Vertical flight profile optimization for a cruise segment with RTA constraints

2019 ◽  
Vol 123 (1265) ◽  
pp. 970-992
Author(s):  
R. Dancila ◽  
R. Botez
Keyword(s):  
Performance Model ◽  
Time Of Arrival ◽  
Flight Performance ◽  
Flight Trajectory ◽  
Flight Profile ◽  
Profile Optimization ◽  
Wind Profiles ◽  
Cruise Flight ◽  
Active Controls ◽  
Required Time Of Arrival

ABSTRACTThis paper presents the results of a research performed at the Research Laboratory in Active Controls, Avionics and Aeroservoelasticty (LARCASE), at ÉTS, concerning optimisation strategies for cruise flight segments with imposed flight time (delimited by waypoints with required time of arrival constraints). Specifically, a new algorithm is presented that identifies the optimal vertical navigation profile (flight altitude and speed optimisation) for a cruise segment with imposed lateral navigation profile, bounded by two waypoints with required time of arrival constraints. The set of evaluated vertical navigation profiles are characterised by identical altitudes and speeds at their initial and final waypoints (at the beginning and the end of the cruise segment under optimisation), a maximum of one altitude step (relative to the initial altitude), and are flown at constant speed. This study investigates the flight performance increase (total cost reduction) for a flight along the optimal vertical navigation profile, relative to a flight at the optimal speed and initial cruise altitude. The evaluation was performed using a medium haul transport aircraft flight performance model, for three lateral navigation profiles and three wind profiles. The algorithm is targeted for Flight Management Systems platforms, to provide the optimal flight trajectory for the imposed lateral flight profile and time constraints.

scholarly journals Aerial course stabilization is impaired in motion-blind flies

2020 ◽  
Author(s):  
Maria-Bianca Leonte ◽  
Aljoscha Leonhardt ◽  
Alexander Borst ◽  
Alex S. Mauss
Keyword(s):  
Motion Detection ◽  
Visual Motion ◽  
Flight Performance ◽  
Wild Type ◽  
Flight Trajectory ◽  
Free Flight ◽  
Motion Vision ◽  
Circling Behaviour ◽  
Course Control ◽  
Self Motion

AbstractVisual motion detection is among the best understood neuronal computations. One assumed behavioural role is to detect self-motion and to counteract involuntary course deviations, extensively investigated in tethered walking or flying flies. In free flight, however, any deviation from a straight course is signalled by both the visual system as well as by proprioceptive mechanoreceptors called ‘halteres’, which are the equivalent of the vestibular system in vertebrates. Therefore, it is yet unclear to what extent motion vision contributes to course control, or whether straight flight is completely controlled by proprioceptive feedback from the halteres. To answer these questions, we genetically rendered flies motion-blind by blocking their primary motion-sensitive neurons and quantified their free-flight performance. We found that such flies have difficulties maintaining a straight flight trajectory, much like control flies in the dark. By unilateral wing clipping, we generated an asymmetry in propulsory force and tested the ability of flies to compensate for this perturbation. While wild-type flies showed a remarkable level of compensation, motion-blind animals exhibited pronounced circling behaviour. Our results therefore unequivocally demonstrate that motion vision is necessary to fly straight under realistic conditions.

scholarly journals Cruise Flight Performance Optimization for Minimizing Green Direct Operating Cost

2019 ◽  
Vol 11 (14) ◽  
pp. 3899 ◽  
Author(s):  
Tian ◽  
Wan ◽  
Ye ◽  
Xing
Keyword(s):  
Environmental Effects ◽  
Performance Optimization ◽  
Meteorological Data ◽  
Operating Cost ◽  
Numerical Test ◽  
Flight Performance ◽  
Time Dynamic ◽  
Direct Operating Cost ◽  
Greenhouse Effects ◽  
Cruise Flight

To cope with the environmental impact of aviation and pollution problems in the future, airlines need to assess environmental impacts and offer countermeasures in advance. In order to measure the influence of environment on the airlines’ operational costs, this paper establishes an aircraft green direct operating cost (GDOC) model to quantify adverse environmental effects, such as air pollution and greenhouse effects, into the direct operating cost (DOC). Furthermore, fuel consumption, flight time, and distance in the cruising stage account for about 80% of the entire flight mission, and optimizing cruise flight performance can contribute greatly to reduce GDOC. Therefore, this paper sets up an optimal control model to minimize GDOC, establishes a discrete time dynamic system for optimizing the cruise altitude and speed profiles, and searches the optimal results by using dynamic programming. Besides, as meteorological conditions affect aircraft aerodynamics, fuel flow rate, contrail formation, and so on, this paper analyzes meteorological uncertainty by using historic meteorological data. Finally, a route is selected as an example, and the rationality of the optimal results is proven by comparing GDOC with DOC. The results and discussion of the numerical test also show that environmental effects on aircraft operation can be reduced significantly by adopting GDOC as the optimization objective, especially the contrail cost, and the step-climb cruise mode can further reduce GDOC effectively.

New flight trajectory optimisation method using genetic algorithms

2021 ◽  
Vol 125 (1286) ◽  
pp. 618-671
Author(s):  
R.I. Dancila ◽  
R.M. Botez
Keyword(s):  
Genetic Algorithms ◽  
Flight Trajectory ◽  
Step Size ◽  
Cost Index ◽  
Local Optima ◽  
Total Cost ◽  
Flight Plan ◽  
Flight Envelope ◽  
Flight Profile ◽  
Trajectory Optimisation

AbstractThis paper presents a new flight trajectory optimisation method, based on genetic algorithms, where the selected optimisation criterion is the minimisation of the total cost. The candidate flight trajectories evaluated in the optimisation process are defined as flight plans with two components: a lateral flight plan (the set of geographic points that define the flight trajectory track segments) and a vertical flight plan (the set of data that define the altitude and speed profiles, as well as the points where the altitude and/or speed changes occur). The lateral components of the candidate flight plans are constructed by selecting a set of adjacent nodes from a routing grid. The routing grid nodes are generated based on the orthodromic route between the flight trajectory’s initial and final points, a selected maximum lateral deviation from the orthodromic route and a selected grid node step size along and across the orthodromic route. Two strategies are investigated to handle invalid flight plans (relative to the aircraft’s flight envelope) and to compute their flight performance parameters. A first strategy is to assign a large penalty total cost to invalid flight profiles. The second strategy is to adjust the invalid flight plan parameters (altitude and/or speed) to the nearest limit of the flight envelope, with priority being given to maintaining the planned altitude. The tests performed in this study show that the second strategy is computationally expensive (requiring more than twice the execution time relative to the first strategy) and yields less optimal solutions. The performance of the optimal profiles identified by the proposed optimisation method, using the two strategies regarding invalid flight profile performance evaluation, were compared with the performance data of a reference flight profile, using identical input data: initial aircraft weight, initial and final aircraft geographic positions, altitudes and speed, cost index, and atmospheric data. The initial and final aircraft geographic positions, and the reference flight profile data, were retrieved from the FlightAware web site. This data corresponds to a real flight performed with the aircraft model used in this study. Tests were performed for six Cost Index values. Given the randomness of the genetic algorithms, the convergence to a global optimal solution is not guaranteed (the solution may be non-optimal or a local optima). For a better evaluation of the performance of the proposed method, ten test runs were performed for each Cost Index value. The total cost reduction for the optimal flight plans obtained using the proposed method, relative to the reference flight plan, was between 0.822% and 3.042% for the cases when the invalid flight profiles were corrected, and between 1.598% and 3.97% for the cases where the invalid profiles were assigned a penalty total cost.

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