Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints

The shape of the blade strongly influences the aerodynamic behavior of wind turbines; therefore, it is essential to optimize its design to maximize the energy harvested from the wind. Some works address this optimized design problem using CFD, a tool that requires a lot of computational resources an...

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Authors: Radi, Jinane, Sierra-García, Jesús Enrique, Santos Peñas, Matilde, Armenta Deu, Carlos, Djebli, Abdelouahed
Format: article
Publication Date:2024
Country:España
Institution:Universidad Complutense de Madrid (UCM)
Repository:Docta Complutense
Language:English
OAI Identifier:oai:docta.ucm.es:20.500.14352/113281
Online Access:https://hdl.handle.net/20.500.14352/113281
Access Level:Open access
Keyword:Airfoil
Metaheuristic optimization
Genetic algorithm
Bblade
Wind turbine
Wind energy
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
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oai_identifier_str oai:docta.ucm.es:20.500.14352/113281
network_acronym_str ES
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repository_id_str
spelling Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack ConstraintsRadi, JinaneSierra-García, Jesús EnriqueSantos Peñas, MatildeArmenta Deu, CarlosDjebli, AbdelouahedAirfoilMetaheuristic optimizationGenetic algorithmBbladeWind turbineWind energyInteligencia artificial (Informática)1203.04 Inteligencia ArtificialThe shape of the blade strongly influences the aerodynamic behavior of wind turbines; therefore, it is essential to optimize its design to maximize the energy harvested from the wind. Some works address this optimized design problem using CFD, a tool that requires a lot of computational resources and time and starts from scratch. This work describes a new automated design method to generate aerodynamic profiles of wind turbines using existing blades as a base, which speeds up the design process. The optimization is performed using heuristic techniques, and the aim is to improve the characteristics of the blade shape which impact resilience and durability. Specifically, the glide ratio is maximized to capture maximum energy while ensuring specific design parameters, such as maximum thickness or optimal angle of attack. This methodology can obtain results more quickly and with lower computational cost, in addition to integrating these two design parameters into the optimization process, aspects that have been largely neglected in previous works. The analytical model of the blades is described by a class of two-dimensional shapes suitable for representing airfoils. The drag and lift coefficients are estimated, and a metaheuristic optimization technique, genetic algorithm, is applied to maximize the glide ratio while reducing the difference from the desired design parameters. Using this methodology, three new airfoils have been generated and compared with the existing starting models, S823, NACA 2424, and NACA 64418, achieving improvements in the maximum lift and maximum glide ratio of up to 13.8% and 39%, respectively. For validation purposes, a small 10 kW horizontal-axis wind turbine is simulated using the best design of the blades. The comparison with the existing blades focuses on the calculation of the generated power, the power coefficient, torque, and torque coefficient. For the new airfoils, improvements of 6.7% in the power coefficient and 5.5% in the torque coefficient were achieved. This validates the methodology for optimizing the blade airfoils.MdpiUniversidad Complutense de Madrid20242024-12-2020242024-12-20journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/113281reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1132812026-06-02T12:44:21Z
dc.title.none.fl_str_mv Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
title Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
spellingShingle Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
Radi, Jinane
Airfoil
Metaheuristic optimization
Genetic algorithm
Bblade
Wind turbine
Wind energy
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
title_short Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
title_full Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
title_fullStr Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
title_full_unstemmed Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
title_sort Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
dc.creator.none.fl_str_mv Radi, Jinane
Sierra-García, Jesús Enrique
Santos Peñas, Matilde
Armenta Deu, Carlos
Djebli, Abdelouahed
author Radi, Jinane
author_facet Radi, Jinane
Sierra-García, Jesús Enrique
Santos Peñas, Matilde
Armenta Deu, Carlos
Djebli, Abdelouahed
author_role author
author2 Sierra-García, Jesús Enrique
Santos Peñas, Matilde
Armenta Deu, Carlos
Djebli, Abdelouahed
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Airfoil
Metaheuristic optimization
Genetic algorithm
Bblade
Wind turbine
Wind energy
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
topic Airfoil
Metaheuristic optimization
Genetic algorithm
Bblade
Wind turbine
Wind energy
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
description The shape of the blade strongly influences the aerodynamic behavior of wind turbines; therefore, it is essential to optimize its design to maximize the energy harvested from the wind. Some works address this optimized design problem using CFD, a tool that requires a lot of computational resources and time and starts from scratch. This work describes a new automated design method to generate aerodynamic profiles of wind turbines using existing blades as a base, which speeds up the design process. The optimization is performed using heuristic techniques, and the aim is to improve the characteristics of the blade shape which impact resilience and durability. Specifically, the glide ratio is maximized to capture maximum energy while ensuring specific design parameters, such as maximum thickness or optimal angle of attack. This methodology can obtain results more quickly and with lower computational cost, in addition to integrating these two design parameters into the optimization process, aspects that have been largely neglected in previous works. The analytical model of the blades is described by a class of two-dimensional shapes suitable for representing airfoils. The drag and lift coefficients are estimated, and a metaheuristic optimization technique, genetic algorithm, is applied to maximize the glide ratio while reducing the difference from the desired design parameters. Using this methodology, three new airfoils have been generated and compared with the existing starting models, S823, NACA 2424, and NACA 64418, achieving improvements in the maximum lift and maximum glide ratio of up to 13.8% and 39%, respectively. For validation purposes, a small 10 kW horizontal-axis wind turbine is simulated using the best design of the blades. The comparison with the existing blades focuses on the calculation of the generated power, the power coefficient, torque, and torque coefficient. For the new airfoils, improvements of 6.7% in the power coefficient and 5.5% in the torque coefficient were achieved. This validates the methodology for optimizing the blade airfoils.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-20
2024
2024-12-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/113281
url https://hdl.handle.net/20.500.14352/113281
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Mdpi
publisher.none.fl_str_mv Mdpi
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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