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2021 | OriginalPaper | Chapter

18. Tuning of Finite Element Model Parameters to Match Nonlinear Reduced Order Models

Authors : Kyusic Park, Matthew S. Allen

Published in: Nonlinear Structures & Systems, Volume 1

Publisher: Springer International Publishing

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Abstract

There has been a growing demand for nonlinear model updating procedures in the structural dynamics community as advanced vehicles have started to incorporate nonlinearity into their designs. Finite element (FE) model updating is difficult for a nonlinear structure for several reasons: there may be many unknown parameters in the FE model so that multiple solutions may exist, and it is very expensive to compute the structure’s nonlinear normal modes from a full FE model that are an excellent metric to use for nonlinear updating. A recent work showed that some of these challenges can be overcome by updating a nonlinear reduced order model (ROM) rather than the full FE model. The updated ROM can be used to compute response statistics, stresses and ultimately life of the structure. However, one drawback of this approach is that one does not gain physical insight into which parameters in the FE model were in error, and so it is difficult to transfer the lessons learned to future FE models. This work explores the feasibility of updating a FE model to correlate with a ROM with a set of known parameters. The target ROM parameters consist of the linear stiffness and the nonlinear stiffness coefficients which have been identified previously by updating the ROM to correlate with experimental measurements. In this process an optimization routine is setup in which the free parameters are those of the FE model, such as boundary stiffness springs, imperfections, pre-stress, etc. The optimization procedure is wrapped around a ROM creation algorithm that iteratively tunes the FE parameters, creates a ROM, and evaluates the ROM parameters with respect to the target ROM in order to minimize the objective function. This work will demonstrate the procedure on a numerical example of a curved beam in order to verify its effectiveness on the nonlinear model updating.

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Literature
1.
go back to reference I Van Damme, C.: Model correlation and updating of geometrically nonlinear structural models using nonlinear normal modes and the multi-harmonic balance method. Ph.D dissertation, University of Wisconsin-Madison (2019) I Van Damme, C.: Model correlation and updating of geometrically nonlinear structural models using nonlinear normal modes and the multi-harmonic balance method. Ph.D dissertation, University of Wisconsin-Madison (2019)
2.
go back to reference Kuether, R.J., Deaner, B.J., Hollkamp, J.J., Allen, M.S.: Evaluation of geometrically nonlinear reduced-order models with nonlinear normal modes. AIAA J. 53(11), 3273–3285 (2015)CrossRef Kuether, R.J., Deaner, B.J., Hollkamp, J.J., Allen, M.S.: Evaluation of geometrically nonlinear reduced-order models with nonlinear normal modes. AIAA J. 53(11), 3273–3285 (2015)CrossRef
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go back to reference Byrd, R.H., Gilbert, J.C., Nocedal, J.: A trust region method based on interior point techniques for nonlinear programming. Math. Program. 89(1), 149–185 (2000)MathSciNetCrossRef Byrd, R.H., Gilbert, J.C., Nocedal, J.: A trust region method based on interior point techniques for nonlinear programming. Math. Program. 89(1), 149–185 (2000)MathSciNetCrossRef
Metadata
Title
Tuning of Finite Element Model Parameters to Match Nonlinear Reduced Order Models
Authors
Kyusic Park
Matthew S. Allen
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-47626-7_18