An LES-DFSD study of transient premixed propane/air flames propagating past obstacles

Simulation of deflagrations requires accurate modelling of transient premixed flames. An initial laminar flame kernel is subjected to progressive stretch by obstacle generated turbulence as it propagates downstream. Large eddy simulation (LES) based flame surface density models may encounter difficulties when a predefined model constant is used for given turbulence characteristics. The reliable description of flame wrinkling considerably determines the transient flame behaviour. In this work, we applied a dynamic flame surface density (DFSD) model to automatically adjust the model parameter based on the instantaneous resolved flame information, whilst recovering laminar flame propagation in the absence of sub-grid turbulence. In the work we have successfully verified the LES DFSD approach against recent experimental data of stoichiometric propane/air deflagrating flames past obstacles. The overpressure, flame front speed and other flame features have been correctly reproduced by the model at various stages of flame propagation for distinct obstacle configurations. The wrinkling factor was identified as an informative parameter to understand the flame dynamics and pressure build-up mechanisms, and the significant contribution from the sub-grid flame surfaces have been demonstrated. By constructing the combustion regime diagram for LES, we have demonstrated that the flame wrinkling is fully resolved during the early propagation, and the flame lies in the thin reaction zone regime when the pressure peak is reached. Through comprehensive parametric studies, we found that ignition modelling has a considerable impact on the time taken to reach the peak pressure yet a much weaker effect on the peak magnitude. A negative correlation was identified between the Smagorinsky constant and the maximum pressure. Early-stage overpressure and flame evolutions were confirmed to be grid-independent, while the pressure peak is slightly mesh-sensitive. It was found that the magnitude of the dynamic parameter adequately self-adjusts to varying grid and filter widths. The satisfactory agreements with experiments and the robust findings ensured by the sensitivity studies indicate the predictive capabilities and the benefits of the LES-DFSD model.