Aerodynamic-aeroacoustic co-design and multi-objective optimization of vertical-axis wind turbines: a critical review of dynamic stall, flow control, and data-driven methods

Vertical-axis wind turbines (VAWTs) are increasingly considered for distributed, urban, and offshore wind-energy applications, but unsteady aerodynamic loading, dynamic stall, wake interactions, and aeroacoustic emissions limit their wider deployment. This critical review examines VAWT development from a coupled aerodynamic-aeroacoustic perspective, with particular emphasis on dynamic stall, flow-control strategies, computational and experimental assessment, multi-objective optimization, and data-driven design. The reviewed evidence shows that dynamic stall should not always be treated solely as a loss mechanism; controlling its timing and severity can improve torque production while reducing transient loading. Passive and active flow-control methods can provide simultaneous aerodynamic and acoustic benefits, although substantial trade-offs also occur. In particular, configurations that strongly increase aerodynamic loading may increase impulsive or blade-passing noise, while reductions in overall sound pressure level can conceal frequency-dependent or directional acoustic penalties. High-fidelity detached-eddy simulation (DES), delayed detached-eddy simulation (DDES), and large-eddy simulation (LES) coupled with acoustic analogies provide valuable information on noise-generation mechanisms. In contrast, unsteady Reynolds-averaged Navier-Stokes (URANS) and lower-fidelity approaches remain useful for screening and optimization. Recent surrogate and machine-learning methods substantially reduce the computational cost of multi-objective searches and have enabled direct power-noise optimization. However, model generalization, uncertainty propagation, synchronized aerodynamic-acoustic experimental validation, and optimization over realistic operating envelopes remain insufficiently developed. The review identifies simultaneous optimization of power output, acoustic emission, and cyclic aerodynamic loading as a fatigue-relevant design metric for VAWTs and proposes a multi-fidelity, physics-supported, data-assisted framework for robust aerodynamic-aeroacoustic co-design.