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.
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
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-42, https://doi.org/10.5194/wes-2025-42, 2025
Preprint under review for WES
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Most human activity happens in the layer of the atmosphere which extends a few hundred meters to a couple of kilometers above the surface of the Earth. The flow in this layer is turbulent. Turbulence impacts wind power production and turbine lifespan. Optimizing wind turbine performance requires understanding how turbulence affects both wind turbine efficiency and reliability. This paper points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
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Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-68, https://doi.org/10.5194/wes-2024-68, 2024
Revised manuscript under review for WES
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The study clarifies the use of probabilistic extrapolation of short/mid-term data for long-term site-specific fatigue assessments. In addition, it assesses the accountability of the Frandsen model in the Lillgrund wind farm as an example of compact layout.
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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
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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
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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
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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.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth N. Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, William Radünz, and Patrick Moriarty
Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, https://doi.org/10.5194/wes-10-1681-2025, 2025
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This study is the first to use real-world atmospheric measurements to show that large wind plants can increase the height of the planetary boundary layer, the part of the atmosphere near the surface where life takes place. The planetary boundary layer height governs processes like pollutant transport and cloud formation and is a key parameter for modeling the atmosphere. The results of this study provide important insights into interactions between wind plants and their local environment.
Leonardo Pagamonci, Francesco Papi, Gabriel Cojocaru, Marco Belloli, and Alessandro Bianchini
Wind Energ. Sci., 10, 1707–1736, https://doi.org/10.5194/wes-10-1707-2025, https://doi.org/10.5194/wes-10-1707-2025, 2025
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The study presents a critical analysis using wind tunnel experiments and large-eddy simulations aimed at quantifying to what extent turbulence affects the wake structures of a floating turbine undergoing large motions. Analyses show that, whenever realistic turbulence comes into play, only small gains in terms of wake recovery are noticed in comparison to bottom-fixed turbines, suggesting the absence of hypothesized superposition effects between inflow and platform motion.
Yelena L. Pichugina, Alan W. Brewer, Sunil Baidar, Robert Banta, Edward Strobach, Brandi McCarty, Brian Carroll, Nicola Bodini, Stefano Letizia, Richard Marchbanks, Michael Zucker, Maxwell Holloway, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-79, https://doi.org/10.5194/wes-2025-79, 2025
Preprint under review for WES
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The truck-based Doppler lidar system was used during the American Wake Experiment (AWAKEN) to obtain the high-frequency, simultaneous measurements of the horizontal wind speed, direction, and vertical-velocity from a moving platform. The paper presents the unique capability of the novel lidar system to characterize the temporal, vertical, and spatial variability of winds at various distances from operating turbines and obtain quantitative estimates of wind speed reduction in the waked flow.
Anna Voss, Konrad B. Bärfuss, Beatriz Cañadillas, Maik Angermann, Mark Bitter, Matthias Cremer, Thomas Feuerle, Jonas Spoor, Julie K. Lundquist, Patrick Moriarty, and Astrid Lampert
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-113, https://doi.org/10.5194/wes-2025-113, 2025
Preprint under review for WES
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This study analyses onshore wind farm wakes in a semi-complex terrain with data conducted with the research aircraft of TU Braunschweig during the AWAKEN project. Vertical profiles of temperature, humidity and wind give insights into the stratification of the atmospheric boundary layer, while horizontal profiles downwind of wind farms reveal an amplification of the reduction in wind speed in a semi-complex terrain in particular in a distance of 10 km.
Alessandro Fontanella, Alberto Fusetti, Stefano Cioni, Francesco Papi, Sara Muggiasca, Giacomo Persico, Vincenzo Dossena, Alessandro Bianchini, and Marco Belloli
Wind Energ. Sci., 10, 1369–1387, https://doi.org/10.5194/wes-10-1369-2025, https://doi.org/10.5194/wes-10-1369-2025, 2025
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This paper investigates the impact of large movements allowed by floating wind turbine foundations on their aerodynamics and wake behavior. Wind tunnel tests with a model turbine reveal that platform motions affect wake patterns and turbulence levels. Insights from these experiments are crucial for optimizing large-scale floating wind farms. The dataset obtained from the experiment is published and can aid in developing simulation tools for floating wind turbines.
Abhinav Anand and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-101, https://doi.org/10.5194/wes-2025-101, 2025
Preprint under review for WES
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We formulate a controller for wind turbines that has three main characteristics. First, it optimizes profit by balancing revenue from power generation with cost. Second, cost includes the effects of cyclic fatigue that, departing from most of the existing literature on control, is rigorously accounted for by an exact cycle counting on receding horizons. Third, it uses a model capable of learning and improving its performance based on measured or synthetic data.
Alessandro Fontanella, Stefano Cioni, Francesco Papi, Sara Muggiasca, Alessandro Bianchini, and Marco Belloli
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-106, https://doi.org/10.5194/wes-2025-106, 2025
Preprint under review for WES
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This study explores how the movement of floating wind turbines affects nearby turbines. Using wind tunnel experiments, we found that certain motions of an upstream turbine can improve the energy produced by a downstream one and change the forces it experiences. These effects depend on how the turbines are spaced and aligned. Our results show that the motion of floating turbines plays a key role in how future offshore wind farms should be designed and operated.
