Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1137-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1137-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2022
Research article |
| 01 Jun 2022
| 01 Jun 2022
FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models
Michael J. LoCascio
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
Christopher J. Bay
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Majid Bastankhah
Department of Engineering, Durham University, Durham DH1 3LE, UK
Garrett E. Barter
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Paul A. Fleming
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Luis A. Martínez-Tossas
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
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Etienne Muller, Simone Gremmo, Felix Houtin-Mongrolle, Laurent Beaudet, Juliette Coussy, Luis A. Martínez-Tossas, and Pierre Bénard
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-37, https://doi.org/10.5194/wes-2026-37, 2026
Preprint under review for WES
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The energy yield of wind turbines within wind farms can be increased with control strategies. One way consists in misaligning the turbines with respect to the wind direction, to redirect their wake away from downstream turbines. Using models and advanced simulations, this work confirms a true potential but stresses how measurement biases on the wind direction and the flow complexity may critically affect both the performance in the field, and the reliability of the field validation campaigns.
James Cutler, Christopher Bay, and Andrew Ning
Wind Energ. Sci., 11, 37–49, https://doi.org/10.5194/wes-11-37-2026, https://doi.org/10.5194/wes-11-37-2026, 2026
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Tilting wind turbines change the airflow behind them, which can lower energy production in wind farms. This research tested both a traditional physics-based model and a deep learning method to predict these effects. While the traditional model improved with added optimization, it struggled with complex wake patterns. The deep learning approach was faster and more accurate, showing potential for better wind farm design and control with reduced computational cost.
Xiaowei Zhu, Shuolin Xiao, Ghanesh Narasimhan, Luis A. Martinez-Tossas, Michael Schnaubelt, Gerard Lemson, Hanxun Yao, Alexander S. Szalay, Dennice F. Gayme, and Charles Meneveau
Wind Energ. Sci., 10, 2821–2840, https://doi.org/10.5194/wes-10-2821-2025, https://doi.org/10.5194/wes-10-2821-2025, 2025
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The paper describes a new approach to democratize access to results from expensive high-performance computer simulations of atmospheric boundary layer flow interacting with wind turbines, in large wind farms. Users interact with the data using a virtual sensor array methodology and essentially stream the data on demand to their analysis or visualization programs rather than having to download files and worrying about data formats, etc.
Katarzyna Patryniak, Maurizio Collu, Jason Jonkman, Matthew Hall, Garrett Barter, Daniel Zalkind, and Andrea Coraddu
Wind Energ. Sci., 10, 2051–2077, https://doi.org/10.5194/wes-10-2051-2025, https://doi.org/10.5194/wes-10-2051-2025, 2025
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This paper studies the instantaneous centre of rotation (ICR) of floating offshore wind turbines (FOWTs). We present a method for computing the ICR and examine the correlations between the external loading, design features, ICR statistics, motions, and loads. We demonstrate how to apply the new insights to successfully modify the designs of the spar and semisubmersible FOWTs to reduce the loads in the moorings, the tower, and the blades, improving the ultimate strength and fatigue properties.
Kenneth Brown, Gopal Yalla, Lawrence Cheung, Joeri Frederik, Dan Houck, Nathaniel deVelder, Eric Simley, and Paul Fleming
Wind Energ. Sci., 10, 1737–1762, https://doi.org/10.5194/wes-10-1737-2025, https://doi.org/10.5194/wes-10-1737-2025, 2025
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This paper presents one half of a companion paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for a wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-154, https://doi.org/10.5194/wes-2025-154, 2025
Revised manuscript under review for WES
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This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.
Cory Frontin, Jeff Allen, Christopher J. Bay, Jared Thomas, Ethan Young, and Pietro Bortolotti
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-103, https://doi.org/10.5194/wes-2025-103, 2025
Revised manuscript under review for WES
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Wind farms produce energy and to do so have to occupy a non-trivial amount of space. Understanding how much energy a proposed wind farm will make (and at what cost) is technically challenging, especially when turbines are packed closely together. Plus, there's a key tradeoff in how much space a farm occupies and how cheap the energy it can produce might be: less space means more costly energy. This work shows an novel way to run computational simulations efficiently to understand that tradeoff.
