Farfield filtering and source imaging for the study of jet noise

Abstract

We present an analysis of the sound field radiated by a high Mach number subsonic jet. The spatial and temporal structures of the sound field are filtered and studied, respectively, by means of Proper Orthogonal Decomposition (POD) and wavelet transforms. The first POD mode is shown to give a near-perfect representation of the fluctuation energy radiation at low angles (in the range 30° ≤ θ ≤ 50°), larger numbers of modes being necessary to completely reproduce the radiation characteristics at higher angles. The wavelet analysis shows, in agreement with previous studies, that the temporal structure of the sound field is characterised by localised high-amplitude events. We implement two threshold intermit-tency metrics which we use to filter the pressure signals based on the scalogram topology. By varying these metrics we characterise the intermittency of the pressure signals as a function of emission angle. We again find that the sound field can be divided into two families: the fluctuations radiated at low angles (30° ≤ θ ≤ 50°) are characterised by higher levels of global intermittency (an intermittency metric defined with respect to the overall fluctuation energy) than the fluctuations radiated in the angular range θ ≥ 60°. However, when Farge's Local Intermittency Measure (defined with respect to the local fluctuation energy at each scale) is used to analyse the data, the fluctuations at all angles show identical behaviour. Results also show that the spectral shapes associated with the high-amplitude events, at all emission angles, are less broadband than those of the unfiltered field, suggesting that the most important source dynamics are not as broadband as the Fourier spectrum would have one believe. Using both the POD and wavelet-filtered signals we decompose the acoustic field into two components: a component which we loosely attribute to coherent structures (CS) and a residuum (R). We compare the CS and R components with the LSS and FSS proposed by Tam et al.1 We find that neither of these filtering criteria produce a natural division of the acoustic field into two components which match the LSS and FSS shapes. We also show, in the appendix, that the three-microphone approach proposed by Nance & Ahuja2 to split the acoustic field into two such pieces is very sensitive to the three microphones which are chosen to perform the operation. Finally, we implement a source imaging algorithm, using the CS part of the farfield signature, for both the POD and wavelet-based filtering, in order to establish if our so-called CS signal ensemble can be associated with wavepacket-like sources. Results show that the CS component of our filtering can be associated with a wavepacket-like source mechanism. © 2010 by P. Jordan.