A sparse-Lagrangian multiple mapping conditioning model for turbulent diffusion flames

Abstract

A sparse-Lagrangian multiple mapping conditioning (MMC) model for turbulent diffusion flames is presented and tested against experimental data for a piloted methane/air jet diffusion flame (Sandia Flame D). The model incorporates a large eddy simulation for the flow field and a stochastic multiple mapping conditioning (MMC) approach for the reactive scalars. The stochastic MMC models the filtered density function of the scalar composition field. The numerical implementation involves a sparse-Lagrangian particle scheme in which there are fewer particles than there are LES grid cells. Predictions of similar accuracy to previously published Flame D simulations are achieved using only 35,000 particles (of these only 10,000 are chemically active). Sub-filter conditional dissipation is modelled by interactions between pairs of particles which are closely located in a reference mixture fraction space interpolated from the underlying Eulerian filtered field. A model is developed for the mixing time-scale which is proportional to the distance between mixing particles. It is shown that the time-scale can be adjusted to achieve good predictions for time-averaged mean and fluctuating statistics of passive and reactive scalars.