Multiple mapping conditioning for silica nanoparticle nucleation in turbulent flows

Abstract

In this paper a study of the sparse-Lagrangian multiple mapping conditioning (MMC) model for the synthesis of silica nanoparticles is presented. This is the first attempt using the model to simulate the formation of kinetically limited solid state species. A simplified differential diffusion model, involving a modified mixing timescale, is used to account for the much lower diffusion of the solid species. The model is validated against DNS of a counterflowing double shear layer of silane and hot combustion products. The reduced reaction mechanism involves silane decomposition and clustering leading to silica. The model is implemented in the context of large eddy simulations (LES). The MMC model allows a sparse distribution of stochastic particles for computing the reactive scalar field, including silica number density, by enforcing the property of localness in mixture fraction space onto the mixing model. Gas phase mixture fraction and fast reactive species are predicted with acceptable accuracy. The prediction of silica number density is qualitatively correct although the magnitude is underpredicted somewhat and the rms is greatly underpredicted. The effects of differential diffusion are clearly visible in the modelling and the simplified model correctly predicts the trend of increasing silica formation when differential diffusion is included.