A stochastic multiple mapping conditioning computational model in OpenFOAM for turbulent combustion. (30th August 2018) - Galindo-Lopez, S.; Salehi, F.; Cleary, M.J.; Masri, A.R.; Neuber, G.; Stein, O.T.; Kronenburg, A.; Varna, A.; Hawkes, E.R.; Sundaram, B.; Klimenko, A.Y.; Ge, Y.
Highlights: Stochastic MMC–LES and MMC–RANS are implemented into OpenFOAM. Code architecture is based on layered template classes and abstract submodels. Mass consistency of the hybrid Eulerian and Lagrangian schemes is demonstrated. Numerical convergence with increasing stochastic particles is demonstrated. Numerical convergence with increasing aerosol species sections is demonstrated. Abstract: Computational models for combustion must account for complex and inherently interconnected physical processes including dispersion, mixing, chemical reactions, particulate nucleation and growth and, critically, the interactions of these with turbulence. The development of affordable and accurate models that are widely applicable is a work in progress. Stochastic multiple mapping conditioning (MMC) is a fast-emerging approach that has been successfully applied to non-premixed, premixed and partially premixed flames as well to the modelling of liquid and solid particulate synthesis. The method solves the conventional PDF transport equation but incorporates an additional constraint in that the mixing is localised in a reference space. This paper describes the numerical implementation of stochastic MMC in an OpenFOAM compatible code called mmcFoam. The model concepts and equations along with alternative submodels, code structure and numerical schemes are explained. A focus is placed on validation of the computational methods in particular demonstrating numerical convergence and mass consistency of the hybrid Eulerian/Lagrangian schemes. Four validation cases are selected including a combustion direct numerical simulation (DNS) case, two combustion experimental jet flame cases and a non-combusting particulate synthesis case. The results show that the total mass and mass distribution of Eulerian and Lagrangian schemes are consistent and confirm that the solutions numerically converge with increasing number of stochastic computational particles and sections for describing particulate size distribution.