori, L., and Grasso, F.: Free-form design of
rotor blades, IOP J. Phys., 524, 012041, https://doi.org/10.1088/1742-6596/524/1/012041, 2014c.
Bottasso, C. L., Campagnolo, F., and Petrovic, 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, 2014d.
Bousman, W. G.: Rotorcraft Airloads Measurements: Extraordinary Costs,
Extraordinary Benefits, Report No. NASA/TP–2014-218374,
https://rotorcraft.arc.nasa.gov/Publications/files/Bousman.Nikolsky31.pdf
(last access: 19 June 2023), 2014.
Branlard, E. and Meyer Forsting, A. R.: Assessing the blockage effect of
wind turbines and wind farms using an analytical vortex model, Wind Energy,
23, 2068–2086, https://doi.org/10.1002/we.2546, 2020.
Bredmose, H., Dixen, M., Ghadirian, A., Larsen, T. J., Schløer, S., Andersen, S. J., Wang, S., Bingham, H. B., Lindberg, O., Christensen, E. D.,
Vested, M. H., Carstensen, S., Engsig-Karup, A. P., Petersen, O. S., Hansen,
H. F., Mariegaard, J. S., Taylor, P. H., Adcock, T. A. A., Obhrai, C., and
Hanson, T. D.: DeRisk – Accurate Prediction of ULS Wave Loads. Outlook and
First Results, Energy Proced., 94, 379-387. https://doi.org/10.1016/j.egypro.2016.09.197, 2016.
Brower, M.: Wind Resource Assessment: A Practical Guide to Developing a Wind
Project, John Wiley & Sons,
https://www.wiley.com/en-br/Wind+Resource+Assessment:+A+Practical+Guide+to+Developing+a+Wind+Project-p-9781118022320
(last access: 19 June 2023), 2012.
Calaf, M., Meneveau, C., and Meyers, J.: Large eddy simulation study of fully developed wind-turbine array boundary layers, Phys. Fluids, 22, 015110, https://doi.org/10.1063/1.3291077, 2010.
Campagnolo, F., Petrović, V., Bottasso, C. L., and Croce, A.: Wind
tunnel testing of wake control strategies, in: Proc. American Control Conf., Boston, MA, USA, 513–518, https://ieeexplore.ieee.org/document/7524965 (last access: 19 June 2023), 2016.
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.
Canet, H., Guilloré, A., and Bottasso, C. L.: The eco-conscious wind turbine: bringing societal value to design, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-37, in review, 2022.
Carroll, J., McDonald, A., Dinwoodie, I., McMillan, D., Revie, M., and Lazakis, I.: Availability, operation and maintenance costs of offshore wind
turbines with different drive train configurations, Wind Energy, 20, 361–378, https://doi.org/10.1002/we.2011, 2016.
Chaviaropoulos, P. K.: Flap/lead-lag aeroelastic stability of wind turbine
blades, Wind Energy, 4, 183–200, https://doi.org/10.1002/we.55, 2001.
Chaviaropoulos, P. K., Politis, E. S., Lekou, D. J., Sørensen, N. N., Hansen, M. H., Bulder, B. H., Winkelaar, D., Lindenburg, C., Saravanos, D.
A., Philippidis, T. P., Galiotis, C., Hansen, M. O. L., and Kossivas, T.:
Enhancing the damping of wind turbine rotor blades, the DAMPBLADE project,
Wind Energy, 9, 163–177, https://doi.org/10.1002/we.183, 2006.
Chen, P., Chen, J., and Hu, Z.: Review of Experimental-Numerical Methodologies and Challenges for Floating Offshore Wind Turbines, J. Mar. Sci. Appl., 19, 339–361, https://doi.org/10.1007/s11804-020-00165-z, 2020.
Clifton, A., Smith, A., and Fields, M. J.: Wind Plant Preconstruction Energy
Estimates. Current Practice and Opportunities, NREL – National Renewable Energy Laboratory, Golden, CO, NREL/TP-5000-64735, https://doi.org/10.2172/1248798, 2016.
Cousins, D. S., Suzuki, Y., Murray, R. E., Samaniuk, J. R., and Stebner, A. P.: Recycling glass fiber thermoplastic composites from wind turbine blades, J. Clean. Product., 209, 1252–1263, https://doi.org/10.1016/j.jclepro.2018.10.286, 2019.
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.
Damiani, R. and Franchi, M.: An innovative second-order design method for
the structural optimization of the SpiderFLOAT offshore wind platform, Ocean
Eng., 228, 108792, https://doi.org/10.1016/j.oceaneng.2021.108792, 2021.
Dao, C., Kazemtabrizi, B., and Crabtree, C.: Wind turbine reliability data
review and impacts on levelised cost of energy, Wind Energy, 22, 1848–1871, https://doi.org/10.1002/we.2404, 2019.
de Vries, E.: Exclusive: How Vestas beat rivals to launch first 15 MW
offshore turbine, Windpower Monthly,
https://www.windpowermonthly.com/article/1706924/exclusive-vestas-beat-rivals-launch-first-15mw-offshore-turbine
(last access: 19 June 2023), 2021.
de Vries, E.: Winergy insights: Giants of the sea, the next ten years',
Windpower Monthly,
https://www.windpowermonthly.com/article/1798540/winergy-insights-giants-sea-next-ten-years
(last access: 19 June 2023), 2022.
Demtröder, J., Kjaer, P., and Hansen, A.: Balancing incremental development and disruptive innovation in the design of a modularized,
scalable powertrain for the modular wind turbine product system EnVentus, in: Dresdner Maschi-nenelemente Kolloquium, Technische Universität Dresden, Dresden, ISBN 13978-3-96548-055-1, 2019.
Dhert, T., Ashuri, T., and Martins, J. R. R. A.: Aerodynamic shape optimization of wind turbine blades using a Reynolds-averaged Navier–Stokes
model and an adjoint method, Wind Energy, 20, 909–926, https://doi.org/10.1002/we.2070, 2016.
Díaz, M. H. and Guedes Soares, C.: Review of the current status,
technology and future trends of offshore wind farms, Ocean Eng., 209,
107381, https://doi.org/10.1016/j.oceaneng.2020.107381, 2020.
Dimitrov, N.: Surrogate models for parameterized representation of wake-induced loads in wind farms, Wind Energy, 50, 1371–1389, https://doi.org/10.1002/we.2362, 2019.
Dimitrov, N. and Natarajan, A.: Wind farm set point optimization with surrogate models for load and power output targets, J. Phys.: Conf. Ser., 2018, 012013, https://doi.org/10.1088/1742-6596/2018/1/012013, 2022.
Dimitrov, N. and Natarajan, A.: Application of simulated lidar scanning
patterns to constrained Gaussian turbulence fields for load validation, Wind
Energy, 20, 79–95, https://doi.org/10.1002/we.1992, 2016.
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From wind to loads:
Wind turbine site-specific load estimation with surrogate models trained on
high-fidelity load databases, Wind Energ. Sci., 3, 767–790,
https://doi.org/10.5194/wes-3-767-2018, 2018.
Doll, G. L.: Surface engineering in wind turbine tribology, Surf. Coat. Technol., 442, 128545, https://doi.org/10.1016/j.surfcoat.2022.128545, 2022.
Doubrawa, P., Churchfield, M. J., Godvik, M., and Sirnivas, S.: Load response of a floating wind turbine to turbulent atmospheric flow, Appl. Energy, 242, 1588–1599, https://doi.org/10.1016/j.apenergy.2019.01.165, 2019.
Dubey, P. K., Mahanth, S. K., and Dixit, A.: Recyclamine® – Novel Amine Building Blocks for a Sustainable World, in: SAMPE neXus Proceedings, 29 June–1 July 2021, virtual event,
https://www.digitallibrarynasampe.org/data/pdfs/s2021_pdfs/TP21-0000000632.pdf
(last access: 19 June 2023), 2021.
Dunne, F., Pao, L. Y., Wright, A. D., Jonkman, B., and Kelley, N.: Adding
feedforward blade pitch control to standard feedback controllers for load
mitigation in wind turbines, Mechatronics, 21, 682–690,
https://doi.org/10.1016/j.mechatronics.2011.02.011, 2011.
Dykes, K., Meadows, R., Felker, F., Graf, P., Hand, M., Lunacek, M., Michalakes, J., Moriarty, P., Musial, W., and Veers, P.: Applications of
Systems Engineering to the Research, Design, and Development of Wind Energy
Systems, NREL – National Renewable Energy Laboratory, Golden, CO, TP-5000-52616, https://www.nrel.gov/docs/fy12osti/52616.pdf (last access: 19 June 2023), 2011.
Dykes, K., Ning, A., King, R., Graf, P., Scott, G., and Veers, P.: Sensitivity Analysis of Wind Plant Performance to Key Turbine Design Parameters: A Systems Engineering Approach, in: Proc. AIAA Science and Tech.
Forum & Expo., January 2014, National Harbor, Maryland, 2014-1087, https://doi.org/10.2514/6.2014-1087, 2014.
Ela, E., Gevorgian, V., Fleming, P., Zhang, Y. C., Singh, M., Muljadi, E.,
Scholbrock, A., Aho, J., Buckspan, A., Pao, L., Singhvi, V., Tuohy, A.,
Pourbeik, P., Brooks, D., and Bhatt, N.: Active Power Controls from Wind
Power: Bridging the Gaps, NREL – National Renewable Energy Laboratory, Golden, CO, NREL/TP-5D00-60574, 154 pp.,
http://www.nrel.gov/docs/fy14osti/60574.pdf (last access: 19 June 2023), 2014.
Eldred, M., Swiler, L. P., and Tang, G.: Mixed aleatory-epistemic uncertainty
quantification with stochastic expansions and optimization-based interval
estimation, Reliab. Eng. Syst. Saf., 96, 1092–1113, https://doi.org/10.1016/j.ress.2010.11.010, 2011.
