Relationship between fuel consumption and altitude for commercial aircraft during descent: Preliminary assessment with a genetic algorithm

Turgut, Enis Turhan; Rosen, Marc A.

doi:10.1016/j.ast.2011.03.007

Cited by 36 publications

(19 citation statements)

References 15 publications

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“…Aunque la literatura sobre este tema es escasa, en el artículo de Turgut [8] se utilizan algoritmos genéticos para analizar la relación entre el consumo de fuel de una aeronave y la altitud de la misma durante la fase de descenso. También hace referencia a un problema similar en el sector aeronáutico el trabajo de Mongeau [5] sobre la optimización de la distribución de la carga de pago en un avión.…”

Section: Introductionunclassified

Repostaje en tierra de un avión mediante algoritmos genéticos

Plaza¹,

Santos²

2020

XXXVIII Jornadas De Automática: Gijón, 6, 7, Y 8 De Septiembre De 2017

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aeronave comercial. En concreto se ha abordado el repostaje de la aeronave antes de un vuelo. El repostaje en tierra consiste en decidir la cantidad de combustible que se dispondrá en cada depósito atendiendo a criterios de seguridad, estabilidad del avión y eficiencia energética. Para ello se define un problema de optimización cuya función de coste nace del conocimiento específico de los factores que influyen en una aeronave que tiene relación con el posicionamiento del combustible. Después su resolución se realiza mediante Algoritmos Genéticos. Los resultados obtenidos en simulación mejoran la solución manual estándar que se viene aplicando.

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Section: Introductionunclassified

Repostaje en tierra de un avión mediante algoritmos genéticos

Plaza¹,

Santos²

2020

XXXVIII Jornadas De Automática: Gijón, 6, 7, Y 8 De Septiembre De 2017

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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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“…This model was also based on real FDRs. A genetic algorithm (GA) model was also implemented by Turgut and Rosen (11) to cover the descent flight phase, but again they excluded the variation of fuel flow rate with Mach number. There is also a fuel flow rate model included in the Base of Aircraft Data (BADA) (12) from Eurocontrol.…”

Section: Introductionmentioning

confidence: 99%

Fuel flow-rate modelling of transport aircraft for the climb flight using genetic algorithms

Baklacıoğlu

2015

Aeronaut.j.

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In this study, development of a new fuel flow rate model for the climbing phase of flight was achieved using a genetic algorithm (GA) method. Two modelling approaches were performed using real flight data records (FDRs) from a medium-weight transport-category aircraft. The first model considered the dependency of fuel consumption only with respect to altitude, whereas the effects of both altitude and true airspeed (TAS) were included in the second model. The proposed models are improvements on existing models because the relationship between fuel flow rate, flight altitude, and TAS can be deduced using the derived formulations. Both modelling approaches were found to provide accurate results after performing an error analysis for fuel flow rate values. It was clear that incorporating the TAS effect into the second model enhanced the accuracy of the model, but the first model was also found to be appropriate for practical usage.

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Relationship between fuel consumption and altitude for commercial aircraft during descent: Preliminary assessment with a genetic algorithm

Cited by 36 publications

References 15 publications

Repostaje en tierra de un avión mediante algoritmos genéticos

Repostaje en tierra de un avión mediante algoritmos genéticos

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

Fuel flow-rate modelling of transport aircraft for the climb flight using genetic algorithms

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