Characteristics of sources and sinks of momentum in a turbulent boundary layer

Abstract

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms In turbulent boundary layers, the wall-normal gradient of the Reynolds shear stress identifies momentum sources and sinks (T = ∂[−uv]/∂y). These motions can be physically interpreted in two ways: (1) as contributors to the turbulence term balancing the mean momentum equation, and (2) as regions of strong local interaction between velocity and vorticity fluctuations. In this paper, the space-time evolution of momentum sources and sinks is investigated in a turbulent boundary layer at the Reynolds number (Re τ ) = 2700, with time-resolved planar particle image velocimetry in a plane along the streamwise and wall-normal directions. Wave number-frequency power spectra of T fluctuations reveal that the wave velocities of momentum sources and sinks tend to match the local streamwise velocity in proximity to the wall. However, as the distance from the wall increases, the wave velocities of the T events are slightly lower than the local streamwise velocities of the flow, which is also confirmed from the tracking in time of the intense momentum sources and sinks. This evidences that momentum sources and sinks are preferentially located in low-momentum regions of the flow. The spectral content of the T fluctuations is maximum at the wall, but it decreases monotonically as the distance from the wall grows. The relative spectral contributions of the different wavelengths remains unaltered at varying wall-normal locations. From autocorrelation coefficient maps, the characteristic streamwise and wall-normal extents of the T motions are respectively 60 and 40 wall units, independent of the wall distance. Both statistics and instantaneous visualizations show that momentum sources and sinks have a preferential tendency to be organized in positive-negative pairs in the wall-normal direction.

Probabilistic Characterization of Sweep and Ejection Events in Turbulent Flows and Its Implications on Sediment Transport

“…This section describes the method used for tracking the sweep and ejection events over time. On the basis of the assumption used by Fiscaletti and Ganapathisubramani (2018), if two events $$e{v}_{1}$$ and $$e{v}_{2}$$ are detected consecutively, and the following condition holds, we treat them as the same event: $$${D}_{\text{cent}}< {D}_{\text{box}}$$$ Where $${D}_{\text{cent}}$$ is the distance between the centroids of $$e{v}_{1}$$ and $$e{v}_{2}$$, and $${D}_{\text{box}}$$ is the diagonal of the smallest rectangle, including all points of $$e{v}_{1}$$ (Yoon et al., 2020).…”

“…The histogram of streamwise length represents the probability that the range in the streamwise direction is influenced by ejection and sweep events (Figures 7e and 7f). Two consecutive events can be merged into a larger event based on the criterion proposed by Fiscaletti and Ganapathisubramani (2018). Dennis and Nickels (2011), among others, used an exponential distribution to represent the streamwise LSM lengths.…”

“…This section describes the method used for tracking the sweep and ejection events over time. On the basis of the assumption used by Fiscaletti and Ganapathisubramani (2018), if two events 𝐴𝐴 𝐴𝐴𝐴𝐴1 and 𝐴𝐴 𝐴𝐴𝐴𝐴2 are detected consecutively, and the following condition holds, we treat them as the same event:…”