One Engine Out Takeoff Trajectory Optimization

Talgorn, Bastien; Laporte, Serge; Bès, Christian; Segonds, Stéphane

doi:10.2514/6.2010-9013

Cited by 5 publications

(3 citation statements)

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“…This method is relatively easy to implement and offers smoother paths than the graph based methods, but may suffer from local minima as described by [16] and confirmed by us in preliminary studies. Finally, recent studies carried out by Talgorn et al [17] appear to use a mix of genetic algorithms for global optimization of engine out paths, and classical least gradient for optimizing local optima. These paths are defined as a succession of set navigation instructions, however these may not define a set track on the ground, and are thus subject to a greater obstacle splay and lesser optimized paths.…”

Section: Literature Reviewmentioning

confidence: 96%

Engine-Out Takeoff Path Optimization out of Terrain Challenging Airports

Masson

Bain

Page

2011

AIAA Guidance, Navigation, and Control Conference

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The purpose of this paper is to present ongoing research that utilizes novel methods inspired by the recent high quality research into UAV path planning to optimize abnormal engine failure takeoff paths of commercial large transport aircraft. Current practice is to manually pre-determine these flight paths, which leads in most cases to a conservative, sub-optimal result, especially for airports surrounded by high terrain. Optimal Flight Paths allowing the highest payload or highest engine derate are computed using a mixture of graph based Path Planning algorithms, and a search space determination based on the aircrafts compromised climb and turn capability. Results are presented for a number of terrain sensitive runways and are compared to current operational flight paths and takeoff weights. Final takeoff paths are determined as a succession of arcs and straight legs as specified in Performance Based Navigation standards.

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Section: Literature Reviewmentioning

confidence: 96%

Engine-Out Takeoff Path Optimization out of Terrain Challenging Airports

Masson

Bain

Page

2011

AIAA Guidance, Navigation, and Control Conference

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“…With g(x, u) and g f (x N ) defined, the cost function J(x, U), the value function V(x, i), and the local variation of value function Q(δx, δu) can be found from Eqs. (12) ( 14) (15). The optimal longitudinal control is solved through the four steps of DDP described in Sec.…”

Section: B Ddp Optimizationmentioning

confidence: 99%

“…A number of methods have been proposed for takeoff modeling. Past studies have focused on takeoff analysis for aircraft sizing and performance analysis [4][5][6][7], takeoff trajectory prediction for aircraft operational level analysis [8,9], takeoff dynamics modeling [10][11][12][13], one-engine-out lateral path strategy [14,15], and lateral controllability at CEI [16,17]. Limitations in preceding research include: 1.…”

Section: Introductionmentioning

confidence: 99%

Differential Dynamic Programming to Critical-Engine-Inoperative Takeoff Certification Analysis

Xie

Harrison

Mavris

2020

Aiaa Aviation 2020 Forum

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Critical-engine-inoperative (CEI) takeoff is a required flight test in transport aircraft type certification. Due to the limited excess power following engine failure, this flight test is potentially dangerous and highly sensitive to the flight controls. To enhance the flight safety in CEI takeoff, an optimal longitudinal control sequence is necessary for the flight test. On the other hand, to reduce the cost associated with type certification process, it is desired to incorporate certification analysis in early design phases. Since the certification regulations pose requirements on aircraft dynamic responses, the point-mass based method used in most of the takeoff analyses for aircraft early design is not suitable. To incorporate flight dynamics in takeoff analysis, a robust longitudinal control law is needed for takeoff performance prediction. This paper proposes to use Differential Dynamic Programming (DDP) for the optimization of elevator control for CEI takeoff certification analysis. To evaluate the method, two test cases are performed on the CEI takeoff of a small single-aisle aircraft model with different initial conditions. The results of two cases suggests that the DDP algorithm is able to optimize the trajectory in terms of minimizing takeoff distance, maximizing the rate of climb, and improving the compliance with respect to takeoff certification constraints. The optimized trajectory is sensitive to the initial control sequence given to the algorithm and the cost function settings.

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