Time-History Statistics of Soot Formation in a Model Gas Turbine Combustor

Soot formation is a complex dynamic and intermittent process determined by properties of the fuel, combustor design, and combustor operation. Although the major steps in soot formation (i.e., formation of precursors, inception, growth and evolution) are similar for a variety of carbonaceous fuels, applications, and operating conditions, it remains unclear when the temporal transition between these steps occurs. An engineering prediction tool coupled with computational fluid physics (CFD), therefore needs to accurately model all these complex steps of soot formation. To develop such a soot modeling tool, we propose a time-history concept to better understand the time dependency of soot formation as a function of local properties (i.e., temperature, velocity, local fuel air ratio, turbulence quantities etc.). We continue our previous work of modeling the DLR aero-combustor with a semi-empirical two-equation soot model by injecting massless tracer particles upstream of the injector region of the combustor to collect time history statistics of the solution variables. The computed correlations between the statistics of certain species with respect to the experimental soot volume fraction data showed that a time-history effect of certain flow variables, including turbulent kinetic energy, and multiple species is indeed important for soot formation. The time-history based correlation analysis was used to identify four key species and their respective concentration ranges deemed critical to improve the soot prediction of the current semi-empirical soot model (C6H5-based nucleation, acetylene-based surface growth, and oxidation with OH and O2). A novel statistical time-history correlation coefficient concept is developed in this work to identify deficiencies and propose areas of improvement in an existing semi-empirical soot model.