A Complete Solution for Modal Pretest Analysis and Model Validation using Test-Analysis Correlation
FEMtools Pretest & Correlation contains tools for:
If a baseline finite element model is available, then this model can be used to simulate tests. This provides test engineers with optimal locations and directions to excite the structure, and to position measurement transducers. The FE model can be reduced and converted into a test model.
Questions that can be answered with pretest analysis include:
Using the pretest analysis tools it is possible to plan an optimal modal test strategy early in the project and increase quality of modal data for validation and updating of FE models.
Correlation analysis quantitatively and qualitatively compares 2 sets of analysis results data. Usually this is a FEA and a test database that are imported in the FEMtools database. However, the tools can be used for FEA-to-FEA and test-to-test correlation as well.
Correlation analysis is used for FE model validation, design of optimal test conditions, evaluate different modeling strategies, identification of modeling errors, damage detection, ...
Results from correlation analysis are used to define targets for FE model updating. Similar mode shapes can be identified in the FE and test database thus providing residues in terms of resonance frequency differences, MAC, modal displacements.
Another application is to provide the analyst with information that can only be measured. An example is modal damping, used in modal superposition methods. Modal damping can be obtained experimentally and applied to the analytical mode shape that, using correlation analysis, was found to best match the experimental one.
Modal correlation analysis is also used to scale test mode shapes obtained by output-only modal analysis. The same scaling as used by the analytical mode shapes (e.g. unity modal mass), can be applied to the correlated test modes.
Unlike global correlation analysis, spatial correlation methods are used to identify areas of better or poorer correlation, which when linked to structural information, can be interpreted in terms of 'modeling error'. Depending on how these tools are used, the results help with the selection of updating variables (parameters), or are used to assess structural damage.