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Poster Communications Year : 2023

An overview of aircraft vibration environment prediction using machine learning

Abstract

• Studying the vibration environment of an aircraft is fundamental to ensure the on-board equipment robustness with respect to vibrations up to 2000 Hz • Challenge: being predictive with the currently available models is a difficult task in the frequency band of interest • Proposed approach: to develop data-driven predictive models, based on flight test data and enhanced by physical models
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2023-04-24 - Poster Cambridge - Thèse Stéphane Février.pdf (3.25 Mo) Télécharger le fichier
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Dates and versions

hal-04185152 , version 1 (28-11-2023)

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  • HAL Id : hal-04185152 , version 1

Cite

Stéphane Février, Stéphane Nachar, Lionel Mathelin, Frédéric Giordano, Bérengère Podvin. An overview of aircraft vibration environment prediction using machine learning. Computational Challenges and Emerging Tools Workshop, Apr 2023, Cambridge, United Kingdom. ⟨hal-04185152⟩
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