The rise of urban air mobility as a solution to alleviate congestion in metropolitan areas has spurred the development of novel vertical takeoff and landing aircraft, renewing research interest in conceptual design. Adjoint-based multidisciplinary design optimization naturally suits aircraft design by efficiently searching high-dimensional design spaces. However, solving system-level large-scale aircraft design problems reliably remains challenging due to numerical conditioning issues arising from simultaneously modeling many design conditions and disciplines. This paper presents a novel demonstration of system-level large-scale multidisciplinary design optimization of an urban air mobility concept using an integrated, physics-based computational model. We solve a gross weight minimization problem of a lift-plus-cruise reference vehicle with 161 design variables and 96 constraints across 15 design conditions, representing nominal mission and failure conditions. Our computational model integrates physics-based models for aerodynamics, propulsion, structures, aeroacoustics, motor performance, and static stability. The optimized design achieves a 6.5% reduction in gross weight. Parameter sweeps over battery energy density and end-of-mission state of charge confirm the robustness of our approach. Using standard computational resources, the average time for solving the large-scale problem is 1.6 h. These findings highlight the potential of large-scale multidisciplinary design optimization in accelerating aircraft conceptual design.
Ruh et al. (Sun,) studied this question.