Abstract

Wake vortices pose a threat to a following aircraft, because they can induce a roll and compromise the safety of everyone on board. Caused by a difference in pressure between the upper and the lower part of the wings, these invisible flows of air are a major hazard and have to be avoided by separating the aircraft at considerable distances. One of the known constraints in airport capacity for both departure and arrival operations is the large headway resulting from the wake spacing separation criteria. Reducing wake vortex separations to a safe level between successive aircraft can increase capacity in the National Airspace System (NAS) with corresponding savings in delay times. One of the main goals of the Wake Encounter Model (WEM) described in this thesis is to assess the outcome from future reduced separation criteria in the NAS. The model has been used to test probable encounters in todayâ s operations, and can also be used to test NextGen scenarios, such as Close Parallel Approaches and reduced in-trail separation flights. This thesis presents model enhancements to account for aircraft turning maneuvers, giving the wake a more realistic shape. Three major airspaces, New York, Southern California and Atlanta, were analyzed using the original and the enhanced WEM to determine if the enhanced model better represents the conditions in todayâ s operations. Additionally, some analysis on the wake lateral travel for closely spaced runways is presented in this thesis. Finally, some extension tools for post -analysis, such as animation tool and various graphs depicting the interactions between wake pairs were developed. Master of Science

Abstract

One way to address the need for increased airspace system capacity is to reduce the separation requirements between aircraft in-flight. A key limiting factor to any reduction in separation is wake turbulence. The potential for aircraft to encounter wake turbulence poses a threat to both safety as well as increased efficiency. This research effort seeks to develop a model that can be used to evaluate the potential for wake encounters in todayâ s flight operations, as well as serve as a tool for evaluating future reduced separation scenarios. The wake encounter model (WEM) achieves this goal by integrating results from NASAâ s TDAWP wake turbulence prediction model with a flight operations model based on radar flight track data. Unique in this modelâ s design, is the ability to evaluate the potential for wake encounters throughout the terminal area versus previous research which has largely been restricted to areas near the runway. Expanding the modelâ s reach provides not only for a more thorough analysis of potential wake encounters, but also creates an effective tool for evaluating future reduced separation scenarios. "p" The WEM model was used to evaluate operations at three metropolitan airspaces in the United States: Atlanta, Los Angeles and New York. The results from these model runs indicated that potential wake encounters in todayâ s operations were few. More importantly, the results from the WEM create a baseline for wake turbulence exposure in todayâ s system, by which future scenarios can be compared against as part of any comprehensive reduced separation safety analysis. Master of Science

Abstract

To accommodate the growing air traffic demand, flights will need to be planned and navigated with a much higher level of precision than today's aircraft flight path. The Next Generation Air Transportation System (NextGen) stands to benefit significantly in safety and efficiency from such movement of aircraft along precisely defined paths. Air Traffic Operations (ATO) relying on such precision--the Precision Air Traffic Operations or PATO--are the foundation of high throughput capacity envisioned for the future airports. In PATO, the preferred method is to manage the air traffic by assigning a speed profile to each aircraft in a given fleet in a given airspace (in practice known as ("italic"speed control"/italic"). In this research, an algorithm has been developed, set in the context of a Hybrid Control System (HCS) model, that determines whether a speed control solution exists for a given fleet of aircraft in a given airspace and if so, computes this solution as a collective speed profile that assures separation if executed without deviation. Uncertainties such as weather are not considered but the algorithm can be modified to include uncertainties. The algorithm first computes all feasible sequences (i.e., all sequences that allow the given fleet of aircraft to reach destinations without violating the FAA's separation requirement) by looking at all pairs of aircraft. Then, the most likely sequence is determined and the speed control solution is constructed by a backward trajectory generation, starting with the aircraft last out and proceeds to the first out. This computation can be done for different sequences in parallel which helps to reduce the computation time. If such a solution does not exist, then the algorithm calculates a minimal path modification (known as "italic"path control"/italic") that will allow separation-compliance speed control. We will also prove that the algorithm will modify the path without creating a new separation violation. The new path will be generated by adding new waypoints in the airspace. As a byproduct, instead of minimal path modification, one can use the aircraft arrival time schedule to generate the sequence in which the aircraft reach their destinations.

Abstract

The U.S. Next Generation Air Transportation System (NextGen) is designed to increase the capacity, safety and efficiency of the air traffic control via the integration of past experiences and advances in technology. However, the system is expected to greatly increase the amount and types of data generated as well as the knowledge to be managed. Additionally, as with all new technology, U.S. NextGen opens the specter of the potential impacts created by cyberattacks. Given this, it appears logical to view the U.S. NextGen system from the lens of Big Data. This study evaluates the U.S. NextGen system using the five differentiated qualitative characteristics of big data: Volume, Velocity, Variety, Veracity and Value. The results indicate that U.S. NextGen system has several big data challenges that must be addressed in order to obtain its maximal potential. Refereed/Peer-reviewed