Fuel Variation Effects in Propagation and Stabilization of Turbulent Counter-Flow Premixed Flames

Abstract

Sensitivity to stretch and differential diffusion of chemical species are known to influence premixed flame propagation, even in the turbulent environment where mass diffusion can be greatly enhanced. In this context, it is convenient to characterize flames by their Lewis number (Le), a ratio of thermal-to-mass diffusion. The work reported in this paper describes a study of flame stabilization characteristics when the Le is varied. The test data is comprised of Le ≪ 1 (Hydrogen), Le ≈ 1 (Methane), and Le > 1 (Propane) flames stabilized at various turbulence levels. The experiments were carried out in a Hot exhaust Opposed-flow Turbulent Flame Rig (HOTFR), which consists of two axially-opposed, symmetric turbulent round jets. The stagnation plane between the two jets allows the aerodynamic stabilization of a flame, and clearly identifies fuel influences on turbulent flames. Furthermore, high-speed Particle Image Velocimetry (PIV), using oil droplet seeding, allowed simultaneous recordings of velocity (mean and rms) and flame surface position. These experiments, along with data processing tools developed through this study, illustrated that in the mixtures with Le ≪ 1, turbulent flame speed increases considerably compared to the laminar flame speed due to differential diffusion effects, where higher burning rates compensate for the steepening average velocity gradient, and keeps these flames almost stationary as bulk flow velocity increases. These experiments are suitable for validating the ability of turbulent combustion models to predict lifted, aerodynamically-stabilized flames. In the final part of this paper, we model the three fuels at two turbulence intensities using the FGM model in a RANS context. Computations reveal that the qualitative flame stabilization trends reproduce the effects of turbulence intensity, however, more accurate predictions are required to capture the influences of fuel variations and differential diffusion.