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An FEM-based AI approach to model parameter identification for low vibration modes of wind turbine composite rotor blades

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Title: An FEM-based AI approach to model parameter identification for low vibration modes of wind turbine composite rotor blades
Authors: Navadeh, N
Gorshko, IO
Zhuk, YA
Soleiman Fallah, A
Item Type: Journal Article
Abstract: An approach to construction of a beam-type simplified model of a horizontal axis wind turbine composite blade based on the finite element method is proposed. The model allows effective and accurate description of low vibration bending modes taking into account the effects of coupling between flapwise and lead–lag modes of vibration transpiring due to the non-uniform distribution of twist angle in the blade geometry along its length. The identification of model parameters is carried out on the basis of modal data obtained by more detailed finite element simulations and subsequent adoption of the ‘DIRECT’ optimisation algorithm. Stable identification results were obtained using absolute deviations in frequencies and in modal displacements in the objective function and additional a priori information (boundedness and monotony) on the solution properties.
Issue Date: 1-Oct-2017
Date of Acceptance: 18-Sep-2017
URI: http://hdl.handle.net/10044/1/51644
DOI: https://dx.doi.org/10.1080/17797179.2017.1382317
ISSN: 1779-7179
Publisher: Taylor & Francis
Start Page: 541
End Page: 556
Journal / Book Title: European Journal of Computational Mechanics
Volume: 26
Issue: 5-6
Copyright Statement: © 2017 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis Group in European Journal of Computational Mechanics on 28 Sept 2017, available online at: http://www.tandfonline.com/10.1080/17797179.2017.1382317
Keywords: 0913 Mechanical Engineering
Publication Status: Published
Online Publication Date: 2017-09-28
Appears in Collections:Aeronautics
Faculty of Engineering