4.7 Article

Objective momentum barriers in wall turbulence

期刊

JOURNAL OF FLUID MECHANICS
卷 941, 期 -, 页码 -

出版社

CAMBRIDGE UNIV PRESS
DOI: 10.1017/jfm.2022.316

关键词

computational methods; turbulent boundary layers; vortex dynamics

资金

  1. German National Science Foundation (DFG) [SPP 1881]
  2. Swiss National Science Foundation (SNSF) [P400P2 199190]
  3. Swiss National Science Foundation (SNF) [P400P2_199190] Funding Source: Swiss National Science Foundation (SNF)

向作者/读者索取更多资源

Using the recent theory of diffusive momentum transport, this study identifies internal barriers in wall-bounded turbulence. These barriers, formed by the invariant manifolds of the velocity field, block the viscous part of the instantaneous momentum flux in the flow. The study introduces new diagnostic tools and normalized trajectory metrics to provide unprecedented visualizations of objective coherent structures.
We use the recent frame-indifferent theory of diffusive momentum transport to identify internal barriers in wall-bounded turbulence. Formed by the invariant manifolds of the Laplacian of the velocity field, the barriers block the viscous part of the instantaneous momentum flux in the flow. We employ the level sets of single-trajectory Lagrangian diagnostic tools, the trajectory rotation average and trajectory stretching exponent, to approximate both vortical and internal wall-parallel momentum transport barrier (MTB) interfaces. These interfaces provide frame-indifferent alternatives to classic velocity-gradient-based vortices and high-shear boundaries between uniform momentum zones (UMZs). Indeed, we find that these elliptic manifold approximations and MTBs outperform standard vortices and UMZ interfaces in blocking diffusive momentum transport, which suggests our momentum barriers are physical features that may be the cause of coherence signatures in statistical and non-objective diagnostics. We also introduce normalized trajectory metrics that provide unprecedented visualizations of objective coherent structures by avoiding strong turbulence biases.

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