Articles | Volume 9, issue 9
https://doi.org/10.5194/wes-9-1811-2024
© 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-9-1811-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Aerodynamic effects of leading-edge erosion in wind farm flow modeling
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Tuhfe Göçmen
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Özge Sinem Özçakmak
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Alexander Meyer Forsting
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Ásta Hannesdóttir
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Pierre-Elouan Réthoré
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
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Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
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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.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, https://doi.org/10.5194/wes-7-1069-2022, 2022
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Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Alessandro Sebastiani, Alfredo Peña, Niels Troldborg, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 875–886, https://doi.org/10.5194/wes-7-875-2022, https://doi.org/10.5194/wes-7-875-2022, 2022
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The power performance of a wind turbine is often tested with the turbine standing in a row of several wind turbines, as it is assumed that the performance is not affected by the neighbouring turbines. We test this assumption with both simulations and measurements, and we show that the power performance can be either enhanced or lowered by the neighbouring wind turbines. Consequently, we also show how power performance testing might be biased when performed on a row of several wind turbines.
Mark Kelly, Søren Juhl Andersen, and Ásta Hannesdóttir
Wind Energ. Sci., 6, 1227–1245, https://doi.org/10.5194/wes-6-1227-2021, https://doi.org/10.5194/wes-6-1227-2021, 2021
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Via 11 years of measurements, we made a representative ensemble of wind ramps in terms of acceleration, mean speed, and shear. Constrained turbulence and large-eddy simulations were coupled to an aeroelastic model for each ensemble member. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % plus ~ 3 % per 0.1 m/s2 of bulk ramp acceleration magnitude. The LES indicates that the ramps (and such loads) persist through the farm.
Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, and Alan Wai Hou Lio
Wind Energ. Sci., 6, 111–129, https://doi.org/10.5194/wes-6-111-2021, https://doi.org/10.5194/wes-6-111-2021, 2021
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Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.
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.
Cited articles
Asgarpour, M. and Sørensen, J. D.: Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms, Energies, 11, 300, https://doi.org/10.3390/en11020300, 2018. a
Bak, C.: Aerodynamic design of wind turbine rotors, in: Advances in Wind Turbine Blade Design and Materials, Elsevier, 59–108, ISBN 978-0-85709-426-1, https://doi.org/10.1533/9780857097286.1.59, 2013. a
Bak, C.: A simple model to predict the energy loss due to leading edge roughness, J. Phys. Conf. Ser., 2265, 032038, https://doi.org/10.1088/1742-6596/2265/3/032038, 2022a. a, b, c
Bak, C.: Airfoil Design, in: Handbook of Wind Energy Aerodynamics, edited by: Stoevesandt, B., Schepers, G., Fuglsang, P., and Yuping, S., Springer, 95–122, ISBN 978-3-030-31306-7, https://doi.org/10.1007/978-3-030-31307-4_3, 2022b. a
