Articles | Volume 9, issue 9
https://doi.org/10.5194/wes-9-1827-2024
© Author(s) 2024. 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-9-1827-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Investigating the interactions between wakes and floating wind turbines using FAST.Farm
National Renewable Energy Laboratory, Golden, CO 80401, USA
Jason Jonkman
National Renewable Energy Laboratory, Golden, CO 80401, USA
Regis Thedin
National Renewable Energy Laboratory, Golden, CO 80401, USA
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Matthew Hall, Lucas Carmo, and Ericka Lozon
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-58, https://doi.org/10.5194/wes-2025-58, 2025
Revised manuscript under review for WES
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This paper presents a frequency-domain dynamics modeling approach for multiple floating wind turbines that are connected by shared mooring lines. It models the wave excitation and response of each floating platform, and computes the shared mooring line reactions based on the relative platform motions. A two-turbine scenario demonstrates the approach, and comparison with an established time-domain model verifies its accuracy. The results reveal a new shared-mooring tension-dynamics phenomenon.
Veronica Liverud Krathe, Jason Jonkman, and Erin Elizabeth Bachynski-Polić
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-92, https://doi.org/10.5194/wes-2025-92, 2025
Preprint under review for WES
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This study looks into how changes in wind direction with height and drivetrain flexibility influence the behavior of large floating wind turbines. Using numerical simulations, it was found that these factors can significantly impact the lifetime of the turbines. These results suggest that standardized design methods may underestimate fatigue and that improved modeling could enhance turbine reliability as turbines continue to grow in size.
Matthew Hall, Lucas Carmo, and Ericka Lozon
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-58, https://doi.org/10.5194/wes-2025-58, 2025
Revised manuscript under review for WES
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This paper presents a frequency-domain dynamics modeling approach for multiple floating wind turbines that are connected by shared mooring lines. It models the wave excitation and response of each floating platform, and computes the shared mooring line reactions based on the relative platform motions. A two-turbine scenario demonstrates the approach, and comparison with an established time-domain model verifies its accuracy. The results reveal a new shared-mooring tension-dynamics phenomenon.
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.
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.
Katarzyna Patryniak, Maurizio Collu, Jason Jonkman, Matthew Hall, Garrett Barter, Daniel Zalkind, and Andrea Coraddu
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-167, https://doi.org/10.5194/wes-2024-167, 2025
Revised manuscript accepted for WES
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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, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024, https://doi.org/10.5194/wes-9-1791-2024, 2024
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This paper presents a study of the popular wind turbine design tool OpenFAST. We compare simulation results to measurements obtained from a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate turbulent flow fields through several techniques. We show that successful validation of the tool is not strongly dependent on the inflow generation technique used for mean quantities of interest. The type of inflow assimilation method has a larger effect on fatigue quantities.
Kelsey Shaler, Eliot Quon, Hristo Ivanov, and Jason Jonkman
Wind Energ. Sci., 9, 1451–1463, https://doi.org/10.5194/wes-9-1451-2024, https://doi.org/10.5194/wes-9-1451-2024, 2024
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This paper presents a three-way verification and validation between an engineering-fidelity model, a high-fidelity model, and measured data for the wind farm structural response and wake dynamics during an evolving stable boundary layer of a small wind farm, generally with good agreement.
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.
Emmanuel Branlard, Jason Jonkman, Cameron Brown, and Jiatian Zhang
Wind Energ. Sci., 9, 1–24, https://doi.org/10.5194/wes-9-1-2024, https://doi.org/10.5194/wes-9-1-2024, 2024
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In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The article present methods to obtain reduced-order models of floating wind turbines. The models are used to form a digital twin which combines measurements from the TetraSpar prototype (a full-scale floating offshore wind turbine) to estimate signals that are not typically measured.
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.
Paula Doubrawa, Kelsey Shaler, and Jason Jonkman
Wind Energ. Sci., 8, 1475–1493, https://doi.org/10.5194/wes-8-1475-2023, https://doi.org/10.5194/wes-8-1475-2023, 2023
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Wind turbines are designed to withstand any wind conditions they might encounter. This includes high-turbulence flow fields found within wind farms due to the presence of the wind turbines themselves. The international standard allows for two ways to account for wind farm turbulence in the design process. We compared both ways and found large differences between them. To avoid overdesign and enable a site-specific design, we suggest moving towards validated, higher-fidelity simulation tools.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
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The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
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.
