Optimal Design of eVTOLs for Urban Mobility using Analytical Target Cascading (ATC)

Over the past few decades, multidisciplinary design optimization (MDO) techniques have shown great potential in generating optimal designs for complex system of systems. Monolithic MDO methods that formulate the design problem as a single optimization problem are effective, but present challenges in coordination at an organizational level. On the other hand, distributed MDO methods decompose the design problem into different optimization problems and hence offer more modularity and flexibility for organizational implementations. In this article, one such distributed MDO method, Analytical Target Cascading (ATC), is investigated as a candidate for the design of electric Vertical Take Off and Landing aircraft (eVTOL). Design of eVTOLs for urban mobility has been a subject of immense interest over the past decade. eVTOLs offer many advantages over conventional modes of urban transport such as being environmentally friendly, utilization of vertical space for transport, and competitive cost of transportation. Most current efforts for eVTOL design are in relatively early stages. Hence, distributed MDO methods that can effectively consider complex interactions between different subsystems and disciplines can help support design efforts for eVTOLs. In this study, ATC is implemented to optimize the total cost per flight for a simple mission, involving take-off to a set altitude, cruising at constant velocity for a range of 50-150 km, and landing, all while carrying a given payload. The key design parameters that are optimized as a part of this study are the mass of aircraft and individual subsystems, cruise velocity, wingspan, and radius of the propeller. Furthermore, proximity of the resulting optimal solution using ATC and monolithic MDO methods is demonstrated. General observations are also articulated regarding potential computational advantages, such as parallelism and tailored solution algorithms, as well as organizational considerations, such as distributed iterative subproblem formulation refinement conducted by human subject matter experts and team coordination.