Bak, C., Fuglsang, P., Johansen, J., and Antoniou, I.: Wind tunnel tests of the NACA 63-415 and a modified NACA 63-415 airfoil, no. 1193(EN) in Denmark, Forskningscenter Risoe, Risoe-R, ISBN 87-550-2716-4, 2000. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energ., 70, 116–123, https://doi.org/10.1016/j.renene.2014.01.002, 2014. a, b, c
Bech, J. I., Hasager, C. B., and Bak, C.: Extending the life of wind turbine blade leading edges by reducing the tip speed during extreme precipitation events, Wind Energ. Sci., 3, 729–748, https://doi.org/10.5194/wes-3-729-2018, 2018. a, b
Cappugi, L., Castorrini, A., Bonfiglioli, A., Minisci, E., and Campobasso, M. S.: Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion, Energ. Convers. Manage., 245, 114567, https://doi.org/10.1016/j.enconman.2021.114567, 2021. a
Castorrini, A., Cappugi, L., Bonfiglioli, A., and Campobasso, M.: Assessing wind turbine energy losses due to blade leading edge erosion cavities with parametric CAD and 3D CFD, J. Phys. Conf. Ser., 1618, 052015, https://doi.org/10.1088/1742-6596/1618/5/052015, 2020. a
Castorrini, A., Venturini, P., Corsini, A., and Rispoli, F.: Machine learnt prediction method for rain erosion damage on wind turbine blades, Wind Energy, 24, 917–934, https://doi.org/10.1002/we.2609, 2021. a
DNV: RP-0573, Evaluation of erosion and delamination for leading edge protection systems of rotor blades, https://tinyurl.com/DNV-RP0573 (last access: 29 July 2024), 2020. a
Doubrawa, P., Quon, E. W., Martinez-Tossas, L. A., Shaler, K., Debnath, M., Hamilton, N., Herges, T. G., Maniaci, D., Kelley, C. L., Hsieh, A. S., Blaylock, M. L., van der Laan, P., Andersen, S. J., Krueger, S., Cathelain, M., Schlez, W., Jonkman, J., Branlard, E., Steinfeld, G., Schmidt, S., Blondel, F., Lukassen, L. J., and Moriarty, P.: Multimodel validation of single wakes in neutral and stratified atmospheric conditions, Wind Energy, 23, 2027–2055, https://doi.org/10.1002/we.2543, 2020. a
Drela, M. and Giles, M. B.: Viscous-inviscid analysis of transonic and low Reynolds number airfoils, AIAA J., 25, 1347–1355, https://doi.org/10.2514/3.9789, 1987. a
Ehrmann, R. S., Wilcox, B., White, E. B., and Maniaci, D. C.: Effect of Surface Roughness on Wind Turbine Performance, Tech. rep., Sandia National Lab. (SNL-NM), Albuquerque, NM (United States), 2017. a
Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S., Højstrup, J., and Thøgersen, M.: Analytical modelling of wind speed deficit in large offshore wind farms, Wind Energy, 9, 39–53, https://doi.org/10.1002/we.189, 2006. a
Gaudern, N.: A practical study of the aerodynamic impact of wind turbine blade leading edge erosion, J. Phys. Conf. Ser., 524, 012031, https://doi.org/10.1088/1742-6596/524/1/012031, 2014. a, b, c
Ge, M., Tian, D., and Deng, Y.: Reynolds number effect on the optimization of a wind turbine blade for maximum aerodynamic efficiency, J. Energ. Eng., 142, 04014056, https://doi.org/10.1061/(ASCE)EY.1943-7897.0000254, 2016. a
Glauert, H.: Airplane Propellers, in: Aerodynamic Theory: A General Review of Progress Under a Grant of the Guggenheim Fund for the Promotion of Aeronautics, Springer Berlin Heidelberg, Berlin, Heidelberg, 169–360, ISBN 978-3-642-91487-4, https://doi.org/10.1007/978-3-642-91487-4_3, 1935. a
Göcmen, T., van der Laan, P., Réthoré, P.-E., Pena Diaz, A., Larsen, G., and Ott, S.: Wind turbine wake models developed at the Technical University of Denmark: A review, Renew. Sust. Energ. Rev., 60, 752–769, https://doi.org/10.1016/j.rser.2016.01.113, 2016. a
Hansen, M.: Aerodynamics of Wind Turbines, Routledge, 3 edn., https://doi.org/10.4324/9781315769981, 2015. a
Herring, R., Dyer, K., Martin, F., and Ward, C.: The increasing importance of leading edge erosion and a review of existing protection solutions, Renew. Sust. Energ. Rev., 115, 109382, https://doi.org/10.1016/j.rser.2019.109382, 2019. a