Eliassen, L. and Andersen, S.: Investigating Coherent Structures in the
Standard Turbulence Models using Proper Orthogonal Decomposition, J. Phys.:
Conf. Ser., 753, 032040, https://doi.org/10.1088/1742-6596/753/3/032040, 2016.
Elyasichamazkoti, F. and Khajehpoor, K.: Application of machine learning for
wind energy from design to energy-Water nexus: A Survey, Energy Nexus, 2, 100011, https://doi.org/10.1016/j.nexus.2021.100011, 2021.
Emeis, S.: Current issues in wind energy meteorology, Meteorol. Appl., 21, 803–819, https://doi.org/10.1002/met.1472, 2014.
Ennis, B., Kelley, C., Naughton, B., Norris, R., Das, S., Lee, D., and Miller, D.: Optimized Carbon Fiber Composites in Wind Turbine Blade Design,
Technical Report, SAND2019-14173, Sandia National Laboratories, Albuquerque,
New Mexico, https://www.osti.gov/servlets/purl/1592956 (last access: 19 June 2023), 2019.
Erdem, E.: Why P2X must be the part of the energy solution?, Environ. Prog.
Sustain. Energ., 40, 8–10, https://doi.org/10.1002/ep.13545, 2021.
Fathy, K., Reyer, J. A., Papalambros, P. Y., and Ulsoy, A. G.: On the coupling between plant and controller optimization problems, in: Proc. American Control Conf., 25–27 June 2001, Arlington, VA USA, 1864–1869, https://doi.org/10.1109/ACC.2001.946008, 2001.
Fields, M. J., Optis, M., Perr-Sauer, J., Todd, A., Lee, J. C. Y., Meissner,
J., Simley, E., Bodini, N., Williams, L., Sheng, S., and Hammond, R.: Wind Plant Performance Prediction Benchmark Phase 1 Technical Report,
NREL – National Renewable Energy Laboratory, Golden, CO, NREL/TP-5000-78715,
https://www.nrel.gov/docs/fy22osti/78715.pdf (last access: 19 June 2023), 2021.
Fischer, B.: Reducing rotor speed variations of floating wind turbines by
compensation of non-minimum phase zeros, IET Renewable Power Generation,
https://doi.org/10.1049/iet-rpg.2012.0263, 2013.
Fleming, P., Gebraad, P. M., Lee, S., van Wingerden, J.-W., Johnson, K.,
Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Simulation
comparison of wake mitigation control strategies for a two-turbine case,
Wind Energy, 18, 2135–2143, https://doi.org/10.1002/we.1810, 2015.
Fleming, P., Annoni, J., Shah, J. J., Wang, L., Ananthan, S., Zhang, Z.,
Hutchings, K., Wang, P., Chen, W., and Chen, L.: Field test of wake steering at an offshore wind farm, Wind Energy Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017, 2017.
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A.,
Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1, Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, 2019a.
Fleming, P. A., Peiffer, A., and Schlipf, D.: Wind turbine controller to mitigate structural loads on a floating wind turbine platform, J. Offshore Mech. Art. Eng., 141, 061901, https://doi.org/10.1115/1.4042938, 2019b.
Fleming, P., King, J., Simley, E., Roadman, J., Scholbrock, A., Murphy, P.,
Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort,
R., Jager, D., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.:
Continued results from a field campaign of wake steering applied at a
commercial wind farm – Part 2, Wind Energ. Sci., 5, 945–958,
https://doi.org/10.5194/wes-5-945-2020, 2020.
FLOATECH: Research project funded by European Union's H2020 program – grant
agreement No. 101007142, https://www.floatech-project.com/ ( last access: 11 April 2021), 2021.
Fortes-Plaza, A., Campagnolo, F., Wang, J., Wang, C., and Bottasso, C. L.: A POD reduced-order model for wake steering control, The Science of Making Torque from Wind, Milano, Italy, June 20–22, 2018, J. Phys.: Conf. Ser., 1037 032014, https://doi.org/10.1088/1742-6596/1037/3/032014, 2018.
Frandsen, S. and Christensen, C. J.: Structural loads in large wind farm
arrays, in: EWEC'94: European Wind Energy Association conference and exhibition, 10–14 October 1994, Thessaloniki, Greece, CONF-941050, RISO-R-797(EN), Risoe National Lab., Roskilde, DK,
https://www.osti.gov/etdeweb/biblio/39765 (last access: 19 June 2023), 1995.
Garcia-Sanz, M.: Control Co-Design: An Engineering Game Changer, Adv. Control Appl., 1, e18, https://doi.org/10.1002/adc2.18, 2019.
General Electric: GE Renewable Energy, COBOD and LafargeHolcim co-develop
record-tall wind turbine towers with 3D-printed concrete bases,
https://www.ge.com/news/press-releases/ge-renewable-energy-cobod-and-lafargeholcim-co-develop-3D
(last access: 19 June 2023), 2020.
Geraci, G., Eldred, M., and Iaccarino, G. A.: A multifidelity multilevel Monte Carlo method for uncertainty propagation in aerospace applications, in:
19th AIAA Non-Deterministic Approaches Conference, 9–13 January 2017, Grapevine, TX, USA, https://doi.org/10.2514/6.2017-1951, 2017.
Göçmen, T., van der Laan, P., Réthoré, P., Peña Diaz,
A., Larsen, G., and Ott, S.: Wind turbine wake models developed at the technical university of Denmark: A review, Renew. Sustain. Energ. Rev., 60, 752–769, https://doi.org/10.1016/j.rser.2016.01.113, 2016.
Gontier, H., Schaffarczyk, A. P., Kleinhans, D., and Friedrich, R.: A comparison of fatigue loads of wind turbine resulting from a non-Gaussian
turbulence model vs. standard ones, J. Phys.: Conf. Ser., 75, 012070,
https://doi.org/10.1088/1742-6596/75/1/012070, 2007.
Gould, B. and Greco, A.: The Influence of Sliding and Contact Severity on
the Generation of White Etching Cracks, Tribol. Lett., 60, 29, https://doi.org/10.1007/s11249-015-0602-6, 2015.
Griffin, O. M., Skop, R. A., and Koopmann, G. H.: The vortex-excited
resonant vibrations of circular cylinders, J. Sound Vibrat., 31, 235–249,
https://doi.org/10.1016/S0022-460X(73)80377-3, 1973.
Griffith, D. T. and Richards, P. W.: The SNL100-03 blade: Design Studies
with Flatback Airfoils for the Sandia 100-meter Blade, Technical report
SAND2014-18129, Sandia National Laboratories,
https://energy.sandia.gov/wp-content/gallery/uploads/dlm_uploads/1418129.pdf
(last access: 19 June 2023), 2014.
Grinderslev, C., Sørensen, N. N., Horcas, S. G., Troldborg, N., and Zahle, F.: Wind turbines in atmospheric flow: fluid–structure interaction simulations with hybrid turbulence modeling, Wind Energ. Sci., 6, 627–643,
https://doi.org/10.5194/wes-6-627-2021, 2021.
Guma, G., Bucher, P., Letzgus, P., Lutz, T., and Wüchner, R.: High-fidelity aeroelastic analyses of wind turbines in complex terrain:
fluid–structure interaction and aerodynamic modeling, Wind Energ. Sci., 7,
1421–1439, https://doi.org/10.5194/wes-7-1421-2022, 2022.
Halevy, A., Norvig, P., and Pereira, F.: The Unreasonable Effectiveness of Data, IEEE Intel. Syst., 24, 8–12, https://doi.org/10.1109/MIS.2009.36, 2009.
GWEC – Global Wind Energy Council: GWEC Global Wind Report 2021,
https://gwec.net/global-wind-report-2021/ (last access: 19 June 2023), 2021.
Hannesdóttir, Aì., Kelly, M., and Dimitrov, N.: Extreme wind fluctuations: joint statistics, extreme turbulence, and impact on wind turbine loads, Wind Energ. Sci., 4, 325–342, https://doi.org/10.5194/wes-4-325-2019, 2019.
Hansen, A. D., Altin, M., and Iov, F.: Provision of enhanced ancillary
services from wind power plants – Examples and challenges, Renew. Energy, 97, 8–18, https://doi.org/10.1016/j.renene.2016.05.063, 2016.
Hansen, K. S., Barthelmie, R. J., Jensen, L. E., and Sommer, A.: The impact
of turbulence intensity and atmospheric stability on power deficits due to
wind turbine wakes at Horns Rev wind farm, Wind Energy, 15, 183–196,
https://doi.org/10.1002/we.512, 2012.
Hansen, M. H.: Aeroelastic instability problems for wind turbines, Wind Energy, 10, 551–577, https://doi.org/10.1002/we.242, 2007.
Hansen, M. O. L., Sørensen, J. N., Voutsinas, S., Sørensen, N. N., and
Madsen, H. A.: State of the art in wind turbine aerodynamics and aeroelasticity, Prog. Aerosp. Sci., 42, 285–330, https://doi.org/10.1016/j.paerosci.2006.10.002, 2006.
Harrison-Atlas, D., King, R. N., and Glaws, A.: Machine learning enables
national assessment of wind plant controls with implications for land use,
Wind Energy, 25, 1095–4244, https://doi.org/10.1002/we.2689, 2021.
Hassanzadeh, A., Naughton, J. W., LoTufo, J., and Hangan, H.: Aerodynamic
Load Distribution and Wake Measurements on a Sub-scale Wind Turbine, J. Phys.: Conf. Ser., 1452, 012059, https://doi.org/10.1088/1742-6596/1452/1/012059, 2020.
