Modeling and detection of rotating stall inception in an axial-flow compressor with inlet distortion

Abstract

The performance of axial-flow compressors is seriously affected by inlet distortion due to a decrease of stability margin. To increase the operational range of compressors, the inlet flow distortion has attracted increasing attention. This paper describes the stall detection based on Deterministic Learning (DL) theory to predict the onset of flow instability for an axial compressor operating in the inlet flow distortion. The inlet flow distortion with plugboard was simulated. Experiments are conducted on a low-speed axial flow compressor test rig of Beihang University at different rotating speeds. Firstly, by installing high response dynamic pressure transducers arranged circumferentially around the casing of the axial compressor, the dynamic data for inlet flow distortion are collected. Secondly, based on deterministic learning theory, the system dynamics underlying stall inception patterns are identified. Finally, based on modeling results, rapid detection of small oscillation faults is used to perform the detection of stall precursors. Results show that this approach successfully detects inception signal of rotating stall under inlet distortion in the compressor test rig.