Regis Thedin, Garrett Barter, Jason Jonkman, Rafael Mudafort, Christopher J. Bay, Kelsey Shaler, and Jasper Kreeft
Wind Energ. Sci., 10, 1033–1053, https://doi.org/10.5194/wes-10-1033-2025, https://doi.org/10.5194/wes-10-1033-2025, 2025
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We investigate asymmetries in terms of power performance and fatigue loading on a five-turbine wind farm subject to wake steering strategies. Both the yaw misalignment angle and the wind direction were varied from negative to positive. We highlight conditions in which fatigue loading is lower while still maintaining good power gains and show that a partial wake is the source of the asymmetries observed. We provide recommendations in terms of yaw misalignment angles for a given wind direction.
Joeri A. Frederik, Eric Simley, Kenneth A. Brown, Gopal R. Yalla, Lawrence C. Cheung, and Paul A. Fleming
Wind Energ. Sci., 10, 755–777, https://doi.org/10.5194/wes-10-755-2025, https://doi.org/10.5194/wes-10-755-2025, 2025
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In this paper, we present results from advanced computer simulations to determine the effects of applying different control strategies to a small wind farm. We show that when there is variability in wind direction over height, steering the wake of a turbine away from other turbines is the most effective strategy. When this variability is not present, actively changing the pitch angle of the blades to increase turbulence in the wake could be more effective.
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.
Eric Simley, Dev Millstein, Seongeun Jeong, and Paul Fleming
Wind Energ. Sci., 9, 219–234, https://doi.org/10.5194/wes-9-219-2024, https://doi.org/10.5194/wes-9-219-2024, 2024
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Wake steering is a wind farm control technology in which turbines are misaligned with the wind to deflect their wakes away from downstream turbines, increasing total power production. In this paper, we use a wind farm control model and historical electricity prices to assess the potential increase in market value from wake steering for 15 US wind plants. For most plants, we find that the relative increase in revenue from wake steering exceeds the relative increase in energy production.
Paul Hulsman, Luis A. Martínez-Tossas, Nicholas Hamilton, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-112, https://doi.org/10.5194/wes-2023-112, 2023
Manuscript not accepted for further review
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This paper presents an approach to analytically estimate the wake deficit within the near-wake region by modifying the curled wake model. This is done by incorporating a new initial condition at the rotor using an azimuth-dependent Gaussian profile, an adjusted turbulence model in the near-wake region and the far-wake region and an iterative process to determine the velocity field, while considering the relation of the pressure gradient and accounting the conservation of mass.
Andrew P. J. Stanley, Christopher J. Bay, and Paul Fleming
Wind Energ. Sci., 8, 1341–1350, https://doi.org/10.5194/wes-8-1341-2023, https://doi.org/10.5194/wes-8-1341-2023, 2023
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Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as a function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.
Jared J. Thomas, Nicholas F. Baker, Paul Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John Jasa, Christopher Bay, Federico Tilli, David Bieniek, Nick Robinson, Andrew P. J. Stanley, Wesley Holt, and Andrew Ning
Wind Energ. Sci., 8, 865–891, https://doi.org/10.5194/wes-8-865-2023, https://doi.org/10.5194/wes-8-865-2023, 2023
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This work compares eight optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. Optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely spaced and roughly on a grid in the interior.
Ryan Scott, Luis Martínez-Tossas, Juliaan Bossuyt, Nicholas Hamilton, and Raúl B. Cal
Wind Energ. Sci., 8, 449–463, https://doi.org/10.5194/wes-8-449-2023, https://doi.org/10.5194/wes-8-449-2023, 2023
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In this work we examine the relationship between wind speed and turbulent stresses within a wind turbine wake. This relationship changes further from the turbine as the driving physical phenomena vary throughout the wake. We propose a model for this process and test the effectiveness of our model against existing formulations. Our approach increases the accuracy of wind speed predictions, which will lead to better estimates of wind plant performance and promote more efficient wind plant design.
