Electric aircraft emerge as a promising solution to mitigate the environmental impact of the aviation sector. The integration of electric propulsion components in such aircraft differs significantly from that of conventional aircraft. In the latter case, initial sizing frameworks typically employ mathematical models that operate solely with high-level propulsion parameters, such as power, thrust, and efficiency. However, in the case of advanced air mobility (AAM) aircraft, the propulsion system integration at the sizing stage is critical to exploring more comprehensive configurations and avoiding infeasible solutions. We introduce a framework for designing AAM aircraft, emphasizing the crucial integration of propulsion components from the initial phase. It combines top-level, low-fidelity methods, higher-fidelity components, and system-level propulsion analysis. This modular approach facilitates realistic aircraft configuration exploration by efficiently balancing high and low-fidelity studies. This integrated approach significantly reduces the risk of infeasible or impractical solutions and allows for adaptable coupling with various models of differing fidelity. We demonstrate the framework’s effectiveness by designing and analyzing a box-wing vertical takeoff and landing electric aircraft. Key results suggest setting the propeller pitch optimally at each mission segment increases the cruising range of aircraft by at least 10%. Similarly, varying the battery cell type with a fixed pack mass results in up to 10 km gain in cruising range for the optimal cruise speed. Propeller pitch, battery cell type, and other parameters are often omitted in the initial sizing stage. Our results emphasize the high sensitivity of aircraft performance to these parameters. The proposed framework illustrates its potential to advance electric aircraft design, contributing to a more sustainable future in aviation.

In this paper, we introduce a new formulation of the multivehicle point-to-point network problem to be utilised in urban air mobility (UAM) vertiport-to-vertiport network operations. Vehicle routing problems (VRPs) and their variants have previously been studied and applied in real-world situations, but these problems require additional depot locations, and not all the vehicles can travel to all the locations. In UAM operations, additional depot locations may not be required, and all vehicles can travel to all locations, meaning that existing routing problems are not suitable for application to the management of UAMs. Therefore, we propose a new formulation for UAM vertiport-to-vertiport operation by introducing new constraints. In addition, we integrate dynamic scheduling with the flight mission by controlling cruise speed and waiting in each UAM at each vertiport location to generate an arrival and departure schedule for different vertiports that can avoid collisions and increase the number of vehicles. A computational experiment is conducted using an MILP model, and the results show that although our formulation satisfies the problem definition, the computation time increases exponentially with an increase in the problem size. A case study is conducted in the Seoul area involving five vertiports, with 10- and 15-vehicle scenarios studied. This case study shows that the cruise speed variable is active only for the lower and upper bounds under dynamic scheduling, whereas the waiting time variable can be controlled between user-defined limits that can be applied to the management of vertiport-to-vertiport UAM operations.

At the early stage of aircraft design, the sizing process plays a critical role in determining key parameters such as mass, geometry, and propulsion system characteristics based on design requirements. However, existing sizing methods, relying on historical data, assumptions, and low-fidelity models, often fail to address uncertainties, leading to costly design modifications, particularly for electric aircraft, where technological uncertainties pose significant challenges. It is crucial to account for uncertainties at the sizing stage to ensure reliable outcomes, highlighting the need for a method that efficiently integrates uncertainty into the process. We propose a possibility-based sizing approach, which accounts for design uncertainties into sizing process, yields possible feasible design outcomes and identifies critical parameters for refinement in later stages. We verified our approach with two case studies meeting CS23 certification. The results demonstrate design feasibility through trade-offs between conservative and optimistic designs, with possibility indices from 0.75 to 1, identifying maximum and stall speeds as key limiting factors. Sensitivity analysis shows that aerodynamic design, airframe, and propulsion efficiencies significantly impact maximum takeoff mass. Propulsion system integration achieves a relative mass reduction of 0.05, outperforming hydrogen fuel cells (0.025) and battery systems (0.009). The study highlights that power density is more critical than battery energy density in high-power segments, providing valuable insights for sizing and key parameters in future hybrid electric aircraft development.

Uncertainty-based aerodynamic shape optimization is challenged by the curse of dimensionality and high computational costs. To address this, we propose a deep generative and probabilistic surrogate modeling for optimizing subsonic airfoils under uncertainty. A variational autoencoder learns a compact latent representation of airfoil geometries for dimensionality reduction and design generation, while a mixture density network predicts aerodynamic uncertainties under varying flight conditions. The proposed framework achieves a mean drag prediction [Formula: see text] of 0.999 and a standard deviation prediction [Formula: see text] of 0.970. Optimization results show that deterministic, robust, and reliability-based designs closely match those obtained from Monte Carlo simulations with 5000 samples, while our method requires only 200 samples per design, trained over the global design space. This shows the potential to mitigate dimensionality and reduce computational costs in uncertainty-based design optimization.