On Low-Emission Annular Combustor Based on Designing of Liner Air Admission Holes

Abstract

Numerical experiments was carried out to predict the total temperature characteristics and formation of nitrogen oxide emissions and pattern factor in an annular combustor liner based on geometrical parameters and location and rows of different air admission holes, for 6 various cases, using computational fluid dynamics (CFD) .The simulation has been performed using ANSYS CFX including finite rate chemistry and eddy dissipation model, for simulation of liquid kerosene (Jet A)-air combustion after fuel droplet evaporation. The spray modeling was performed, including Rosin-Rammler droplet distribution. Thermal and prompt nitrogen oxide (NO x) formation was performed to predicting NO x emission characteristics with a k-ε model of turbulent. In this investigation the 3D CAD model of the realistic annular combustion chamber is presented for the simulation with double radial air swirler for the better mixing fuel with air. Beside this the characteristic and the flame structure is presented including the contour plots of total temperature and NO concentration at the outlet of the combustor liner and in cross section plane along the X axis from the injector center of the combustor including the chart of the velocity and NO, CO, CO 2 , O 2 and the total temperature along the liner from the injector center. For the combustion of kerosene with air 2 step kinetic schemes are presented in this study. The results show that the best result with the low concentration of NO is the case 5 but with a high percentage of pressure drop and the case 3 have the maximum concentration of NO with the low percentage of pressure drop.

Performance and Emissions of a Microturbine and Turbofan Powered by Alternative Fuels

“…In the modelling of combustors, thermodynamic equations are used to describe combustion and heat transport [18,19]. When designing new combustors with reduced emissions, complex numerical CFD models are used [20,21]. An engine emissions data bank [22] and machine learning [23] models using statistical methods [24] or artificial intelligence are also used to predict emissions.…”