Abstract:

There is growing interest in commercial aircraft formation flight as a means of reducing both airspace congestion and the carbon footprint of air transportation. Wake vortex surfing has been researched extensively and proven to have significant fuel-saving benefits, however, commercial air transportation has yet to take advantage of these formation benefits due to understandable safety concerns. The realization of these formations requires serious consideration of formation contingencies and safety during closer-in maneuvering of large commercial aircraft. Formation contingency scenarios are much more complex than those of individual aircraft and have not yet been studied in depth. This thesis investigates the utility of optimization modeling in providing insight into generation of aircraft escape paths for formation contingency planning. Three high-altitude commercial aircraft formation scenarios are presented; formation join, formation emergency exit, and formation escape. The model-generated paths are compared with pilot-generated escape plans using the author’s pilot expertise. The model results compare well with pilot intuition and are useful in presenting solutions not previously considered, in evaluating separation requirements for improvement of escape path planning and in confirming the viability of the pilot-generated plans. The novel optimization model formulation presented in this thesis is the first model shown to be capable of generating escape paths comparable to pilot-generated escape plans and is also the first to incorporate avoidance of persistent and drifting wake turbulence within the formation. 

Bio:

Safa served in the US Air Force as an F-15C pilot and an experimental fighter test pilot specializing in engine-out flight, departure from controlled flight, and high-risk flutter test. Afterwards, she flew the 737NG and 737MAX for American Airlines and then for Oman Air. She has master’s degrees in theoretical physics and flight test engineering and is currently a PhD candidate in mechanical engineering in the Robotics, Intelligent Systems, and Control (RISC) Lab at King Abdullah University of Science and Technology in Saudi Arabia.

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