Resonance Effects in the NASA Transonic Flutter Cascade Facility

摘要

Investigations of unsteady pressure loadings on the blades of fans operating near the stall flutter boundary are carried out under simulated conditions in the NASA Transonic Flutter Cascade facility (TFC). It has been observed that for inlet Mach numbers of about 0.8, the cascade flowfield exhibits intense low-frequency pressure oscillations. The origins of these oscillations were not clear. It was speculated that this behavior was either caused by instabilities in the blade separated flow zone or that it was a tunnel resonance phenomenon. It has now been determined that the strong low-frequency oscillations, observed in the TFC facility, are not a cascade phenomenon contributing to blade flutter, but that they are solely caused by the tunnel resonance characteristics. Most likely, the self-induced oscillations originate in the system of exit duct resonators. For sure, the self-induced oscillations can be significantly suppressed for a narrow range of inlet Mach numbers by tuning one of the resonators. A considerable amount of flutter simulation data has been acquired in this facility to date, and therefore it is of interest to know how much this tunnel self-induced flow oscillation influences the experimental data at high subsonic Mach numbers since this facility is being used to simulate flutter in transonic fans. In short, can this body of experimental data still be used reliably to verify computer codes for blade flutter and blade life predictions To answer this question a study on resonance effects in the NASA TFC facility was carried out. The results, based on spectral and ensemble averaging analysis of the cascade data, showed that the interaction between self-induced oscillations and forced blade motion oscillations is very weak and can generally be neglected. The forced motion data acquired with the mistuned tunnel, when strong self-induced oscillations were present, can be used as reliable forced pressure fluctuations provided that they are extracted from raw data sets by an ensemble averaging procedure.