Hadi Hoghooghi and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-98, https://doi.org/10.5194/wes-2025-98, 2025
Preprint under review for WES
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We formulate and demonstrate a new digital shadow (i.e. a virtual copy) for wind turbines. The digital shadow is designed in order to be capable of mirroring the response of the machine even in complex inflow conditions. Results from field measurements illustrate the ability of the shadow to estimate loads with good accuracy, even with minimal tuning.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
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Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Will Wiley, Jason Jonkman, and Amy Robertson
Wind Energ. Sci., 10, 941–970, https://doi.org/10.5194/wes-10-941-2025, https://doi.org/10.5194/wes-10-941-2025, 2025
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Numerical models, used to assess loads on floating offshore wind turbines, require many input parameters to describe air and water conditions, system properties, and load calculations. All parameters have some possible range, due to uncertainty and/or variations with time. The selected values can have important effects on the uncertainty in the resulting loads. This work identifies the input parameters that have the most impact on ultimate and fatigue loads for extreme storm load cases.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-66, https://doi.org/10.5194/wes-2025-66, 2025
Preprint under review for WES
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We proposed a new method for active power control that uniquely combines induction control with wake steering to maximize power tracking margins. Our methodology results in significantly improved robustness against wind fluctuations and fatigue loading when compared to the state of the art.
Helge Aagaard Madsen, Alejandro Gomez Gonzalez, Thanasis Barlas, Anders Smærup Olsen, Sigurd Brabæk Ildvedsen, and Andreas Fischer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-75, https://doi.org/10.5194/wes-2025-75, 2025
Revised manuscript under review for WES
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In this article we present the measurements of local aerodynamic sectional characteristics on a full-scale rotor blade with a novel add-on instrumentation comprising a wake rake, a pressure belt, and a five hole Pitot tube. In general, the demonstration of this instrumentation opens a range of promising new options for optimizing airfoil sectional performance in its real operating environment, e.g. the size and position of VG's.
Julian Quick, Edward Hart, Marcus Binder Nilsen, Rasmus Sode Lund, Jaime Liew, Piinshin Huang, Pierre-Elouan Rethore, Jonathan Keller, Wooyong Song, and Yi Guo
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-63, https://doi.org/10.5194/wes-2025-63, 2025
Revised manuscript under review for WES
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Wind turbine main bearings often fail prematurely, creating costly maintenance challenges. This study examined how wake effects – where upstream turbines create disturbed airflow that impacts downstream turbines – affect bearing lifespans. Using computer simulations, we found that wake effects reduce bearing life by 16% on average. The direction of wake impact matters significantly due to interactions between wind forces and gravity, informing better wind turbine and farm farm design strategies.
Andre Thommessen, Abhinav Anand, Christoph M. Hackl, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-72, https://doi.org/10.5194/wes-2025-72, 2025
Preprint under review for WES
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We present a method to forecast inertia that accounts for wake effects in a wind farm. The approach is based on mapping forecasted site conditions to each single wind turbine in the farm through a wake model. The resulting inflow conditions are used to predict the inertia that the wind farm can provide to the grid, taking the wind turbine control strategies and operational limits into account.
Bogdan Pamfil, Henrik Bredmose, and Taeseong Kim
Wind Energ. Sci., 10, 827–856, https://doi.org/10.5194/wes-10-827-2025, https://doi.org/10.5194/wes-10-827-2025, 2025
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A floating wind turbine time domain model, which considers dynamic stall, is used to develop Coleman-free aero-elastic stability analysis methods, namely Hill's and Floquet's. We clarify how the floater tilt is involved in the stability analysis, show damping effects of aerodynamic states, prove that results of both methods agree and can reproduce the forward- and backward-whirling rotor modes in a Coleman-based analysis, and demonstrate that both methods can be applied to a two-bladed rotor.
Abhinav Anand, Robert Braunbehrens, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-67, https://doi.org/10.5194/wes-2025-67, 2025
Revised manuscript has not been submitted
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We present a new method for wind farm control, based on the optimization of an economic cost function that accounts for revenue from power production and cost due to operation and maintenance. The new formulation also includes constraints to ensure a desired lifetime duration. The application to relevant scenarios shows consistently improved profit when compared to alternative formulations from the recent literature.
Branko Kosović, Sukanta Basu, Jacob Berg, Larry K. Berg, Sue E. Haupt, Xiaoli G. Larsén, Joachim Peinke, Richard J. A. M. Stevens, Paul Veers, and Simon Watson
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-42, https://doi.org/10.5194/wes-2025-42, 2025
Preprint under review for WES
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Most human activity happens in the layer of the atmosphere which extends a few hundred meters to a couple of kilometers above the surface of the Earth. The flow in this layer is turbulent. Turbulence impacts wind power production and turbine lifespan. Optimizing wind turbine performance requires understanding how turbulence affects both wind turbine efficiency and reliability. This paper points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.
