Lagrangian tracking of soot particles in LES of gas turbines

Abstract

Expected stringent legislation on particulate matter (PM) emission by gas turbine combustors is currently motivating considerable efforts to better understand, model and predict soot formation. This complex phenomenon is very difficult to study in detail with experiment, and numerical simulation is an essential complementary tool. Considering that the chemistry of soot particles strongly depends on their size, the numerical prediction of soot formation requires the description of their size distribution. To do so, either Eulerian methods (sectional or moments), or stochastic Lagrangian approaches are reported in the literature. In the present work a far more simple semi-deterministic Lagrangian approach is proposed. Combined to the semi-empirical model of Leung et al. (1991) for soot chemistry, the Lagrangian approach is first validated on a one-dimensional premixed ethylene-air flame. The model is then applied to a gaseous non-premixed ethylene-air burner measured at DLR and computed with Large Eddy Simulation (LES). The gaseous chemistry is described with an Analytically Reduced Chemistry (ARC) to guarantee a good prediction of combustion and gaseous soot precursors. Results are validated against experiment and compared, in terms of accuracy and CPU cost, to an Eulerian semi-empirical model. To the authors knowledge, it is the first time that such Lagrangian particle tracking approach is used for soot. Results obtained in terms of accuracy and computing time are very encouraging.