Roberto Salvador Félix Patrón

Vertical profile optimization for the Flight Management System CMA-9000 using the golden section search method

The current Flight Management System (FMS), CMA-9000, from CMC Electronics - Esterline, only optimizes the vertical flight profile in terms of aircraft speeds. This article defines a methodology that optimizes the vertical flight profile in terms of speeds and altitudes, obtaining a trajectory that reduces the global flight cost. The Performance Database (PDB) provided by CMC Electronics - Esterline is presently used on airplanes such as the Lockheed L-lOll and the Airbus A310. This PDB is used as the reference to design different trajectory optimization algorithms in order to obtain the altitude where the aircrafts fuel efficiency is the best. These algorithms are compared with the Part-Task Trainer (PTT), simulator that represents the FMS CMA-9000, supplied by CMC Electronics - Esterline as well. To validate the results, the FlightSIM® software is used, which considers a complete aircraft aerodynamic model for its simulations, giving accurate results and very close to reality.

Flight trajectories optimization under the influence of winds using genetic algorithms

The current Flight Management System (FMS), CMA-9000, from CMC Electronics – Esterline, does not include a cruise optimization. In the presence of winds, the aircraft fuel consumption is highly affected. If the aircraft experiments “head winds”, the consumption will increase, and the consumption will decrease in the case of “tail winds”. The cruise phase is analyzed using genetic algorithms to obtain the maximal optimization possible in the least calculation time. In this article, an algorithm will be defined, that will be capable of selecting an alternative trajectory to take advantage of “tail winds” in the flight path, and to avoid as much as possible the presence of “head winds”, to reduce fuel burn.

New methods of optimization of the flight profiles for performance database-modeled aircraft

Researchers have been attempting to reduce aircraft fuel consumption for decades to minimize aviation’s emissions to the atmosphere. This article presents an algorithm which improves the trajectories created by a commercial flight management system. A complete analysis of the climb, cruise, and descent was performed and a genetic algorithm has been implemented to evaluate the effects of the possible changes to aircraft speeds and altitudes, as well as the influence of the wind vector on the lateral and vertical profiles, all to obtain the flight trajectory that most reduces the global flight fuel consumption.

Flight Trajectory Optimization Through Genetic Algorithms Coupling Vertical and Lateral Profiles

For long flights, the cruise is the longest phase and is where the largest proportion of fuel is consumed. A new flight trajectory calculation method utilizing genetic algorithms is proposed here. The lateral and vertical navigation profiles are analyzed to obtain the optimal cruise trajectory in terms of fuel consumption. With a complete analysis of the wind currents, a 3D grid is created for all along the cruise phase, including latitudes, longitudes and altitudes. Different flight trajectories are calculated using genetic algorithms. To improve calculation time and precision, the flight trajectories are calculated using the performance databases for commercial aircraft, databases which are used in actual flight management system platforms. This optimization process indicates that fuel cost savings of up to 5.6% can be achieved.Copyright

Low calculation time interpolation method on the altitude optimization algorithm for the FMS CMA-9000 improvement on the A310 and L-1011 aircraft

In order to implement a trajectories optimization algorithm into the flight management system CMA-9000 from CMC Electronics – Esterline, an algorithm with a short calculation time has to be designed. The performance databases supplied by CMC Electronics – Esterline represent the numerical model of aircraft such as the Airbus A310, the Lockheed L-1011 and the Sukhoi Superjet 100, and to create flight trajectories, these databases have to be interpolated. An interpolation procedure is defined in this algorithm to calculate fuel burn, distance traveled and flight time, with the lowest algorithm execution time possible.

Flight Trajectory Optimization Through Genetic Algorithms for Lateral and Vertical Integrated Navigation

For long flights, the cruise is the longest phase and where the largest amount of fuel is consumed. An in-cruise optimization method has been implemented to calculate the optimal trajectory that reduces the flight cost. A three-dimensional grid has been created, coupling lateral navigation and vertical navigation profiles. With a dynamic analysis of the wind, the aircraft can perform a horizontal deviation or change altitudes via step climbs to reduce fuel consumption. As the number of waypoints and possible step climbs is increased, the number of flight trajectories increases exponentially; thus, a genetic algorithm has been implemented to reduce the total number of calculated trajectories compared to an exhaustive search. The aircraft’s model has been obtained from a performance database, which is currently used in the commercial flight management system studied in this paper. A 5% average flight cost reduction has been obtained.

Climb, Cruise and Descent 3D Trajectory Optimization Algorithm for a Flight Management System

Global warming is one of the major issues in the Earth today. Many studies are intended to reduce aircraft’s fuel consumption to minimize aviation’s footprint. This article presents the combination between two different trajectories’ optimization types: one optimizing the vertical navigation profile, and the other optimizing the lateral navigation profile. The aircraft model is obtained from a performance database, which offers an improved precision over aircraft modeled trough equations of motion, constantly used on the literature. The calculation of the optimal trajectory is obtained by implementing dynamic weather information. The VNAV algorithm calculates the optimal altitudes, speeds and step climbs to reduce fuel consumption, while the LNAV algorithm searches for alternative horizontal trajectories. The aircraft takes advantage of tail winds and avoids head winds. The results were compared with real flight information, and the fuel burn reduction obtained is encouraging.