Kelsey Shaler, Benjamin Anderson, Luis A. Martínez-Tossas, Emmanuel Branlard, and Nick Johnson
Wind Energ. Sci., 8, 383–399, https://doi.org/10.5194/wes-8-383-2023, https://doi.org/10.5194/wes-8-383-2023, 2023
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Free-vortex wake (OLAF) and low-fidelity blade-element momentum (BEM) structural results are compared to high-fidelity simulation results for a flexible downwind turbine for varying inflow conditions. Overall, OLAF results were more consistent than BEM results when compared to SOWFA results under challenging inflow conditions. Differences between OLAF and BEM results were dominated by yaw misalignment angle, with varying shear exponent and turbulence intensity causing more subtle differences.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
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This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
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).
Julian Quick, Ryan N. King, Garrett Barter, and Peter E. Hamlington
Wind Energ. Sci., 7, 1941–1955, https://doi.org/10.5194/wes-7-1941-2022, https://doi.org/10.5194/wes-7-1941-2022, 2022
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Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of alignment with the incoming wind, thereby steering wakes away from downstream turbines. Trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present an optimization approach for efficiently exploring the trade-offs between power and loading during wake steering.
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022, https://doi.org/10.5194/wes-7-991-2022, 2022
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Using highly accurate simulations within a design cycle is prohibitively computationally expensive. We implement and present a multifidelity optimization method and showcase its efficacy using three different case studies. We examine aerodynamic blade design, turbine controls tuning, and a wind plant layout problem. In each case, the multifidelity method finds an optimal design that performs better than those obtained using simplified models but at a lower cost than high-fidelity optimization.
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022, https://doi.org/10.5194/wes-7-741-2022, 2022
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In wind plants, turbines can be yawed to steer their wakes away from downstream turbines and achieve an increase in plant power. The yaw angles become expensive to solve for in large farms. This paper presents a new method to solve for the optimal turbine yaw angles in a wind plant. The yaw angles are defined as Boolean variables – each turbine is either yawed or nonyawed. With this formulation, most of the gains from wake steering can be reached with a large reduction in computational expense.
Jared J. Thomas, Christopher J. Bay, Andrew P. J. Stanley, and Andrew Ning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-4, https://doi.org/10.5194/wes-2022-4, 2022
Revised manuscript not accepted
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We wanted to determine if and how optimization algorithms may be exploiting inaccuracies in the simple models used for wind farm layout optimization. Comparing optimization results from a simple model to large-eddy simulations showed that even a simple model provides enough information for optimizers to find good layouts. However, varying the number of wind directions in the optimization showed that the wind resource discretization can negatively impact the optimization results.
Andrew P. J. Stanley, Jennifer King, Christopher Bay, and Andrew Ning
Wind Energ. Sci., 7, 433–454, https://doi.org/10.5194/wes-7-433-2022, https://doi.org/10.5194/wes-7-433-2022, 2022
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In this paper, we present a computationally inexpensive model to calculate wind turbine blade fatigue caused by waking and partial waking. The model accounts for steady state on the blade, as well as wind turbulence. The model is fast enough to be used in wind farm layout optimization, which has not been possible with more expensive fatigue models in the past. The methods introduced in this paper will allow for farms with increased energy production that maintain turbine structural reliability.
Ethan Young, Jeffery Allen, John Jasa, Garrett Barter, and Ryan King
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-7, https://doi.org/10.5194/wes-2022-7, 2022
Preprint withdrawn
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In this study, we present ways to measure the phenomenon of wind plant blockage, or the the velocity slowdown upstream from a farm, and carry out turbine layout optimizations to reduce this effect. We find that farm-wide measurements provide a better characterization of blockage compared to more localized measurements and that, in the absence of any constraint on total power output, layouts which minimize the effect of blockage are frequently characterized by streamwise alignment of turbines.
