Integrated Surrogate Optimization of a Vertical Axis Wind Turbine
In this work, a 3D computational model based on computational fluid dynamics (CFD) is built to simulate the aerodynamic behavior of a Savonius-type vertical axis wind turbine with a semi-elliptical profile. This computational model is used to evaluate the performance of the wind turbine in terms of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/1/233 |
| _version_ | 1797499103778701312 |
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| author | Marco A. Moreno-Armendáriz Eddy Ibarra-Ontiveros Hiram Calvo Carlos A. Duchanoy |
| author_facet | Marco A. Moreno-Armendáriz Eddy Ibarra-Ontiveros Hiram Calvo Carlos A. Duchanoy |
| author_sort | Marco A. Moreno-Armendáriz |
| collection | DOAJ |
| description | In this work, a 3D computational model based on computational fluid dynamics (CFD) is built to simulate the aerodynamic behavior of a Savonius-type vertical axis wind turbine with a semi-elliptical profile. This computational model is used to evaluate the performance of the wind turbine in terms of its power coefficient (Cp). Subsequently, a full factorial design of experiments (DOE) is defined to obtain a representative sample of the search space on the geometry of the wind turbine. A dataset is built on the performance of each geometry proposed in the DOE. This process is carried out in an automated way through a scheme of integrated computational platforms. Later, a surrogate model of the wind turbine is fitted to estimate its performance using machine learning algorithms. Finally, a process of optimization of the geometry of the wind turbine is carried out employing metaheuristic optimization algorithms to maximize its Cp; the final optimized designs are evaluated using the computational model for validating their performance. |
| first_indexed | 2024-03-10T03:43:34Z |
| format | Article |
| id | doaj.art-9204b2c085894b8b841d9ee27ea0b147 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T03:43:34Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9204b2c085894b8b841d9ee27ea0b1472023-11-23T11:27:32ZengMDPI AGEnergies1996-10732021-12-0115123310.3390/en15010233Integrated Surrogate Optimization of a Vertical Axis Wind TurbineMarco A. Moreno-Armendáriz0Eddy Ibarra-Ontiveros1Hiram Calvo2Carlos A. Duchanoy3Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de Mexico 07738, MexicoInstituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de Mexico 07738, MexicoInstituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de Mexico 07738, MexicoGus Chat, Av. Paseo de la Reforma 26-Piso 19, Ciudad de Mexico 06600, MexicoIn this work, a 3D computational model based on computational fluid dynamics (CFD) is built to simulate the aerodynamic behavior of a Savonius-type vertical axis wind turbine with a semi-elliptical profile. This computational model is used to evaluate the performance of the wind turbine in terms of its power coefficient (Cp). Subsequently, a full factorial design of experiments (DOE) is defined to obtain a representative sample of the search space on the geometry of the wind turbine. A dataset is built on the performance of each geometry proposed in the DOE. This process is carried out in an automated way through a scheme of integrated computational platforms. Later, a surrogate model of the wind turbine is fitted to estimate its performance using machine learning algorithms. Finally, a process of optimization of the geometry of the wind turbine is carried out employing metaheuristic optimization algorithms to maximize its Cp; the final optimized designs are evaluated using the computational model for validating their performance.https://www.mdpi.com/1996-1073/15/1/233vertical axis wind turbineCAE modelcomputational fluid dynamicsmachine learningsurrogate modeloptimization |
| spellingShingle | Marco A. Moreno-Armendáriz Eddy Ibarra-Ontiveros Hiram Calvo Carlos A. Duchanoy Integrated Surrogate Optimization of a Vertical Axis Wind Turbine Energies vertical axis wind turbine CAE model computational fluid dynamics machine learning surrogate model optimization |
| title | Integrated Surrogate Optimization of a Vertical Axis Wind Turbine |
| title_full | Integrated Surrogate Optimization of a Vertical Axis Wind Turbine |
| title_fullStr | Integrated Surrogate Optimization of a Vertical Axis Wind Turbine |
| title_full_unstemmed | Integrated Surrogate Optimization of a Vertical Axis Wind Turbine |
| title_short | Integrated Surrogate Optimization of a Vertical Axis Wind Turbine |
| title_sort | integrated surrogate optimization of a vertical axis wind turbine |
| topic | vertical axis wind turbine CAE model computational fluid dynamics machine learning surrogate model optimization |
| url | https://www.mdpi.com/1996-1073/15/1/233 |
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