Bayesian estimator is a commonly used statistical optimization technique for "nite element model updating. This paper presents a modi"ed Bayesian estimator and discusses its unbiasedness, e$ciency, learning ability and robustness. The main di!erences from other estimators, for instance, the least-sq
On the treatment of discretisation errors in finite element model updating
β Scribed by J.E. Mottershead; E.L. Goh; W. Shao
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 462 KB
- Volume
- 9
- Category
- Article
- ISSN
- 0888-3270
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β¦ Synopsis
It is well known that the eigenvalues of finite element models are influenced considerably by mesh refinement. Eigenvalue errors, due to shape function discretisation, persist in the frequency range of interest until the model is fully converged, and generally have a serious affect upon updated parameters. The requirement of a converged model for updating generally conflicts with design requirements which may be satisfied by a coarser mesh. A two-level Gauss-Newton approach is proposed which allows the simultaneous and efficient correction of design and updating models. The method may be applied to modal or FRF test data. Simulated and experimental tests are conducted to illustrate the effectiveness of the technique.
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