The power coefficients (Cp) 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 Cp across the H-type, helical, and Φ-type turbines with various structural parameters and turbulence intensity (Iu). Three-dimensional computational fluid dynamics (CFD) simulations, validated against experimental data, were conducted to generate a reliable dataset and elucidate the flow mechanisms. Cp 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 (Cm) and Cp. 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 Cm. Positive pitch angles (β), orienting leading edges inward, decrease the negative Cm and increase Cp. For helical turbines, increased twist angles reduce Cp, and the effects of parameters on Cp are mutually independent. The curvature ratio of the Φ-type turbine and Iu only slightly affect Cp. The particle swarm optimization algorithm incorporated with the ML model effectively improves Cp for all turbines.
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