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Simulations for a pressure-assisted multi-stream injector designed for urea-dosing in a selective catalytic reduction (SCR) exhaust gas system have been carried out and compared to measurements taken in an optically accessible high-idelity low test rig. The experimental data comprises four different combinations of mass low rate and temperature for the gas stream with unchanged injection parameters for the spray. First, a parametric study is carried out to determine the importance of various spray sub-models, including atomization, spray-wall interaction, buoyancy as well as droplet coalescence. Optimal parameters are determined using experimental data for one reference operating condition. The model is subsequently applied to all operating conditions with unaltered parameters and validated by means of the measured droplet velocity ields, droplet diameter distributions and spray-tip propagations which have been characterized by means of Particle Image Velocimetry (PIV), Phase Doppler Anemometry (PDA) and shadow imaging. Finally, the model is used to investigate in detail phenomena characteristic of inclined sprays in cross-lows. Effects such as formation of vortices behind the spray core for low cross low velocities, greater entrainment of droplets at high low rates as well as wall ilm dynamics and the interaction of droplets also with the side walls are discussed in detail, highlighting the importance of the respective phenomena at the different operating conditions.

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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. AbstractComputational 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 A C C E P T E D M A N U S C R I P T 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.