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On the detection of internal interfacial layers in turbulent flows

Published online by Cambridge University Press:  07 June 2019

Duosi Fan Open the ORCID record for Duosi Fan [Opens in a new window] ,
Jinglei Xu Open the ORCID record for Jinglei Xu [Opens in a new window] ,
Matthew X. Yao Open the ORCID record for Matthew X. Yao [Opens in a new window]  and
Jean-Pierre Hickey Open the ORCID record for Jean-Pierre Hickey [Opens in a new window]
Duosi Fan
Affiliation:
School of Energy and Power Engineering, Beihang University, Xueyuan Road, Beijing 100191, China Department of Mechanical and Mechatronics Engineering, University of Waterloo, University Avenue, Waterloo N2L 3G1, Canada
Jinglei Xu
Affiliation:
School of Energy and Power Engineering, Beihang University, Xueyuan Road, Beijing 100191, China Department of Mechanical and Mechatronics Engineering, University of Waterloo, University Avenue, Waterloo N2L 3G1, Canada
Matthew X. Yao
Affiliation:
Department of Mechanical and Mechatronics Engineering, University of Waterloo, University Avenue, Waterloo N2L 3G1, Canada
Jean-Pierre Hickey*
Affiliation:
Department of Mechanical and Mechatronics Engineering, University of Waterloo, University Avenue, Waterloo N2L 3G1, Canada
*
†Email address for correspondence: j6hickey@uwaterloo.ca
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Abstract

A novel approach to identify internal interfacial layers, or IILs, in wall-bounded turbulent flows is proposed. Using a fuzzy cluster method (FCM) on the streamwise velocity component, a unique and unambiguous grouping of the uniform momentum zones (UMZs) is achieved, thus allowing the identification of the IILs. The approach overcomes some of the key limitations of the histogram-based IIL identification methods. The method is insensitive to the streamwise domain length, can be used on inhomogeneous grids, uses all the available flow field data, is trivially extended to three dimensions and does not need user-defined parameters (e.g. number of bins) other than the number of zones. The number of zones for a given snapshot can be automatically determined by an a priori algorithm based on a kernel density estimation algorithm, or KDE. This automated approach is applied to compute the average number of UMZs as a function of Reynolds number $Re_{\unicode[STIX]{x1D70F}}$ in turbulent channel flows in several numerical simulations. This systematic approach reveals a dependence of the Reynolds number on the average number of UMZs in the channel flow; this supports previously reported observations in the boundary layer. The fuzzy clustering approach is applied to the turbulent boundary layer (experimental, planar particle image velocimetry) and channel flow (numerical, direct numerical simulation) at varying Reynolds numbers. The interfacial layers are characterized by a strong concentration of spanwise vorticity, with the outer-most layer located at the upper edge of the log layer. The three-dimensional interface identification reveals a streak-like organization. The large-scale motion (LSM) at the outer region of the channel flow boundary layer modulates the outer IIL. The corrugations of the outer IIL are aligned with the LSM and the conditional correlation of the inner and outer IIL height shows that extreme near-wall events leave their mark on the outer IIL corrugations.

JFM classification

Boundary Layers: Boundary layer structure Turbulent Flows: Turbulent boundary layers
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 872 , 10 August 2019 , pp. 198 - 217
Copyright
© 2019 Cambridge University Press 

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