Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil

Ice formation on structures like wind turbine blade airfoils significantly reduces their aerodynamic efficiency. The presence of ice on airfoils causes deformation in their geometry and an increase in their surface roughness, enhancing turbulence, particularly on the suction side of the airfoil at h...

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Autores:
Contreras Montoya, Leidy Tatiana
Ilinca, Adrian
Laín Beatove, Santiago
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/15897
Acceso en línea:
https://hdl.handle.net/10614/15897
https://doi.org/10.3390/pr11123371
https://red.uao.edu.co/
Palabra clave:
Turbulent flow
Rime and glaze ice
Wind turbine rough surface
CFD simulation
Aerodynamic loss
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openAccess
License
Derechos reservados - MDPI, 2023
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dc.title.eng.fl_str_mv Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil
title Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil
spellingShingle Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil
Turbulent flow
Rime and glaze ice
Wind turbine rough surface
CFD simulation
Aerodynamic loss
title_short Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil
title_full Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil
title_fullStr Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil
title_full_unstemmed Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil
title_sort Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoil
dc.creator.fl_str_mv Contreras Montoya, Leidy Tatiana
Ilinca, Adrian
Laín Beatove, Santiago
dc.contributor.author.none.fl_str_mv Contreras Montoya, Leidy Tatiana
Ilinca, Adrian
Laín Beatove, Santiago
dc.subject.proposal.eng.fl_str_mv Turbulent flow
Rime and glaze ice
Wind turbine rough surface
CFD simulation
Aerodynamic loss
topic Turbulent flow
Rime and glaze ice
Wind turbine rough surface
CFD simulation
Aerodynamic loss
description Ice formation on structures like wind turbine blade airfoils significantly reduces their aerodynamic efficiency. The presence of ice on airfoils causes deformation in their geometry and an increase in their surface roughness, enhancing turbulence, particularly on the suction side of the airfoil at high angles of attack. An approach for understanding this phenomenon and assessing its impact on wind turbine operation is modeling and simulation. In this contribution, a computational fluid dynamics (CFD) study is conducted using FENSAP-ICE 2022 R1 software available in the ANSYS package. The objective was to evaluate the influence of surface roughness modeling (Shin et al. and beading models) in combination with different turbulence models (Spalart–Allmaras and k-! shear stress transport) on the estimation of the aerodynamic performance losses of wind turbine airfoils not only under rime ice conditions but also considering the less studied case of glaze ice. Moreover, the behavior of the commonly less explored pressure and skin friction coefficients is examined in the clean and iced airfoil scenarios. As a result, the iced profile experiences higher drag and lower lift than in the no-ice conditions, which is explained by modifying skin friction and pressure coefficients by ice. Overall, the outcomes of both turbulence models are similar, showing maximum differences not higher than 10% in the simulations for both ice regimes. However, it is demonstrated that the influence of blade roughness was critical and cannot be disregarded in ice accretion simulations on wind turbine blades. In this context, the beading model has demonstrated an excellent ability to manage changes in roughness throughout the ice accretion process. On the other hand, the widely used roughness model of Shin et al. could underestimate the lift and overestimate the drag coefficients of the wind turbine airfoil in icy conditions
publishDate 2023
dc.date.issued.none.fl_str_mv 2023
dc.date.accessioned.none.fl_str_mv 2024-11-13T19:23:37Z
dc.date.available.none.fl_str_mv 2024-11-13T19:23:37Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.eng.fl_str_mv Text
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dc.identifier.citation.spa.fl_str_mv Contreras Montoya, L. T.; Ilinca, A. y Laín Beatove, S. (2023). Influence of Surface Roughness Modeling on the Aerodynamics of an Iced Wind Turbine S809 Airfoil. Processes. 29 p. https://doi.org/10.3390/pr11123371
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/15897
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.3390/pr11123371
dc.identifier.eissn.spa.fl_str_mv 22279717
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Respositorio Educativo Digital UAO
dc.identifier.repourl.none.fl_str_mv https://red.uao.edu.co/
identifier_str_mv Contreras Montoya, L. T.; Ilinca, A. y Laín Beatove, S. (2023). Influence of Surface Roughness Modeling on the Aerodynamics of an Iced Wind Turbine S809 Airfoil. Processes. 29 p. https://doi.org/10.3390/pr11123371
22279717
Universidad Autónoma de Occidente
Respositorio Educativo Digital UAO
url https://hdl.handle.net/10614/15897
https://doi.org/10.3390/pr11123371
https://red.uao.edu.co/
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.citationendpage.spa.fl_str_mv 29
dc.relation.citationissue.spa.fl_str_mv 12
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 11
dc.relation.ispartofjournal.eng.fl_str_mv Processes
dc.relation.references.none.fl_str_mv 1. O’Brien, J.M.; Young, T.M.; O’Mahoney, D.C.; Griffin, P.C. Horizontal axis wind turbine research: A review of commercial CFD, FE codes and experimental practices. Prog. Aerosp. Sci. 2017, 92, 1–24. [CrossRef]
2. Han, W.; Kim, J.; Kim, B. Study on correlation between wind turbine performance and ice accretion along a blade tip airfoil using CFD. J. Renew. Sustain. Energy 2018, 10, 023306. [CrossRef]
3. Pedersen, M.C.; Sørensen, H. Towards a CFD Model for Prediction ofWind Turbine Power Losses due to Icing in Cold Climate. In Proceedings of the 16th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery, Honolulu, HI, USA, 10–15 April 2016; p. 7.
