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Characteristics of small-scale shear layers in a temporally evolving turbulent planar jet

Published online by Cambridge University Press:  14 June 2021

Masato Hayashi
Affiliation:
Department of Mechanical Systems Engineering, Nagoya University, Nagoya464-8603, Japan
Tomoaki Watanabe*
Affiliation:
Department of Aerospace Engineering, Nagoya University, Nagoya464-8603, Japan
Koji Nagata
Affiliation:
Department of Aerospace Engineering, Nagoya University, Nagoya464-8603, Japan
*
Email address for correspondence: watanabe.tomoaki@c.nagoya-u.jp

Abstract

Characteristics of small-scale shear layers are studied with direct numerical simulations of a temporally evolving turbulent planar jet. The shear layers that internally exist in turbulence are detected with a tensor of shearing motion, which is extracted from a velocity gradient tensor with a triple decomposition. Flow visualization of the shear intensity confirms the existence of layer structures with intense shear. The mean flow characteristics around local maxima of the shear intensity are investigated with averages taken in the shear coordinate system, which is defined based on the shear orientation. The mean flow pattern reveals that the shear layer is formed in a biaxial strain field, which consists of extensive strain in the vorticity direction of the shear and compressive strain in the direction perpendicular to the shear layer. The velocity components associated with the shear and biaxial strain rapidly change around the shear layer. The Kolmogorov scales characterize the mean characteristics of shear layers, such as velocity jumps, thickness and the intensities of shear and biaxial strain. These quantities normalized by the Kolmogorov scales only weakly depend on lateral positions in the planar jet. Although the turbulent planar jet evolves under the influence of mean shear, a large number of the shear layers do not align with the mean shear direction. The typical shear layer thickness is about six times the Kolmogorov length scale. Furthermore, the shear layer thickness is well predicted by the Burgers vortex layer.

Type
JFM Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

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