Optimization of a Vertical-Axis Wind Turbine Airfoils Using Machine Learning, Numerical and Experimental Methodologies
type: CreativeWork
description: This study aimed to enhance the aerodynamic performance of a Vertical-Axis Wind Turbine (VAWT) airfoil through a multidisciplinary approach that combines Machine Learning (ML), Computational Fluid Dynamics (CFD), and experimental validation. The focus was on enhancing the lift-to-drag coefficients ratio (퐶 푙 /퐶 퐷 ), particularly at higher Angles of Attack (AoA ≥ 20°), a critical operational regime for VAWTs. A baseline airfoil of 12-inch chord length and 10-inch wingspan was analyzed using ANSYS Fluent across a range of AoA (0°–90°) at a constant freestream velocity of 10 m/s (Re ≈ 2.0 × 10 5 ). This served as a performance benchmark. Using ANSYS Design Explorer and Python-coded constraints, ML-based optimization—employing adjoint solvers and stochastic gradient descent—was applied to the baseline geometry at 20° AoA. The resulting AI-optimized airfoil was then evaluated across all AoAs, both with and without specially designed endplates. CFD simulations revealed substantial aerodynamic improvements, with the AI airfoil delivering markedly higher 퐶 푙 /퐶 퐷 across the full AoA range. In the critical 10°– 15° AoA zone, the AI airfoil with endplates achieved more than twice the 퐶 푙 /퐶 퐷 of the baseline without endplates, highlighting improvements in lift generation, drag reduction, and vortex control. Experimental validation using a 3D-printed scale model in an open circuit wind tunnel further substantiated the CFD findings. Across all AoAs, the AI-optimized airfoil—particularly with endplates—demonstrated superior aerodynamic performance, closely aligning with CFD predictions (within 2%–7.8% deviation). Experimental results confirmed a 퐶 푙 /퐶 퐷 gain of over 133% at 0° AoA and consistent improvements of 40–55% between 5° and 15° AoA when using endplates. At higher AoAs (25°–50°), the AI airfoil maintained elevated performance levels, benefitting from delayed stall and improved flow coherence enabled by the endplates. This investigation confirms the dominant role of airf…
abstract: This study aimed to enhance the aerodynamic performance of a Vertical-Axis Wind Turbine (VAWT) airfoil through a multidisciplinary approach that combines Machine Learning (ML), Computational Fluid Dynamics (CFD), and experimental validation. The focus was on enhancing the lift-to-drag coefficients ratio (퐶 푙 /퐶 퐷 ), particularly at higher Angles of Attack (AoA ≥ 20°), a critical operational regime for VAWTs. A baseline airfoil of 12-inch chord length and 10-inch wingspan was analyzed using ANSYS Fluent across a range of AoA (0°–90°) at a constant freestream velocity of 10 m/s (Re ≈ 2.0 × 10 5 ). This served as a performance benchmark. Using ANSYS Design Explorer and Python-coded constraints, ML-based optimization—employing adjoint solvers and stochastic gradient descent—was applied to the baseline geometry at 20° AoA. The resulting AI-optimized airfoil was then evaluated across all AoAs, both with and without specially designed endplates. CFD simulations revealed substantial aerodynamic improvements, with the AI airfoil delivering markedly higher 퐶 푙 /퐶 퐷 across the full AoA range. In the critical 10°– 15° AoA zone, the AI airfoil with endplates achieved more than twice the 퐶 푙 /퐶 퐷 of the baseline without endplates, highlighting improvements in lift generation, drag reduction, and vortex control. Experimental validation using a 3D-printed scale model in an open circuit wind tunnel further substantiated the CFD findings. Across all AoAs, the AI-optimized airfoil—particularly with endplates—demonstrated superior aerodynamic performance, closely aligning with CFD predictions (within 2%–7.8% deviation). Experimental results confirmed a 퐶 푙 /퐶 퐷 gain of over 133% at 0° AoA and consistent improvements of 40–55% between 5° and 15° AoA when using endplates. At higher AoAs (25°–50°), the AI airfoil maintained elevated performance levels, benefitting from delayed stall and improved flow coherence enabled by the endplates. This investigation confirms the dominant role of airf…
author: Torres Angel, Leovigildo
keywords: AI design, Computational fluid dynamics, Machine learning, Optimization, Renewable energy, Wind power, Engineering
date published: 2025-01-01T08:00:00Z
date modified: 2026-07-24T01:41:17Z
provider: Claremont Graduate University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Claremont Graduate University