On the Importance of Engine-Representative Models for Fan Flutter Predictions

Abstract

Discrepancies between rig tests and numerical predictions of the flutter boundary for fan blades are usually attributed to the deficiency of computational fluid dynamics (CFD) models for resolving flow at off-design conditions. However, as will be demonstrated in this paper, there are a number of other factors, which can influence the flutter stability of fan blades and lead to differences between measurements and numerical predictions. This research was initiated as a result of inconsistencies between the flutter predictions of two rig fan blades. The numerical results agreed well with rig test data in terms of flutter speed and nodal diameter (ND) for both fans. However, they predicted a significantly higher flutter margin for one of the fans, while measured flutter margins were similar for both blades. A new set of flutter computations including the whole low-pressure system was therefore performed. The new set of computations considered the effects of the acoustic liner and mistuning for both blades. The results of this work indicate that the previous discrepancies between CFD and tests were caused by, first, differences in the effectiveness of the acoustic liner in attenuating the pressure wave created by the blade vibration and second, differences in the level of unintentional mistuning of the two fan blades. In the second part of this research, the effects of blade mis-staggering and inlet temperature on aerodynamic damping were investigated. The data presented in this paper clearly show that manufacturing and environmental uncertainties can play an important role in the flutter stability of a fan blade. They demonstrate that aeroelastic similarity is not necessarily achieved if only aerodynamic properties and the traditional aeroelastic parameters, reduced frequency and mass ratio, are maintained. This emphasizes the importance of engine-representative models, in addition to accurate and validated CFD codes, for the reliable prediction of the flutter boundary.