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An affordable technique is proposed for fast quantitative analysis of aerobatics and other complex flight domains of highly maneuverable aircraft. A generalized autonomous situational model of the “pilot (automaton) – vehicle – operational environment” system is employed as a “virtual test article”. Using this technique, a systematic knowledge of the system behavior in aerobatic flight can be generated on a computer, much faster than real time. This information can be analyzed via a set of knowledge mapping formats using a 3-D graphics visualization tool. Piloting and programming skills are not required in this process. Possible applications include: aircraft design and education, applied aerodynamics, flight control systems design, planning and rehearsal of flight test and display programs, investigation of aerobatics-related flight accidents and incidents, physics-based pilot training, research into new maneuvers, autonomous flight, and onboard AI.

The ultimate goal of knowledge-based aircraft design, pilot training and flight operations is to make flight safety an inherent, built-in feature of the flight vehicle, such as its aerodynamics, strength, economics and comfort are. Individual flight accidents and incidents may vary in terms of quantitative characteristics, circumstances, and other external details. However, their cause-and-effect patterns often reveal invariant structure or essential causal chains which may re-occur in the future for the same or other vehicle types. The identification of invariant logical patterns from flight accident reports, time-histories and other data sources is very important for enhancing flight safety at the level of the ‘pilot - vehicle -operational conditions’ system. The objective of this research project was to develop and assess a method for ‘mining’ knowledge of typical cause-and-effect patterns from flight accidents and incidents.

A technique is proposed for examining complex behaviors in the “pilot - vehicle - operational conditions” system using an autonomous situational model of flight. The goal is to identify potentially critical flight situations in the system behavior early in the design process. An exhaustive set of flight scenarios can be constructed and modeled on a computer by the designer in accordance with test certification requirements or other inputs. Distinguishing features of the technique include the autonomy of experimentation (the pilot and a flight simulator are not involved) and easy planning and quick modeling of complex multi-factor flight cases. An example of mapping airworthiness requirements into formal scenarios is presented. Simulation results for various flight situations and aircraft types are also demonstrated.