Hassanzadeh, A., Naughton, J. W., LoTufo, J., and Hangan, H.: Instrumentation development and testing of a wind turbine blade for sub-scale wake studies, J. Renew. Sustain. Energ., 14, 013302, https://doi.org/10.1063/5.0042011, 2022.
Haupt, S. E., Kosovic, B., Shaw, W., Berg, L. K., Churchfield, M., Cline, J.,
Draxl, C., Ennis, B., Koo, E., Kotamarthi, R., Mazzaro, L., Mirocha, J., Moriarty, P., Muñoz-Esparza, D., Quon, E., Rai, R. K., Robinson, M., and Sever, G.: On Bridging a Modeling Scale Gap: Mesoscale to Microscale Coupling for Wind Energy, B. Am. Meteorol. Soc., 100, 2533–2550, https://doi.org/10.1175/BAMS-D-18-0033.1, 2019.
Hawbecker, P., Basu, S., and Manuel, L.: Realistic simulations of the July 1, 2011 severe wind event over the Buffalo Ridge Wind Farm, Wind Energy, 20, 1803–1822, https://doi.org/10.1002/we.2122, 2017.
Heinz, J. C., Sorensen, N. N., Zahle, F., and Skrzypiński, W.: Vortex-induced vibrations on a modern wind turbine blade, Wind Energy, 19,
2041–2051, https://doi.org/10.1002/we.1967, 2016.
Herber, R. and Allison, J. T.: Nested and Simultaneous Solution Strategies
for General Combined Plant and Control Design Problems, J. Mech. Design, 141, 011402, https://doi.org/10.1115/1.4040705, 2019.
Herges, T., Maniaci, D., Debnath, M., Fao, R., Hamilton, N., Krishnamurthy,
R., and Naughton, J.: Wind Energy Instrumentation Development Roadmap, Sandia
National Laboratories, in preparation, 2023.
Hills, R. G., Maniaci, D. C., and Naughton, J.: V&V Framework, Sandia
National Laboratories, SAND2015-0854 R, https://www.osti.gov/servlets/purl/1214246 (last access: 19 June 2023), 2015.
Horcas, S. G., Debrabandere, F., Tartinville, B., Hirsch, C., and Coussement, G.: Rotor-tower interactions of DTU 10 MW reference wind turbine with a non-linear harmonic method, Wind Energy, 619–636, https://doi.org/10.1002/we.2027, 2016.
Horcas, S. G., Barlas, T., Zahle, F., and Sørensen, N. N.: Vortex induced
vibrations of wind turbine blades: Influence of the tip geometry, Phys. Fluids, 32, 065104, https://doi.org/10.1063/5.0004005, 2020.
Howard, K. B., Singh, A., Sotiropoulos, F., and Guala, M.: On the statistics
of wind turbine wake meandering: An experimental investigation, Phys. Fluids, 27, 075103, https://doi.org/10.1063/1.4923334, 2015.
Howison, J., Thomas, J., and Ekici, K.: Aeroelastic analysis of a wind turbine blade using the harmonic balance method, Wind Energy, 21, 226–241,
https://doi.org/10.1002/we.2157, 2017.
Howland, M. F., Lele, S., and Dabiri, J.: Wind farm power optimization through wake steering, P. Natl. Acad. Sci. USA, 116, 14495–14500, https://doi.org/10.1073/pnas.1903680116, 2019.
Hsieh, A., Maniaci, D. C., Herges, T. G., Geraci, G., Seidl, D. T., Eldred, M. S., Blaylock, M. L., and Houchens, B. C.: Multilevel Uncertainty Quantification Using CFD and OpenFAST Simulations of the SWiFT Facility,
AIAA Scitech 2020 Forum, 6–10 January 2020, Orlando, FL, USA, https://doi.org/10.2514/6.2020-1949, 2020.
IEC – International Electrotechnical Commission: Edition 2: Wind turbines – Part 4: Design requirements for wind turbine gearboxes, IEC/CD 61400-4,
https://webstore.iec.ch/publication/5447 (last access: 19 June 2023), 2012.
IEC – International Electrotechnical Commission: Edition 4: Wind energy generation systems – Part 1: Design requirements, IEC 61400-1,
https://webstore.iec.ch/publication/26423 (last access: 19 June 2023), 2019.
Jasa, J., Glaws, A., Bortolotti, P., Vijayakumar, G., and Barter, G.: Wind
turbine blade design with airfoil shape control using invertible neural networks, J. Phys.: Conf. Ser., 2265 042052, https://doi.org/10.1088/1742-6596/2265/4/042052, 2022.
Jay, A., Myers, A. T., Torabian, S., Mahmoud, A., Smith, E., Agbayani, N.,
and Schafer, B. W.: Spirally welded steel wind towers: Buckling experiments,
analyses, and research needs, J. Construct. Steel Res., 125, 218–226, 2016.
Jensen, P. H. and Natarajan, A.: INNWIND.EU: Overview of project and recent results, https://backend.orbit.dtu.dk/ws/portalfiles/portal/87807100/INNWIND.pdf, (last access: 19 June 2023) 2014.
Jiménez, A. Crespo, A., and Migoya, E.: Application of a LES technique to
characterize the wake deflection of a wind turbine in yaw, Wind Energy, 13, 559–572, https://doi.org/10.1002/we.380, 2009.
Johnson, K. E., Fingersh, L., Balas, M., and Pao, L. Y.: Methods for
Increasing Region 2 Power Capture on a Variable-Speed Wind Turbine, J. Solar Energ. Eng., 126, 1092–1100, https://doi.org/10.1115/1.1792653, 2004.
Johnson, K. E., Pao, L. Y., Balas, M. J., and Fingersh, L. J.: Control of
variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture, IEEE Control Syst. Mag., 26, 70–81, https://doi.org/10.1109/MCS.2006.1636311, 2006.
Johnson, S. J., Baker. J. P., van Dam, C., and Berg, D.: An overview of
active load control techniques for wind turbines with an emphasis on microtabs, Wind Energy, 13, 239–253, https://doi.org/10.1002/we.356, 2009.
Jonkman, J.: Influence of Control on the Pitch Damping of a Floating Wind Turbine, NREL/CP-500-42589 March 2008, https://www.nrel.gov/docs/fy08osti/42589.pdf (last access: 19 June 2023), 2008.
Jonkman, J., Wright, A., Barter, G., Hall, M., Allison, J., and Herber, D.
R.: Functional Requirements for the WEIS Toolset to Enable Controls
Co-Design of Floating Offshore Wind Turbines, in: International Conference on
Offshore Mechanics and Arctic Engineering, Vol. 84768, American Society of
Mechanical Engineers, V001T01A007,
https://www.nrel.gov/docs/fy21osti/77123.pdf (last access: 19 June 2023), 2021.
Jonkman, J. M. and Buhl, M. L.: FAST User's Guide, NREL – National
Renewable Energy Laboratory, Golden, CO, NREL/EL-500-38230,
https://www.nrel.gov/docs/fy06osti/38230.pdf (last access: 19 June 2023), 2005.
Kaimal, J. C. and Wyngaard, J. C.: The Kansas and Minnesota experiments,
Bound.-Lay. Meteorol., 50, 31–47, https://doi.org/10.1007/BF00120517, 1990.
Keller, J., Sheng, S., Guo, Y., Gould, B., and Greco, A.: Wind Turbine
Drivetrain Reliability and Wind Plant Operations and Maintenance Research
and Development Opportunities, NREL – National Renewable Energy Laboratory, Golden, CO, NREL/TP-5000-80195, https://www.nrel.gov/docs/fy21osti/80195.pdf (last access: 19 June 2023), 2021.
Kim, E. and Manuel, L.: Simulation of Wind, Waves and Currents During
Hurricane Sandy for Planned Assessment of Offshore Wind Turbines, J. Offshore Mech. Arct. Eng., 141, 061904, https://doi.org/10.1115/1.4043777, 2019.
Kim, E. and Manuel, L.: Loads on a Jacket-Supported Wind Turbine During
Hurricane Sandy Simulation, J. Offshore Mech. Arct. Eng., 144, 012001, https://doi.org/10.1115/1.4052482,
2021.
Kim, E., Manuel, L., Curcic, M., Chen, S. C., Phillips, C., and Veers, P.: On
the Use of Coupled Wind, Wave, and Current Fields in the Simulation of Loads
on Bottom-Supported Offshore Wind Turbines during Hurricanes: March 2012–September 2015, NREL/TP-5000-65283,
https://www.nrel.gov/docs/fy16osti/65283.pdf (last access: 19 June 2023), 2015.
Klein, A. C., Bartholomay, S., Marten, D., Lutz, T., Pechlivanoglou, G., Nayeri, C. N., Paschereit, C. O., and Krämer, E.: About the suitability of different numerical methods to reproduce model wind turbine measurements in a wind tunnel with a high blockage ratio, Wind Energ. Sci., 3, 439–460, https://doi.org/10.5194/wes-3-439-2018, 2018.
Kothe, D., Qualters, I., and Lee, S.: Exascale Computing in the United States, IEEE Computer Society: Computing in Science & Engineering, https://doi.org/10.1109/MCSE.2018.2875366, 2019.
Lackner, M. A. and Rotea, M. A.: Structural control of floating wind
turbines, Mechatronics, 21, 704–719, https://doi.org/10.1016/j.mechatronics.2010.11.007, 2011.
Laks, J., Pao, L. Y., Wright, A. D., Kelley, N., and Jonkman, B.: The use of
preview wind measurements for blade pitch control, Mechatronics, 21, 668–681, https://doi.org/10.1016/j.mechatronics.2011.02.003, 2011.
Larsen, T. J. and Hansen, A. M.: How 2 HAWC2, the user's manual, Report No. Risø-R-1597 (ver. 4–6) (EN), Risø, 1–112,
https://www.hawc2.dk/install/hawc2-manual (last access: 19 June 2023), 2015.
