Coherent flow structures in a depth‐limited flow over a gravel surface: The role of near‐bed turbulence and influence of Reynolds number

Abstract

[1] In gravel bed rivers, the microtopography of the bed exerts a significant effect on the generation of turbulent flow structures. Although field and laboratory measurements have indicated that flows over gravel beds contain coherent macroturbulent flow structures, the origin of these phenomena, and their relationship to the ensemble of individual roughness elements forming the bed, is not quantitatively well understood. Here we report upon a flume experiment in which flow over a gravel surface is quantified through the application of digital particle imaging velocimetry, which allows study of the downstream and vertical components of velocity over the entire flow field. The results indicate that as the Reynolds number increases (1) the visual distinctiveness of the coherent flow structures becomes more defined, (2) the upstream slope of the structures increases, and (3) the turbulence intensity of the structures increases. Analysis of the mean velocity components, the turbulence intensity, and the flow structure using quadrant analysis demonstrates that these large-scale turbulent structures originate from flow interactions with the bed topography. Detection of the dominant temporal length scales through wavelet analysis enables calculation of mean separation zone lengths associated with the gravel roughness through standard scaling laws. The calculated separation zone lengths demonstrate that wake flapping is a dominant mechanism in the production of large-scale coherent flow structures in gravel bed rivers. Thus, we show that coherent flow structures over gravels owe their origin to bed-generated turbulence and that large-scale outer layer structures are the result of flow-topography interactions in the near-bed region associated with wake flapping.

High-resolution numerical modelling of flow—vegetation interactions

“…In particular, whilst intrusive flow measurement devices might be used, they have the disadvantage of perturbing not only the flow, but also the canopy behaviour which is a critical concern of the modelling. Use of PIV has the advantage that, whilst it may lose some representation within the canopy, it does allow quantitative flow visualisation of the entire flow field through time without any flow or canopy intrusion (Hardy et al 2009(Hardy et al , 2010 and for this reason it has been proven as a useful method for assessing CFD predictions (Hardy et al 2005). This represents an increase in complexity from most canopy studies which have Fourier transform (FFT)-based spatial cross-correlation technique was applied to consecutive images to determine both velocity components (Westerweel 1997).…”

“…Wavelet analysis involves the decomposition of a time series into a set of scaled and translated versions of a wavelet function (Hardy et al 2009). …”

Does the canopy mixing layer model apply to highly flexible aquatic vegetation? Insights from numerical modelling

“…The Morlet wavelet is fitted to the data across scales from 0.04 s to 20.48 s, centred at each point in the time series to calculate the wavelet power spectrum. Points that do not have statistically significant wavelet power compared to a white noise spectrum, and those subject to edge effects are discarded and the wavelet scale is converted to the equivalent Fourier period for comparison with other data [20,74]. For the power spectral analysis, the Welch periodogram method was applied to the time series data, with two non-overlapping windows [75].…”

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