Hybrid Numerical-Experimental Model Update Based on Correlation Approach for Turbine Components

Abstract

Bladed-disks in turbomachines experience high cycle fatigue failures due to high vibration amplitudes. Therefore, it is important to accurately predict their dynamic characteristics including the mechanical joints at blade-disk interfaces. Before the experimental identification of these joints, it is of paramount importance to accurately measure the interface degrees-of-freedom (DoF). However, they are largely inaccessible for the measurements. For this reason, expansion techniques can be used in order to update the single components. But the expansion can be affected adversely if the measurements are not properly correlated with the updated model. Therefore, a frequency domain expansion method called System Equivalent Model Mixing (SEMM) is used to expand a limited set of measurements to a larger set of numerical DoF. Different measured models—termed the overlay models—are taken from an impact testing campaign of a blade and a disk and coupled to the numerical model according to the SEMM. The expanded models—termed the hybrid models—are then correlated with the validation channels in a round-robin way by means of Frequency Response Assurance Criteria (FRAC). The global correlations depict whether or not a measurement and the respective expansion is properly correlated. By this approach, the least correlated channels can be eliminated from the measurements to have a better updated hybrid model. The method is tested on both the structures (the blade and the disk) and it is successfully shown that removing the uncorrelated channels does improve the quality of the hybrid models.