4. Fei, C.-W.; Han, Y.-J.; Wen, J.-R.; Li, C.; Han, L.; Choy, Y.-S. Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk. Propuls. Power Res. 2023. [CrossRef]
5. Chen, J.-Y.; Feng, Y.-W.; Teng, D.; Lu, C.; Fei, C.-W. Support vector machine-based similarity selection method for structural transient reliability analysis. Reliab. Eng. Syst. Saf. 2022, 223, 108513. [CrossRef]
6. Hacıefendio˘ glu, K.; Ba¸sa˘ ga, H.B.; Yavuz, Z.; Karimi, M.T. Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method. Renew. Energy 2022, 182, 1–16. [CrossRef]
7. Fakorede, O.; Feger, Z.; Ibrahim, H.; Ilinca, A.; Perron, J.; Masson, C. Ice protection systems for wind turbines in cold climate: Characteristics, comparisons and analysis. Renew. Sustain. Energy Rev. 2016, 65, 662–675. [CrossRef]
8. Hu, L.; Zhu, X.; Hu, C.; Chen, J.; Du, Z. Wind turbines ice distribution and load response under icing conditions. Renew. Energy 2017, 113, 608–619. [CrossRef]
9. Hu, L.; Zhu, X.; Chen, J.; Shen, X.; Du, Z. Numerical simulation of rime ice on NREL Phase VI blade. J. Wind Eng. Ind. Aerodyn. 2018, 178, 57–68. [CrossRef]
10. Rizk, P.; Younes, R.; Ilinca, A.; Khoder, J. Wind turbine ice detection using hyperspectral imaging. Remote Sens. Appl. Soc. Environ. 2022, 26, 100711. [CrossRef]
11. Laín, S.; Contreras, L.T.; López, O. A review on computational fluid dynamics modeling and simulation of horizontal axis hydrokinetic turbines. J. Braz. Soc. Mech. Sci. Eng. 2019, 41, 375. [CrossRef]
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15. Jin, J.Y.; Virk, M.S. Study of ice accretion along symmetric and asymmetric airfoils. J. Wind Eng. Ind. Aerodyn. 2018, 179, 240–249. [CrossRef]
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17. Yirtici, O.; Cengiz, K.; Ozgen, S.; Tuncer, I.H. Aerodynamic validation studies on the performance analysis of iced wind turbine blades. Comput. Fluids 2019, 192, 104271. [CrossRef]
18. Yirtici, O.; Ozgen, S.; Tuncer, I.H. Predictions of ice formations on wind turbine blades and power production losses due to icing. Wind Energy 2019, 22, 945–958. [CrossRef]
19. Pedersen, M.C. Modelling Icing on Structures forWind Power Applications. Ph.D. Dissertation, Aalborg University, Aalborg, Denmark, 2018.
20. Shu, L.; Li, H.; Hu, Q.; Jiang, X.; Qiu, G.; He, G.; Liu, Y. 3D numerical simulation of aerodynamic performance of iced contaminated wind turbine rotors. Cold Reg. Sci. Technol. 2018, 148, 50–62. [CrossRef]
21. Jin, J.Y.; Virk, M.S.; Hu, Q.; Jiang, X. Study of Ice Accretion on Horizontal Axis Wind Turbine Blade Using 2D and 3D Numerical Approach. IEEE Access 2020, 8, 166236–166245. [CrossRef]
22. IEA Wind. Wind Energy in Cold Climates Available Technologies—Report; VTT Technical Research Centre on Finland Ltd.: Tampere, Finland, 2016; p. 120.