Pietro Bortolotti, Lee Jay Fingersh, Nicholas Hamilton, Arlinda Huskey, Chris Ivanov, Mark Iverson, Jonathan Keller, Scott Lambert, Jason Roadman, Derek Slaughter, Syhoune Thao, and Consuelo Wells
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-8, https://doi.org/10.5194/wes-2025-8, 2025
Revised manuscript accepted for WES
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This study compares a wind turbine with blades behind the tower (downwind) to the traditional upwind design. Testing a 1.5 MW turbine at NREL’s Flatirons Campus, we measured performance, loads, and noise. Numerical models matched well with observations. The downwind setup showed higher fatigue loads and sound variations but also an unexpected power improvement. Downwind rotors might be a valid alternative for future floating offshore wind applications.
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
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This study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
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Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1547–1575, https://doi.org/10.5194/wes-9-1547-2024, https://doi.org/10.5194/wes-9-1547-2024, 2024
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We develop a new simple model to predict power losses incurred by a wind turbine when it yaws out of the wind. The model reveals the effects of a number of rotor design parameters and how the turbine is governed when it yaws. The model exhibits an excellent agreement with large eddy simulations and wind tunnel measurements. We showcase the capabilities of the model by deriving the power-optimal yaw strategy for a single turbine and for a cluster of wake-interacting turbines.
Shadan Mozafari, Jennifer Rinker, Paul Veers, and Katherine Dykes
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-68, https://doi.org/10.5194/wes-2024-68, 2024
Revised manuscript under review for WES
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The study clarifies the use of probabilistic extrapolation of short/mid-term data for long-term site-specific fatigue assessments. In addition, it assesses the accountability of the Frandsen model in the Lillgrund wind farm as an example of compact layout.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
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A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
Jenna Iori, Carlo Luigi Bottasso, and Michael Kenneth McWilliam
Wind Energ. Sci., 9, 1289–1304, https://doi.org/10.5194/wes-9-1289-2024, https://doi.org/10.5194/wes-9-1289-2024, 2024
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The controller of a wind turbine has an important role in regulating power production and avoiding structural failure. However, it is often designed after the rest of the turbine, and thus its potential is not fully exploited. An alternative is to design the structure and the controller simultaneously. This work develops a method to identify if a given turbine design can benefit from this new simultaneous design process. For example, a higher and cheaper turbine tower can be built this way.
Andrea Gamberini, Thanasis Barlas, Alejandro Gomez Gonzalez, and Helge Aagaard Madsen
Wind Energ. Sci., 9, 1229–1249, https://doi.org/10.5194/wes-9-1229-2024, https://doi.org/10.5194/wes-9-1229-2024, 2024
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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 3month 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.
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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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.
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
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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.
Helena Canet, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci., 8, 1029–1047, https://doi.org/10.5194/wes-8-1029-2023, https://doi.org/10.5194/wes-8-1029-2023, 2023
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We propose a new approach to design that aims at optimal trade-offs between economic and environmental goals. New environmental metrics are defined, which quantify impacts in terms of CO2-equivalent emissions produced by the turbine over its entire life cycle. For some typical onshore installations in Germany, results indicate that a 1 % increase in the cost of energy can buy about a 5 % decrease in environmental impacts: a small loss for the individual can lead to larger gains for society.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
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The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
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
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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
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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
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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
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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
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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
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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
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We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
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
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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
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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
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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
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A novel way of wind farm control is presented where the wake is deflected vertically to reduce interactions with downstream turbines. This is achieved by moving ballast in a floating offshore platform in order to pitch the support structure and thereby tilt the wind turbine rotor disk. The study considers the effects of this new form of wake control on the aerodynamics of the steering and wake-affected turbines, on the structure, and on the ballast motion system.
Stefan Loew and Carlo L. Bottasso
Wind Energ. Sci., 7, 1605–1625, https://doi.org/10.5194/wes-7-1605-2022, https://doi.org/10.5194/wes-7-1605-2022, 2022
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This publication presents methods to improve the awareness and control of material fatigue for wind turbines. This is achieved by enhancing a sophisticated control algorithm which utilizes wind prediction information from a laser measurement device. The simulation results indicate that the novel algorithm significantly improves the economic performance of a wind turbine. This benefit is particularly high for situations when the prediction quality is low or the prediction time frame is short.
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
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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
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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
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The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Lidar-assisted control (LAC) is used to redesign the rotor and tower of three turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save
mass, increase hub height, or extend lifetime. The first two strategies yield reductions in the cost of energy only for the tower of the largest machine, while more interesting benefits are obtained for lifetime extension.
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
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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
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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
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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
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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
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A previously published wind sensing method is applied to an experimental dataset obtained from a 3.5 MW turbine and a nearby hub-tall met mast. The method uses blade load harmonics to estimate rotor-equivalent shears and wind directions at the rotor disk. Results indicate the good quality of the estimated shear, both in terms of 10 min averages and of resolved time histories, and a reasonable accuracy in the estimation of the yaw misalignment.
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
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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
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The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
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
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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
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This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
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
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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
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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
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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
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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
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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
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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.
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...
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