Todd A. Lauderdale’s research while affiliated with Ames Research Center and other places

The primary motivation for this paper is to quantify the operational benefits (energy consumption and flight duration) of flying wind-optimal lateral trajectories for short flights (less than 60 miles) anticipated in the urban environment. The optimal control model presented includes a wind model for quantifying the effect of wind on the lateral trajectory. The optimal control problem is numerically solved using the direct collocation method. Energy consumption and flight duration flying wind-optimal lateral trajectories are compared with corresponding values obtained flying great-circle paths between the same origin and destination pairs to determine the operational benefits of wind-optimal routing for short flights. The flight duration results for different scenarios are validated using a simulation tool designed and developed at NASA for exploring advanced air traffic management concepts. This research study suggests that for short flights in an urban environment, operational benefits of the wind-optimal lateral trajectories over the corresponding great-circle trajectories in terms of energy consumption and flight duration per flight are dependent on: i) wind field’s spatial variability, ii) wind magnitude, iii) the direction of route relative to the wind field, and iv) cruise segment length. The operational benefits observed in realistic flyable wind scenarios are less than 2.5%; these could be translated to an equivalent of a maximum of 2 min of cruise flight duration savings in the urban air mobility environment. As expected, headwinds and tailwinds along the flight route most significantly impact energy consumption and flight duration.

View Video Presentation: https://doi.org/10.2514/6.2022-3704.vid Remotely Piloted Aircraft (RPA) for cargo operations in the national airspace system will impact safety due to, among other factors, the latency and reliability of command & control, and of communication. This paper investigates the safety impact with increasing mix of RPA amidst manned traffic in a generic arrival pattern with three merging flows. Latency was modelled as the response time between air traffic control's determination of a resolution and the RPAs' initiation of the maneuver. Reliability was modelled as a message drop probability. The experiment was repeated with two different aircraft types having different performance characteristics as representatives of RPA for conducting automated cargo operations. Overall response time above thirty seconds and message drop probability over twenty percent caused losses of separation. Specific results depended on the RPA aircraft type. The detailed impacts of latency and reliability with increasing mix of RPA traffic are provided. Applications of the approach for further studies at increasing levels of automation are also discussed.

View Video Presentation: https://doi.org/10.2514/6.2022-3828.vid The fuel-efficiencies of arrival flights that were managed by an automated system were compared to the fuel-efficiencies of arrival flights that were managed by air traffic controllers. It was infeasible to have the automated system control arrivals in real operations, so the comparison was accomplished by setting up a fast-time simulation where the automated system could manage arrivals with the same initial conditions and flight plans as those that operated in real operations during a selected comparison period and in the same background traffic. For this study, Newark Liberty International Airport was selected as the arrival airport because its high traffic load and constrained arrival procedures were expected to highlight fuel-efficiency benefits of an automated system. In the simulation, the automated system managed Newark arrivals, and the other flights (arrivals to other airports, departures, and overflights) composed the background traffic. To match the simulation and the real operations background traffic, the other flights flew in simulation the same trajectory that they flew in real operations during the comparison period. Fuel-efficiency was measured by calculating fuel burns of the arrival trajectories. The fuel-efficiencies of arrival trajectories produced in the simulation were compared with the estimated fuel-efficiencies of arrival trajectories recorded from real operations during the comparison period. Results showed that automation managed arrivals burned 346 lbs less fuel per flight on average than controller managed arrivals.