23. Pedersen, M.C.; Yin, C. Preliminary Modelling Study of Ice Accretion on Wind Turbines. Energy Procedia 2014, 61, 258–261. [CrossRef]
24. Contreras Montoya, L.T.; Lain, S.; Ilinca, A. A Review on the Estimation of Power Loss Due to Icing inWind Turbines. Energies 2022, 15, 1083. [CrossRef]
25. ANSYS Inc. In-Flight Icing Simulation. Available online: https://www.ansys.com/products/fluids/ansys-fensap-ice (accessed on 10 November 2022).
26. Hann, R.; Hearst, R.J.; Sætran, L.R.; Bracchi, T. Experimental and Numerical Icing Penalties of an S826 Airfoil at Low Reynolds Numbers. Aerospace 2020, 7, 46. [CrossRef]
27. Knobbe-Eschen, H.; Stemberg, J.; Abdellaoui, K.; Altmikus, A.; Knop, I.; Bansmer, S.; Balaresque, N.; Suhr, J. Numerical and experimental investigations of wind-turbine blade aerodynamics in the presence of ice accretion. In Proceedings of the AIAA Scitech 2019 Forum, San Diego, CA, USA, 7–11 January 2019.
28. Li, Y.; Wang, S.; Sun, C.; Yi, X.; Guo, W.; Zhou, Z.; Feng, F. Icing distribution of rotating blade of horizontal axis wind turbine based on Quasi-3D numerical simulation. Therm. Sci. 2018, 22, 681–691. [CrossRef]
29. Homola, M.C.; Virk, M.S.; Nicklasson, P.J.; Sundsbø, P.A. Performance losses due to ice accretion for a 5 MW wind turbine. Wind Energy 2012, 15, 379–389. [CrossRef]
30. Makkonen, L.; Laakso, T.; Marjaniemi, M.; Finstad, K.J. Modelling and Prevention of Ice Accretion onWind Turbines. Wind Eng. 2001, 25, 3–21. [CrossRef]
31. Virk, M.; Mughal, U.; Hu, Q.; Jiang, X. Multiphysics Based Numerical Study of Atmospheric Ice Accretion on a Full Scale Horizontal AxisWind Turbine Blade. Int. J. Multiphys. 2016, 10, 237–246. [CrossRef]
32. Fu, P.; Farzaneh, M. A CFD approach for modeling the rime-ice accretion process on a horizontal-axis wind turbine. J. Wind Eng. Ind. Aerodyn. 2010, 98, 181–188. [CrossRef]
33. Bai, C.-J.; Wang, W.-C. Review of computational and experimental approaches to analysis of aerodynamic performance in horizontal-axis wind turbines (HAWTs). Renew. Sustain. Energy Rev. 2016, 63, 506–519. [CrossRef]
34. Menter, F. Zonal Two Equation k-w Turbulence Models For Aerodynamic Flows. In Proceedings of the 23rd Fluid Dynamics, Plasmadynamics, and Lasers Conference, Orlando, FL, USA, 6–9 July 1993.
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36. Shin, J.; Berkowitz, B.; Chen, H.; Cebeci, T. Prediction of ice shapes and their effect on airfoil performance. In Proceedings of the 29th Aerospace Sciences Meeting, Reno, NV, USA, 7–10 January 1991; p. 264.
37. Caccia, F.; Guardone, A. Numerical simulation of ice accretion on wind turbine blades: Are performance losses due to ice shape or surface roughness? Wind Energy Sci. 2023, 8, 341–362. [CrossRef]
38. ANSYS Inc. ANSYS FENSAP-ICE 18.1 User Manual; ANSYS Inc.: Canonsburg, PA, USA, 2017.
39. Hildebrandt, S.; Sun, Q. Evaluation of operational strategies on wind turbine power production during short icing events. J. Wind Eng. Ind. Aerodyn. 2021, 219, 104795. [CrossRef]
40. Blasco, P.; Palacios, J.; Schmitz, S. Effect of icing roughness on wind turbine power production. Wind Energy 2017, 20, 601–617. [CrossRef]
41. Turkia, V.; Huttunen, S.;Wallenius, T. Method for estimating wind turbine production losses due to icing. VTT Tech. Res. Cent. Finl. 2013, 114, 44.
42. Fortin, G.; Perron, J.Wind Turbine Icing and De-Icing. In Proceedings of the 47th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, FL, USA, 5–8 January 2009; Aerospace Sciences Meetings. American Institute of Aeronautics and Astronautics: Reston, VA, USA, 2009.
