Parametric analysis and optimization of lift-type vertical axis wind turbines using machine learning techniques

The power coefficients (C_p) of helical and Φ-type vertical axis wind turbines remain insufficiently explored compared with the H-type ones. A unified machine learning (ML) model was developed to conduct the parametric study of C_p across the H-type, helical, and Φ-type turbines with various structural parameters and turbulence intensity (I_u). Three-dimensional computational fluid dynamics (CFD) simulations, validated against experimental data, were conducted to generate a reliable dataset and elucidate the flow mechanisms. C_p of the Φ-type turbine is insensitive to the aspect ratio (AR) due to minimal tip loss, whereas a larger AR of the helical turbine expands midspan regions unaffected by tip vortices, improving the maximum torque coefficient (C_m) and C_p. Higher solidity (σ) reduces the optimal tip speed ratio and induces larger angles of attack, slightly aggravating the dynamic stall of helical turbines and decreasing C_m. Positive pitch angles (β), orienting leading edges inward, decrease the negative C_m and increase C_p. For helical turbines, increased twist angles reduce C_p, and the effects of parameters on C_p are mutually independent. The curvature ratio of the Φ-type turbine and I_u only slightly affect C_p. The particle swarm optimization algorithm incorporated with the ML model effectively improves C_p for all turbines.