In air traffic management research, aircraft performance models are often used to generate and analyze aircraft trajectories. Although a crucial part of the aircraft performance model, the aerodynamic property of aircraft is rarely available for public research purposes, as it is protected by aircraft manufacturers for commercial reasons. In many studies, a simplified quadratic drag polar model is assumed to compute the drag of an aircraft based on the required lift. In this paper, using surveillance data, we take on the challenge of estimating the drag polar coefficients based on a novel stochastic total energy model that employs Bayesian computing. The method is based on a stochastic hierarchical modeling approach, which is made possible given accurate open aircraft surveillance data and additional analytical models from the literature. Using this proposed method, the drag polar models for 20 of the most common aircraft types are estimated and summarized. By combining additional data from the literature, we propose additional methods allowing aircraft total drag to be calculated under other configurations, such as when flaps and landing gears are deployed. We also include additional models allowing the calculation of wave drag caused by compressibility at high Mach number. Though uncertainties exist, it has been found that the estimated drag polars agree with existing models, as well as CFD simulation results. The trajectory data, performance models, and results related to this study are shared publicly.
|Number of pages||14|
|Journal||Transportation Research. Part C: Emerging Technologies|
|Publication status||Published - 2020|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Aircraft performance
- Drag polar
- Aerodynamic coefficient
- Bayesian computing
- Stochastic total energy model