Quantifying the Effects of Uncertainty in a Decentralized Model of the National Airspace System
The modernization of the National Air Traffic Control System is on the horizon, and with it, the possible introduction of autonomous air vehicles into the national airspace. Per the FAA Aerospace Forecast (FAA, 2013), U.S. carrier passenger traffic is expected to average 2.2 percent growth per year over the next 20 years with government statistics indicating that the average domestic load factor for airlines in 2014 was approximately 84.4 percent (US Department of Transportation, 2015). Adding to that demand, the potential introduction of unmanned and autonomous air vehicles motivates reconsideration of control schemes. One of the proposed solutions (Eby, 1994) would involve a decentralized control protocol. Equipping each aircraft with the information necessary to navigate safely through integrated airspace becomes an information sharing problem: how much information about other aircraft is required for a pilot to safely fly the gamut of a heavily populated airspace and what paradigm shifts may be necessary to safely and efficiently utilize available airspace? This thesis describes the development of a tool for testing alternative traffic management systems, centralized or decentralized, in the presence of uncertainty.
Applying a computational fluid dynamics-inspired approach to the problem creates a simulation tool to model both the movement of traffic within the airspace and also allows study of the effects of interactions between vehicles. By incorporating a Smoothed Particle Hydrodynamics (SPH) based model, discrete particle aircraft each carry a set of unique deterministic and stochastic properties. With this model, aircraft interaction can be studied to better understand how variations in the nondeterministic properties of the system affect its overall efficiency and safety. The tool is structured to be sufficiently flexible as to allow incorporation of different collision detection and avoidance rules for aircraft traffic management.