From industry-wide parameters to aircraft-centric on-flight inference: Improving aeronautics performance prediction with machine learning

Abstract

Abstract Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines which are slightly modified throughout the aircraft life cycle via the tuning of a single factor, enabling better fuel predictions. However, this has limitations, in particular, they do not reflect the evolution of each feature impacting the aircraft performance. Our goal here is to overcome this limitation. The key contribution of the present article is to foster the use of machine learning to leverage the massive amounts of data continuously recorded during flights performed by an aircraft and provide models reflecting its actual and individual performance. We illustrate our approach by focusing on the estimation of the drag and lift coefficients from recorded flight data. As these coefficients are not directly recorded, we resort to aerodynamics approximations. As a safety check, we provide bounds to assess the accuracy of both the aerodynamics approximation and the statistical performance of our approach. We provide numerical results on a collection of machine learning algorithms. We report excellent accuracy on real-life data and exhibit empirical evidence to support our modeling, in coherence with aerodynamics principles. Impact Statement Current airline operations in both flight preparation (on-ground) and flight management (in-air) are mainly based on the performance of an aircraft. For instance, a trajectory is set before the flight and managed in-air by the Flight Management System using the manufacturer’s performance model. This numerical model is calibrated during in-service period using monitoring systems developed by manufacturers. However, the calibration is based on the tuning of a single parameter, and this overly simplified modeling leads to a lack of precision in optimizing fuel consumption. In this paper, we propose performance models that take into account real flight conditions and switch from industry-wide to aircraft-centric calibration of relevant parameters. To do this, we use massive collections of in-air data recorded by the Quick Access Recorder, which reflect the actual behavior of the aircraft. We then resort to machine learning algorithms to learn sufficiently accurate models from this data to infer the actual performance of the aircraft. The present paper describes our overall approach and its application to predicting the lift and drag coefficients. Bounds for the prediction errors are provided to assess the accuracy of the models. We aim at a twofold impact: (a) improve on the inference of in-flight parameters to optimize trajectories (e.g., regarding fuel consumption) and (b) provide a principled data-centric modeling approach which could be replicated in other intensive data-generating industries.