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二元高超声速进气道不起动状态的信号特征及预警 被引量:8

Signal Characteristics and Prediction of Unstarting Process for Two-dimensional Hypersonic Inlet
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摘要 对高超声速进气道的不起动进行了研究,首先给出了某二元高超声速进气道由起动到不起动全过程的壁面压强信号历程,并利用短时傅里叶变换(STFT)和小波变换(WT)方法对不起动过程中壁面压强信号的时-频特性进行了分析,然后采用累积和(CUSUM)和最大似然比(GLR)变化检测算法,对高超声速进气道的喘振现象进行了预测。研究结果表明,高超声速进气道喘振发生时,壁面压强信号的时-频特性存在显著改变,能量谱密度(PSD)分布出现了有规律的集中,其基频在200~340Hz范围,并随着堵塞度(TR)的增加而增加。另外,在喘振发生前,进气道收敛段的下壁面附近存在大尺度的分离包,其有规律的尺度变化和前后移动导致了明显的壁面压强脉动特征,因此可作为一种前兆来预测喘振现象的出现。在本文条件下,两种变化检测算法均可在大喘发生前约220ms发出警报,表明了该喘振预警思路的可行性。 Unstart is an abnormal state,and special care is devoted to the phenomenon for a hypersonic inlet.In this article,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.Then the pressure signals are analyzed by means of such time-frequency signal-processing methods as short-time Fourier transform(STFT)and wavelet transform(WT).Furthermore,two change-detection algorithms,i.e.the cumulative sum(CUSUM)and generalized likelihood ratio(GLR),are utilized to predict the buzz phenomenon.Results indicate that when a buzz starts,considerable fluctuation of the pressure signals accompanied by clustering power spectrum density(PSD)can be observed and the base frequency increases with the throttling ratio(TR),ranging from 200 Hz to 340 Hz.In addition,ahead of the buzz,due to the regular variation and movement of the large-scale separation bubble existing near the bottom surface of the duct entrance,substantial fluctuation of the wall static pressure can be noted,suggesting the probability of predicting the onset of an inlet unstart.According to the experimental data,the alarm time calculated by CUSUM and GLR are both approximately 220 ms ahead of the big buzz,which verifies the feasibility of the methods adopted.
出处 《航空学报》 EI CAS CSCD 北大核心 2010年第12期2324-2331,共8页 Acta Aeronautica et Astronautica Sinica
基金 国家自然科学基金(90916014 50776044)
关键词 高超声速进气道 不起动 喘振 时-频分析 傅里叶变换 小波变换 hypersonic inlet unstart buzz time-frequency analysis Fourier transform wavelet transform
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参考文献16

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