Larsen, T. J. and Hanson, T. D.: A method to avoid negative damped low frequent tower vibrations for a floating, pitch controlled wind turbine, J.
Phys.: Conf. Ser., 75, 012073, https://doi.org/10.1088/1742-6596/75/1/012073, 2007.
Larsen, T. J., Hansen, A. M., and Buhl, T.: Aeroelastic effects of large
blade deflections for wind turbines, Sci.Mak. Torque from Wind, 238–246,
https://backend.orbit.dtu.dk/ws/portalfiles/portal/12090982/A7_4_096paper_final_rev1.pdf
(last access: 19 June 2023), 2004.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature,
521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Lee, J. C. and Fields, M. J.: An overview of wind-energy-production prediction bias, losses, and uncertainties, Wind Energ. Sci., 6, 311–365, https://doi.org/10.5194/wes-6-311-2021, 2021.
Lehnhoff, S., Gómez González, A., and Seume, J. R.: Full-scale
deformation measurements of a wind turbine rotor in comparison with aeroelastic simulations, Wind Energ. Sci., 5, 1411–1423,
https://doi.org/10.5194/wes-5-1411-2020, 2020.
Leishman, J. G.: Challenges in modelling the unsteady aerodynamics of wind turbines, Wind Energy, 5, 85–132, https://doi.org/10.1002/we.62, 2002.
Letzgus, P., El Bahlouli, A., Leukauf, D., Hofsäß, M., Lutz, T., and
Krämer, E.: Microscale CFD Simulations of a Wind Energy Test Site in the
Swabian Alps with Mesoscale Based Inflow Data, J. Phys.: Conf. Ser., 1618,
062021, https://doi.org/10.1088/1742-6596/1618/6/06202, 2020.
Li, X. and Zhang, W.: Long-term assessment of a floating offshore wind turbine under environmental conditions with multivariate dependence structures, Renew. Energy, 147, 764–775, https://doi.org/10.1016/j.renene.2019.09.076 2020.
Lim, H., Manuel, L., and Low, Y. M.: On Efficient Surrogate Model
Development for Prediction of the Long-Term Extreme Response of a Moored
Floating Structure, J. Offshore Mech. Arct. Eng., 143, 011703, https://doi.org/10.1115/1.4047545, 2021.
Lin, Y., Eto, J. H., Johnson, B. B., Flicker, J. D., Lasseter, R. H.,
Villegas Pico, H. N., Seo, B.-S., Pierre, B. J., and Ellis, A.: Research
Roadmap on Grid-Forming Inverters, NREL/TP-5D00-73476, NREL – National Renewable Energy Laboratory, Golden, CO,
https://www.nrel.gov/docs/fy21osti/73476.pdf (last access: 19 June 2023), 2020.
Lu, N.-Y., Hawbecker, P., Basu, S., and Manuel, L.: On Wind Turbine Loads
During Thunderstorm Downbursts in Contrasting Atmospheric Stability Regimes,
Energies, 12, 2773, https://doi.org/10.3390/en12142773, 2019.
Lundquist, J. K., Badger, J., Heinemann, D., Steinfeld, G., Dörenkämper, M., King, R., Draxl, C., Fischereit, J., Xia, G.,
Larsén, X., and Sanchez Gomez, M.: Mesoscale Wind Plant Wakes, Wind Energy Science, in preparation, 2023.
Machefaux, E., Larsen, G. C., Koblitz, T., Troldborg, N., Kelly, M. C.,
Chougule, A., Hansen, K. S., and Rodrigo, J. S.: An experimental and numerical study of the atmospheric stability impact on wind turbine wakes, Wind Energy, 19, 1785–1805, https://doi.org/10.1002/we.1950, 2015.
Madsen, H. A., Bak, C., Schmidt Paulsen, U., Gaunaa, M., Fuglsang, P., Romblad, J., Olesen, N. A., Enevoldsen, P., Laursen, J., and Jensen, L.: The
DAN-AERO MW Experiments: Final report, Technical report No. 1726(EN),
Danmarks Tekniske Universitet, Risø Nationallaboratoriet for
Bæredygtig Energi, https://www.osti.gov/etdeweb/servlets/purl/990865 (last access: 19 June 2023), 2010a.
Madsen, H. A., Fuglsang, P., Romblad, J., Enevoldsen, P., Laursen, J.,
Jensen, L., Bak, C., Paulsen, U. S., Gaunaa, M., Sorensen, N. N., and Olsen,
N. A.: The DAN-AERO MW Experiments, in: 48th AIAA Aerospace Sciences Meeting
Including the New Horizons Forum and Aerospace Exposition, American
Institute of Aeronautics and Astronautics, https://doi.org/10.2514/6.2010-645, 2010b.
Madsen, H. A., Özçakmak, Ö. S., Bak, C., Troldborg, N., Sørensen, N. N., and Sørensen, J. N.: Transition characteristics
measured on a 2 MW 80 m diameter wind turbine rotor in comparison with
transition data from wind tunnel measurements, in: AIAA Scitech 2019 Forum,
7–11 January 2019, San Diego, CA, USA, https://doi.org/10.2514/6.2019-0801, 2019a.
Madsen, M. H. A., Zahle, F., Sørensen, N. N., and Martins, J. R. R. A.:
Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW
wind turbine, Wind Energy Sci., 4, 163–192, https://doi.org/10.5194/wes-4-163-2019, 2019b.
Madsen, H. A., Zahle, F., Meng, F., Barlas, T., Rasmussen, F., and Rudolf, R. T.: Initial performance and load analysis of the LowWind turbine in comparison with a conventional turbine, J. Phys.: Conf. Ser., 1618,
032011, https://doi.org/10.1088/1742-6596/1618/3/032011, 2020a.
Madsen, H. A., Juul Larsen, T., Raimund Pirrung, G., Li, A., and Zahle, F.:
Implementation of the blade element momentum model on a polar grid and its
aeroelastic load impact, Wind Energ. Sci., 5, 1–27,
https://doi.org/10.5194/wes-5-1-2020, 2020b.
Madsen, H. A., Barlas, T., Fischer, A., Olsen, A. S., and Gomez Gonzalez, A:
Inflow and pressure measurements on a full scale turbine with a pressure
belt and a five hole pitot tube, J. Phys.: Conf. Ser., 2265, 022096,
https://doi.org/10.1088/1742-6596/2265/2/022096, 2022.
Maniaci, D., Frankel, A., Geraci, G., Blaylock, M., and Eldred, M.: Multilevel Uncertainty Quantification of a Wind Turbine Large Eddy Simulation Model, 6th European Conference on Computational Mechanics, in: 7th European Conference on Computational Fluid Dynamics, June 2018, Glasgow, UK, https://congress.cimne.com/eccm_ecfd2018/admin/files/filePaper/p2122.pdf (last access: 19 June 2023), 2018.
Maniaci, D. C. and Naughton, J. W.: V&V Integrated Program Planning for Wind Plant Performance, Report SAND2019-6888, Sandia National Laboratorie,
https://www.osti.gov/servlets/purl/1762662 (last access: 19 June 2023), 2019.
Mann, A.: Core Concept: Nascent exascale supercomputers offer promise, present challenges, P. Natl. Acad. Sci. USA, 117, 22623–22625, https://doi.org/10.1073/pnas.2015968117, 2020.
Manuel, L., Nguyen, H. H., and Barone, M. F.: On the Use of a Large Database
of Simulated Wind Turbine Loads to Aid in Assessing Design Standard Provisions, in: 51st AIAA Aerospace Sciences Meeting including
the New Horizons Forum and Aerospace Exposition, AIAA 2013-197, 7–10 January 2013, Grapevine, TX, USA, https://doi.org/10.2514/6.2013-197, 2013.
Manuel, L., Nguyen, P. T. T., Canning, J., Coe, R. G., Eckert-Gallup, A. C.,
and Martin N.: Alternative approaches to develop environmental contours from
metocean data, J. Ocean Eng. Mar. Energ., 4, 293–310, https://doi.org/10.1007/s40722-018-0123-0, 2018.
Martin, R. W., Sabato, A., Schoenberg, A., Giles, R. H., and Niezrecki, C.:
Comparison of nondestructive testing techniques for the inspection of wind
turbine blades' spar caps, Wind Energy, 21, 980–996, https://doi.org/10.1002/we.2208, 2018.
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, 1047–1060, https://doi.org/10.1002/we.1747, 2015.
McMorland, J., Collu, M., McMillan, D., and Carroll, J.: Operation and
maintenance for floating wind turbines: A review, Renew. Sustain. Energ. Rev., 163, 112499, https://doi.org/10.1016/j.rser.2022.112499, 2022.
Medici, D. and Alfredsson, P. H.: Measurements behind model wind turbines:
further evidence of wake meandering, Wind Energy, 11, 211–217, https://doi.org/10.1002/we.247, 2007.
Mehta, M., Zaayer, M., and von Terzi, D.: Optimum Turbine Design for Hydrogen Production from Offshore Wind, J. Phys. Conf. Ser., 2265, 042061, https://doi.org/10.1088/1742-6596/2265/4/042061, 2022.
Michalke, G. and Hansen, A. D.: Grid Support Capabilities of Wind Turbines,
in: Handbook of Wind Power Systems, edited by: Pardalos, P. M., Rebennack,
S., Pereira, M. V. F., Iliadis, N. A., and Pappu, V., Springer, Berlin, Heidelberg, 569–590, https://doi.org/10.1007/978-3-642-41080-2_16, 2014.
Michel, R. K., Ellingwood, B. R., Hagerman, G. M., Ibsoe, J. B., Manuel, L.,
Musial, W., Sheppard, R. E., Simiu, E., Stewart, S. W., and Wisch, D. J.:
Structural Integrity of Offshore Wind Turbines – Oversight of Design,
Fabrication, and Installation, Committee of Structural and Operating Safety,
Special Report 305, Transportation Research Board of the National Academies,
140 pp., https://doi.org/10.17226/13159, 2011.
