Aerodynamic characterization of a Horizontal Axis Wind Turbine through Reverse Engineering and Computational Fluid Dynamics
DOI:
https://doi.org/10.18687/LACCEI2026.1.1.2235Palabras clave:
Wind, Energy, CFD, Simulation, SDG 7Resumen
The research aimed to study a three-bladed horizontal-axis wind turbine from the Renewable Energy Laboratory (RENOVA) at Metropolitan University using Computational Fluid Dynamics (CFD), contributing to the development of sustainable energy solutions in line with SDG 7 (Affordable and Clean Energy). A reverse engineering methodology was applied, beginning with 3D scanning to digitize the turbine geometry, followed by the creation of a CAD model based on the scan, measurements, and images. This model was used to conduct CFD simulations to determine the aerodynamic performance of the turbine, generating its characteristic curves. The simulation results were validated against literature, showing good agreement. The study revealed that while the aerodynamic design is capable of capturing power above the rated value, the actual system capacity is limited by the generator’s power, which is protected by the power controller. The work concludes by providing a documented foundation of the turbine’s behavior, identifying the generator as the system’s limiting factor, and establishing a replicable methodology for future studies at the university.Descargas
Publicado
2026-07-27
Número
Sección
Articles
Derechos de autor
Derechos de autor 2026 LACCEI
Licencia
Esta obra está bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.
LACCEI conserva el copyright de todos los artículos publicados bajo los términos de su acuerdo de transferencia de copyright. Como titular del copyright, LACCEI distribuye los artículos al público bajo la Licencia Internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0 (CC BY-NC-SA 4.0).
Cómo citar
Figueroa, E., Palencia, J., Cadenas, P., & Smith-Perera, A. (2026). Aerodynamic characterization of a Horizontal Axis Wind Turbine through Reverse Engineering and Computational Fluid Dynamics. LACCEI, 1(14). https://doi.org/10.18687/LACCEI2026.1.1.2235