An improved wiener filter-based method for identifying stall inception of transonic compressor

Abstract

The development of the modern aviation industry poses high demands on the design of aircraft engines recently. However, the stability of compressor flow is one of the key factors affecting further improvements in engine performance. The design of next-generation aircraft engines imposes higher requirements on compressor loading, which leads to the emergence of many new stall inceptions. As a result, the onset and evolution of flow instability become more complex. For accurately capturing stall inceptions of transonic compressors, the strong pressure disturbances caused by shock waves at the blade tip and the complex flow within blade passages result in significant challenges. To address this issue, this study draws inspiration from the design methods of Wiener filters in the field of speech recognition. Based on the characteristic signal mutations during rotating stall in compressors, a Wiener filter approach that uses time-delayed signals as noise estimates during filter training is developed. The method can be used for both offline and online analysis. It was applied to analyze the stall signals of a single rotor from a 1.5-stage transonic axial compressor under distorted inlet conditions at transonic rotational speeds and the entire stage under uniform inlet conditions at subsonic rotational speeds. The results indicate that, under inlet distortion, the compressor generates disturbance signals in the distorted sector before stall, and the earliest spike-inception disturbance occurs at the circumferential position of the rotor leaving the distorted sector. Under uniform inlet conditions, random disturbances could be detected throughout the circumference before stall onset, developing into spike waves at a circumferential location that subsequently triggered stall. Compared to conventional low-pass filters, discrete wavelet transforms, and empirical mode decomposition, the Wiener filter yielded more prominent spike wave structures in the filtered signals. Under distorted inlet conditions, the Wiener-filtered signals showed a 1 % decrease in autocorrelation coefficient and a 3.7 % increase in root mean square (RMS) upon the appearance of spike waves, more pronounced than the 0.5 % decrease in autocorrelation coefficient and 1.8 % increase in RMS achieved by conventional methods. Under uniform inlet conditions, the Wiener filter also detected a 9.7 % increase in RMS upon the appearance of spike waves, more pronounced than the 6.3 % increase observed with conventional methods.