A Modified Inverse Eigensensitivity Method for Large Finite Element Models

Date

2016-01-28

Author

Unlu, Dogus
Ciğeroğlu, Ender
Özgen, Gökhan Osman

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Finite element models should represent the dynamic behavior of real structures accurately to be subsequently used in design purposes. Therefore, finite element model updating methods have been developed in order to decrease the difference between analytical model and modal test results. In this paper, inverse eigensensitivity method as a sensitivity-based model updating method is summarized. Inverse eigensensitivity method with improved sensitivity computation which decreases the total calculation time of the updating procedure is introduced. A method based on the parameter sensitivities is integrated into the developed code in order to decrease the number of model updating parameters making it is possible to update large finite element models. Initially a simple plate structure is considered as an application. An aerospace structure is considered as an example where modal test is performed and modal parameters are extracted. The developed updating method is applied on the finite element model of the aerospace structure and the results of the updated model are compared with experimental ones.

Subject Keywords

Inverse eigensensitivity method, Computational model updating, Sensitivity based model updating;, Large order finite element models, Finite element model updating

URI

https://hdl.handle.net/11511/36698

DOI

https://doi.org/10.1007/978-3-319-30249-2_26

Conference Name

34th IMAC Conference and Exposition on Structural Dynamics

Collections

Department of Mechanical Engineering, Conference / Seminar

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Citation Formats

D. Unlu, E. Ciğeroğlu, and G. O. Özgen, “A Modified Inverse Eigensensitivity Method for Large Finite Element Models,” presented at the 34th IMAC Conference and Exposition on Structural Dynamics, Orlando, FL, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36698.