The sooting propensity of laminar diffusion flames employing ethylene/methane mixture fuel is investigated by numerical simulation. Detailed gas phase chemistry and moments method are used to describe the chemical reaction process and soot particle dynamics, respectively. The numerical model captures the primary features experimentally observed previously. At constant temperatures of air and fuel mixture, both maximum soot volume fraction and soot yield monotonically decrease with increasing the fraction of carbon from methane in the fuel mixture. However, when the temperatures of air and fuel mixture are preheated so that the adiabatic temperatures of all flames are same, the variation of the maximum soot yield becomes higher than what would be expected from a linear combination of the flames of pure ethylene and pure methane, showing a synergistic phenomenon in soot formation. Further analysis of the details of the numerical results suggests that the synergistic phenomenon is caused by the combined effects of the variations in the concentrations of acetylene (C2H2) and methyl radical (CH3). When the fraction of carbon from methane in fuel mixture increases, the concentration of C2H2 monotonically decreases, whereas that of methyl radical increases, resulting in a synergistic phenomenon in the variation of propargyl (C3H3) radical concentration due to the reactions C2H2 + CH3 = PC3H4 + H and PC3H4 + H = C3H3 + H2. This synergistic phenomenon causes a qualitatively similar variation trend in the concentration of pyrene (A4) owing to the reaction paths C3H3 → A1 (benzene) → A2 (naphthalene) → A3 (phenanthrene) → A4. Consequently, the synergistic effect occurs for soot inception and PAH condensation rates, leading to the synergistic phenomenon in soot yield. The similar synergistic phenomenon is not observed in the variation of peak soot volume fraction, since the maximum surface growth rate monotonically decreases, as the fraction of carbon from methane in fuel mixture increases.
The influence of aliphatics on soot inception modelling