Paul Fleming, Michael Sinner, Tom Young, Marine Lannic, Jennifer King, Eric Simley, and Bart Doekemeijer
Wind Energ. Sci., 6, 1521–1531, https://doi.org/10.5194/wes-6-1521-2021, https://doi.org/10.5194/wes-6-1521-2021, 2021
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The paper presents a new validation campaign of wake steering at a commercial wind farm. The campaign uses fixed yaw offset positions, rather than a table of optimal yaw offsets dependent on wind direction, to enable comparison with engineering models of wake steering. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions.
Eric Simley, Paul Fleming, Nicolas Girard, Lucas Alloin, Emma Godefroy, and Thomas Duc
Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, https://doi.org/10.5194/wes-6-1427-2021, 2021
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Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to deflect their low-velocity wakes away from downstream turbines, increasing overall power production. Here, we present results from a two-turbine wake-steering experiment at a commercial wind plant. By analyzing the wind speed dependence of wake steering, we find that the energy gained tends to increase for higher wind speeds because of both the wind conditions and turbine operation.
Andrew P. J. Stanley, Owen Roberts, Jennifer King, and Christopher J. Bay
Wind Energ. Sci., 6, 1143–1167, https://doi.org/10.5194/wes-6-1143-2021, https://doi.org/10.5194/wes-6-1143-2021, 2021
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Wind farm layout optimization is an essential part of wind farm design. In this paper, we present different methods to determine the number of turbines in a wind farm, as well as their placement. Also in this paper we explore the effect that the objective function has on the wind farm design and found that wind farm layout is highly sensitive to the objective. The optimal number of turbines can vary greatly, from 15 to 54 for the cases in this paper, depending on the metric that is optimized.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
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Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Jennifer King, Paul Fleming, Ryan King, Luis A. Martínez-Tossas, Christopher J. Bay, Rafael Mudafort, and Eric Simley
Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, https://doi.org/10.5194/wes-6-701-2021, 2021
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This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
Luis A. Martínez-Tossas, Jennifer King, Eliot Quon, Christopher J. Bay, Rafael Mudafort, Nicholas Hamilton, Michael F. Howland, and Paul A. Fleming
Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, https://doi.org/10.5194/wes-6-555-2021, 2021
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In this paper a three-dimensional steady-state solver for flow through a wind farm is developed and validated. The computational cost of the solver is on the order of seconds for large wind farms. The model is validated using high-fidelity simulations and SCADA.
Cited articles
Barthelmie, R. J., Frandsen, S. T., Nielsen, M., Pryor, S., Rethore, P.-E., and Jørgensen, H. E.: Modelling and measurements of power losses and
turbulence intensity in wind turbine wakes at Middelgrunden offshore wind
farm, Wind Energy, 10, 517–528, 2007. a
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. a
Draxl, C., Clifton, A., Hodge, B.-M., and McCaa, J.: The wind integration
national dataset (wind) toolkit, Appl. Energy, 151, 355–366, 2015. a
Feng, J. and Shen, W. Z.: Solving the wind farm layout optimization problem
using random search algorithm, Renew. Energy, 78, 182–192, 2015. a
Gebraad, P., Thomas, J. J., Ning, A., Fleming, P., and Dykes, K.: Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control, Wind Energy, 20, 97–107, 2017. a
González, J. S., García, Á. L. T., Payán, M. B., Santos, J. R., and Rodríguez, Á. G. G.: Optimal wind-turbine micro-siting of
offshore wind farms: A grid-like layout approach, Appl. Energy, 200, 28–38, 2017. a
