A comparative study of coupled and decoupled fan flutter prediction methods under variation of mass ratio and blade stiffness

Abstract

Flutter is a long standing issue for fan blades of civil aero-engines and becomes of further concern for modern light-weight designs with increasing fan diameters to reach ultra-high bypass ratios. Accurate flutter prediction is therefore of prime consideration in the design process in order to avoid catastrophic blade failure in operation or expensive redesign iterations if spotted in ground or flight tests. The traditional energy method, which is based on the assumption of negligible aeroelastic coupling, has been used to great extend to predict flutter of turbomachinery components including aero-engine fan blades, and is today by far the most widely applied technique. The underlying assumption of fluid–structure decoupling, however, has to be questioned for large fan blades that are characterized by low mass ratios and low stiffness. Implications of the violation of the system decoupling assumption on the prediction capabilities of the energy method are important to understand for the fan designer in order to allow an informed decision on the flutter prediction tool to use. In this work a comprehensive comparative study is presented in which the energy method is contrasted to the predictions of a strongly coupled fluid–structure interaction method for varying values of mass ratio and blade stiffness of a transonic three-dimensional fan rotor. The strength of aeroelastic coupling is evaluated in terms of the aeroelastic frequency shift and its impact on the prediction accuracy of the energy method is investigated. The results show the capability of the energy method to accurately predict flutter for a wide range of mass ratio and stiffness configurations, but its prediction accuracy is reduced for combined low mass ratio and low stiffness blades. Mechanisms governing the aeroelastic frequency shift are explained to allow a better understanding of the effect and a method for its prediction based on results of a decoupled analysis is shown.