Optimization of a Vertical-Axis Wind Turbine Airfoils Using Machine Learning, Numerical and Experimental Methodologies
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 airfoil geometry and endplate design in aerodynamic optimization. The study highlights the reliability of CFD-ML methods for airfoil design. Future research will implement these AI-optimized geometries in a full VAWT setup, evaluating torque, power coefficients with tip-speed ratio (TSR) through transient CFD and further experimental validation. These results lay a strong foundation for next-generation VAWT development driven by computational intelligence and aerodynamic refinement.
AI design, Computational fluid dynamics, Machine learning, Optimization, Renewable energy, Wind power, Engineering
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