Predictions of NOx and CO emissions from a low-emission concentric staged combustor for civil aeroengines

Abstract

This study is aimed to establish a detailed chemical reactor network model based on the analysis of complex reaction flowfield structures in aeroengine combustors, so that the emissions of nitrogen oxides and carbon monoxide from advanced civil aeroengines can be predicted quickly and accurately. In this study, a low-emission concentric staged combustor with three axial swirlers is designed for civil aeroengines, and numerical simulations of the three-dimensional reaction flowfields of the combustor during four load phases of takeoff, climb, approach, and idle, are conducted. Based on the numerical results, a simple chemical reactor network model with seven perfectly stirred reactors and a detailed chemical reactor network model using up to 15 perfectly stirred reactors are established. Using the developed chemical reactor network models and the detailed JP10 chemical reaction mechanism—composed of 374 step elementary reactions and 82 species, the emission variations of nitrogen oxides and carbon monoxide are predicted and compared with those estimated using an empirical formula and with the numerical results as a function of the combustion load. Using a combined chemical reactor network–computational fluid dynamics analysis method, the variations of the formation path, the mechanism, and the amounts of nitrogen oxides in the combustor and in the perfectly stirred reactors, are analyzed as a function of the combustion load. In addition, the effects of fuel and air pilot-to-total ratio on nitrogen oxides emissions for the 100% load condition are also analyzed. It is found that at high loads, the production rate of the thermal NO is the highest, while at low loads, the production rate of the prompt NO is the highest. The nitrogen oxide is mainly produced in the pilot zone and the recirculation zone, while its production in the outer main stage zone is low. The results show that the NOx emissions predicted by the complex chemical reactor network model are most consistent with those elicited using the empirical formula.

Low-Order Reactor-Network-Based Prediction of Pollutant Emissions Applied to FLOX® Combustion

“…Bhargava et al [16] x Single-nozzle experiments and numerical modeling, study of pressure effects Falcitelli et al [17] x NOx prediction in industrial energy combustion systems Falcitelli et al [18] x NOx prediction in industrial furnaces, NOx reduction techniques Falcitelli et al [19] x Methodical assessment of ERN pollutant prediction from CFD Mohamed et al [20] x NOx, UHC, and CO for gas turbine combustion (model only) Novosselov et al [21] x NOx and CO in a swirl-stabilized burner Russo et al [22] x NOx and CO in a recuperated micro gas turbine Benedetto et al [23] x Industrial furnace facilities Fichet et al [24] x NOx emissions in a staged gas turbine combustor Lee et al [25] x NOx emissions in a simplified combustor, GE7FA gas turbine Lyra and Cant [26] x NOx emissions in a high-pressure nozzle test case De Toni et al [27] x NOx emissions in a BERL 300 kW furnace combustor Colorado et al [28] x NOx emissions in a C60 gas turbine combustor Nguyen [29] x NOx emissions for a generic gas turbine burner using Chemkin Nguyen et al [30] x NOx prediction with Chemkin for a gas turbine combustor Innocenti et al [31] x NOx and CO in a swirl-stabilized aero-engine combustor Kaluri et al [32] x Real-time reactor network for LBO predictions Nguyen [2] x Emissions in a swirl-stabilized combustor using Chemkin Gupta et al [33] x Real-time reactor network for LBO predictions Perpignan et al [34] x NOx and CO emissions at flameless oxidation combustion Zhang et al [35] x NOx and CO emissions in a swirl-stabilized aero-engine combustor sector Surprisingly, the degree of model fidelity does not necessarily increase with time. Many early studies use automated algorithms for network extraction from CFD data [17][18][19]21], whereas few rely on using CFD results as guidance for manual network creation only [22,23,27].…”