Hybrid binomial Langevin-multiple mapping conditioning modeling of a reacting mixing layer

The hybrid binomial Langevin-MMC (Multiple Mapping Conditioning) method combines the advantages of the binomial Langevin and MMC models in a consistent manner to overcome difficulties in each. The binomial Langevin method provides joint velocity-scalar statistics, but the treatment of scalars is complex since specification of the bounds is not trivial. The MMC method is capable of dealing with the mixing of any number of scalars, but it can be difficult to specify coefficients involving averages of the scalars and the introduced reference space. The difficulties are overcome by using the velocity statistics from the binomial Langevin model to obtain the reference variable for MMC and, subsequently, the mixing of MMC scalars is performed in a manner that minimises the difference between the mixture fractions for each submodel. The current work expands past studies of NO conversion in a mixing layer to include a study of the Sandia D Flame in preparation for the application to more complex combustion phenomena. Results compare favourably with experimental data and other models