Surrogate-Assisted Multi-Objective Aeroacoustic Optimization of a Small-Scale Rotor in Hover Mode

Abstract

Small-scale rotor design must balance hover efficiency and acoustic performance; however, costly aeroacoustic evaluations make multi-objective optimization computationally demanding. This study develops a surrogate-assisted framework using eight radial basis-function variables to parameterize spanwise chord and twist variations. Aerodynamic loads are evaluated with a reformulated vortex-particle method, while acoustic models estimate tonal and broadband noise. Baseline validation results in a 3.1% thrust coefficient error and captures the principal acoustic directivity trend. Gradient-boosted regression trees guide adaptive sampling, with candidate designs required to retain at least 95% of the baseline thrust coefficient. A total of 304 direct evaluations identify a 16-design thrust-feasible Pareto front. At 5400 RPM, the maximum-FM design improves FM by 11.31% while reducing OASPL by 1.06 dB, whereas the minimum-noise design reduces OASPL by 2.97 dB while increasing FM by 3.00%. Thrust-matched reassessment confirms that these performance benefits are maintained with lower shaft-power requirements. The improvements are primarily the result of the selective spanwise redistribution of thrust and torque rather than uniform unloading; the minimum-noise design shifts loading inboard and weakens the outer-span wake, whereas the maximum-FM design increases thrust while limiting torque growth and produces stronger downstream momentum transfer.