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.
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022, https://doi.org/10.5194/wes-7-559-2022, 2022
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This paper summarizes efforts done to understand the impact of design parameter variations in the physical system (e.g., mass, stiffness, geometry, aerodynamic, and hydrodynamic coefficients) on the linearized system using OpenFAST in support of the development of the WEIS toolset to enable controls co-design of floating offshore wind turbines.
Emmanuel Branlard, Ian Brownstein, Benjamin Strom, Jason Jonkman, Scott Dana, and Edward Ian Baring-Gould
Wind Energ. Sci., 7, 455–467, https://doi.org/10.5194/wes-7-455-2022, https://doi.org/10.5194/wes-7-455-2022, 2022
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In this work, we present an aerodynamic tool that can model an arbitrary collections of wings, blades, rotors, and towers. With these functionalities, the tool can be used to study and design advanced wind energy concepts, such as horizontal-axis wind turbines, vertical-axis wind turbines, kites, or multi-rotors. This article describes the key features of the tool and presents multiple applications. Field measurements of horizontal- and vertical-axis wind turbines are used for comparison.
Matthias Kretschmer, Jason Jonkman, Vasilis Pettas, and Po Wen Cheng
Wind Energ. Sci., 6, 1247–1262, https://doi.org/10.5194/wes-6-1247-2021, https://doi.org/10.5194/wes-6-1247-2021, 2021
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We perform a validation of the new simulation tool FAST.Farm for the prediction of power output and structural loads in single wake conditions with respect to measurement data from the offshore wind farm alpha ventus. With a new wake-added turbulence functionality added to FAST.Farm, good agreement between simulations and measurements is achieved for the considered quantities. We hereby give insights into load characteristics of an offshore wind turbine subjected to single wake conditions.
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. a
Angelou, N., Mann, J., and Dubreuil-Boisclair, C.: Revealing inflow and wake conditions of a 6 MW floating turbine, Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, 2023. a
Churchfield, M. and Lee, S.: SOWFA: Simulator fOr Wind Farm Applications, https://www.nrel.gov/wind/nwtc/sowfa.html (last access: 5 September 2024), 2015. a
Doubrawa, P., Annoni, J., and Jonkman, J.: Optimization-based calibration of FAST.Farm parameters against large-eddy simulations, in: 2018 Wind Energy Symposium,Kissimmee, Florida, USA, 8–12 January 2018, p. 0512, https://doi.org/10.2514/6.2018-0512, 2018. a
Equinor: Hywind Scotland, https://www.equinor.com/energy/hywind-scotland (last access: 5 September 2024), 2023. a
Equinor: Hywind Tampen, https://www.equinor.com/energy/hywind-tampen (last access: 5 September 2024), 2024. a
Fleming, P., Gebraad, P., 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, 2015. a
Fu, S., Jin, Y., Zheng, Y., and Chamorro, L.: Wake and power fluctuations of a model wind turbine subjected to pitch and roll oscillations, Appl. Energ., 253, 113605, https://doi.org/10.1016/j.apenergy.2019.113605, 2019. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Roland, F., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: IEA Wind TCP Task 37: Definition of the IEA 15-megawatt offshore reference wind turbine, Tech. rep., National Renewable Energy Lab. (NREL), Golden, CO (United States), https://doi.org/10.2172/1603478, 2020. a
Hall, M.: MoorDyn user's guide, Tech. rep., University of Maine, Orono, ME, USA, https://www.nrel.gov/wind/nwtc/moordyn.html (last access: 5 September 2024), 2015. a
Huang, Y. and Wan, D.: Investigation of interference effects between wind turbine and spar-type floating platform under combined wind-wave excitation, Sustainability, 12, 246, https://doi.org/10.3390/su12010246, 2019. a
