Numerical Study on the Effect of Shape, Stagger, Separation Distance, and Number of Obstacles on Methane-Air Flame Acceleration in Partially Confined Geometry

The presence of obstacles in confined spaces results in high overpressure from premixed flame combustion and the specific obstacle configurations significantly affect flame dynamics. Although linear plate-type obstacles had been extensively explored for flame acceleration, the present study focused on obstacles with volume blockage. This paper investigated the effects of different shapes of such obstacles and their configurations on the flame propagation characteristics inside a partially confined geometry. Four different flame surface density models were tested: Algebraic Flame Surface Wrinkling model, Turbulent Flame Speed Closure, Algebraic model and Transport model. The Transport model by Weller was selected with the dynamic k-equation Large Eddy Simulation model for turbulence modelling. Four shapes of obstacles, triangular, rectangular, elliptical and circular were examined. The effect of separation distance (standard-100 mm, spaced-out-150 mm and squeezed-in-70 mm) between the obstacles was investigated, along with their number and configuration (in-line and staggered). The results revealed that for standard separation, the overpressure peak is maximum for triangular and minimum for circular obstacles. Staggering the obstacles reduced the peak overpressure. Further, the overpressure peak reduced with both increasing and reducing separation compared to the standard case for triangle, ellipse, and circle-shaped obstacles, whereas it increased with greater separation for rectangular obstacles. The most significant reduction across all cases was observed upon reducing the separation distance. Oscillatory pressure behaviour owing to combustion in unburnt mixture pockets is reported for rectangle and triangle obstacles, attributed to their minimal sphericity. The flame surface area, representative of the turbulence generated, is observed to be directly correlated with the peak overpressure value across the dataset.