Integrated Surrogate Optimization of a Vertical Axis Wind Turbine

Marco A. Moreno-Armendáriz, Eddy Ibarra-Ontiveros, Hiram Calvo and Carlos A. Duchanoy
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Marco A. Moreno-Armendáriz: Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de Mexico 07738, Mexico
Eddy Ibarra-Ontiveros: Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de Mexico 07738, Mexico
Hiram Calvo: Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz s/n, Ciudad de Mexico 07738, Mexico
Carlos A. Duchanoy: Gus Chat, Av. Paseo de la Reforma 26-Piso 19, Ciudad de Mexico 06600, Mexico

Energies, 2021, vol. 15, issue 1, 1-21

Abstract: 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.

Keywords: vertical axis wind turbine; CAE model; computational fluid dynamics; machine learning; surrogate model; optimization; evolutionary algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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