Vertical Axis Wind Turbines: A Comprehensive Critical Review of Aerodynamic Theory, Design Configurations, Performance Analysis, and Future Perspectives

Abstract

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio λ=ωR/V∞ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity σ=Nc/R (0.1–0.4), chord-based Reynolds number Re_c (105–106), and peak power coefficient Cp_max (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque CTθ and the cycle-averaged Cp. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: k–ωSST URANS captures peak-region Cp to within ±5–10% but over-predicts torque below λopt; the γ–Re_θ transition SST model reduces this error to ±3–5%; DES, DDES, and LES reach ±2–3% at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15D upstream, 10D downstream, and 20D lateral; rotating sub-domain Drot ≈1.5D; y+≤1; Δθ≤0.1°; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported Cp gains of 5–30%. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below Re_c = 5×105; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment.