This paper employs a Kriging-based surrogate model combined with a multi-objective genetic algorithm to optimize rotor blade airfoils with respect to thrust, power, and broadband noise. Three airfoil parameterization methods—ParFoil, PARSEC, and CST—are compared in generating airfoil geometries for the surrogate modeling and optimization process. The optimized airfoil shapes and their corresponding aerodynamic and acoustic performance metrics are presented and analyzed. Low-fidelity aerodynamic analyses are performed using XFOIL and blade element momentum theory, while acoustic predictions are obtained using Lee’s wall-pressure spectrum model and Amiet’s turbulent boundary-layer trailing-edge noise model implemented in UCD-QuietFly. The study focuses on two configurations: a small-scale ideally twisted rotor and a scaled XV-15 rotor blade. Optimization of the ideally twisted rotor across the three parameterization methods demonstrates an A-weighted overall sound pressure level reduction of approximately 4 dBA, primarily attributed to decreases in the chordwise pressure gradient and wall shear stress. Similarly, optimization of the XV-15 blade using the ParFoil method achieves a noise reduction of 3.47 dBA. Further analyses conducted under vertical climb conditions reveal that these hover-optimized blades maintain noise reductions of 3.0–4.0 dBA relative to the baseline configuration.
Won et al. (Thu,) studied this question.