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Revisiting turbulence small-scale behavior using velocity gradient triple decomposition

Fluid Dynamics 2020-08-26 v1

Abstract

Turbulence small-scale behavior has been commonly investigated in literature by decomposing the velocity-gradient tensor (AijA_{ij}) into the symmetric strain-rate (SijS_{ij}) and anti-symmetric rotation-rate (WijW_{ij}) tensors. To develop further insight, we revisit some of the key studies using a triple decomposition of the velocity-gradient tensor. The additive triple decomposition formally segregates the contributions of normal-strain-rate (NijN_{ij}), pure-shear (HijH_{ij}) and rigid-body-rotation-rate (RijR_{ij}). The decomposition not only highlights the key role of shear, but it also provides a more accurate account of the influence of normal-strain and pure rotation on important small-scale features. First, the local streamline topology and geometry are described in terms of the three constituent tensors in velocity-gradient invariants' space. Using DNS data sets of forced isotropic turbulence, the velocity-gradient and pressure field fluctuations are examined at different Reynolds numbers. At all Reynolds numbers, shear contributes the most and rigid-body-rotation the least toward the velocity-gradient magnitude (A2A^2). Especially, shear contribution is dominant in regions of intermittency (high values of A2A^2). It is also shown that the high-degree of enstrophy intermittency reported in literature is due to the shear contribution toward vorticity rather than that of rigid-body-rotation. The study also provides an explanation for the absence of intermittency of the pressure-Laplacian, despite the strong intermittency of enstrophy and dissipation fields. Overall, it is demonstrated that triple decomposition offers unique and deeper understanding of velocity-gradient behavior in turbulence.

Keywords

Cite

@article{arxiv.1912.11507,
  title  = {Revisiting turbulence small-scale behavior using velocity gradient triple decomposition},
  author = {Rishita Das and Sharath S. Girimaji},
  journal= {arXiv preprint arXiv:1912.11507},
  year   = {2020}
}
R2 v1 2026-06-23T12:56:02.261Z