Michelsen, J. A.: Basis3D – a Platform for Development of Multiblock PDE
Solvers, Report No. AFM 92-05, Department of Fluid Mechanics, Technical
University of Denmark,
https://orbit.dtu.dk/en/publications/basis3d-a-platform-for-development-of-multiblock-pde-solvers-%CE%B2-re
(last access: 19 June 2023), 1992.
Michelsen, J. A.: Block structured Multigrid solution of 2D and 3D elliptic
PDE's, Report No. AFM 94-06, Department of Fluid Mechanics, Technical
University of Denmark, 1994.
Moeng, C. H. and Rotunno, R.: Vertical-Velocity Skewness in the Buoyancy-Driven Boundary Layer, J. Atmos. Sci., 47, 1149–1162,
https://doi.org/10.1175/1520-0469(1990)047<1149:VVSITB>2.0.CO;2, 1990.
Moon, J. S. and Sahasakkul, W.: On the Use of Site Data to Define Extreme
Turbulence Conditions for Wind Turbine Design, J. Sol. Energy Eng., 136, 044506,
https://doi.org/10.1115/1.4028721, 2014.
Moravec, D., Barták, V., Puš, V., and Wild, J.: Wind turbine impact
on near-ground air temperature, Renew. Energy, 123, 627–633,
https://doi.org/10.1016/j.renene.2018.02.091, 2018.
Moriarty, P., Holley, W., and Butterfield, S.: Probabilistic Methods for
Predicting Wind Turbine Design Loads, in: ASME 2003 Wind Energy Symposium,
6–9 January 2003, Reno, NV, USA, https://doi.org/10.1115/WIND2003-864, 2003.
Moriarty, P., Hamilton, N., Debnath, M., Fao, R., Roadman, J., van Dam, J.,
Herges, T., Isom, B., Lundquist, J., Maniaci, D., and Naughton, B.: American
WAKe ExperimeNt (AWAKEN), NREL/TP-5000-75789, NREL – National Renewable Energy Laboratory, Golden, CO, https://www.nrel.gov/docs/fy20osti/75789.pdf (last access: 19 June 2023), 2020.
Morkovin, M. V.: Bypass Transition to Turbulence and Research Desiderata,
National Aeronautics and Space Administration, Lewis Research Center
Transition in Turbines, 161–204, https://ntrs.nasa.gov/api/citations/19850023129/downloads/19850023129.pdf (last access: 19 June 2023), 1985.
Murcia, J. P., Réthoré, P. E., Dimitrov, N., Natarajan, A.,
Sørensen, J. D., Graf, P., and Kim, T.: Uncertainty propagation through
an aeroelastic wind turbine model using polynomial surrogates, Renew.
Energy, 119, 910–922, https://doi.org/10.1016/j.renene.2017.07.070, 2018.
Musial, W., Beiter, P., Spitsen, P., Nunemaker, J., Gevorgian, V.,
Cooperman, A., Hammond, R., and Shields, M.: 2019 Offshore Wind Technology Data Update, NREL/TP-5000-77411, NREL – National Renewable Energy Laboratory, Golden, CO, https://www.nrel.gov/docs/fy21osti/77411.pdf (last access: 19 June 2023), 2020.
Najm, H. N.: Uncertainty Quantification and Polynomial Chaos Techniques
in Computational Fluid Dynamics, Annu. Rev. Fluid Mech., 41, 35–52, https://doi.org/10.1146/annurev.fluid.010908.165248, 2009.
Nejad, A. R. and Torsvik, J.: Drivetrains on floating offshore wind turbines: lessons learned over the last 10 years, Forsch. Ingenieurwesen, 85, 335–343, https://doi.org/10.1007/s10010-021-00469-8, 2021.
Nejad, A. R., Keller, J., Guo, Y., Sheng, S., Polinder, H., Watson, S., Dong, J., Qin, Z., Ebrahimi, A., Schelenz, R., Gutiérrez Guzmán, F., Cornel, D., Golafshan, R., Jacobs, G., Blockmans, B., Bosmans, J., Pluymers, B., Carroll, J., Koukoura, S., Hart, E., McDonald, A., Natarajan, A., Torsvik, J., Moghadam, F. K., Daems, P.-J., Verstraeten, T., Peeters, C., and Helsen, J.: Wind turbine drivetrains: state-of-the-art technologies and future development trends, Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, 2022.
Newsom, R. K., Berg, L. K., Shaw, W. J., and Fischer, M. L.: Turbine-scale
wind field measurements using dual-Doppler lidar, Wind Energy, 18, 219–235,
https://doi.org/10.1002/we.1691, 2013.
Nguyen, P. H. H., Manuel, L., and Veers, P. S.: Wind turbine loads during
simulated thunderstorm microbursts, J. Renew. Sustain. Energ., 3, 053104, https://doi.org/10.1063/1.3646764, 2011.
Nguyen, P. T. T., Manuel, L., and Coe, R. G.: On the Development of an
Efficient Surrogate Model for Predicting Long-Term Extreme Loads on a Wave
Energy Converter, J. Offshore Mech. Arct. Eng., 141, 061103, https://doi.org/10.1115/1.4042944, 2019.
Ning, A., Dykes, K., and Quick, J.: Systems engineering and optimization of
wind turbines and power plants, in: Wind Energy Modeling and
Simulation - Volume 2: Turbine and System, 2, edited by: Veers, P., Institution of Engineering and Technology, 235–292, https://doi.org/10.1049/pbpo125g_ch7, 2019.
NREL FLORIS: FLOw Redirection and Induction in Steady State, Version 2.3.0,
National Renewable Energy Laboratory, GitHub [code], https://github.com/NREL/floris (last access: 19 June 2023), 2021.
NREL ROSCO: Reference OpenSource Controller, Version 2.4.1, National
Renewable Energy Laboratory, GitHub [code], https://github.com/NREL/ROSCO (last access: 19 June 2023), 2021.
NREL WEIS: Wind Energy with Integrated Servo-controls toolset, Version 1.1,
National Renewable Energy Laboratory, GitHub [code], https://github.com/WISDEM/WEIS (last access: 19 June 2023), 2022.
Nygaard, N. G.: Wakes in very large wind farms and the effect of neighbouring wind farms, J. Phys.: Conf. Ser., 524, 012162, https://doi.org/10.1088/1742-6596/524/1/012162, 2014.
Oberkampf, W. L. and Roy, C. J: Verification and Validation in Scientific
Computing, in: 1st Edn., Cambridge University Press, https://doi.org/10.1017/CBO9780511760396, 2010.
Ogale, A., Zhang, M., and Jing, J.: Recent advances in carbon fibers derived
from biobased precursors, J. Appl. Polymer Sci., 133, 43794, https://doi.org/10.1002/APP.43794, 2016.
OpenFAST: v2.6.0, GitHub [code], https://github.com/openfast/openfast/ (last access: 14 June 2021), 2021.
Orszaghova, J., Taylor, P., Wolgamot, H., Madsen, F., Pegalajar-Jurado, A.,
and Bredmose, H.: Wave- and drag-driven subharmonic responses of a floating
wind turbine, J. Fluid Mech., 929, A32, https://doi.org/10.1017/jfm.2021.874, 2021.
Özçakmak, Ö. S., Madsen, H. A., Sørensen, N. N., and Sørensen, J. N.: Laminar-turbulent transition characteristics of a 3-D
wind turbine rotor blade based on experiments and computations, Wind Energ.
Sci., 5, 1487–1505, https://doi.org/10.5194/wes-5-1487-2020, 2020.
Pao, L. Y.: Active Power Control of Wind Power Plants for Grid Integration,
in: Encyclopedia of Systems and Control, 2nd Edn., edited by: Baillieul, J. and Samad, T., Springer Nature Switzerland, 6 pp., https://doi.org/10.1007/978-3-030-44184-5_272, 2014.
Pao, L. Y., Zalkind, D. S., Griffith, D. T., Chetan, M., Selig, M. S., Ananda, G. K., Bay, C. J., Stehly, T., and Loth, E.: Control co-design of 13 MW downwind two-bladed rotors to achieve 25 % reduction in levelized cost of wind energy, Annu. Rev. Control, 51, 331–343, https://doi.org/10.1016/j.arcontrol.2021.02.001, 2021.
Park, J., Basu, S., and Manuel, L.: Large-eddy simulation of stable boundary
layer turbulence and estimation of associated wind turbine loads, Wind
Energy, 17, 359–384, https://doi.org/10.1002/we.1580, 2014.
Park, J., Manuel, L., and Basu, S.: Toward Isolation of Salient Features in
Stable Boundary Layer Wind Fields that Influence Loads on Wind Turbines,
Energies, 8, 2977–3012, https://doi.org/10.3390/en8042977, 2015.
Park, S., Lackner, M. A., Pourazarm, P., Tsouroukdissian, A. R., and Cross-Whiter, J.: An investigation on the impacts of passive and semiactive
structural control on a fixed bottom and a floating offshore wind turbine,
Wind Energy, 22, 1451–1471, https://doi.org/10.1002/we.2381, 2019.
Patil, R., Filipi, Z., and Fathy, H.: Computationally Efficient Combined
Plant Design and Controller Optimization Using a Coupling Measure, J. Mech. Design, 134, 071008, https://doi.org/10.1115/1.4006828, 2012.
Petrović, V. and Bottasso, C. L.: Wind turbine envelope protection
control over the full wind speed range, Renew. Energy, 111, 836–848,
https://doi.org/10.1016/j.renene.2017.04.021, 2017.
