Abstract

The present work focuses on the effect of the manufacturing geometrical variability on the high-pressure compressor of a turbofan engine for civil aviation. The deviations of the geometry over the axial compressor blades are studied and modeled for the representation in the computational models. Such variability is of particular interest for the forced response problem, where small deviations of the geometry from the ideal nominal model can cause significant differences in the vibrational responses. The information regarding the geometrical mistuning is extracted from a set of manufactured components surface scans of a blade integrated disk (blisk) rotor. The optically measured geometries are parameterized, defining a set of opportune variables to describe the deviations. The dimension of the variables domain is reduced using the principal component analysis approach and a reconstruction of the modeled geometries is performed for the implementation in CFD and FEM solvers. The generated model allows a stochastic representation of the variability, providing an optimal set of variables to represent it. The aeroelastic analyses considering geometry based mistuning is carried out on a test-rig case, focusing on how such variability can affect the modal forcing generated on the blades. The force generated by the unsteady pressure field over the selected vibrational mode shapes of the rotor blades is computed through a validated CFD model. The uncertainty quantification of the geometrical variability effect on the modal forcing is performed employing Monte Carlo methods on a reduced model for the CFD solution, based on a single passage multi-blade row setup. The amplitude shift of the unsteady modal forcing is studied for different engine orders. In particular the scatter of the main engine orders forcing amplitudes for the manufactured blades can be compared with the nominal responses to predict the possible amplification due to the geometrical variability. Finally the results are compared to a full assembly computational model to assess the influence of multiple variable blades.

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