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Article

Experimental and Dynamic Modeling of a Variable-Pitch VAWT Using a Neural Network and the DMST Model

by
Luz M. Sanchez-Rivera
*,
Jorge Díaz-Salgado
,
Oliver M. Huerta-Chávez
and
Jesús García-Barrera
División de Ingeniería Mecánica, Mecatrónica e Industrial, Posgrado de Ingeniería Mecatrónica, Tecnológico Nacional de México (TecNM), Campus Ecatepec–Tecnológico de Estudios Superiores de Ecatepec (TESE), Ecatepec de Morelos 55210, Estado de México, Mexico
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(20), 10989; https://doi.org/10.3390/app152010989 (registering DOI)
Submission received: 6 September 2025 / Revised: 28 September 2025 / Accepted: 8 October 2025 / Published: 13 October 2025
(This article belongs to the Special Issue Advanced Wind Turbine Control and Optimization)

Abstract

The mathematical modeling and experimental validation of a non-conventional vertical-axis wind turbine (VAWT) with a variable-pitch angle are presented, employing the Double-Multiple Streamtube (DMST) method to simulate aerodynamic performance. The aerodynamic coefficients required by the model are obtained through a data-driven approach using a multi-input, two-output multilayer perceptron artificial neural network (MLP–ANN). The model is validated through numerical simulations under two distinct wind input profiles. An experimental evaluation with a prototype replicates the step input. It shows strong agreement with the simulations, particularly in the angular velocity response, which fluctuates between 35 and 55 RPM, with an average value in the range of 40–45 RPM. This hybrid methodology enhances the modeling fidelity of VAWTs and provides a scalable framework for real-time aerodynamic analysis and control.
Keywords: VAWT; wind turbine; pitch control; neural network VAWT; wind turbine; pitch control; neural network

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MDPI and ACS Style

Sanchez-Rivera, L.M.; Díaz-Salgado, J.; Huerta-Chávez, O.M.; García-Barrera, J. Experimental and Dynamic Modeling of a Variable-Pitch VAWT Using a Neural Network and the DMST Model. Appl. Sci. 2025, 15, 10989. https://doi.org/10.3390/app152010989

AMA Style

Sanchez-Rivera LM, Díaz-Salgado J, Huerta-Chávez OM, García-Barrera J. Experimental and Dynamic Modeling of a Variable-Pitch VAWT Using a Neural Network and the DMST Model. Applied Sciences. 2025; 15(20):10989. https://doi.org/10.3390/app152010989

Chicago/Turabian Style

Sanchez-Rivera, Luz M., Jorge Díaz-Salgado, Oliver M. Huerta-Chávez, and Jesús García-Barrera. 2025. "Experimental and Dynamic Modeling of a Variable-Pitch VAWT Using a Neural Network and the DMST Model" Applied Sciences 15, no. 20: 10989. https://doi.org/10.3390/app152010989

APA Style

Sanchez-Rivera, L. M., Díaz-Salgado, J., Huerta-Chávez, O. M., & García-Barrera, J. (2025). Experimental and Dynamic Modeling of a Variable-Pitch VAWT Using a Neural Network and the DMST Model. Applied Sciences, 15(20), 10989. https://doi.org/10.3390/app152010989

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