Fuel burn prediction algorithm for cruise, constant speed and level flight segments

Dancila, Bogdan Dumitru; Botez, Ruxandra Mihaela; Labour, Dominique

doi:10.1017/s0001924000008149

Cited by 46 publications

(28 citation statements)

References 6 publications

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“…Dancila et al researched fuel burn prediction algorithm for cruise, constant speed, and level flight segments and considered the continuous fuel burn rate variation with time caused by the gross weight modification due to the fuel burn process itself [16]. Patron et al put forward low calculation time interpolation method on the altitude optimization algorithm for the FMS CMA-9000 improvement on the A310 and L-1011 aircraft [17].…”

Section: Introductionmentioning

confidence: 99%

Optimization Plug Mode of External Fuel and Weapons for Less Changing Aircraft Center of Gravity

Zhou

Huang

et al. 2017

Mathematical Problems in Engineering

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To achieve the minimum change of aircraft center of gravity, plug mode optimization was carried out. Plug mode, including weight and position of external fuel and weapon load, was selected as design mode. Numerical model for calculating aircraft center of gravity was established and used to generate mode samples. Random search algorithm (RSA) was designed and applied for solving the optimization plug mode, and the precise solution of plug mode was determined by full array method (FAM). Compared with typical plug mode, the optimization mode can reach a minimum change in aircraft center of gravity with fewer samples than the solution by FAM. RSA coupled with FAM is an effective scheme for the optimization of aircraft plug mode.

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Section: Introductionmentioning

confidence: 99%

Optimization Plug Mode of External Fuel and Weapons for Less Changing Aircraft Center of Gravity

Zhou

Huang

et al. 2017

Mathematical Problems in Engineering

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“…Beam search, a variation of branch and cut, was suggested in [18], and it was later improved to reduce the computation time [19] finding the global optimal solution. An algorithm able to find the optimal altitude for a fixed altitude cruise and a fuel burn estimator was studied in [20].…”

Section: Introductionmentioning

confidence: 99%

Vertical Reference Flight Trajectory Optimization with the Particle Swarm Optimisation

Murrieta-Mendoza¹,

Ruiz²,

Botez³

2017

Modelling, Identification and Control

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The consumption of fossil fuels in order to power flights leads to undesirable pollution particles to be released to the atmosphere. Fuel also represents an important expense for airlines. For these reasons, it is of interest to reduce fuel burn for a given flight. In this article, the altitudes followed by a commercial aircraft during the cruise phase of a flight, also called vertical reference trajectory, were optimized in terms of fuel burn. The airspace was modelled under the form of a unidirectional graph. Fuel burn was computed using a numerical performance model. The weather forecast was obtained from the model delivered by Environment Canada. The selection of waypoints where to execute the changes in altitudes that provided the most economical flight cost in terms of fuel burn was determined using the particle swarm optimisation (PSO) algorithm. The trajectories provided by the algorithm developed in this paper were compared against simple geodesic trajectories to validate its optimization potential, and against as flown trajectories. Results have showed that up to 6.5% of fuel burn can be saved comparing against simple trajectories, and up to 3.1% was optimized comparing against as flown trajectories.

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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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Fuel burn prediction algorithm for cruise, constant speed and level flight segments

Cited by 46 publications

References 6 publications

Optimization Plug Mode of External Fuel and Weapons for Less Changing Aircraft Center of Gravity

Optimization Plug Mode of External Fuel and Weapons for Less Changing Aircraft Center of Gravity

Vertical Reference Flight Trajectory Optimization with the Particle Swarm Optimisation

Lateral Reference Trajectory Algorithm Using Ant Colony Optimization

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