Flight Altitude Optimization Using Genetic Algorithms Considering Climb and Descent Costs in Cruise with Flight Plan Information 2015-01-2542
Flight trajectory optimization algorithms reduce flight cost and fuel consumption, thereby reducing the polluting emissions released to the atmosphere. Ground teams and avionics equipment such as the Flight Management System evaluate different routes to minimize flight costs. The optimal trajectory represents the flight plan given to the crew. The resulting flight plan contains waypoints and weather information such as the wind speed and direction and the temperature for each waypoint. The flight plan is normally introduced manually into the Flight Management System.
In this paper, genetic algorithms were applied to the waypoints available in a flight plan to find the altitudes that minimize total fuel consumption, taking into account the cruise-climb and cruise-descent steps' costs.
The genetic algorithms emulate the evolution process through a predefined number of generations. Here, an individual is defined as a set of altitudes, whose fitness depends on its ability to improve the flight cost. The most-fitted individuals are selected to reproduce and create a new generation of individuals. As new generations are created, the fitness of the individuals improves and an optimal set of altitudes to reduce the flight cost is found.
Aircraft fuel consumption in this algorithm was computed using a Performance Database, which was developed and validated by our industrial partner using experimental flight data. This approach differs from the Equations of Motion commonly used in the field and in the literature.
Preliminary results showed that the set of altitudes provided by the genetic algorithm reduces the flight cost. This fuel reduction has a direct impact on the level of polluting emissions.
Citation: Murrieta-Mendoza, A., Botez, R., and Félix Patrón, R., "Flight Altitude Optimization Using Genetic Algorithms Considering Climb and Descent Costs in Cruise with Flight Plan Information," SAE Technical Paper 2015-01-2542, 2015, https://doi.org/10.4271/2015-01-2542. Download Citation
Alejandro Murrieta-Mendoza, Ruxandra Mihaela Botez, Roberto S Félix Patrón