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Rapidly Building a Parametric Model to Estimate the Structural Fatigue of a Fast Jet

Abstract : To effectively plan maintenance actions for a fleet of fast jets requires accurately monitoring the fatigue of the structure. In this application, a parametric model is used to estimate the stress experienced at different locations on the aircraft given flight parameters (acceleration forces, speed etc.) collected from on-board sub-systems. The estimated stress is then combined with previous stress cycles to calculate the overall fatigue of the structure at different locations. The problem addressed in this paper, is the rapid generation of new parametric models that accurately estimate the stress experienced when new aircraft configurations are introduced. Firstly, an initial parametric model is constructed using a physics-based model of the new aircraft configuration. Secondly, and the main focus of this paper, a tool based on numerical optimisation methods is used to refine the parametric model using data collected from test flights, where flight parameters and strain measures at different locations are recorded on a test aircraft in the new configuration. This paper presents how an optimisation tool can be utilised by a structural engineer to refine an existing parametric model to estimate the structural fatigue of the new aircraft configuration and presents a brief analysis of different parameters within the tool. The paper concludes that the tool enables a parametric model to be rapidly generated that accurately estimates fatigue, and hence enables the maintenance procedures to be scheduled for new aircraft configurations.
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Submitted on : Tuesday, July 8, 2014 - 9:41:34 AM
Last modification on : Tuesday, March 13, 2018 - 5:00:02 PM
Long-term archiving on: : Wednesday, October 8, 2014 - 11:21:28 AM


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  • HAL Id : hal-01020306, version 1



Antony Waldock, Stephen Hunt, Glen Kelsey. Rapidly Building a Parametric Model to Estimate the Structural Fatigue of a Fast Jet. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01020306⟩



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