Fuzzy Finite Element Model Updating of the DLR AIRMOD Test Structure

H Haddad Khodaparast (Swansea University), Y Govers (DLR, Germany), I Dayyani (Cranfield University), S Adhikari (Swansea University), M Link (University of Kassel, Germany), MI Friswell (Swansea University), JE Mottershead (University of Liverpool) & J Sienz (Swansea University)

Applied Mathematical Modelling, Vol. 52, December 2017, pp. 512-526

Abstract

This article presents the application of finite-element fuzzy model updating to the DLR AIRMOD structure. The proposed approach is initially demonstrated on a simulated mass-spring system with three degrees of freedom. Considering the effect of the assembly process on variability measurements, modal tests were carried out for the repeatedly disassembled and reassembled DLR AIRMOD structure. The histograms of the measured data attributed to the uncertainty of the structural components in terms of mass and stiffness are utilised to obtain the membership functions of the chosen fuzzy outputs and to determine the updated membership functions of the uncertain input parameters represented by fuzzy variables. In this regard, a fuzzy parameter is introduced to represent a set of interval parameters through the membership function, and a meta model (kriging, in this work) is used to speed up the updating. The use of non- probabilistic models, i.e. interval and fuzzy models, for updating models with uncertainties is often more practical when the large quantities of test data that are necessary for probabilistic model updating are unavailable.

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Link to paper using doi: 10.1016/j.apm.2017.08.001

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