Novel parametric reduced order model for aeroengine blade dynamics

Yuan, J., Allegri, G., Scarpa, F. and Rajasekaran, R. (2014) Novel parametric reduced order model for aeroengine blade dynamics. In, Allen, Matt and Mayes, Randy (eds.) Conference Proceedings of the Society for Experimental Mechanics Series. (Conference Proceedings of the Society for Experimental Mechanics Series, 1) Proceedings of the 32nd IMAC: A Conference and Exposition on Structural Dynamics (03/02/14 - 06/02/14) Springer. (doi:10.1007/978-3-319-04501-6_39).

Abstract

The work proposes a reduced order modelling (ROM) technique for turbofan engine blades. The aim is to develop a simplified structural layout that allows describing the dynamic behaviour associated with the first six modes of fullscale fan blades. This is done by introducing equivalent frame models for the blade, which can be tailored to represent coupled flexural/torsional mode shapes, the relevant natural frequencies and static masses. Both 2D and 3D frame models are considered with initial configurations obtained from structural identification equations. The frame configurations are refined via an optimization process based on simulated annealing with stochastic tunnelling. The cost function comprises a linear combination of relative errors on the vibration frequencies, the individual modal assurance criteria (MAC) and the static mass. We demonstrate that an optimized 3D frame can represent the blade dynamic behaviour with a 6 % error on the MAC and a 1 % error on the associated modal frequencies. The approach proposed in this paper is considerably more accurate than ROMs based on single equivalent beams, either Euler–Bernoulli or Timoshenko, and highly computational efficient. Therefore, this technique is suitable for application to the analysis of mistuned bladed discs, particularly for determining the sensitivity to manufacturing and assembly tolerances in joints. © The Society for Experimental Mechanics, Inc. 2014.

This record has no associated files available for download.

Contributors

Author: J. Yuan

Download statistics