43. Sagol, E. Three Dimensional Numerical Prediction of Icing Related Power and Energy Losses on aWind Turbine. Ph.D. Thesis, École Polytechnique de Montréal, PolyPublie Polytechnique Montréal, Montréal, QC, Canada, 2014.
44. Farzaneh, M. Atmospheric Icing of Power Networks, 1st ed.; Springer: Berlin/Heidelberg, Germany, 2008; p. 381.
45. Battisti, L. Aerodynamic Performances on Ice Contaminated Rotors. In Wind Turbines in Cold Climates, 1st ed.; Springer International Publishing: Cham, Switzerland, 2015; p. 341.
46. Somers, D.M. Design and Experimental Results for the S809 Airfoil; National Renewable Energy Laboratory (NREL): Golden, CO, USA; U.S. Department of Energy, Office of Scientific and Technical Information: Oak Ridge, TN, USA, 1997.
47. Tan, H. CFD Analysis of aWind Turbine Airfoil with Flap. Master’s Thesis, Washinton University, St. Louis, MO, USA, 2020.
48. Han, Y.; Palacios, J.; Schmitz, S. Scaled ice accretion experiments on a rotating wind turbine blade. J. Wind Eng. Ind. Aerodyn. 2012, 109, 55–67. [CrossRef]
49. Hand, M.M.; Simms, D.A.; Fingersh, L.J.; Jager, D.W.; Cotrell, J.R.; Schreck, S.; Larwood, S.M. Unsteady Aerodynamics Experiment Phase VI WindTunnel Test Configurations and Available Data Campaigns; National Renewable Energy Laboratory—NREL: Golden, CO, USA, 2001; p. 310.
50. Li, Y.; Sun, C.; Jiang, Y.; Yi, X.; Zhang, Y. Effect of liquid water content on blade icing shape of horizontal axis wind turbine by numerical simulation. Therm. Sci. 2019, 23, 1637–1645. [CrossRef]
51. Burton, T.; Jenkins, N.; Sharpe, D.; Bossanyi, E. Aerodynamics of Horizontal Axis Wind Turbines. In Wind Energy Handbook; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2011; pp. 39–136.
52. Airfoil Tools. NREL’s S809 Airfoil (s809-nr). Available online: http://airfoiltools.com/airfoil/details?airfoil=s809-nr (accessed on 5 February 2022).
53. Zanon, A.; De Gennaro, M.; Kühnelt, H.Wind energy harnessing of the NREL 5 MW reference wind turbine in icing conditions under different operational strategies. Renew. Energy 2018, 115, 760–772. [CrossRef]
54. Tardif d’Hamonville, T. Modélisation et Analyse des Phénomènes Aéroélastiques Pour Une Pale D’éolienne. Master’s Thesis, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada, 2009.
55. Hudecz, A.; Koss, H.; Laver, M.O. Ice Accretion onWind Turbine Blades. In Proceedings of the 15th International Workshop on Atmospheric Icing of Structures (IWAIS XV), St. John’s, NL, Canada, 8–13 September 2013; p. 8.
56. ANSYS Inc. ANSYS Fluent User’s Guide Release 15.0; ANSYS Inc.: Canonsburg, PA, USA, 2013; p. 2692.
57. Bodenlle-Toral, D.; García-Regodeseves, P.; Pandal-Blanco, A. Performance evaluation of an airfoil under ice accretion using CFD simulations. J. Phys. Conf. Ser. 2022, 2217, 012088. [CrossRef]
58. Jasinski, W.J.; Noe, S.C.; Selig, M.S.; Bragg, M.B. Wind Turbine Performance Under Icing Conditions. J. Sol. Energy Eng. 1998, 120, 60–65. [CrossRef]
59. Jonkman, J.; Butterfield, S.; Musial, W.; Scott, G. Definition of a 5-MW Reference Wind Turbine for Offshore System Development; NREL/TP-500-38060; TRN: US200906%%69 United States 10.2172/947422 TRN: US200906%%69 NREL English; National Renewable Energy Lab. (NREL): Golden, CO, USA, 2009; p. 75.
