Aircraft Drag Polar Estimation Based on a Stochastic Hierarchical Model

The aerodynamic properties of an aircraft determine a crucial part of the aircraft performance model. Deriving accurate aerodynamic coefficients requires detailed knowledge of the aircraft’s design. These designs and parameters are well protected by aircraft manufacturers. They rarely can be used in public research. Very detailed aerodynamic models are often not necessary in air traffic management related research, as they often use a simplified point-mass aircraft performance model. In these studies, a simple quadratic relation often assumed to compute the drag of an aircraft based on the required lift. This so-called drag polar describes an approximation of the drag coefficient based on the total lift coefficient. The two key parameters in the drag polar are the zero-lift drag coefficient and the factor to calculate the lift-induced part of the drag coefficient. Thanks to this simplification of the flight model together with accurate flight data, we are able to estimate these aerodynamic parameters based on flight data. In this paper, we estimate the drag polar based on a novel stochastic total energy model using Bayesian computing and Markov chain Monte Carlo sampling. The method is based on the stochastic hierarchical modeling approach. With sufficiently accurate flight data and some basic knowledge of aircraft and their engines, the drag polar can be estimated. We also analyze the results and compare them to the commonly used Base of Aircraft Data model. The mean absolute difference among 20 common aircraft for zero-lift drag coefficient and lift-induced drag factor are 0.005 and 0.003 respectively. At the end of this paper, the drag polar models in different flight phases for these common commercial aircraft types are shared.