Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Flight Trajectory Planning for Fixed-Wing Aircraft in Loss of Thrust
  Emergencies   Loss of thrust emergencies-e.g., induced by bird/drone strikes or fuel
exhaustion-create the need for dynamic data-driven flight trajectory planning
to advise pilots or control UAVs. While total loss of thrust trajectories to
nearby 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 be
considered for accuracy. In this paper, we propose a new Dynamic Data-Driven
Avionics Software (DDDAS) approach which during flight updates a damaged
aircraft performance model, used in turn to generate plausible flight
trajectories to a safe landing site. Our damaged aircraft model is
parameterized on a baseline glide ratio for a clean aircraft configuration
assuming best gliding airspeed on straight flight. The model predicts purely
geometric criteria for flight trajectory generation, namely, glide ratio and
turn radius for different bank angles and drag configurations. Given actual
aircraft performance data, we dynamically infer the baseline glide ratio to
update the damaged aircraft model. Our new flight trajectory generation
algorithm thus can significantly improve upon prior Dubins based trajectory
generation work by considering these data-driven geometric criteria. We further
introduce a trajectory utility function to rank trajectories for safety. As a
use case, we consider the Hudson River ditching of US Airways 1549 in January
2009 using a flight simulator to evaluate our trajectories and to get sensor
data. In this case, a baseline glide ratio of 17.25:1 enabled us to generate
trajectories up to 28 seconds after the birds strike, whereas, a 19:1 baseline
glide ratio enabled us to generate trajectories up to 36 seconds after the
birds strike. DDDAS can significantly improve the accuracy of generated flight
trajectories thereby enabling better decision support systems for pilots in
emergency conditions.

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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
2
The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph