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Loss of thrust emergencies-e.g., induced by bird/drone strikes or fuelexhaustion-create the need for dynamic data-driven flight trajectory planningto advise pilots or control UAVs. While total loss of thrust trajectories tonearby airports can be pre-computed for all initial points in a 3D flight plan,dynamic aspects such as partial power and airplane surface damage must beconsidered for accuracy. In this paper, we propose a new Dynamic Data-DrivenAvionics Software (DDDAS) approach which during flight updates a damagedaircraft performance model, used in turn to generate plausible flighttrajectories to a safe landing site. Our damaged aircraft model isparameterized on a baseline glide ratio for a clean aircraft configurationassuming best gliding airspeed on straight flight. The model predicts purelygeometric criteria for flight trajectory generation, namely, glide ratio andturn radius for different bank angles and drag configurations. Given actualaircraft performance data, we dynamically infer the baseline glide ratio toupdate the damaged aircraft model. Our new flight trajectory generationalgorithm thus can significantly improve upon prior Dubins based trajectorygeneration work by considering these data-driven geometric criteria. We furtherintroduce a trajectory utility function to rank trajectories for safety. As ause case, we consider the Hudson River ditching of US Airways 1549 in January2009 using a flight simulator to evaluate our trajectories and to get sensordata. In this case, a baseline glide ratio of 17.25:1 enabled us to generatetrajectories up to 28 seconds after the birds strike, whereas, a 19:1 baselineglide ratio enabled us to generate trajectories up to 36 seconds after thebirds strike. DDDAS can significantly improve the accuracy of generated flighttrajectories thereby enabling better decision support systems for pilots inemergency conditions.

Flight Trajectory Planning for Fixed-Wing Aircraft in Loss of Thrust Emergencies