Popko, W., Robertson, A., Jonkman, J., Wendt, F., Thomas, P., Müller, K., Kretschmer, M., Hagen, T. R., Galinos, C., Le Dreff, J., Gilbert, P., Auriac, B., Oh, S., Qvist, J., Sørum, S. H., Suja-Thauvin, L., Shin, H., Molins, C., Trubat, P., Bonnet, P., Bergua, R., Wang, K., Fu, P., Cai, J., Cai, Z., Alexandre, A., and Harries, R.: Validation of Numerical Models of the Offshore Wind Turbine From the Alpha Ventus Wind Farm Against Full-Scale Measurements Within OC5 Phase III, ASME, J. Offshore Mech. Arct. Eng., 143, 012002, https://doi.org/10.1115/1.4047378, 2021.
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and
Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 174, 1–59, https://doi.org/10.1007/s10546-019-00473-0, 2020.
Qin, C., Saunders, G., and Loth, E.: Offshore wind energy storage concept
for cost-of-rated-power savings, Appl. Energy, 201, 148–157,
https://doi.org/10.1016/j.apenergy.2017.04.077, 2017.
Quarton, D. C. and Ainslie, J. F.: Turbulence in wind-turbine wakes, J.
Wind Eng. Indust. Aerodynam., 61, 15–23,
https://doi.org/10.1016/0167-6105(95)00033-X, 1990.
Rajewski, D. A., Takle, E. S., Prueger, J. H., and Doorenbos, R. K.: Toward
understanding the physical link between turbines and microclimate impacts
from in situ measurements in a large wind farm, J. Geophys. Res.-Atmos., 121, 13392–13414, https://doi.org/10.1002/2016JD025297, 2016.
Rakib, M. I., Evans, S. P., and Clausen, P. D.: Measured gust events in the urban environment, a comparison with the IEC standard, Renew. Energy, 146, 1134–1142, https://doi.org/10.1016/j.renene.2019.07.058, 2020.
Ramos-García, N., Kontos, S., Pegalajar-Jurado, A., González Horcas, S., and Bredmose, H.: Investigation of the floating IEA Wind 15 MW RWT using vortex methods Part I: Flow regimes and wake recovery. Wind Energy, 25, 468–504, https://doi.org/10.1002/we.2682, 2022.
Rasmussen, F., Thirstrup Petersen, J., Winkelaar, D., and Rawlinson-Smith, R.: Response of stall regulated wind turbines, Stall induced vibrations,
Report No. RISO-R-691(EN), https://orbit.dtu.dk/en/publications/response-of-stall-regulated-wind-turbines-stall-induced-vibration (last access: 19 June 2023), 1993.
Rasmussen, F., Petersen, J. T., and Madsen, H. A.: Dynamic Stall and Aerodynamic Damping, J. Sol. Energ. Eng. Trans. ASME, 121, 150–155,
https://doi.org/10.1115/1.2888426, 1999.
Rasmussen, F., Hansen, M. H., Thomsen, K., Larsen, T. J., Bertagnolio, F.,
Johansen, J., Madsen, H. A., Bak, C., and Hansen, A. M.: Present Status of
Aeroelasticity of Wind Turbines, Wind Energy, 6, 213–228, https://doi.org/10.1002/we.98, 2003.
Rebello, E., Watson, D., and Rodgers, M.: Ancillary services from wind turbines: automatic generation control (AGC) from a single Type 4 turbine,
Wind Energ. Sci., 5, 225–236, https://doi.org/10.5194/wes-5-225-2020, 2020.
Ren, Z., Verma, A. S., Li, Y., Teuwen, J. J. E., and Jiang, Z.: Offshore wind
turbine operations and maintenance: A state-of-the-art review, Renew. Sustain. Energ. Rev., 144, 110886, https://doi.org/10.1016/j.rser.2021.110886, 2021.
Richard, C.: ZF building powertrain test rig for 30 MW wind turbines,
WINDPOWER Monthly,
https://www.windpowermonthly.com/article/1800181/zf-building-powertrain-test-rig-30mw-wind-turbines
(last access: 19 June 2023), 2022.
Rife, D. L., Pinto, J. O., Monaghan, A. J., Davis, C. A., and Hannan, J. R.:
Global Distribution and Characteristics of Diurnally Varying Low-Level Jets,
J. Climate, 23, 5041–5064, https://doi.org/10.1175/2010JCLI3514.1, 2010.
Rinker, J. M.: PyConTurb: an open-source constrained turbulence generator, J. Phys.: Conf. Ser., 1037, 062032, https://doi.org/10.1088/1742-6596/1037/6/062032, 2018.
Riziotis, V. A., Voutsinas, S. G., Politis, E. S., and Chaviaropoulos, P. K.: Aeroelastic stability of wind turbines: the problem, the methods and the
issues, Wind Energy, 7, 373–392, https://doi.org/10.1002/we.133, 2004.
Roach, D. P., Neidigk, S., Rice, T. M., Duvall, R. L., and Paquette, J. A.: Development and Assessment of Advanced Inspection Methods for Wind Turbine Blades Using a Focused WINDIE Experiment, in: 33rd Wind Energy Symposium, 5–9 January 2015, Kissimmee, Florida USA, https://doi.org/10.2514/6.2015-0998, 2015.
Robertson, A., Bachynski, E. E., Gueydon, S., Wendt, F., and Schünemann, P.: Total experimental uncertainty in hydrodynamic testing of a semisubmersible wind turbine, considering numerical propagation of systematic uncertainty, Ocean Eng., 195, 10660, https://doi.org/10.1016/j.oceaneng.2019.106605, 2020.
Robertson, A. N., Shaler, K., Sethuraman, L., and Jonkman, J.: Sensitivity
analysis of the effect of wind characteristics and turbine properties on wind turbine loads, Wind Energ. Sci., 4, 479–513, https://doi.org/10.5194/wes-4-479-2019, 2019.
Rott, A., Doekemeijer, B., Seifert, J. K., van Wingerden, J.-W., and Kühn, M.: Robust active wake control in consideration of wind direction variability and uncertainty, Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, 2018.
Roy, C. and Oberkampf, W.: A Complete Framework for Verification, Validation, and Uncertainty Quantification in Scientific Computing, in: AIAA Aerospace Sciences Meeting, 4–7 January 2010 Orlando, Florida, USA, https://doi.org/10.2514/6.2010-124, 2010.
Saranyasoontorn, K. Manuel, L., and Veers, P.: A Comparison of Standard
Coherence Models for Inflow Turbulence with Estimates from Field Measurements, J. Sol. Energ. Eng., 126, 1069–1082, https://doi.org/10.1115/1.1797978, 2004.
Sareen, A., Sapre, C. A., and Selig, M. S.: Effects of leading edge erosion on wind turbine blade performance, Wind Energy, 1531–1542, https://doi.org/10.1002/we.1649, 2013.
Sathe, A., Mann, J., Barlas, T., Bierbooms, W. A. A. M., and van Bussel, G. J. W.: Influence of atmospheric stability on wind turbine loads, Wind Energy, 16, 1013–1032, https://doi.org/10.1002/we.1528, 2012.
Schaffarczyk, A. P., Schwab, D., and Breuer, M.: Experimental detection of
laminar-turbulent transition on a rotating wind turbine blade in the free
atmosphere, Wind Energy, 20, 211–220, https://doi.org/10.1002/we.2001, 2016.
Schepers, J. G., Brand, A. J., Bruining, A., Graham, J. M. R., Paynter, R.
J. H., Hand, M. M., Simms, D. A., Madsen, H. A., and Infield, D. G.: Final Report of IEA Annex XIV: Field Rotor Aerodynamics, vol. 97, issue 27 of ECN.: C-serie, ISSN 1381-1479, Netherlands Energy Research Foundation ECN, 1997.
Schepers, J. G., Brand, A., Madsen, H. A., Stefanatos, N., Simms, D., Hand,
M., Bruining, A., Van Rooij, R., Shimizu, Y., Maeda, T., Graham, M., and
Paynter, J. H.: Final report of IEA Annex XVIII, Enhanced field rotor
aerodynamics database, Report No: ECN-C-02-016, 2002.
Schepers, J. G., Boorsma, K., Cho, T., Gomez-iradi, S., Schaffarczyk, P.,
Jeromin, A., Shen, W. Z., Lutz, T., Meister, K., Stoevesandt, B., Schreck, S., Micallef, D., Pereira, R., Sant, T., Madsen, H. A., and Sørensen, N. N.: Final report of IEA Task 29, Mexnext (Phase 1): Analysis of Mexico wind tunnel measurements, Report No. ECN-E-12-004, 312 pp.,
https://backend.orbit.dtu.dk/ws/portalfiles/portal/56752175/ecn_e12004.pdf
(last access: 19 June 2023), 2012.
Schepers, J. G., Boorsma, K., Sørensen, N., Voutsinas, V., Sieros, G.,
Rahimi, H., Heisselmann, H., Jost, E., Lutz, T., Maeder, T., Gonzalez, A.,
Ferreira, C., Stoevesandt, B., Barakos, G., Lampropoulos, N., Croce, A., and
Madsen, J.: Final results from the EU project AVATAR: Aerodynamic modelling
of 10 MW wind turbines, J. Phys. Conf. Ser., 1037, 022013, https://doi.org/10.1088/1742-6596/1037/2/022013, 2018.