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spelling Contreras Montoya, Leidy TatianaIlinca, AdrianLaín Beatove, Santiagovirtual::5771-12024-11-13T19:23:37Z2024-11-13T19:23:37Z2023Contreras Montoya, L. T.; Ilinca, A. y Laín Beatove, S. (2023). Influence of Surface Roughness Modeling on the Aerodynamics of an Iced Wind Turbine S809 Airfoil. Processes. 29 p. https://doi.org/10.3390/pr11123371https://hdl.handle.net/10614/15897https://doi.org/10.3390/pr1112337122279717Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/Ice formation on structures like wind turbine blade airfoils significantly reduces their aerodynamic efficiency. The presence of ice on airfoils causes deformation in their geometry and an increase in their surface roughness, enhancing turbulence, particularly on the suction side of the airfoil at high angles of attack. An approach for understanding this phenomenon and assessing its impact on wind turbine operation is modeling and simulation. In this contribution, a computational fluid dynamics (CFD) study is conducted using FENSAP-ICE 2022 R1 software available in the ANSYS package. The objective was to evaluate the influence of surface roughness modeling (Shin et al. and beading models) in combination with different turbulence models (Spalart–Allmaras and k-! shear stress transport) on the estimation of the aerodynamic performance losses of wind turbine airfoils not only under rime ice conditions but also considering the less studied case of glaze ice. Moreover, the behavior of the commonly less explored pressure and skin friction coefficients is examined in the clean and iced airfoil scenarios. As a result, the iced profile experiences higher drag and lower lift than in the no-ice conditions, which is explained by modifying skin friction and pressure coefficients by ice. Overall, the outcomes of both turbulence models are similar, showing maximum differences not higher than 10% in the simulations for both ice regimes. However, it is demonstrated that the influence of blade roughness was critical and cannot be disregarded in ice accretion simulations on wind turbine blades. In this context, the beading model has demonstrated an excellent ability to manage changes in roughness throughout the ice accretion process. On the other hand, the widely used roughness model of Shin et al. could underestimate the lift and overestimate the drag coefficients of the wind turbine airfoil in icy conditions29 páginasapplication/pdfengMDPIBasel, SwitzerlandDerechos reservados - MDPI, 2023https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Influence of surface roughness modeling on the aerodynamics of an iced wind turbine s809 airfoilArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a852912111Processes1. O’Brien, J.M.; Young, T.M.; O’Mahoney, D.C.; Griffin, P.C. Horizontal axis wind turbine research: A review of commercial CFD, FE codes and experimental practices. Prog. Aerosp. Sci. 2017, 92, 1–24. [CrossRef]2. Han, W.; Kim, J.; Kim, B. Study on correlation between wind turbine performance and ice accretion along a blade tip airfoil using CFD. J. Renew. Sustain. Energy 2018, 10, 023306. [CrossRef]3. Pedersen, M.C.; Sørensen, H. Towards a CFD Model for Prediction ofWind Turbine Power Losses due to Icing in Cold Climate. In Proceedings of the 16th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery, Honolulu, HI, USA, 10–15 April 2016; p. 7.4. Fei, C.-W.; Han, Y.-J.; Wen, J.-R.; Li, C.; Han, L.; Choy, Y.-S. Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk. Propuls. Power Res. 2023. [CrossRef]5. Chen, J.-Y.; Feng, Y.-W.; Teng, D.; Lu, C.; Fei, C.-W. Support vector machine-based similarity selection method for structural transient reliability analysis. Reliab. Eng. Syst. Saf. 2022, 223, 108513. [CrossRef]6. Hacıefendio˘ glu, K.; Ba¸sa˘ ga, H.B.; Yavuz, Z.; Karimi, M.T. Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method. Renew. Energy 2022, 182, 1–16. [CrossRef]7. Fakorede, O.; Feger, Z.; Ibrahim, H.; Ilinca, A.; Perron, J.; Masson, C. Ice protection systems for wind turbines in cold climate: Characteristics, comparisons and analysis. Renew. Sustain. Energy Rev. 2016, 65, 662–675. [CrossRef]8. Hu, L.; Zhu, X.; Hu, C.; Chen, J.; Du, Z. Wind turbines ice distribution and load response under icing conditions. Renew. Energy 2017, 113, 608–619. [CrossRef]9. Hu, L.; Zhu, X.; Chen, J.; Shen, X.; Du, Z. Numerical simulation of rime ice on NREL Phase VI blade. J. Wind Eng. Ind. Aerodyn. 2018, 178, 57–68. [CrossRef]10. Rizk, P.; Younes, R.; Ilinca, A.; Khoder, J. Wind turbine ice detection using hyperspectral imaging. Remote Sens. Appl. Soc. Environ. 2022, 26, 100711. [CrossRef]11. Laín, S.; Contreras, L.T.; López, O. 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