Modeling and detection of spike-type stall in axial compressors via deterministic learning theory

Rotating stall is one of aerodynamic instabilities in which reduced flow rate gives rise to flow separation and the formation of stall cells, causing enormous vibratory stresses in the blades to limit the performance of compressors. When stall is imminent, an easily recognizable flow breakdown process, known as spike-type stall, is observed in most modern compressors. It is more meaningful for active control to capture the emergence of spike-type initial perturbation during transients into rotating stall or surge. Based on the deterministic learning theory, this paper conducts the approximately accurate modeling and rapid detection of spike-type stall precursors for the Mansoux-C3 model. Firstly, based on a high-dimensional ODE model (the Mansoux model), it gives the mathematical expression of the Mansoux-C3 model. According to the analysis of the stall inception process of the Mansoux-C3, the spike-type stall inception is considered to occur. For the spike-type stall, we perform the locally-accurate identification of the system dynamics via deterministic learning theory. Secondly, based on the approximately accurate modeling of the dominant system dynamics, rapid detection for spike-type stall precursors is achieved by rapid detection of small oscillation faults. Simulation studies show that the detection for rotating stall via deterministic learning is an effective method for the spike-type stall.