With the increase in air traffic over the past decades, the reduction of aircraft noise is one of the major challenges facing stakeholders. Flight operating conditions that decrease noise may possibly increase the fuel consumption of aircraft, which is an important factor in airline cost management. In this thesis, a methodology to support flight path planning with the aim of optimizing both perceived noise and fuel consumption is presented. In this approach, an aircraft flight trajectory is decomposed into surface and altitude paths to model relevant air transportation constraints. The shortest path for the surface projection is found using Dubins path method and an improved A* algorithm, which considers guide points according to the flight destination, runway angles, spatial separatio...[ Read more ]

With the increase in air traffic over the past decades, the reduction of aircraft noise is one of the major challenges facing stakeholders. Flight operating conditions that decrease noise may possibly increase the fuel consumption of aircraft, which is an important factor in airline cost management. In this thesis, a methodology to support flight path planning with the aim of optimizing both perceived noise and fuel consumption is presented. In this approach, an aircraft flight trajectory is decomposed into surface and altitude paths to model relevant air transportation constraints. The shortest path for the surface projection is found using Dubins path method and an improved A* algorithm, which considers guide points according to the flight destination, runway angles, spatial separation of aircraft near the airport, population distribution, and steering motion. The altitude path is optimized for low-perceived noise and low-fuel consumption, which is determined by solving the longitudinal governing equations of motion of flight using the distance computed from this surface path. A modified nondominated sorting genetic algorithm II for discrete optimization is developed to obtain Pareto fronts of the optimum altitude paths with reduced computational effort. The methodology is demonstrated by simulating flights for three different origin-destination routes. The results are then compared with Quick Access Recorder data and Standard Instrument Departure (SID). Although certain factors in air transportation that affect departure path planning, such as weather patterns and air traffic mix, are not considered in this method, the resulting surface path exhibits a close similarity with SID tracks. The resulting Pareto front exhibit reductions in fuel consumption and perceived noise levels. The trade-offs between fuel consumption and perceived noise levels are also discussed based on the relevant flight physics for the different routes.