Jensen, N.: A note on wind generator interaction, no. 2411 in Risø-M, Risø National Laboratory, ISBN 87-550-0971-9, 1983. a
Keegan, M. H., Nash, D. H., and Stack, M. M.: On erosion issues associated with the leading edge of wind turbine blades, J. Phys. D Appl. Phys., 46, 383001, https://doi.org/10.1088/0022-3727/46/38/383001, 2013. a
Kruse, E., Bak, C., and Olsen, A.: Wind tunnel experiments on a NACA 633-418 airfoil with different types of leading edge roughness, Wind Energy, 24, 1263–1274, https://doi.org/10.1002/we.2630, 2021. a
Kruse, E. K., Sørensen, N. N., and Bak, C.: Predicting the Influence of Surface Protuberance on the Aerodynamic Characteristics of a NACA 633-418, J. Phys. Conf. Ser., 1037, 022008, https://doi.org/10.1088/1742-6596/1037/2/022008, 2018. a
Larsen, G., Madsen Aagaard, H., Bingöl, F., Mann, J., Ott, S., Sørensen, J., Okulov, V., Troldborg, N., Nielsen, N., Thomsen, K., Larsen, T., and Mikkelsen, R.: Dynamic wake meandering modeling, no. 1607(EN) in Denmark. Forskningscenter Risoe, Risoe-R, Risø National Laboratory, ISBN 978-87-550-3602-4, 2007. a
Lee, J. C. Y., Stuart, P., Clifton, A., Fields, M. J., Perr-Sauer, J., Williams, L., Cameron, L., Geer, T., and Housley, P.: The Power Curve Working Group's assessment of wind turbine power performance prediction methods, Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, 2020. a
Li, D., Li, R., Yang, C., and Wang, X.: Effects of Surface Roughness on Aerodynamic Performance of a Wind Turbine Airfoil, 2010 Asia-Pacific Power and Energy Engineering Conference Chengdu, China, 1–4, https://doi.org/10.1109/APPEEC.2010.5448702, 2010. a
Maniaci, D. C., White, E. B., Wilcox, B., Langel, C. M., van Dam, C., and Paquette, J. A.: Experimental Measurement and CFD Model Development of Thick Wind Turbine Airfoils with Leading Edge Erosion, J. Phys. Conf. Ser., 753, 022013, https://doi.org/10.1088/1742-6596/753/2/022013, 2016. a
Maniaci, D. C., Westergaard, C., Hsieh, A., and Paquette, J. A.: Uncertainty Quantification of Leading Edge Erosion Impacts on Wind Turbine Performance, J. Phys. Conf.-Ser., 1618, 052082, https://doi.org/10.1088/1742-6596/1618/5/052082, 2020. a, b
Menter, F. R.: Zonal two-equation k−ω models for aerodynamic flows, AIAA paper 93-2906, https://ntrs.nasa.gov/api/citations/19960044572/downloads/19960044572.pdf (last access: 29 July 2024), 1993. a
Meyer Forsting, A., Olsen, A., Gaunaa, M., Bak, C., Sørensen, N., Madsen, J., Hansen, R., and Veraart, M.: A spectral model generalising the surface perturbations from leading edge erosion and its application in CFD, J. Phys. Conf. Ser., 2265, 032036, https://doi.org/10.1088/1742-6596/2265/3/032036, 2022a. a, b, c, d, e
Meyer Forsting, A., Sørensen, N., Bak, C., and Olsen, A.: LERAP: Leading Edge Repair and Performance. Commissioned by The Energy Innovation Cluster, no. I-1212 in DTU Wind Energy I, DTU Wind and Energy Systems, https://doi.org/10.11581/DTU.00000264, 2022b. a, b
Meyer Forsting, A., Olsen, A. S., Sørensen, N. N., and Bak, C.: The impact of leading edge damage and repair on sectional aerodynamic performance, Proceedings of AIAA SCITECH 2023 Forum, Aerospace Research Central (ARC), https://doi.org/10.2514/6.2023-0968, 2023. a, b, c
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a
Michelsen, J. A.: Forskning i aeroelasticitet (Research in aeroelasticity) EFP-2001, https://backend.orbit.dtu.dk/ws/portalfiles/portal/7712406/ris_r_1349.pdf (last access: 29 July 2024), 2002. a
Mishnaevsky, L.: Repair of wind turbine blades: Review of methods and related computational mechanics problems, Renew. Energ., 140, 828–839, https://doi.org/10.1016/j.renene.2019.03.113, 2019. a
