Article: Sukheswalla P, Vaithianathan T, Collins LR, (2013) Simulation of Homogeneous Turbulent Shear Flows at Higher Reynolds Numbers: numerical challenges and a remedy. International Journal of Turbulence, 14 (5): 60-97
Abstract: In a recent study, Isaza and Collins [J. Fluid Mech., 637 (2009), pp. 213-239] found the asymptotic state of homogeneous turbulent shear flows (HTSFs) to be sensitively dependent on the initial shear parameter (S* equivalent to Sq(2) /epsilon) and yet be almost independent of the initial Reynolds number (R-lambda equivalent to q lambda/nu). The stringent resolution criteria they employed, however, restricted their studies to relatively low Reynolds numbers. In this paper, we present higher resolution direct numerical simulations of HTSFs over a wider range of Reynolds numbers, aided in part by an improved parallelisation scheme that utilises two-dimensional domain decomposition. We maximise the time-window for our simulations by determining appropriate settings for the initial energy spectrum, viscosity and domain configuration, thereby ensuring that we attain the highest possible asymptotic Reynolds number at the chosen grid resolution. In the course of our study, we find that the pseudo-spectral method suffers from Gibbs oscillations while resolving the thin vortical structures that tend to form in HTSFs.
The nonlinear growth of these oscillations leads to spurious energy buildup in the high-wavenumber region of the spectrum, and contaminates the flow field. Consequently, the growth of the integral length scale is found to be numerically stunted, well before the intended final Reynolds number is attained. The issue is rectified by the application of exponential-type spectral filters, which stabilize the simulations and extend the runtime window, permitting attainment of larger asymptotic Reynolds numbers. Various large-scale flow statistics are then studied, and their dependence on the initial value of the shear parameter and Reynolds number corroborates the findings of Isaza and Collins.