Share:


A kinematic approach to segmented-trajectory generation for the total loss of thrust emergency

Abstract

Contemporary twin-engine airliners are more vulnerable to total loss of thrust than yesterday‘s three and four engine airliners, due to reduced engine redundancy. In the event of a total loss of thrust, flight crews have only one chance for landing, because the aircraft cannot gain altitude. Therefore, there is a pressing need to explore the idea of an engines-out landing trajectory optimization for commercial jets. A few past studies addressed this safety issue for general aviation aircraft and fighter jets but not commercial jets, primarily because the essential aircraft-specific aerodynamic data are not publicly available. To fill in this gap, this study adopts a kinematic approach to aircraft trajectory optimization. Unlike conventional trajectory optimization methods, the kinematic algorithm requires minimal amount of aircraft-specific aerodynamic data that can be effortlessly collected in a full flight simulator. The paper describes the kinematic algorithm and applies it to a realistic bird strike scenario. Flight simulation tests are conducted in a full flight simulator to verify the accuracy of the algorithm. The results demonstrate that the algorithm can compute the optimum trajectory with a less than 3.0 percent error. Since the algorithm is accurate and computationally-undemanding, it is promising for real-world applications.

Keyword : aircraft, commercial, dual-engine failure, glide, jetliner, performance, power-off, powerless, engines- out, trajectory optimization, simulation, total loss of power

How to Cite
Avrenli, K. A., & Dempsey, B. J. (2015). A kinematic approach to segmented-trajectory generation for the total loss of thrust emergency. Aviation, 19(3), 138-149. https://doi.org/10.3846/16487788.2015.1104847
Published in Issue
Nov 13, 2015
Abstract Views
663
PDF Downloads
468
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.