The present paper reports an investigation of the statistical properties of pressure fluctuations in the near field of subsonic compressible jets. The data-base analyzed has been obtained numerically by DNS and LES of two single-stream circular jets, having diameter-based Reynolds numbers of 3125 and 100,000 and Mach number 0.9, respectively, initially laminar and highly disturbed. Pressure fluctuations are extracted from several virtual probes positioned in the near field of the jets and covering a region from 0 to 20 diameters in the axial direction and from 0.5 to 3 diameters in the radial. An azimuthal decomposition of the pressure fluctuations is performed, and the statistical analysis is applied to the axisymmetric 0-mode component and compared to the results obtained from the full original signals. The intermittent behavior is investigated by the estimation of standard statistical indicators, such as probability distribution functions and flatness factor, as well as through conditional statistics based on the application of the wavelet transform. It is shown that downstream of the potential core, intermittency estimated through the traditional indicators is relevant even at the lowest Re for the full signals, whereas it is apparently not significant for the 0-mode component. The wavelet analysis provides an estimation of intermittency scale-by-scale and allows for the calculation of a frequency-dependent FF. This approach reveals that the 0-mode component has a relevant degree of intermittency around the frequencies associated with the Kelvin–Helmholtz instability. The statistics of the intermittent events, in terms of their temporal appearance and energy content, are shown to be weakly sensitive to the jet Reynolds number and the universal behavior can be reproduced by simple stochastic models.

本文报道了对亚音速可压缩射流近场压力波动统计特性的研究。已通过DNS和LES从数值上获得了两个单流圆形喷嘴的分析数据库，这两个喷嘴的直径为基础的雷诺数分别为3125和100,000，马赫数为0.9，最初是层流型的，并且受到严重干扰。压力波动是从几个虚拟探针中提取的，这些虚拟探针位于射流的近场中，并且在轴向上覆盖直径从0到20的直径，在径向上覆盖0.5到3的直径的区域。进行压力波动的方位角分解，并将统计分析应用于轴对称0模式分量，并将其与从完整原始信号获得的结果进行比较。通过估计标准统计指标（例如概率分布函数和平坦度因子）以及通过基于小波变换的条件统计来研究间歇行为。结果表明，在潜在核心的下游，通过传统指标估算的间断性即使在整个信号的Re最低时也很重要，而对于0模式分量显然并不重要。小波分析提供了按比例缩放间歇性的估计，并允许计算频率相关的FF。这种方法揭示了0模式分量在与Kelvin–Helmholtz不稳定性相关的频率附近具有相关的间歇性。间歇事件的统计信息，