Seulki Kim

(Advisor: Prof. Dimitri Mavris)

will defend a doctoral thesis entitled,

From Strategic Planning to Tactical Adjustments: An eVTOL Trajectory Management Framework for Urban Air Mobility

On

Wednesday, April 24 at 2:00 p.m.

Collaborative Visualization Environment (CoVE)
Weber Space Science and Technology Building (SST II)

And

Microsoft Teams

Abstract

Urban Air Mobility (UAM) represents a transformative approach to urban transportation, aiming to alleviate ground congestion and reduce urban pollution through the use of Electric Vertical Take-Off and Landing (eVTOL) aircraft. As cities continue to grow, the demand for efficient, sustainable, and rapid transit solutions escalates, positioning UAM as a promising alternative mode to conventional ground transportation modalities.

The motivation of this research begins by observing the distinct operational complexities associated with managing eVTOL flights. These complexities include low-altitude operations in obstacle-rich urban environments, limited battery electric energy storage, and specific electrical/thermal powertrain constraints. In addition to these difficulties, traditional human-driven trajectory management systems may not suffice for high traffic densities, fast-paced operations, and constrained traffic separation anticipated for UAM operations. Consequently, there is a critical need for an automation system that ensures safe, energy-efficient, and scalable management of eVTOL trajectories.

In response, this research develops a comprehensive automated trajectory management framework. The framework comprises two primary components: the strategic planner and the tactical planner. The strategic planner is designed to generate pre-departure trajectories that optimize battery energy efficiency and collision avoidance, taking into account the intricate powertrain constraints. This planner builds upon mixed-integer linear programming (MILP) optimization, integrated with vertical takeoff and landing as well as battery discharge and thermal behavior modeling. The tactical planner, on the other hand, facilitates real-time adjustments to planned trajectories in response to evolving flight conditions and unforeseen contingencies, employing a Receding Horizon MILP (RH-MILP) approach. This planner also incorporates a diversion decision-making tool that helps pilots manage in-flight contingencies by automatically selecting the best alternate landing sites and continuously adjusting the trajectory until the safe diversion is completed.

Collectively, these components form a robust automation framework capable of effectively managing UAM trajectories under both regular and irregular operational scenarios. This framework offers considerable benefits to UAM stakeholders, including pilots, dispatchers, and air traffic controllers, by automating the burden of creating and continuously managing trajectories throughout the flight. Through the integration of advanced trajectory optimization with real-time decision-making capabilities, the framework is expected to establish a solid foundation for safe, energy-efficient, and scalable UAM operations in densely populated urban landscape.

Committee

·         Prof. Dimitri Mavris – School of Aerospace Engineering (Advisor)

·         Prof. Daniel P. Schrage – School of Aerospace Engineering

·         Prof. Brian German – School of Aerospace Engineering

·         Dr. Cedric Y. Justin – School of Aerospace Engineering

·         David Sizoo – Federal Aviation Administration (FAA)