MMC-LES modelling of premixed and stratified combustion

Research project: MMC-LES modelling of premixed and stratified combustion

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.



Movie 1: The animation shows the ATF-MMC-LES simulation of the turbulent stratified jet flame TSF-A. The left half shows the Eulerian LES temperature (from ATF) which is used as a reference variable for MMC. The right half illustrates the sparse-Lagrangian solution of the CO mass fraction predicted by MMC. The iso-contours indicate equivalence ratios of phi=0.75 (red) and phi=0.3 (black) from the LES solution and demonstrate the different compositions (stratification) within the burner setup.

Related publications

  1. C. Straub, A. Kronenburg, O. T. Stein, S. Galindo-Lopez, and M. J. Cleary, “Mixing time scale models for multiple mapping conditioning with two reference variables,” Flow, Turbul. Combust., vol. 106, pp. 1143–1166, (2021).
  2. C. Straub, A. Kronenburg, O. T. Stein, S. Galindo-Lopez, and M. J. Cleary, “Mixing time scale models for multiple mapping conditioning of partially premixed flames,” in Proc. 11th Medit. Combust. Symp., Tenerife, Spain, (2019).
  3. C. Straub, A. Kronenburg, O. T. Stein, R. S. Barlow, and D. Geyer, “Modelling stratified flames with and without shear using multiple mapping conditioning,” Proc. Combust. Inst., vol. 37, pp. 2317–2324, (2019).
  4. C. Straub, A. Kronenburg, O. T. Stein, G. Kuenne, J. Janicka, R. S. Barlow, and D. Geyer, “Multiple mapping conditioning coupled with an artificially thickened flame model for turbulent premixed combustion,” Combust. Flame, vol. 196, pp. 325–336, (2018).
  5. C. Straub, A. Kronenburg, O. T. Stein, G. Kuenne, and K. Vogiatzaki, “Modelling of turbulent premixed stratified combustion with multiple mapping conditioning mixing model,” in Proc. 8th Europ. Combust. Meeting, Dubrovnik, Croatia, (2017).
  6. C. Straub, S. De, A. Kronenburg, and K. Vogiatzaki, “The effect of timescale variation in multiple mapping conditioning mixing of PDF calculations for Sandia Flame series D–F,” Combust. Theo. Mod., vol. 20, pp. 894–912, (2016).


This image shows Andreas Kronenburg

Andreas Kronenburg

Univ.-Prof. Dr.

Director of the Institute

This image shows Carmen Straub

Carmen Straub


Scientific staff

This image shows Nadezhda Iaroslavtceva

Nadezhda Iaroslavtceva


Scientific staff

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