The Influence of Transient Inlet Distortions on the Instability Inception of a Low-Pressure Compressor in a Turbofan Engine

Abstract

While studies on compressor flow instabilities under the presence of inlet distortions have been carried out with steady distortions in the past, the investigation presented here focuses on the influence of transient inlet distortions as generated by variable geometry engine intakes of super- and hypersonic aircraft on the characteristic and the nature of the instability inception of a LPC. The flow patterns (total pressure distortion with a superimposed co- or counterrotating swirl) of the distortions are adopted from a hypersonic concept aircraft. A LARZAC 04 twin-spool turbofan was operated with transient inlet distortions, generated by a moving delta wing, and steady total pressure distortions starting close to the LPC’s stability limit until it stalled. High-frequency pressure signals are recorded at different engine power settings. Instabilities are investigated with regard to the inception process and the early detection of stall precursors for providing data for a future stability control device. It turned out that the transient distortion does not have an influence on the surge margin of the LPC compared to the steady distortion, but that it changes the nature of stall inception. The pressure traces are analyzed in the time and frequency domain and also with tools like Spatial FFT, Power Spectral Density, and Traveling Wave Energy. A Wavelet Transformation algorithm is applied as well. While in the case of clean inlet flow, the compressor exhibits different types of stall inception depending on the engine speed, stall is always initiated by spike-type disturbances under the presence of steady or transient distortions. Modal disturbances are present in the mid-speed range that do not grow into stall, but rather interact with the inlet flow and produce short length scale disturbances. The obtained early warning times prior to stall are adversely affected by transient distortions in some cases. The problem of appropriate thresholding becomes evident. The best warning times have been acquired using a statistical evaluation of the Wavelet coefficients, which might be promising to apply in a staged active control system. This system could include different phases of detection and actuation depending on the current precursor.

Stall Control and Recovery in a Low-Speed Axial Fan Through the Use of Variable Pitch in Motion Blades

“…The authors infer that, as a consequence of the aerodynamic characteristics of the studied fan rotor, the pressure disturbances borne as stall spots in the range of 0.3 to 0.5 shaft orders during the stall recovery grew further and disappeared at the shaft rate. Others have reported similar observations under dynamic inflow distortion with tip injection [34] and in a similar axial fan [35]. Several small components initially formed the cell, which therefore rotated at low speed and thus facilitated the 'quasi-static' cell to bubble up to recovery at normal conditions.…”

Stall Inception, Evolution and Control in a Low Speed Axial Fan With Variable Pitch in Motion

“…Others have reported similar observations under dynamic inflow distortion with tip injection [34] and in a similar axial fan [35]. Several small components initially formed the cell, which therefore rotated at highest speed, as described in the pressure signal's visual inspection.…”

Stall inception and its control in an axial flow fan under dynamic inflow distortion

“…In contrast, a modal wave occupies length scales comparable to the annulus circumference and travels at slower speeds. Several other researchers have also used this approach to explore the stall inception mechanism under clean [6][7][8] and distorted inflow conditions [2,[9][10][11][12]. Nie et al [3] demonstrated the effects of dynamic inlet flow distortion on a low-speed singlestage axial flow compressor.…”

“…The numerical results agreed well with the experimental observations. Leinhos et al [6] analysed the stall inception process in a turbofan engine under transient inflow distortion. They carried out the data analysis using various tools such as spatial fast fourier transform (FFT), power spectral density, travelling wave energy, and wavelet transform.…”