Flight Altitude Optimization Using Genetic Algorithms Considering Climb and Descent Costs in Cruise with Flight Plan Information

Murrieta-Mendoza, Alejandro; Botez, Ruxandra Mihaela; Patrón, Roberto Salvador Félix

doi:10.4271/2015-01-2542

Cited by 9 publications

(5 citation statements)

References 41 publications

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“…Other algorithms have been developed by using a numerical performance model obtained from experimental flight test data. Using this database, the vertical reference trajectory optimization has been solved by using search space reduction techniques [24,25], golden section search [26], fuel estimators [27], genetic algorithms [28,29], and branch and bound inspired techniques [30,31]. All these algorithms have shown the vertical optimization opportunities.…”

Section: Introductionmentioning

confidence: 99%

Lateral Reference Trajectory Algorithm Using Ant Colony Optimization

Murrieta-Mendoza

Hamy

Botez

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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The aeronautical industry requires important quantities of fossil fuel to sustain flights. Fuel burn releases pollution particles to the atmosphere such as carbon dioxide. The aeronautical industry has set itself the goal of diminishing CO 2 emissions for the forthcoming years. A way of reducing fuel consumption is by improving en-route operations. For long haul flights, cruise is the flight stage that requires the most fuel consumption. Taking advantage of tailwinds reduces the flight time, thus reducing the fuel requirements. In this paper, an algorithm based on the Ant Colony Optimization was developed to optimize the lateral navigation reference trajectory of a commercial aircraft. Following a constant flight level, the algorithm was able to identify favorable wind areas, and to lead the aircraft to take advantage of these winds to optimize the flight cost. The aircraft model was a numerical performance model constructed from experimental flight data, where weather information was obtained by using information from Weather Canada. Results showed important fuel savings for different long haul flights. NomenclatureCDA = Continuous Descent Approach CDG = Paris Charles De Gaulle International airport (France) CI = Cost Index LNAV = Lateral Navigation NRT = Tokyo Narita International Airport (Japan) Pherom i = Pheromone level at a given path. PDB = Performance Database P i = Waypoint Selection Probability PTP = International Airport of Point à Pitre (Guadeloupe) TOC = Top of climb TOD = Top of decent VNAV = Vertical Navigation WA = Wind Azimuth WS = Wind Speed YUL = Montreal Pierre Eliot Trudeau International Airport (Canada) γ = Pheromone evaporation rate

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Section: Introductionmentioning

confidence: 99%

Lateral Reference Trajectory Algorithm Using Ant Colony Optimization

Murrieta-Mendoza

Hamy

Botez

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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“…The VNAV reference trajectory for all flight phases has been optimized by using the golden search section and step climbs [19], a search space reduction algorithms were developed in [20,21], and the branch and bound algorithm was implemented in [22], both algorithms were later coupled to reduce the computation time [23]. Genetic Algorithms were also used to find the waypoints where to maximize fuel savings [24].…”

Section: Introductionmentioning

confidence: 99%

Horizontal flight trajectory optimization considering RTA constraints

2020

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The increasing of flights around the world has led to various problems for the aeronautical industry such as saturated air space and higher levels of fossil fuel consumption. The way in which en-route flights are handled should be improved in order to increase airways’ capacity. A solution is to make aircraft to arrive at specific waypoints at a time constraint called Required Time of Arrival (RTA). Fossil fuel brings as a consequence the release of polluting particles to the atmosphere such as carbon dioxide and nitrogen oxides. It is thus desirable to compute the most economical trajectory in terms of fuel burn while fulfilling the RTA constraint. This article proposes a horizontal reference trajectory optimization algorithm based on the Particle Swarm Optimization technique in order to reduce fuel burn while fulfilling the RTA constraint. Results showed that for a flight without RTA constraint, up to 4% of fuel can be saved comparing against the trajectory of reference. The algorithm was normally able to meet the RTA constrain. However, aggressive RTA constraints might reduce the optimization levels of fuel compared with flights without RTA constraint.

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“…Several studies in the literature have investigated steps for aircraft fuel consumption optimization, such as adopting approaches with better performance in all phases, 19–27 analyzing statistical relationships, 5,7,8,10 removing unnecessary weight, 28 using automatic air traffic management systems, 29,30 and optimizing flight path. 31 In addition, other studies have investigated steps for fuel consumption minimization, such as the use of single-engine taxiing, electric power, and auxiliary power units, as well as the best scheduling of fleets.…”

Section: Introductionmentioning

confidence: 99%

Fuel consumption minimization of transport aircraft using real-coded genetic algorithm

Singh¹

2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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The issue of rising carbon dioxide emissions from aviation fuel consumption is increasingly crucial in terms of the Paris Agreement on climate change, which was adopted in 2015. The rapid growth of the global transport network has been affecting the environment adversely owing to the emission of greenhouse gases. Therefore, researchers are attempting to minimize aviation fuel consumption. This study presents a fuel consumption minimization problem in transport aircraft design. A real-coded genetic algorithm with direction-based crossover is employed to determine the optimum design for minimum fuel consumption. To mimic the aircraft fuel consumption issues, the proposed real-coded genetic algorithm employs the following three operators: ranking selection, direction-based crossover, and dynamic random mutation. Compared with earlier studies that used uniform crossover, the present study reduces the fuel consumption by applying real-coded genetic algorithm with a direction-based crossover operator. Comprehensive results show that the proposed real-coded genetic algorithm achieves remarkably faster convergence and improved search performance than the compared method. Direction-based crossover significantly enhances the fitness by guiding the crossover along a certain direction. In addition, it ensures a higher probability of locating the global optimum. Therefore, adopting such a technique can reduce the aircraft development cost.

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Flight Altitude Optimization Using Genetic Algorithms Considering Climb and Descent Costs in Cruise with Flight Plan Information

Cited by 9 publications

References 41 publications

Lateral Reference Trajectory Algorithm Using Ant Colony Optimization

Lateral Reference Trajectory Algorithm Using Ant Colony Optimization

Horizontal flight trajectory optimization considering RTA constraints

Fuel consumption minimization of transport aircraft using real-coded genetic algorithm

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