Herbert-Acero, J. F., Probst, O., Réthoré, P.-E., Larsen, G. C., and
Castillo-Villar, K. K.: A review of methodological approaches for the design
and optimization of wind farms, Energies, 7, 6930–7016, 2014. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW
reference wind turbine for offshore system development, Tech. rep., National
Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/947422, 2009. a
King, R., Glaws, A., Geraci, G., and Eldred, M. S.: A Probabilistic Approach to Estimating Wind Farm Annual Energy Production with Bayesian Quadrature, in: AIAA Scitech 2020 Forum, Orlando, FL, p. 1951, https://doi.org/10.2514/6.2020-1951, 2020. a
Martínez-Tossas, L. A., Annoni, J., Fleming, P. A., and Churchfield, M. J.: The aerodynamics of the curled wake: a simplified model in view of flow control, Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, 2019. a
Martínez-Tossas, L. A., King, J., Quon, E., Bay, C. J., Mudafort, R., Hamilton, N., Howland, M. F., and Fleming, P. A.: The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows, Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, 2021. a
Meyers, J. and Meneveau, C.: Optimal turbine spacing in fully developed wind
farm boundary layers, Wind Energy, 15, 305–317, 2012. a
Murcia, J., Réthoré, P.-E., Natarajan, A., and Sørensen, J. D.: How many model evaluations are required to predict the AEP of a wind power
plant?, J. Phys.: Conf. Ser., 625, 012030, https://doi.org/10.1088/1742-6596/625/1/012030, 2015. a
Ning, A. and Petch, D.: Integrated design of downwind land-based wind turbines using analytic gradients, Wind Energy, 19, 2137–2152, 2016. a
NREL: FLORIS, Version 2.4, https://github.com/NREL/floris, last access: 12 October 2021.
a
Nygaard, N. G., Steen, S. T., Poulsen, L., and Pedersen, J. G.: Modelling
cluster wakes and wind farm blockage, J. Phys.: Conf. Ser., 1618, 062072, https://doi.org/10.1088/1742-6596/1618/6/062072, 2020. a
Padrón, A. S., Thomas, J., Stanley, A. P. J., Alonso, J. J., and Ning, A.: Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization, Wind Energ. Sci., 4, 211–231, https://doi.org/10.5194/wes-4-211-2019, 2019. a
Pedersen, M. M., van der Laan, P., Friis-Møller, M., Rinker, J., and
Réthoré, P.-E.: DTUWindEnergy/PyWake: PyWake, Zenodo [code], https://doi.org/10.5281/zenodo.2562662, 2019. a
Perez-Moreno, S. S., Dykes, K., Merz, K. O., and Zaaijer, M. B.:
Multidisciplinary design analysis and optimisation of a reference offshore
wind plant, J. Phys.: Conf. Ser., 1037, 042004, https://doi.org/10.1088/1742-6596/1037/4/042004, 2018. a
Stanley, A. P., Ning, A., and Dykes, K.: Optimization of turbine design in wind farms with multiple hub heights, using exact analytic gradients and
structural constraints, Wind Energy, 22, 605–619, 2019. a
Stanley, A. P. J. and Ning, A.: Massive simplification of the wind farm layout optimization problem, Wind Energ. Sci., 4, 663–676, https://doi.org/10.5194/wes-4-663-2019, 2019. a
Stanley, A. P. J., Roberts, O., King, J., and Bay, C. J.: Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant, Wind Energ. Sci., 6, 1143–1167, https://doi.org/10.5194/wes-6-1143-2021, 2021. a
Tian, L., Zhu, W., Shen, W., Zhao, N., and Shen, Z.: Development and validation of a new two-dimensional wake model for wind turbine wakes, J. Wind Eng. Indust. Aerodynam., 137, 90–99, 2015. a
Tian, L., Zhu, W., Shen, W., Song, Y., and Zhao, N.: Prediction of multi-wake
problems using an improved Jensen wake model, Renew. Energy, 102, 457–469, 2017. a
Short summary
This work introduces the FLOW Estimation and Rose Superposition (FLOWERS) wind turbine wake model. This model analytically integrates the wake over wind directions to provide a time-averaged flow field. This new formulation is used to perform layout optimization. The FLOWERS model provides a smooth flow field over an entire wind plant at fraction of the computational cost of the standard numerical integration approach.
This work introduces the FLOW Estimation and Rose Superposition (FLOWERS) wind turbine wake...
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