Sparse-Lagrangian MMC modelling of the Sandia DME flame series

A series of turbulent, piloted dimethyl ether (DME)/air jet flames (Sandia DME flames D–G’), with Reynolds numbers ranging from 29,300 to 73,250, has been simulated using a sparse-Lagrangian multiple mapping conditioning (MMC) approach coupled to a large eddy simulation (LES) flow field solver. Mixing between the Monte-Carlo particles is modelled by a generalised form of MMC combined with a sparse distribution of particles leading to significant computational savings compared to what is required for conventional mixing models. This is achieved by pairwise mixing of particles that are selected dependent on their distance in an extended space comprised of a reference variable, given by the LES mixture fraction, and spatial location. The MMC-LES method successfully predicts the flame structure and composition field for the full flame series. Numerical results are compared against conditional statistics and spatially resolved experimental data acquired with Raman/Rayleigh scattering and laser-induced fluorescence measurements. They show good agreement even for flame DME-G’ where large turbulence-chemistry interactions lead to significant local extinction and large deviations from a flamelet structure. The influence of the mixing time on the predicted flame structure is investigated, and the systematic validation of the time scale models with the aid of measurements of the entire flame series has corroborated the findings of earlier DNS and single flame studies: a modified time scale model is needed to provide accurate predictions of conditional fluctuations and thus of possible deviations from a flamelet-like combustion regime.