Titre :

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

Auteur(s) :

Dewez, Florent [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Guedj, Benjamin [Auteur]
Department of Computer science [University College of London] [UCL-CS]
MOdel for Data Analysis and Learning [MODAL]
Vandewalle, Vincent [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
MOdel for Data Analysis and Learning [MODAL]

Titre de la revue :

Data-Centric Eng.

Nom court de la revue :

Data-Centric Eng.

Numéro :

1

Pagination :

-

Date de publication :

2022-09-20

ISSN :

2632-6736

Mot(s)-clé(s) en anglais :

Aeronautics performance prediction
aircraft performance
aircraft performance monitoring
machine learning
statistical modeling

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines calibrated on one single aircraft, with performance modelling ...
Lire la suite >Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines calibrated on one single aircraft, with performance modelling for all similar aircrafts (\emph{i.e.} same model) relying solely on that. In particular, it may poorly reflect on the current performance of a given aircraft. However, for each aircraft, flight data are continuously recorded and as such, not used to improve on the existing models. The key contribution of the present article is to foster the use of machine learning to leverage the massive amounts of collected data and update the models to reflect the actual performance of the aircraft. 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.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2023-11-15T10:16:54Z
2024-01-12T14:29:50Z