Arrival management for eVTOL aircraft in on-demand urban air mobility

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Pradeep, Priyank
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Peng Wei
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Aerospace Engineering

The Department of Aerospace Engineering seeks to instruct the design, analysis, testing, and operation of vehicles which operate in air, water, or space, including studies of aerodynamics, structure mechanics, propulsion, and the like.

The Department of Aerospace Engineering was organized as the Department of Aeronautical Engineering in 1942. Its name was changed to the Department of Aerospace Engineering in 1961. In 1990, the department absorbed the Department of Engineering Science and Mechanics and became the Department of Aerospace Engineering and Engineering Mechanics. In 2003 the name was changed back to the Department of Aerospace Engineering.

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  • Department of Aerospace Engineering and Engineering Mechanics (1990-2003)

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The electric vertical takeoff and landing (eVTOL) aircraft can alleviate transportation congestion on the ground by utilizing three-dimensional airspace efficiently. However, the endurance (specific energy) of Lithium-ion Polymer (Li-Po) batteries imposes severe constraints on the operational time-span of an eVTOL on urban air mobility (UAM) passenger transportation mission.

The first part of the research focuses on the generation of energy efficient trajectories for eVTOLs with the assigned required times of arrival (RTA)s. The problem formulations are performed in multiphase optimal control framework with energy as the performance index for the following eVTOL aircraft types: (i) multirotor and (ii) tandem tilt-wing. These two types of eVTOLs are chosen because of their performance characteristics falling at the two extremes of the performance spectrum of eVTOLs. The proposed multiphase optimal control problem formulations and the corresponding numerical solutions enable an eVTOL to meet the assigned RTA and achieve the most energy efficient arrival trajectory, which is a critical enabler for the safe and efficient future eVTOL operations for passenger transportation and cargo delivery in UAM environment. The problem formulations are applied to a UAM passenger transport use cases with (i) EHang 184, (ii) Airbus Vahana, and (iii) the Uber Elevate proposed vertiport concept in numerical simulations.

The second part of the research involves arrival sequencing and scheduling problem formulation in UAM context for a mixed fleet (winged/wingless) of eVTOLs expected to land on a vertiport. Based on anticipated UAM traffic density in emergent (low) and early expanded (moderate/high) operations, two separate vertiport arrival procedures have been proposed for the problem. The arrival procedure for early expanded operations is proposed based on arrival procedure of emergent operations as a baseline with the addition of metering gate(s) on the boundary of the terminal area (a circular area of radius 400 m around a vertiport) and multiple landing pads on the vertiport. The objective of the problem is to minimize the makespan (landing completion time) of a given set of eVTOLs, which is equivalent to maximizing the vertiport arrival throughput. A heuristic approach called insertion, and local search (ILS) [\cite{Waqar}] combined with two different scheduling methods: i) mixed-integer linear programming (MILP) and ii) time-advance (TA) are proposed to optimize the landing order (sequence) and makespan of the mixed fleet of eVTOLs. Next, the impact of the number of landing pads (N) on the vertiport arrival throughput is studied to aid in early expanded UAM operations (moderate/high traffic density). Finally, sensitivity analysis is performed to see the impact of the following on the sequencing and scheduling algorithms: i) the number of eVTOLs expected to land (n) and ii) the number of eVTOLs used in the local neighborhood search (k). Through numerical simulations and sensitivity analysis, our algorithms demonstrated real-time scheduling capabilities for on-demand UAM arrival operations, which can be used as a potential future service for UAM vertiports and terminal airspace.

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Wed May 01 00:00:00 UTC 2019