Mishnaevsky Jr., L. and Thomsen, K.: Costs of repair of wind turbine blades: Influence of technology aspects, Wind Energy, 23, 2247–2255, https://doi.org/10.1002/we.2552, 2020. a
Niayifar, A. and Porté-Agel, F.: A new analytical model for wind farm power prediction, J. Phys. Conf. Ser., 625, 012039, https://doi.org/10.1088/1742-6596/625/1/012039, 2015. a
NREL: FLORIS Version 2.4, GitHub [code], https://github.com/NREL/floris (last access: 29 July 2024), 2021. a
offshoreWIND: Siemens Gamesa Starts Repairing Anholt Blades, London Array Up Next, https://www.offshorewind.biz/2018/04/26/siemens-gamesa-starts-repairing-anholt-blades-london-array-up-next/ (last access: 29 March 2023), 2018. a
Ott, S., Berg, J., and Nielsen, M.: Linearised CFD Models for Wakes, no. 1772(EN) in Denmark. Forskningscenter Risoe. Risoe-R, Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi, ISBN 978-87-550-3892-9, 2011. a
Panthi, K. and Iungo, G. V.: Quantification of wind turbine energy loss due to leading-edge erosion through infrared-camera imaging, numerical simulations, and assessment against SCADA and meteorological data, Wind Energy, 26, 266–282, https://doi.org/10.1002/we.2798, 2023. a, b
Pedersen, M. M., Forsting, A. M., van der Laan, P., Riva, R., Romàn, L. A. A., Risco, J. C., Friis-Møller, M., Quick, J., Christiansen, J. P. S., Rodrigues, R. V., Olsen, B. T., and Réthoré, P.-E.: PyWake 2.5.0: An open-source wind farm simulation tool, https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake (last access: 29 July 2024), 2023. a, b
Prieto, R. and Karlsson, T.: A model to estimate the effect of variables causing erosion in wind turbine blades, Wind Energy, 24, 1031–1044, https://doi.org/10.1002/we.2615, 2021. a
Pryor, S. C., Barthelmie, R. J., and Shepherd, T. J.: Wind power production from very large offshore wind farms, Joule, 5, 2663–2686, https://doi.org/10.1016/j.joule.2021.09.002, 2021. a
Sareen, A., Sapre, C. A., and Selig, M. S.: Effects of leading edge erosion on wind turbine blade performance, Wind Energy, 17, 1531–1542, https://doi.org/10.1002/we.1649, 2014. a, b, c
Shields, M., Beiter, P., Nunemaker, J., Cooperman, A., and Duffy, P.: Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind, Appl. Energ., 298, 117189, https://doi.org/10.1016/j.apenergy.2021.117189, 2021. a
Sørensen, N.: General purpose flow solver applied to flow over hills, Tech. Rep. Risø-R-827(EN), RisøNational Laboratory, https://backend.orbit.dtu.dk/ws/portalfiles/portal/12280331/Ris_R_827.pdf (last access: 29 July 2024), 1995. a
Verma, A. S., Jiang, Z., Caboni, M., Verhoef, H., van der Mijle Meijer, H., Castro, S. G., and Teuwen, J. J.: A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system, Renew. Energ., 178, 1435–1455, https://doi.org/10.1016/j.renene.2021.06.122, 2021. a
Visbech, J., Göçmen, T., Hasager, C. B., Shkalov, H., Handberg, M., and Nielsen, K. P.: Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations, Wind Energ. Sci., 8, 173–191, https://doi.org/10.5194/wes-8-173-2023, 2023. a, b, c, d
Wang, X., Tang, Z., Yan, N., and Zhu, G.: Effect of Different Types of Erosion on the Aerodynamic Performance of Wind Turbine Airfoils, Sustainability, 14, 12344, https://doi.org/10.3390/su141912344, 2022. a
Witha, B., Hahmann, A., Sile, T., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Leroy, G., and Navarro, J.: WRF model sensitivity studies and specifications for the NEWA mesoscale wind atlas production runs, Zenodo, https://doi.org/10.5281/zenodo.2682604, 2019. a
Short summary
Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This...
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