ADS-B and Mode S Data for Aviation Meteorology and Aircraft Performance Modelling

Master Thesis (2018)
Author(s)

Huy Vû (TU Delft - Aerospace Engineering)

Contributor(s)

J.M. Hoekstra – Mentor

J. Ellerbroek – Graduation committee member

Junzi Sun – Coach

Faculty
Aerospace Engineering
Copyright
© 2018 Huy Vû
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Huy Vû
Graduation Date
01-10-2018
Awarding Institution
Delft University of Technology
Programme
Aerospace Engineering | Control & Simulation
Faculty
Aerospace Engineering
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Abstract

With the increase in air traffic volume, aircraft are required to be equipped with a transponder by aviation authorities to increase the surveillance in air traffic control. These transponders transmit periodically Automatic Dependent Surveillance-Broadcast (ADS-B) signals and by interrogation the Mode S signals. These signals can be received by anyone with a simple receiver and are a new source for aviation research. It contains flight trajectories and aircraft states which were previously only available for air traffic control and aircraft operators. This paper uses ADS-B and Mode S data to derive an accurate meteorological model and uses this model to determine aircraft performance parameters, which are usually not publicly available. A Meteo-Particle model is used and validated with the European Centre for Medium-Range Weather Forecasts (ECMWF) model. For wind speed, wind direction and temperature the mean absolute error are 2.73 m/s, 7.73 degrees and 1.32 K respectively. The true airspeed is the actual airspeed that the aircraft experiences. It is required to determine aircraft performances. The true airspeed can be obtained with the wind derived in the Meteo-Particle model and ADS-B ground speed. Using the true airspeed information, aircraft performance parameters for climb, cruise and descend phase are determined for 16 aircraft types. These derived aircraft performance parameters, based on a large quantity of ADS-B and Mode S data, are a good alternative to the close-sourced performance parameters.

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