Influence of computational drop representation in LES of a droplet-laden mixing layer

Abstract

The objective of this work is to quantify the influence of the number of computational drops and grid spacing on the accuracy of predicted flow statistics and to possibly identify the minimum number, or, if not possible, the optimal number of computational drops that provides minimal error in flow prediction. For this purpose, Large Eddy Simulation (LES) of a mixing layer with evaporating drops has been performed using the dynamic Smagorinsky model and employing various numbers of computational drops. The LES were performed by reducing the number of physical drops by a factor varying from 8 to 128 to obtain the ensemble of computational drops, and by utilizing either a coarse or a fine grid. A set of first order, second order and drop statistics are extracted from LES predictions and are compared to results obtained by filtering a Direct Numerical Simulation (DNS) database. First order statistics such as Favre averaged streamwise velocity, Favre averaged vapor mass fraction, and the drop streamwise velocity are predicted accurately independent of the number of computational drops and grid spacing. Second order flow statistics depend both on the number of computational drops and on grid spacing. The scalar variance and turbulent vapor flux are predicted accurately by the fine mesh LES only when N R is less than 32 and by the coarse mesh LES reasonably accurately for all N R values. This is attributed to the fact that when the grid spacing is coarsened, the number of drops in a computational cell must be kept approximately the same as in the DNS.

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