Schepers, J. G., Boorsma, K., Madsen, H. A., Pirrung, G. R., Bangga, G., Guma, G., Lutz, T., Potentier, T., Braud, C., Guilmineau, E., Croce, A., Cacciola, S., Schaffarczyk, A. P., Lobo, B. A., Ivanell, S., Asmuth, H.,
Bertagnolio, F., Sørensen, N. N., Shen, W. Z., Grinderslev, C., Forsting,
A. M., Blondel, F., Bozonnet, P., Boisard, R., Yassin, K., Honing, L.,
Stoevesandt, B., Imiela, M., Greco, L., Testa, C., Magionesi, F., Vijayakumar, G., Ananthan, S., Sprague, M. A., Branlard, E., Jonkman, J.,
Carrion, M., Parkinson, S., and Cicirello, E.: Final report of Task 29,
Phase IV: Detailed Aerodynamics of Wind Turbines, Technical Report of IEA
TCP Wind Task 29, Zenodo [report], https://doi.org/10.5281/zenodo.4817875, 2021.
Schneider, D.: The Exascale Era is Upon Us: The Frontier supercomputer may
be the first to reach 1,000,000,000,000,000,000 operations per second, IEEE Spectrum, 59, 34–35, https://doi.org/10.1109/MSPEC.2022.9676353, 2022.
Schreck, S.: The NREL full-scale wind tunnel experiment Introduction to the
special issue, Wind Energy, 5, 77–84, https://doi.org/10.1002/we.72, 2002.
Schreck, S.: IEA Wind Annex XX: HAWT Aerodynamics and Models from Wind
Tunnel Measurements, NREL/TP-500-43508, NREL – National Renewable Energy Laboratory, Golden, CO, https://www.nrel.gov/docs/fy09osti/43508.pdf
(last access: 19 June 2023), 2008.
Scott, R., Viggiano, B., Dib, T., Ali, N., Hölling, M., Peinke, J., and
Cal, R. B.: Wind turbine partial wake merging description and quantification, Wind Energy, 23, 1610–1618, https://doi.org/10.1002/we.2504, 2020.
Seidl, D.T, Geraci, G., King, R., Menhorn, F., Glaws, A., and Eldred, M. S.:
Multifidelity strategies for forward and inverse uncertainty quantification
of wind energy applications, in: AIAA Scitech 2020 Forum, January 2020, Orlando, Florida, USA, https://doi.org/10.2514/6.2020-1950, 2020.
Selig, M. S. and Maughmer, M. D.: Multipoint inverse airfoil design method
based on conformal mapping, AIAA J., 30, 1162–1170, https://doi.org/10.2514/3.11046, 1992.
Selig, M. S. and Tangler, J. L.: Development and Application of a Multipoint
Inverse Design Method for Horizontal Axis Wind Turbines, Wind Eng., 19, 91–105, 1995.
Shaler, K., Jonkman, J., and Hamilton, N.: Effects of Inflow Spatiotemporal
Discretization on Wake Meandering and Turbine Structural Response using FAST.Farm, J. Phys.: Conf. Ser., 1256, 012023,
https://doi.org/10.1088/1742-6596/1256/1/012023, 2019.
Shin, H. K., Park, M., Hak-Yong Kim, H.-Y., and Park, S.-J.: An overview of new oxidation methods for polyacrylonitrile-based carbon fibers, Carbon Lett., 16, 11–18, https://doi.org/10.5714/CL.2015.16.1.011, 2015.
Simley, E., Fleming, P., and King, J.: Design and analysis of a wake steering controller with wind direction variability, Wind Energ. Sci., 5, 451–468, https://doi.org/10.5194/wes-5-451-2020, 2020.
Simpson, J. and Loth, E.: Field Tests and Simulations of Tower Shadow Effect for a Downwind Turbine, in: AIAA SciTech 2021 Forum, AIAA 2021-1718,
Virtual Event, https://doi.org/10.2514/6.2021-1718, 2021.
Sinner, M. N., Petrović, V., Langidis, A., Berger, F., Neuhaus, L.,
Hölling, M., Kühn, M., and Pao, L. Y.: Experimental Testing of a
Preview-Enabled Model Predictive Controller for Blade Pitch Control of Wind
Turbines, IEEE T. Control Syst. Technol., 30, 583–597, https://doi.org/10.1109/TCST.2021.3070342, 2021.
Sloan, J.: Carbon fiber suppliers gear up for next-gen growth, Composites
World, 12–13,
https://www.compositesworld.com/articles/carbon-fiber-suppliers-gear-up-for-next-gen-growth
(last access: 20 June 2023), 2020.
Smith, K. J. and Griffin, D.: Supersized Wind Turbine Blade Study: R&D
Pathways for Supersized Wind Turbine Blades, Lawrence Berkeley National Laboratory, Document No.: 10080081-HOU-R-01, https://doi.org/10.2172/1498695, 2019.
Solimine, J., Niezrecki, C., and Inalpolat, M.: An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades, Struct. Health Monit., 19, https://doi.org/10.1177/1475921719895588, 2020.
Sørensen, J. D. and Toft, H. S.: Probabilistic Design of Wind Turbines,
Energies, 3, 241–257, https://doi.org/10.3390/en3020241, 2010.
Sørensen, J. N., Mikkelsen, R., Sarmast, S., Ivanell, S., and Henningson,
D.: Determination of Wind Turbine Near-Wake Length Based on Stability Analysis, J. Phys.: Conf. Ser., 524, 012155, https://doi.org/10.1088/1742-6596/524/1/012155, 2014.
Sørensen, N. N.: General purpose flow solver applied to flow over hills,
Report No. 827-(EN), Technical University of Denmark, Roskilde, Denmark,
https://backend.orbit.dtu.dk/ws/portalfiles/portal/12280331/Ris_R_827.pdf (last access: 19 June 2023), 1995.
Sprague, M. A., Boldyrev, S., Fischer, P., Grout, R., Gustafson Jr., W. I., and Moser, R.: Turbulent Flow Simulation at the Exascale: Opportunities and
Challenges Workshop, August 4–5, 2015, NREL/TP-2C00-67648, NREL, Washington, DC, https://www.nrel.gov/docs/fy17osti/67648.pdf (last access: 20 June 2023), 2017.
Sprague, M. A., Ananthan, S., Vijayakumar, G., and Robinson, M.: ExaWind: A
multifidelity modeling and simulation environment for wind energy, J. Phys.
Conf. Ser., 1452, 012071, https://doi.org/10.1088/1742-6596/1452/1/012071, 2020.
Stehly, T., and Duffy, P.: 2021 Cost of Wind Energy Review, NREL/TP-5000-84774, NREL – National Renewable Energy Laboratory, Golden, CO,
https://www.nrel.gov/docs/fy23osti/84774.pdf (last access: 20 June 2023), 2022.
Stengel, K., Glaws, A., Hettinger, D., and King, R. N.: Adversarial super-resolution of climatological wind and solar data, P. Natl. Acad. Sci. USA, 117, 16805–16815, https://doi.org/10.1073/pnas.1918964117, 2020.
Stevens, Rick, Taylor, V., Nichols, J., Maccabe, A. B., Yelick, K., and Brown, D.: AI for Science: Report on the Department of Energy (DOE) Town
Halls on Artificial Intelligence AI) for Science, Argonne National Laboratory, https://doi.org/10.2172/1604756, 2020.
Stol, K., Zhao, W., and Wright, A.: Individual Blade Pitch Control for the
Controls Advanced Research Turbine (CART), ASME J. Sol. Energ. Eng., 128, 498–505, https://doi.org/10.1115/1.2349542, 2006.
Strasser, D., Thoma, F., Yuksek, S., and Schmaltz, P.: From a Safety Factor
driven Concept to Reliability Engineering: Development of an Multi-Mega-Watt
Wind Energy Gearbox, Conference for Wind Power Drives 2015, Germany, ISBN-10 9783752805314, ISBN-13 978-3752805314, 2015.
Sullivan, P. P., McWilliams, J. C., and Patton, E. G.: Large-Eddy Simulation
of Marine Atmospheric Boundary Layers above a Spectrum of Moving Waves, J. Atmos. Sci., 71, 4001–4027, https://doi.org/10.1175/JAS-D-14-0095.1, 2014.
Sun, H., Gao, X. and Yang, H.: A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect, Renew. Sustain. Energ. Rev., 132, 110042, https://doi.org/10.1016/j.rser.2020.110042, 2020.
Swisher, P., Murcia Leon, J. P., Gea-Bermúdez, J., Koivisto, M., Madsen,
H. A., and Münster, M.: Competitiveness of a low specific power, low
cut-out wind speed wind turbine in North and Central Europe towards 2050,
Appl. Energy, 304, 118043, https://doi.org/10.1016/j.apenergy.2021.118043, 2022.
Tang, X., Yin, M., Shen, C., Xu, Y., Dong, Z. Y., and Zou, Y.: Active Power
Control of Wind Turbine Generators via Coordinated Rotor Speed and Pitch
Angle Regulation, IEEE T. Sustain. Energ., 10, 822–832, 2019.
Technical University of Denmark: Sophia HPC cluster, https://doi.org/10.57940/FAFC-6M81, 2019.
Thirstrup Petersen, J., Frandsen, S., and Courtney, M. S.: Stall Induced
Vibrations of Wind Turbine Blades, in: European Community Wind Energy Conference, proceedings, European Community Wind Energy Conference 1988,
ECWEC'88, 6–10 June 1988, Herning Congress Centre, Denmark, edited by: Stephens, H. S., 246–251, ISBN-10 0951027174, ISBN-13 978-0951027172, 1988.
Tibaldi, C., Hansen, M. H., and Henriksen, L. C.: Optimal tuning for a
classical wind turbine controller, J. Phys. Conf. Ser., 555,
012099, https://doi.org/10.1088/1742-6596/555/1/012099, 2014.
Troldborg, N., Bak, C., Madsen, H. A., and Skrzypinski, W. R.: DANAERO MW:
Final Report, Technical report No. 0027(EN), DTU Wind Energy, Denmark,
https://backend.orbit.dtu.dk/ws/portalfiles/portal/80542014/DanaeroFinalReport.pdf
(last access: 19 June 2023), 2013.
