Real-time unstart prediction and detection of hypersonic inlet based on recursive Fourier transform

Unstart is an abnormal operation, and special care is devoted to this phenomenon for a hypersonic inlet. The process of a two-dimensional hypersonic inlet from start to unstart is experimentally demonstrated by recording the corresponding time history of the wall static pressure. By analyzing the pressure signals, it can be found that the oscillation of shock train flow can be used to characterize the location of shock train leading-edge. The register of shock train leading-edge at T14 located close to inlet throat is a good precursor for unstart prediction. T8 located in the upstream of last ramp compression shock is the optimum transducer for unstart detection because its pressure just goes through steady flow and unstart flow. Based on the differences in frequency domain among steady flow, shock train flow and unstart flow, recursive Fourier transform for unstart prediction and detection is introduced. The algorithm description, algorithm application, and parameters influence of the detection method are discussed, and the results show the validity of the detection method.