A Systematic Comparison of Detailed Soot Models and Gas-Phase Chemical Mechanisms in Laminar Premixed Flames. Issue 7 (2nd July 2016) - Roy, Somesh P.; Haworth, Daniel C.
ABSTRACT: This research is part of an ongoing assessment to identify the most promising soot models for applications to laboratory turbulent flames, and ultimately to practical combustion systems. To that end, it is of interest to compare soot model simulation results with experiments over a broad range of conditions. Here five steady, one-dimensional, laminar, premixed ethylene flames have been targeted that cover a range of C/O ratios (0.68 to 0.88), pressures (1 atm to 10 bar), and peak soot levels (0.03 to 3.2 ppm). Seven different chemical mechanisms have been considered. For each mechanism, results from several soot model variants are compared. Two different approaches for soot aerosol dynamics are used: a discrete sectional method (DSM), and a method of moments with interpolative closure (MOMIC). DSM and MOMIC results are also compared with those from a widely used semi-empirical two-equation soot model. The main figure-of-merit is the ratio of computed-to-measured peak soot volume fraction. The computed soot volume fraction is most sensitive to variations in the surface chemistry scheme. Sensitivities to variations in model configurations are reduced at high pressure. The DSM-based models yield slightly lower soot volume fraction and slightly larger particle size compared to the corresponding MOMIC-based models. A modified semi-empirical two-equation model produced a good match to experiment for all five flames. Particle sizes and number densities from the models are discussed for a low-sooting flame where such data are available. The DSM-based models produce bimodal particle size distributions that are qualitatively consistent with those observed experimentally. The best results overall are obtained using an underlying chemical mechanism that incorporates recent understanding of polycyclic aromatic hydrocarbons kinetics. There is no clear advantage of DSM over MOMIC in predicting global quantities, such as soot volume fraction, particle number density, and average particle size, while the computational cost of DSM is significantly higher than that of MOMIC.