Abstract
Important variations in response behaviors of power plant generators are observed in a population of nominally identical installations due to numerous and significant sources of variability. As a result, it proves to be extremely difficult to implement a predictive and reliable physics-based model. The present study attempts to leverage an existing non validated numerical model to reconstruct information on unobserved degrees of freedom (dofs) based on the results of modal tests. An expansion method is proposed based on the concept of the constitutive relation error (CRE). This method leads to minimization of an energy-based functional that takes into account both errors in the model and in the test data. Due to lack of knowledge, commonplace in this kind of complex system, the expansion will be presented in the framework of robust approach. More precisely, the first objective of this article is to assess the robustness of the mode shape expansion in presence of large epistemic uncertainties that are represented as info-gap models. Secondly, a strategy will be presented to maximize the robustness of the expansion by appropriately selecting the model decision variables for a given horizon of uncertainty. The proposed methodology is illustrated on a simple academic test case.
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Notes
- 1.
Detailed descriptions of these methods are out-of-scope of the paper.
- 2.
In practice, the Guyan stiffness matrix is often used.
- 3.
In the case of large number of dofs, Metis renumbering method available in MD Nastran®; permits to minimize the decomposition CPU-time.
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Kuczkowiak, A., Cogan, S., Ouisse, M., Foltête, E., Corus, M. (2014). Robust Expansion of Experimental Mode Shapes Under Epistemic Uncertainties. In: Atamturktur, H., Moaveni, B., Papadimitriou, C., Schoenherr, T. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-04552-8_42
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