Study On Front Stall Characteristics And Stall Prediction For An Axial Compressor

Fluid Machinery represented by axial compressors is the core components of rotating power plants such as aero-engines and gas turbines.With the continuous updating and upgrading of power plants,the demand for designing high-performance and high-stability compressors is becoming more and more urgent.However,the problem of compressor flow instability represented by rotating stall has become the main obstacle restricting the development of high-performance and high-stability compressors.When rotating stall occurs,the aerodynamic performances(pressure ratio and efficiency)of the compressor decrease rapidly.This is easy to cause a surge phenomenon with low frequency and large amplitude,which drastically reduces the performance and stability of the entire rotating power plant,and even be damaged.In fact,when the compressor is close to the rotating stall,the internal flow of the compressor already exhibits three-dimensional strong nonlinear characteristics.The theoretical model and numerical simulation are usually not easy to objectively and truly restore the flow phenomenon,while the experimental measurement is the most reliable way to directly obtain the flow characteristics near the stall.In-depth analysis of the static pressure signal evolution in throttling process can not only deepen the understanding of stall inception mechanism of axial flow compressor,but also provide a theoretical basis for the construction of stall inception real-time monitoring,stall warning and active control methods.According to the static pressure experimental data of a low-speed axial compressor during throttling process,the characteristics of the front stall signal are gradually and deeply studied from the following three aspects.Firstly,the classical spectral analysis methods(Fourier analysis and wavelet spectral method)were used to explore the flow characteristics of the compressor in the stall stage,and the characteristics of different methods were compared and analyzed.It is found that Fourier analysis can obtain the basic flow structures of the compressor and identify the broadband multi-scale disturbance structures,but cannot describe the evolution process of the spike stall.The wavelet spectrum method can describe the instantaneous evolution process of stall,and identify the key stall characteristics such as the moment of stall inception,the number of stall cells,the propagation speed,and the channels’ range of their coverages.Although the above methods have successfully excavated the physical characteristics of the stall stage,it is too late to implement the active stall control based on the stall evolution characteristics,and it is necessary to carry out research on the evolution law of flow instability in the front stall stage.Furthermore,the evolution law of flow instability in the front stall stage is studied based on the time-delay cross-correlation analysis method and the disturbance phase measurement method.When the traditional cross-correlation coefficient analysis is applied to the investigation of flow instability in the front stall stage,there is a problem of inconsistency in the initial phase,and it cannot accurately describe the real physical flow characteristics.Therefore,a time-delay cross-correlation analysis method is developed.It effectively eliminates the time lag phase difference of the signal caused by the circumferential spatial position.It also avoids the non-physical interference of the circumferential spatial position,and better presents the microphysical characteristics of the compressor’s flow before entering the stall state.In order to monitor the phase change between the signals,a perturbation phase measurement method is proposed.According to the spatiotemporal characteristic image of the perturbation phase,it is found that the compressor’s geometry has circumferential unevenness.Based on the maximum disturbance phase duration,the phase change mechanism of the cross-correlation coefficient and the characteristics of flow instability in the front stall stage are analyzed.It is further confirmed that the phase difference is the cause of the change of the cross-correlation coefficient,and it is found that the stability of the flow in the front stall state is characterized by a sudden stage change.Finally,the compressor stall signal is predicted based on the induction learning algorithm,to develop a method that can not only measure the stability of the front stall flow,but also elucidate the spatiotemporal evolution mechanism of the spike stall.Using the GMDH algorithm to predict the analytic function,it is found that the traditional time series prediction method has the problems of error superposition and false high precision.Therefore,a discrete mapping method is proposed,which greatly improves the prediction accuracy.However,this method is still difficult to apply to the prediction of the time dimension of the stall signal with strong nonlinearity.Therefore,a feasible method for spatial stall signal prediction is considered,and the evolution trend of the stall signal is successfully predicted,which confirms the existence of small perturbations with circumferential propagation characteristics in the near-stall stage.The evolution law of the front stall flow is studied by using the spatiotemporal images of the weight coefficients in the full-time domain,and it is found that the extreme value characteristics of the weight coefficients have the nature of stage changes.This phenomenon is consistent with the perturbation phase results,indicating that the front stall flow has a stage change characteristic,which is similar to the spike stall inception.