Experimental and numerical investigation of the transition from non sooting to sooting premixed n-butane flames, encompassing the nucleation flame conditions

International audienceIn a small range of rich equivalence ratios, premixed flat flames, so-called nucleation flames, were shown to produce very small-size (2-4 nm) soot particles, which undergo negligible growth with the residence time. In such flames, the contribution of the soot nucleation step with respect to the soot growth process is larger than in standard sooting flames, making nucleation flames ideal target flames to better understand the inception process. In order to scrutinize the transition from non sooting to sooting flames, three premixed atmospheric n-butane/oxygen/nitrogen flames surrounding the nucleation flame conditions were investigated: the flame at =1.6 is a fuel-rich non-sooting flame, the flame at =1.75 is a nucleation flame and the flame at =1.95 is a lightly sooting flame. The soot volume faction (SVF) profiles were previously measured by laser-induced incandescence as well as the soot size distributions. In the present study, mole fractions of stable species up to benzene were measured by gas chromatography while those of naphthalene and pyrene were obtained by jet-cooled laser-induced fluorescence. It is found that acetylene, propyne, benzene, naphthalene and pyrene species show the highest sensitivity with the equivalence ratio, while a two orders of magnitude increase of SVF is observed. The chemical flame structure of these flames was modeled using three kinetic mechanisms of the literature to which a recently developed soot code, based on a sectional approach, was coupled. The chosen model is representative of the current state-of-the-art of models describing all steps of soot formation and growth and fully coupled with the gas phase. The ability of such a model to predict soot volume fraction with reasonable predictions of gas-phase species is validated here based on the new experimental database presented in this work. It also highlights the improvement required on particle inception modeling to correctly predict particle size distributions