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

• URL: http://arxiv.org/abs/2005.05286v3
• Date: Thu, 4 Feb 2021 10:22:43 GMT
• Title: From industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning
• Authors: Florent Dewez and Benjamin Guedj and Vincent Vandewalle
• Abstract summary: 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. This has limitations, in particular they do not reflect the evolution of each feature impacting the aircraft performance. 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.
• Score: 5.171090309853363
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 modelling, in coherence with aerodynamics principles.

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