Bayesian Optimization of Tiltrotor Airfoils in Cruise and Hover Conditions

Tiltrotor aerodynamic performance is critically dependent on rotor design, which must accommodate the distinct requirements of hover, cruise, and transition flight modes. This research presents a rotor airfoil design methodology based on Bayesian optimization. The airfoil geometry is parameterized with the Class-Shape Transformation (CST) technique, and an efficient analysis workflow is established by coupling NeuralFoil and Xrotor, with rotor performance as the optimization objective. Under fixed thrust constraints, optimization for a single condition achieved a 6.58% efficiency increase in cruise and a 51.34% increase in hover. However, each optimized airfoil suffered significant performance degradation in the off-design condition, which specifies that fixed-geometry airfoils are inherently limited for tiltrotor application. A comprehensive design strategy accounting for multi-condition requirements is therefore important, with multi-objective optimization and morphing rotors identified as promising paths for future work.