Abstract
The structure of inner-outer interactions in a smooth-wall turbulent boundary layer is investigated using high-frame-rate particle image velocimetry (PIV) with two overlapping fields of view. A refractive-index-matched facility is used to enable the resolution of turbulent structures very close to the wall where these modulation effects are dominant. Amplitude and frequency modulation correlation coefficients were first investigated by computing single-, two-, and multiple-point correlation analyses now routinely reported in the literature. The wall-normal trends in amplitude modulation for the streamwise and wall-normal velocities and in frequency modulation for the streamwise velocity are consistent with those previously reported in the literature, validating the efficacy of the PIV approach used herein. The frequency modulation behavior for wall-normal velocity, however whose behavior in this regard has received little attention in the literature, differs from its amplitude modulation counterpart in the near-wall region. It is not clear whether this difference is physical or a by-product of enhanced sensitivity to measurement noise and/or finite spatial resolution of the measurements. Leveraging the spatial and temporal nature of the PIV data, a conditional average-based method is extended to the PIV data to directly capture the spatiotemporal signature of these interactions. This spatiotemporal picture reveals that the small-scale variance in streamwise and wall-normal velocity fluctuations not only correlates with the slow variations of the large scales in the logarithmic region, but also leads the local large scales (measured using a single hot wire, for example). These observations clearly indicate a structure akin to that proposed by Baars et al. [W. J. Baars et al., Exp. Fluids 56, 188 (2015)], including the relative positions of the modulated and modulating scales. The independence of these observations from filter choice is also shown, with equivalent correlation coefficients developed based on the conditional averaging analysis.
3 More- Received 17 October 2018
DOI:https://doi.org/10.1103/PhysRevFluids.4.034607
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