Trajectory optimization to minimize the environmental impact of departing and arriving aircraft

Trajectory optimization to minimize the environmental impact of departing and arriving aircraft

van Dam, Leanne (TU Delft Aerospace Engineering)

Hoekstra, J.M. (graduation committee)
Sun, Junzi (mentor)

Delft University of Technology

Aerospace Engineering

2022-11-18

The aviation industry faces the challenge of reducing its climate eects. This paper aims to determine whether optimizing flight trajectories can reduce the environmental impact of aircraft during the climb and descent phases. It addresses the research gap in sustainable flight trajectory optimization by combining state-of-the-art research on optimization techniques with research on aviation’s climate eects. A genetic algorithm is created for the climb and descent phases to minimize the impact of gaseous emissions; the objective function entails the climate and air quality costs of the emission of carbon dioxide, carbon monoxide, nitrogen oxides, sulfur oxides, and water vapor. OpenAP is used to evaluate the emissions of aircraft. The optimization includes variable mass and speed, wind, departure and arrival procedures, and airspace constraints. The optimization considers both the vertical and horizontal flight paths simultaneously. The model was tested for more than 22,000 flights in June 2018 at Amsterdam Schiphol Airport. It has been found that an average reduction in environmental costs of 8.6% and 18.6% is achievable for departing and arriving flights, respectively. The reduction depends on the aircraft type, weather conditions, and whether the aircraft departs or arrives. The open-source optimization model can be used for further research, for instance, evaluating the reduction possibilities at dierent locations. The research shows that the aviation industry can reduce its environmental impact considerably by optimizing departing and arriving trac trajectories.

trajectory optimization
environmental impact
genetic algorithm
aviation

http://resolver.tudelft.nl/uuid:534309cb-8067-49c7-8e13-8ab4ce1701b2

Student theses

master thesis

© 2022 Leanne van Dam