Parametric study examines rotor radius ratio impacts on aerodynamic efficiency and stability in tiltwing vehicles, indicating optimal configurations for urban air mobility.
Urban air mobility (UAM) is introducing innovative electric vertical takeoff and landing (eVTOL) vehicles with a tiltwing configuration that offer efficient transition between vertical and forward flight. However, its design poses challenges because of complex interactions among the fuselage, wing, and rotor systems. Prior studies have investigated component-level performance in isolation without fully addressing how fuselage–wing–rotor interactions affect system-level design tradeoffs or how rotor sizing affects aerodynamic efficiency, stability, and acoustic characteristics. To address these gaps, this study introduces an advanced multidisciplinary design analysis and optimization (MDAO) framework for a [Formula: see text] tiltwing eVTOL designed for a 130-knot cruise mission. This work specifically investigates the effect of varying the main-to-tail rotor radius ratio ([Formula: see text]), a critical geometric parameter governing fore-aft thrust distribution, on overall vehicle performance. The MDAO framework captures coupled aerodynamic, structural, and acoustic characteristics by integrating automated geometry evaluation, iterative trim analysis, midfidelity aerodynamic simulations, and an in-house noise prediction module. To accelerate aerodynamic predictions, a multilayer perceptron neural network surrogate model is employed, enabling efficient parametric exploration. A parametric study of [Formula: see text] values ranging from 1.0 to 1.5 reveals that [Formula: see text] offers the most favorable tradeoff, achieving a lift-to-drag ratio of 7.2 while reducing mission energy consumption by approximately 9% and side/rear observer noise by up to [Formula: see text] compared with nonoptimal rotor radius ratios. These results provide design guidance and demonstrate the practical feasibility of an optimized tiltwing configuration for future UAM deployment.
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Choi et al. (2026) studied this question.
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