IEC: Wind turbines – Part 1: Design requirements, Tech. rep., International Electrotechnical Commission (IEC), https://webstore.iec.ch/en/publication/26423 (last access: 5 September 2024), 2005. a
Johlas, H., Martínez-Tossas, L., Schmidt, D., Lackner, M., and Churchfield, M.: Large eddy simulations of floating offshore wind turbine wakes with coupled platform motion, J. Phys. Conf. Ser., 1256, 012018, https://doi.org/10.1088/1742-6596/1256/1/012018, 2019. a, b
Johlas, H., Martínez-Tossas, L., Lackner, M., Schmidt, D., and Churchfield, M.: Large eddy simulations of offshore wind turbine wakes for two floating platform types, J. Phys. Conf. Ser., 1452, 012034, https://doi.org/10.1088/1742-6596/1452/1/012034, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Johlas, H., Schmidt, D., and Lackner, M.: Large eddy simulations of curled wakes from tilted wind turbines, Renew. Energ., 188, 349–360, 2022. a
Jonkman, B.: TurbSim user's guide: Version 1.50, Tech. rep., National Renewable Energy Lab. (NREL), Golden, CO, USA, https://www.nrel.gov/docs/fy09osti/46198.pdf (last access: 5 September 2024), 2009. a
Jonkman, B., Mudafort, R., Platt, A., Branlard, E., Sprague, M., Jonkman, J., Ross, H., Hayman, G., Hall, M., Vijayakumar, G., Buhl, M., Bortolotti, P., Ananthan, S., Rood, J., Damiani, R., Mendoza, N., Long, H., Schunemann, P., Slaughter, D., Sharma, A., Shaler, K., Housner, S., Sakievich, P., Bendl, K., Carmo, L., Quon, E., Phillips, M., Kusuno, N., and Salcedo, A.: OpenFAST/openfast: v3.4.1, Zenodo [code], https://doi.org/10.5281/zenodo.6324287, 2023. a, b, c
Jonkman, J., Hayman, G., Jonkman, B., Damiani, R., and Murray, R.: AeroDyn v15 user's guide and theory manual, Tech. rep., National Renewable Energy Lab. (NREL), Golden, CO, USA, https://www.nrel.gov/wind/nwtc/assets/pdfs/aerodyn-manual.pdf (last access: 5 September 2024), 2015. a
Jonkman, J., Annoni, J., Hayman, G., Jonkman, B., and Purkayastha, A.: Development of FAST.Farm: A new multi-physics engineering tool for wind-farm design and analysis, in: 35th Wind Energy Symposium, p. 0454, Grapevine, Texas, USA, 9–13 January 2017, https://doi.org/10.2514/6.2017-0454, 2017. a, b, c
Jonkman, J., Doubrawa, P., Hamilton, N., Annoni, J., and Fleming, P.: Validation of FAST.Farm against large-eddy simulations, J. Phys. Conf. Ser., 1037, 062005, https://doi.org/10.1088/1742-6596/1037/6/062005, 2018. a
Kim, S., Shin, H., Joo, Y., and Kim, K.: A study of the wake effects on the wind characteristics and fatigue loads for the turbines in a wind farm, Renew. Energ., 74, 536–543, https://doi.org/10.1016/j.renene.2014.08.054, 2015. a
Kretschmer, M., Jonkman, J., Pettas, V., and Cheng, P. W.: FAST.Farm load validation for single wake situations at alpha ventus, Wind Energ. Sci., 6, 1247–1262, https://doi.org/10.5194/wes-6-1247-2021, 2021. a, b
Larsen, G., Madsen, H., Thomsen, K., and Larsen, T.: Wake meandering: a pragmatic approach, Wind Energy, 11, 377–395, https://doi.org/10.1002/we.267, 2008. a
Li, Z., Dong, G., and Yang, X.: Onset of wake meandering for a floating offshore wind turbine under side-to-side motion, J. Fluid Mech., 934, A29, https://doi.org/10.1017/jfm.2021.1147, 2022. a
Messmer, T., Hölling, M., and Peinke, J.: Enhanced recovery caused by nonlinear dynamics in the wake of a floating offshore wind turbine, J. Fluid Mech., 984, A66, https://doi.org/10.1017/jfm.2024.175, 2024. a
Nunemaker, J. and Abbas, N.: pCrunch, GitHub [code], https://github.com/NREL/pCrunch (last access: 5 September 2024), 2023. a
Ocean Winds: WindFloat Atlantic, https://www.oceanwinds.com/projects/windfloat-atlantic-project/ (last access: 5 September 2024), 2023. a
Quon, E.: SAMWICH Box: A Python-based toolbox for Simulated And Measured Wake Identification and CHaracterization, GitHub [code], https://github.com/ewquon/waketracking, 2017. a
