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Automated Planning, Exploration and Mapping of Complex Operational Domains of Flight Using Multifactor Situational Trees
ISSN: 1946-3855, e-ISSN: 1946-3901
Published October 18, 2011 by SAE International in United States
Citation: Burdun, I., "Automated Planning, Exploration and Mapping of Complex Operational Domains of Flight Using Multifactor Situational Trees," SAE Int. J. Aerosp. 4(2):1149-1175, 2011, https://doi.org/10.4271/2011-01-2659.
A critical situation can suddenly develop in the ‘pilot (automaton) - aircraft - operational environment’ system behavior as a result of unfavorable mixing and cross-coupling of several demanding operational factors. The latter can include adverse weather effects, pilot (automaton) errors, mechanical failures and hidden design flaws. These factors are typically linked by strong cause-and-effect relationships, which can disturb the normal flow of external forces and moments acting on the aircraft. As a result, a multifactor situation can quickly propagate towards a chain reaction type accident. Specialists (designers, flight test pilots/engineers, regulators, investigators, educators/instructors, line pilots) have limited resources to address multifactor cases during the aircraft life cycle. The main difficulty is combinatorics (‘the curse of dimensionality’) which determines technical, time and budget constraints. Potentially unsafe complex domains of flight can be identified and screened in advance using the system dynamics model as a virtual flight test article. The developed methodology makes it possible to automatically plan, explore, analyze and map a broad set of realistic multifactor scenarios in autonomous fast-time modeling and simulation experiments. The outcome is a situational tree. This is a collection of branching (what-if) flight paths that are specially planted around a baseline situation to thread a complex operational domain of interest. Special techniques are used to mine and granulate the system level flight safety knowledge from these data structures. Multifactor situational trees can be helpful to locate potential anomalies in the system behavior, quantify critical combinations of events and processes (accident precursors), suggest available recovery options, and depict the aircraft's safety performance under multifactor conditions using ‘a bird's eye view’ knowledge maps. In this paper, the key concepts, algorithms, data structures, research steps and application examples of the developed methodology are presented using realistic flight cases.