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Synthetic velocity gradient tensors and the identification of statistically significant aspects of the structure of turbulence

Christopher J. Keylock*

  • Sheffield Fluid Mechanics Group and Department of Civil and Structural Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom

  • *Present address: Visiting Researcher, Department of Aeronautics, Imperial College London, London, SW7 2AZ, United Kingdom; c.keylock@sheffield.ac.uk
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Phys. Rev. Fluids 2, 084607 – Published 23 August, 2017

DOI: https://doi.org/10.1103/PhysRevFluids.2.084607

Abstract

A method is presented for deriving random velocity gradient tensors given a source tensor. These synthetic tensors are constrained to lie within mathematical bounds of the non-normality of the source tensor, but we do not impose direct constraints upon scalar quantities typically derived from the velocity gradient tensor and studied in fluid mechanics. Hence, it becomes possible to ask hypotheses of data at a point regarding the statistical significance of these scalar quantities. Having presented our method and the associated mathematical concepts, we apply it to homogeneous, isotropic turbulence to test the utility of the approach for a case where the behavior of the tensor is understood well. We show that, as well as the concentration of data along the Vieillefosse tail, actual turbulence is also preferentially located in the quadrant where there is both excess enstrophy (Q>0) and excess enstrophy production (R<0). We also examine the topology implied by the strain eigenvalues and find that for the statistically significant results there is a particularly strong relative preference for the formation of disklike structures in the (Q<0,R<0) quadrant. With the method shown to be useful for a turbulence that is already understood well, it should be of even greater utility for studying complex flows seen in industry and the environment.

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Phys. Rev. Fluids 2, 084607– Published 23 August, 2017
  • Received 2 November 2016

©2017 American Physical Society

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