Peer-Reviewed Journal Details
Mandatory Fields
Agius, D,Wallbrink, C,Hu, WP,Kajtaz, M,Wang, CH,Kourousis, KI
Aerospace Science And Technology
On the utilisation of nonlinear plasticity models in military aircraft fatigue estimation: A preliminary comparison
Optional Fields
Cyclic plasticity Kinematic hardening Isotropic hardening Ratcheting Fatigue life Aircraft fatigue BEHAVIOR
Strain-life methodologies are commonly employed for fatigue estimation in military aircraft structures. These methodologies rely on models describing the elastoplastic response of the material under cycling. Despite the numerous advanced plasticity models proposed and utilised in various engineering problems over the past decades, the Masing model remains a popular choice in fatigue analysis software, mainly due to its simplicity. However, in the case of military aircraft load spectra including scattered overloads the Masing model fails to represent adequately transient cyclic phenomena, such as mean stress relaxation and strain ratcheting. In this study, four well-known constitutive plasticity models have been selected as potential substitutes for the Masing model within a defence organisation in-house developed fatigue analysis software. These models assessed were the well-known Multicomponent Armstrong Frederick Model (MAF) and three of its derivatives: MAF with threshold (MAFT), Ohno-Wang (OW) and MAF with Multiplier (MAFM). The models were calibrated with the use of existing experimental data, obtained from aircraft aluminium alloy tests. Optimisation of the parameters was performed through a genetic algorithm-based commercial software. The models were incorporated in the fatigue analysis software and their performance was evaluated statistically and compared against each other and with the Masing model for a series of different flight load spectra for a military aircraft. The results show that all four models have achieved a drastic improvement in fatigue analysis, with the MAFT model giving a slightly better performance. Crown Copyright (C) 2017 Published by Elsevier Masson SAS. All rights reserved.
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