Quon, E., Doubrawa, P., and Debnath, M.: Comparison of rotor wake identification and characterization methods for the analysis of wake dynamics and evolution, J. Phys. Conf. Ser., 1452, 012070, https://doi.org/10.1088/1742-6596/1452/1/012070, 2020. a
Rivera-Arreba, I., Eliassen, L., and Bachynski-Polić, E.: Effect of the vertical wake deflection on the response of a 12MW semisubmersible FWT, J. Phys. Conf. Ser., 2626, 012057, https://doi.org/10.1088/1742-6596/2626/1/012057, 2023a. a
Rivera-Arreba, I., Li, Z., Yang, X., and Bachynski-Polić, E.: Validation of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes, arXiv [preprint], https://doi.org/10.48550/arXiv.2308.01004, 2023b. a, b, c
Rivera-Arreba, I., Wise, A., Eliassen, L., and Bachynski-Polić, E.: Effect of atmospheric stability on the dynamic wake meandering model applied to two 12MW floating wind turbines, Wind Energy, 26, 1235–1253, 2023c. a
Robertson, A., Jonkman, J., Masciola, M., Song, H., Goupee, A., Coulling, A., and Luan, C.: Definition of the semisubmersible floating system for phase II of OC4, Tech. rep., National Renewable Energy Lab. (NREL), Golden, CO, USA, https://www.nrel.gov/docs/fy14osti/60601.pdf (last access: 5 September 2024), 2014. a, b
Rockel, S., Peinke, J., Hölling, M., and Cal, R.: Dynamic wake development of a floating wind turbine in free pitch motion subjected to turbulent inflow generated with an active grid, Renew. Energ., 112, 1–16, 2017. a
Sant, T., Bonnici, D., Farrugia, R., and Micallef, D.: Measurements and modelling of the power performance of a model floating wind turbine under controlled conditions, Wind Energy, 18, 811–834, 2015. a
Schliffke, B., Aubrun, S., and Conan, B.: Wind tunnel study of a “floating” wind turbine's wake in an atmospheric boundary layer with imposed characteristic surge motion, J. Phys. Conf. Ser., 1618, 062015, https://doi.org/10.1088/1742-6596/1618/6/062015, 2020. a
Shaler, K., Jonkman, J., Barter, G. E., Kreeft, J. J., and Muller, J.: Loads assessment of a fixed-bottom offshore wind farm with wake steering, Wind Energy, 25, 1530–1554, https://doi.org/10.1002/we.2756, 2022. a, b
Thedin, R., Barter, G., Jonkman, J., Mudafort, R., Bay, C. J., Shaler, K., and Kreeft, J.: Load assessment of a wind farm considering negative and positive yaw misalignment for wake steering, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2024-6, in review, 2024. a
Thomsen, K. and Sørensen, P.: Fatigue loads for wind turbines operating in wakes, J. Wind Eng. Ind. Aerod., 80, 121–136, https://doi.org/10.1016/S0167-6105(98)00194-9, 1999. a
WAMIT, I.: WAMIT User Manual – Version 7.4, Chestnut Hill, MA, USA, https://www.wamit.com/manualupdate/v74_manual.pdf (last access: 5 September 2024), 2020. a
Wen, B., Tian, X., Dong, X., Peng, Z., and Zhang, W.: Influences of surge motion on the power and thrust characteristics of an offshore floating wind turbine, Energy, 141, 2054–2068, 2017. a
Wen, B., Dong, X., Tian, X., Peng, Z., Zhang, W., and Wei, K.: The power performance of an offshore floating wind turbine in platform pitching motion, Energy, 154, 508–521, 2018. a
Wise, A. and Bachynski, E.: Wake meandering effects on floating wind turbines, Wind Energy, 23, 1266–1285, 2020. a
Short summary
As floating wind turbines progress to arrays with multiple units, it becomes important to understand how the wake of a floating turbine affects the performance of other units in the array. Due to the compliance of the floating substructure, the wake of a floating wind turbine may behave differently from that of a fixed turbine. In this work, we present an investigation of the mutual interaction between the motions of floating wind turbines and wakes.
As floating wind turbines progress to arrays with multiple units, it becomes important to...
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