Articles | Volume 6, issue 3
https://doi.org/10.5194/wes-6-917-2021
© Author(s) 2021. 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-6-917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A method for preliminary rotor design – Part 2: Wind turbine Optimization with Radial Independence
Suzlon Blade Science Center, Brendstrupgaardsvej 13, 8210 Aarhus, Denmark
Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Christian Bak
Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Michael McWilliam
Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Related authors
Ang Li, Mac Gaunaa, Georg Raimund Pirrung, and Kenneth Lønbæk
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-30, https://doi.org/10.5194/wes-2025-30, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This study improves the analysis of curved wind turbine blades, such as those with sweep or prebend. Existing methods often blend different effects on blade performance, making design optimization challenging. We developed a framework that disentangles these effects, providing clearer insights. Our findings show that the aerodynamic influences of sweep and prebend can be modeled separately and combined, simplifying modeling processes and supporting more efficient blade design.
Kenneth Loenbaek, Christian Bak, Jens I. Madsen, and Michael McWilliam
Wind Energ. Sci., 6, 903–915, https://doi.org/10.5194/wes-6-903-2021, https://doi.org/10.5194/wes-6-903-2021, 2021
Short summary
Short summary
We present a model for assessing the aerodynamic performance of a wind turbine rotor through a different parametrization of the classical blade element momentum model. The model establishes an analytical relationship between the loading in the flow direction and the power along the rotor span. The main benefit of the model is the ease with which it can be applied for rotor optimization and especially load constraint power optimization.
Nanako Sasanuma, Akihiro Honda, Christian Bak, Niels Troldborg, Mac Gaunaa, Morten Nielsen, and Teruhisa Shimada
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-130, https://doi.org/10.5194/wes-2025-130, 2025
Preprint under review for WES
Short summary
Short summary
We verify wake effects between two turbines in complex terrain using Supervisory Control and Data Acquisition data. By identifying “wake conditions” and “no-wake conditions” by the blade pitch angle of the upstream wind turbine, we evaluate wake effects on wind speed, turbulent intensity, and power output. Results show that flow downhill has a significant impact on wake effects compared to flow uphill. The method offers a practical alternative to field measurements in complex terrain.
Ang Li, Mac Gaunaa, Georg Raimund Pirrung, and Kenneth Lønbæk
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-30, https://doi.org/10.5194/wes-2025-30, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This study improves the analysis of curved wind turbine blades, such as those with sweep or prebend. Existing methods often blend different effects on blade performance, making design optimization challenging. We developed a framework that disentangles these effects, providing clearer insights. Our findings show that the aerodynamic influences of sweep and prebend can be modeled separately and combined, simplifying modeling processes and supporting more efficient blade design.
Tahir H. Malik and Christian Bak
Wind Energ. Sci., 10, 269–291, https://doi.org/10.5194/wes-10-269-2025, https://doi.org/10.5194/wes-10-269-2025, 2025
Short summary
Short summary
This research integrates custom sensors into wind turbine simulation models for improved performance monitoring utilising a turbine performance integral (TPI) method developed here. Real-world data validation demonstrates that appropriate sensor selection improves wind turbine performance monitoring. This approach addresses the need for precise performance assessments in the evolving wind energy sector, ultimately promoting sustainability and efficiency.
Tahir H. Malik and Christian Bak
Wind Energ. Sci., 10, 227–243, https://doi.org/10.5194/wes-10-227-2025, https://doi.org/10.5194/wes-10-227-2025, 2025
Short summary
Short summary
This study investigates how wind turbine blades damaged by erosion, along with changing wind conditions, affect power output. Even minor blade damage can lead to significant energy losses, especially in turbulent winds. Using simulations, it was discovered that standard power data analysis methods, including time averaging, can hide these losses. This research highlights the need for better blade damage detection and careful wind data analysis to optimise wind farm performance.
Tahir H. Malik and Christian Bak
Wind Energ. Sci., 9, 2017–2037, https://doi.org/10.5194/wes-9-2017-2024, https://doi.org/10.5194/wes-9-2017-2024, 2024
Short summary
Short summary
We explore the effect of blade modifications on offshore wind turbines' performance through a detailed analysis of 12 turbines over 12 years. Introducing the turbine performance integral method, which utilises time-series decomposition that combines various data sources, we uncover how blade wear, repairs and software updates impact efficiency. The findings offer valuable insights into improving wind turbine operations, contributing to the enhancement of renewable energy technologies.
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
Short summary
Short summary
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.
Kenneth Loenbaek, Christian Bak, Jens I. Madsen, and Michael McWilliam
Wind Energ. Sci., 6, 903–915, https://doi.org/10.5194/wes-6-903-2021, https://doi.org/10.5194/wes-6-903-2021, 2021
Short summary
Short summary
We present a model for assessing the aerodynamic performance of a wind turbine rotor through a different parametrization of the classical blade element momentum model. The model establishes an analytical relationship between the loading in the flow direction and the power along the rotor span. The main benefit of the model is the ease with which it can be applied for rotor optimization and especially load constraint power optimization.
