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

Dewez, F; Guedj, B; Vandewalle, V; (2020) From industry-wide parameters to aircraft-centric on-flight inference: Improving aeronautics performance prediction with machine learning. Data-Centric Engineering , 1 , Article e11. 10.1017/dce.2020.12. Green open access

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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.

Type: Article
Title: From industry-wide parameters to aircraft-centric on-flight inference: Improving aeronautics performance prediction with machine learning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1017/dce.2020.12
Publisher version: https://doi.org/10.1017/dce.2020.12
Language: English
Additional information: Copyright © The Author(s), 2020. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Keywords: Aeronautics performance prediction, aircraft performance aircraft performance monitoring, machine learning, statistical modeling
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10128712
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