Vali, M., Petrović, V., Steinfeld, G., Pao, L. Y., and Kühn, M.: An
active power control approach for wake-induced load alleviation in a fully
developed wind farm boundary layer, Wind Energ. Sci., 4, 139–161,
https://doi.org/10.5194/wes-4-139-2019, 2019.
Vali, M., Petrović, V., Pao, L. Y., and Kühn, M.: Model Predictive
Active Power Control for Optimal Structural Load Equalization in Waked Wind
Farms, IEEE T. Control Syst. Technol., 30, 30–44, https://doi.org/10.1109/TCST.2021.3053776, 2022.
van der Veen, G., Couchman, I., and Bowyer, R.: Control of floating wind
turbines, in: Proc. American Control Conf., 27–29 June 2012, Montreal, Canada, 3148–3153, https://doi.org/10.1109/ACC.2012.6315120, 2012.
van Kuik, G. A. M., Peinke, J., Nijssen, R., Lekou, D., Mann, J., Sørensen, J. N., Ferreira, C., van Wingerden, J. W., Schlipf, D., Gebraad, P., Polinder, H., Abrahamsen, A., van Bussel, G. J. W., Sørensen, J. D., Tavner, P., Bottasso, C. L., Muskulus, M., Matha, D., Lindeboom, H. J., Degraer, S., Kramer, O., Lehnhoff, S., Sonnenschein, M., Sørensen, P. E., Künneke, R. W., Morthorst, P. E., and Skytte, K.: Long-term research challenges in wind energy – a research agenda by the European Academy of Wind Energy, Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, 2016.
van Wingerden, J. W., Pao, L. Y., Aho, J., and Fleming, P.: Active Power
Control of Waked Wind Farms, Proc. IFAC World Congress, Toulouse, France,
IFAC PapersOnLine, 50, 4484–4491, https://doi.org/10.1016/j.ifacol.2017.08.378, 2017.
Veers, P. (Ed.): Wind Energy Modeling and Simulation: Volume 1: Atmosphere and Plant, IET Publishing, ISBN-13 978-1-78561-521-4, 2019.
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O.,
Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki,
V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P.,
Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J.,
Robertson, A., Rodrigo, J. S., Sempreviva, A. M., Smith, J. C., Tuohy, A., and Wiser, R.: Grand challenges in the science of wind energy, Science, 366, 1–17, https://doi.org/10.1126/science.aau2027, 2019.
Veers, P., Sethuraman, L., and Keller, J.: Wind-Power Generator Technology
Research Aims to Meet Global-Wind Power Ambitions, Joule, 4, 1861–1863,
https://doi.org/10.1016/j.joule.2020.08.019, 2020.
Vermeer, L. J., Sørensen, J. N., and Crespo, A.: Wind turbine wake
aerodynamics, Progress in Aerospace Sciences, 39, 467–510,
https://doi.org/10.1016/S0376-0421(03)00078-2, 2003.
Vijayakumar, G., Jung, Y. S., Baeder, J. D., and Ananthan, S.: Design-space
exploration for inverse-design of wind turbine blades using data-driven
methods, in: SciTECH 2022, 3–7 January 2022, San Diego, CA, and online, AIAA 2022-1293, https://doi.org/10.2514/6.2022-1293, 2021.
Viré, A., Derksen, A., Folkersma, M., and Sarwar, K.: Two-dimensional
numerical simulations of vortex-induced vibrations for a cylinder in conditions representative of wind turbine towers, Wind Energ. Sci., 5,
793–806, https://doi.org/10.5194/wes-5-793-2020, 2020.
Viselli, A. M., Goupee, A. J., and Dagher, H. J.: Model Test of a 1:8-Scale
Floating Wind Turbine Offshore in the Gulf of Maine, J. Offshore Mech. Arct. Eng., 137, 041901, https://doi.org/10.1115/1.4030381, 2015.
Voutsinas, S. G.: Vortex methods in aeronautics: how to make things work, Int. J. Comut. Fluid Dynam., 20, 3–18, https://doi.org/10.1080/10618560600566059, 2006.
Wang, C., Campagnolo, F., Canet, H., Barreiro, D. J., and Bottasso, C. L.: How realistic are the wakes of scaled wind turbine models?, Wind Energ. Sci., 6, 961–981, https://doi.org/10.5194/wes-6-961-2021, 2021.
Wang, S. and Seiler, P. J.: LPV Active Power Control and Robust Analysis for
Wind Turbines, in: Proc. AIAA Aerospace Sciences Meeting, 5–9 January 2015, Kissimmee, Florida, 14 pp., https://doi.org/10.2514/6.2015-1210, 2015.
Watson, S., Moro, A., Reis, V., Baniotopoulos, C., Barth, S., Bartoli, G.,
Bauer, F., Boelman, E., Bosse, D., Cherubini, A., Croce, A., Fagiano, L., Fontana, M., Gambier, A., Gkoumas, K., Golightly, C., Jamieson, P., and Wiser, R.: Future emerging technologies in the wind power sector: A European perspective, Renew. Sustain. Energ. Rev., 113, 109270, https://doi.org/10.1016/j.rser.2019.109270, 2019.
Weisenberger, M. and Boyer, C.: Preparation of Mesophase Pitch Feedstock for
Carbon Fiber, IACMI/R005-2019/6.18, Institute for Advanced Composite Manufacturing Innovation, Knoxville, TN,
https://iacmi.org/wp-content/uploads/2021/08/IACMI-6.18-Final-Project-Report-Approved.pdf
(last access: 20 June 2023), 2019.
Wetzel, K., Lee, K., Tran, A., Stakenborghs, B., and Woodward, R. J.: Volumetric inspection of wind turbine blades using a microwave interferometric technique, Mater. Eval., 74, 477–484, 2016.
Williamson, C. H. K. and Govardhan, R.: Vortex-Induced Vibrations, Annu.
Rev. Fluid Mech., 36, 413–455, https://doi.org/10.1146/annurev.fluid.36.050802.122128, 2004.
WISDEM – Wind Plant Integrated System Design and Engineering Model: v3.3.0,
GitHub [code], https://github.com/WISDEM/WISDEM (last access: 14 June 2021), 2021.
Wiser, R., Bolinger, M., and Lantz, E.: Assessing wind power operating costs
in the United States: Results from a survey of wind industry experts, Renew. Energ. Focus, 30, 46–57, https://doi.org/10.1016/j.ref.2019.05.003, 2019.
Wiser, R., Millstein, D., Bolinger, M., Jeong, S., and Mills, A.: The hidden
value of large-rotor, tall-tower wind turbines in the United States, Wind
Eng., 45, 857–871, https://doi.org/10.1177/0309524X20933949, 2020.
Xie, S. and Archer, C. L.: A Numerical Study of Wind-Turbine Wakes for Three
Atmospheric Stability Conditions, Bound.-Lay. Meteorol., 165, 87–112, https://doi.org/10.1007/s10546-017-0259-9, 2017.
Xiu, D. and Karniadakis, G.: The Wiener–Askey Polynomial Chaos for Stochastic Differential Equations, SIAM J. Sci. Comput., 24, 619–644, https://doi.org/10.1137/S1064827501387826, 2002.
Yao, S., D. Griffith, T., Chetan, M., Bay, C. J., Damiani, R., Kaminski, M.,
Loth, E.: A gravo-aeroelastically scaled wind turbine rotor at field-prototype scale with strict structural requirements, Renew. Energy, 156, 535–547, https://doi.org/10.1016/j.renene.2020.03.157, 2020.
Yin, D., Passano, E., Jiang, F., Lie, H., Wu, J., Ye, N., Sævik, S., and Leira, B. J.: State-of-the-Art Review of Vortex-Induced Motions of Floating
Offshore Wind Turbine Structures, J. Mar. Sci. Eng., 10, 1021,
https://doi.org/10.3390/jmse10081021, 2022.
Yu, W., Lemmer, F., Schlipf, D., Cheng, P. W., Visser, B., Links, H., Gupta,
N., Dankemann, S., Counago, B., and Serna, J.: Evaluation of control methods
for floating offshore wind turbines, J. Phys.: Conf. Ser., 1104, 012033,
https://doi.org/10.1088/1742-6596/1104/1/012033, 2018.
Zahle, F., Tibaldi, C., Verelst, D. R., Bak, C., Bitche, R., and Albegaria Amaral Blasques, J. P.: Aero-Elastic Optimization of a 10 MW Wind Turbine, in: AIAA SciTech, 33rd Wind Energy Symposium, 5–9 January 2015, Kissimmee, Florida, 23 pp., https://doi.org/10.2514/6.2015-0491, 2015.
Zalkind, D. S., Ananda, G. K., Chetan, M., Martin, D. P., Bay, C. J., Johnson, K. E., Loth, E., Griffith, D. T., Selig, M. S., and Pao, L. Y.: System-level design studies for large rotors, Wind Energ. Sci., 4, 595–618, https://doi.org/10.5194/wes-4-595-2019, 2019.
Zalkind, D. S., Dall'Anese, E., and Pao, L. Y.: Automatic controller tuning
using a zeroth-order optimization algorithm, Wind Energ. Sci., 5, 1579–1600, https://doi.org/10.5194/wes-5-1579-2020, 2020.
Zalkind, D. S., Nicotra, M., and Pao, L. Y.: Constrained power reference
control for wind turbines, Wind Energy, 25, 914–934, https://doi.org/10.1002/we.2705, 2021.
Zou, F., Riziotis, V. A., Voustinas, S. G., and Wang, J.: Analysis of vortex-induced and stall-induced vibrations at standstill conditions using a
free wake aerodynamic code, Wind Energy, 18, 2145–2169, https://doi.org/10.1002/we.1811, 2014.
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.
Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind...
Altmetrics
Final-revised paper
Preprint