Cited articles
Bak, C.: Aerodynamic design of wind turbine rotors, in: vol. 1, Woodhead
Publishing Limited, Rosklilde, Denmark, https://doi.org/10.1533/9780857097286.1.59, 2013. a
Bak, C., Zahle, F., Bitsche, R., Yde, A., Henriksen, L. C., Nata, A., and
Hansen, M. H.: Description of the DTU 10 MW Reference Wind Turbine, DTU
Wind Energy Report-I-0092, DTU, Rosklilde, Denmark, 1–138, https://doi.org/10.1017/CBO9781107415324.004, 2013. a
Bottasso, C. L., Campagnolo, F., and Croce, A.: Multi-disciplinary constrained optimization of wind turbines, Multibody Syst. Dynam., 27, 21–53, https://doi.org/10.1007/s11044-011-9271-x, 2012. a
Buck, J. A. and Garvey, S. D.: Analysis of Force-Capping for Large Wind
Turbine Rotors, Wind Eng., 39, 213–228, https://doi.org/10.1260/0309-524X.39.2.213, 2015a.
a
Buck, J. A. and Garvey, S. D.: Redefining the design objectives of large
offshore wind turbine rotors, Wind Energy, 18, 835–850, https://doi.org/10.1002/we.1733, 2015b. a
Dykes, K. and Meadows, R.: Applications of systems engineering to the research, design, and development of wind energy systems, Wind Power:
Systems Engineering Applications and Design Models, National Renewable Energy Laboratory, Golden, Colorado, 1–91, 2012. a
Fingersh, L., Hand, M., and Laxson, A.: Wind Turbine Design Cost and Scaling
Model, Tech. Rep. December, NREL – National Renewable Energy Laboratory,
Golden, CO, https://doi.org/10.2172/897434, 2006. a
Fuglsang, P., Bak, C., Schepers, J. G., Bulder, B., Cockerill, T. T., Claiden, P., Olesen, A., and van Rossen, R.: Site-specific Design Optimization of Wind Turbines, Wind Energy, 5, 261–279, https://doi.org/10.1002/we.61, 2002. a
HAWC2: DTU 10-MW Reference Wind Turbine, available at: https://www.hawc2.dk/Download/HAWC2-Model/DTU-10-MW-Reference-Wind-Turbine, last access: August 2020. a
Hjort, S., Dixon, K., Gineste, M., and Olsen, A. S.: Fast prototype blade
design, Wind Eng., 33, 321–334, https://doi.org/10.1260/030952409789685726, 2009. a
Jamieson, P.: Innovation in Wind Turbine Design, John Wiley & Sons Ltd,
Chichester, UK, https://doi.org/10.1002/9781119137924, 2018. a
Jamieson, P.: Top-level rotor optimisations based on actuator disc theory,
Wind Energ. Sci., 5, 807–818, https://doi.org/10.5194/wes-5-807-2020, 2020. a, b, c
Kuhn, H. W. and Tucker, A. W.: Nonlinear programming, University of
California Press, Berkeley, California, USA, 1951. a
Manwell, J. F., McGowan, J. G., and Rogers, A. L.: Aerodynamics of Wind
Turbines, in: Wind Energy Explained, 2, John Wiley & Sons, Ltd, Chichester, UK, 91–155, https://doi.org/10.1002/9781119994367.ch3, 2010. a
Perez-Moreno, S. S., Zaaijer, M. B., Bottasso, C. L., Dykes, K., Merz, K. O.,
Réthoré, P. E., and Zahle, F.: Roadmap to the multidisciplinary design analysis and optimisation of wind energy systems, J. Phys.: Conf. Ser., 753, 062011, https://doi.org/10.1088/1742-6596/753/6/062011, 2016.
a
Sørensen, J. N.: The general momentum theory, in: vol. 4, Springer, London, https://doi.org/10.1007/978-3-319-22114-4_4, 2016. a, b
Stehly, T. J. and Beiter, P. C.: 2018 Cost of Wind Energy Review, Tech. Rep. December, NREL – National Renewable Energy Laboratory, Golden, CO, USA, https://doi.org/10.2172/1581952, 2020.
a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N.,
Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I.,
Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., and van Mulbregt, P.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Meth., 17, 261–272,
https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Short summary
A novel wind turbine rotor optimization methodology is presented. Using an assumption of radial independence it is possible to obtain the Pareto-optimal relationship between power and loads through the use of KKT multipliers, leaving an optimization problem that can be solved at each radial station independently. Combining it with a simple cost function it is possible to analytically solve for the optimal power per cost with given inputs for the aerodynamics and the cost function.
A novel wind turbine rotor optimization methodology is presented. Using an assumption of radial...
Altmetrics
Final-revised paper
Preprint