<p>As Urban Air Mobility (UAM) is developed and brought into fruition via electric vertical takeoff and landing (eVTOL) vehicles, contingencies associated with this new distributed electric propulsion technology in metropolitan areas must be considered. On the state of knowledge on contingencies for eVTOL vehicles, these can be Epistemological Risks or Ontological Risks. Epistemological Risks include known-knowns (probabilistic risks) and known-unknowns (gaps in knowledge). Ontological Risks include, unknown-knowns (hidden knowledge), unknown-unknowns (fog of ignorance). As UAM operations at large scale do not have as much historical accidents data as General Aviation or Commercial Aviation, it is challenging to estimate its accident failure rate per 100,000 flight hours. While battery thermal runaway, battery energy uncertainty, software issues, and common mode power failures are some failure cases listed in this thesis, it is the undiscovered contingency (i.e., unknown-unknown) or unprepared contingency (i.e., unknown-known), along with other external factors, that can lead to an accident. UAM is expected to operate at 1500 feet AGL and at high frequencies over dense metropolitan areas. In an in-flight emergency at these altitudes, any startle response experienced by on-board or remote pilots can lead to longer response times. This study aims to create a framework for contingency planning and risk mitigation using a Reachable Ground Footprint model for eVTOL aircraft under 100% power failure scenarios in-flight. This framework utilizes all existing, public aerodrome infrastructures in metropolitan areas as potential contingency landing sites. Metrics such as Contingency Landing Assurance Percentage and Cruise Altitude Floor requirement are introduced to quantitatively measuring the safety of any UAM trip and provide recommendations on safe cruising altitudes. A demonstration case in the Chicago Metropolitan Area between DuPage Regional Airport and John H. Stroger Hospital Helipad is shown and discussed. Furthermore, aggregate analysis of 434 UAM trips in Chicago Metropolitan Area between Regional Airports, between Regional and Heliports, and between Heliports is performed, along with sensitivity studies involving wind and turn control restrictions. The results discuss variations in Cruise Altitude Floor, Flight Time, and Energy Consumption of these trips using an eVTOL vehicle.</p>

Rising concerns related to the effects of traffic congestion have led to the search for alternative transportation solutions. Advances in battery technology have resulted in an increase of electric vehicles (EVs), which serve to reduce the impact of many of the negative consequences of congestion, including pollution and the cost of wasted fuel. Furthermore, the energy-efficiency and quiet operation of electric motors have made feasible concepts such as Urban Air Mobility (UAM), in which electric aircraft transport passengers in dense urban areas prone to severe traffic slowdowns. Electrified transportation may be the solution needed to combat urban gridlock, but many logistical questions related to the design and operation of the resultant transportation networks remain to be answered. This research begins by examining the near-term effects of EV charging networks. Stationary plug-in methods have been the traditional approach to recharge electric ground vehicles; however, dynamic charging technologies that can charge vehicles while they are in motion have recently been introduced that have the potential to eliminate the inconvenience of long charging wait times and the high cost of large batteries. Using an agent-based model verified with traffic data, different network designs incorporating these dynamic chargers are evaluated based on the predicted benefit to EV drivers. A genetic optimization is designed to optimally locate the chargers. Heavily-used highways are found to be much more effective than arterial roads as locations for these chargers, even when installation cost is taken into consideration. This work also explores the potential long-term effects of electrified transportation on urban congestion by examining the implementation of a UAM system. Interdependencies between potential electric air vehicle ranges and speeds are explored in conjunction with desired network structure and size in three different regions of the United States. A method is developed to take all these considerations into account, thus allowing for the creation of a network optimized for UAM operations when vehicle or topological constraints are present. Because the optimization problem is NP-hard, five heuristic algorithms are developed to find potential solutions with acceptable computation times, and are found to be within 10% of the optimal value for the test cases explored. The results from this exploration are used in a second agent-based transportation model that analyzes operational parameters associated with UAM networks, such as service strategy and dispatch frequency, in addition to the considerations associated with network design. General trends between the effectiveness of UAM networks and the various factors explored are identified and presented.

No description available.