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Journal Article

Comparison of Lagrangian and Eulerian frames of passive scalar turbulent mixing


Emran,  Mohammad Shah
Laboratory for Fluid Dynamics, Pattern Formation and Biocomplexity, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Götzfried, P., Emran, M. S., Villermaux, E., & Schumacher, J. (2019). Comparison of Lagrangian and Eulerian frames of passive scalar turbulent mixing. Physical Review Fluids, 4(4): 044607. doi:10.1103/PhysRevFluids.4.044607.

Cite as: http://hdl.handle.net/21.11116/0000-0003-995B-B
The mixing of a passive scalar in a three-dimensional, statistically stationary turbulent Navier-Stokes flow at a constant and moderate Taylor microscale Reynolds number R-lambda = 42 is studied by means of direct numerical simulations for Schmidt numbers between 1 and 64. The freely decaying passive scalar is represented in two different ways: (1) in the Lagrangian frame of reference as a cloud of up to 4.8 billion individually advected massless tracer particles subject to a stochastic Wiener process along the tracer tracks that describes scalar diffusion or (2) in the standard Eulerian frame of reference as an advection-diffusion equation of the continuum concentration field. In both cases, the scalar is initially seeded in a small cubic subvolume. The mean mixing time < t(s)> is determined by the mean compressive strain rate <lambda(3)> < 0 which is obtained from the probability density functions of the local finite-time Lyapunov exponents in the Lagrangian frame, lambda(i)(t) with i = 1, 2 and 3. The direct comparison of freely decaying Lagrangian and Eulerian passive scalars gives a good agreement of the scalar variance for times t less than or similar to 10 < t(s)> and for the probability density functions P(Theta, t) taken with respect to the whole simulation domain. We also show how the multilayer aggregations of scalar filaments and sheets in the Lagrangian frame are increasingly influenced by the noise due to discreteness with progressing dilution of the initially high tracer particle concentration. This limits the Lagrangian approach in its present form and for the obtainable Schmidt numbers to studies of shorter time periods. A simple one-dimensional advection-diffusion model of a solitary strip is finally applied to the problem at hand to derive the probability density function of the scalar concentration, P(Theta, t), from the one of the compressive local finite-time Lyapunov exponent, p(lambda(3), t). Model prediction with and without self-convolution and numerical data of the scalar distributions agree qualitatively, however with quantitative differences particularly for small scalar concentrations. The present Lagrangian approach to passive scalar mixing in turbulence opens the application of more flexible passive scalar injection and boundary conditions and allows to relax the resolution constraints for high-Schmidt number mixing studies.