Purpose This study aims to develop an efficient and scalable optimization framework to address the growing need for high-performance, low-noise tiltrotor designs, particularly for electric vertical takeoff and landing (eVTOL) aircraft in urban air mobility applications. Design/methodology/approach To manage the high dimensionality and complexity of tiltrotor design, a multi-objective Bayesian optimization framework is established, targeting simultaneous improvements in aerodynamic efficiency and tonal noise reduction. A comprehensive parametric model encompassing 40 design variables is constructed, including pitch angles, blade number, rotor diameter, airfoil shapes using class shape transformation and chord and twist distributions. Aerodynamic performance is evaluated using a modified blade element vortex theory, while tonal noise is predicted through an acoustic analogy-based model. Four-objective optimization is performed under both cruise and hover conditions. Findings The proposed framework efficiently identifies Pareto-optimal designs, capturing trade-offs between total mission energy consumption and average perceived noise levels across multiple flight modes. Results demonstrate that the method achieves up to 22% reduction in total energy consumption and over 75% reduction in average noise compared with the baseline design. In the balanced optimization scenario, the framework yields a 17.5% decrease in total energy, a 16.3% reduction in average noise, demonstrating the framework’s capability to efficiently explore trade offs in high dimensional design spaces. Originality/value This study introduces a high-dimensional, multi-objective optimization methodology tailored to tiltrotor systems, integrating advanced aerodynamic and aeroacoustic modeling with Bayesian optimization. It offers a robust tool for next-generation rotor design under diverse operational scenarios, contributing to the development of quiet and efficient eVTOL propulsion systems.
Liu et al. (2026) studied this question.