Typical concepts for pollutant reduction include lean premixed combustion as under these conditions thermal NOx and soot formation can be minimised. Perfect premixing is hard to achieve due to incomplete mixing or it may not necessarily be the primary design objective as (desired) local variations in the equivalence ratio can improve combustion stability. Modern combustion systems therefore attempt to control the local mixture properties by introducing varying degrees of stratification. This project addresses the need for detailed numerical models to predict stratified and premixed turbulent flames. The multiple mapping conditioning (MMC) approach is a probability density function (PDF) method, where the PDF is represented by Lagrangian Monte Carlo particles. It provides an accurate modelling framework for turbulent combustion, where one or more reference variables are used to determine the properties of turbulent mixing of the stochastic particles. This accuracy is obtained by localness of particle pairs both in physical and reference variable space. MMC has been successfully employed for turbulent non-premixed flames and is extended here to stratified and premixed combustion. The MMC model is coupled to a large eddy simulation (LES) solver for an accurate description of turbulence. A coupled approach using the artificially thickened flame (ATF) and flamelet generated manifold (FGM) methods serves to provide the reference variable(s) for MMC. The localness of mixing in MMC allows for a sparse particle distribution. The expression “sparse” refers to the number of stochastic particles that can be as low as one stochastic particle per 30 LES cells. The newly developed model is able to capture the flame structure without any prior assumptions on the regime of premixed combustion. The model results are validated against state-of-the-art laser experiments from the Darmstadt/Sandia stratified flame series.
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