Assessment of RANS-Based Turbulent Combustion Models for Prediction of Emissions from Lean Premixed Combustion of Methane

Abstract

Reynolds-Averaged Navier-Stokes (RANS) simulations of Lean Premixed Combustion (LPC) of methane–air in a bluff-body stabilized combustor were performed with several widely used turbulent combustion methodologies in order to assess their prediction capabilities. The methods employed are the Eddy Dissipation Concept (EDC), the Composition Probability Density Function (CPDF) and the Joint Velocity–Frequency-Composition PDF (VFCPDF) models. Where needed, two different models were employed for turbulent transport closure, namely the Renormalization Group (RNG) k-ϵ and Reynolds Stress Transport (RSM) models. The combustion chemistry was represented by two separate augmented reduced mechanisms (ARM9 and ARM19) in order to assess the influence of chemical mechanisms on calculations. Mean temperature and major species predictions of all of the employed methodologies compared well with the experimental data. Intermediate and emission species predictions were sensitive to the resolution of turbulence viscosity, which changes the effective diffusivity of the species. NO emissions predictions were in error by an average ±5 ppm with the EDC models and the CPDF model, with the VFCPDF model showing a somewhat better prediction of NO_x. Calculations for some intermediate species (especially H₂) deviated qualitatively from the experimental data, which highlights some of the limitations of these methodologies commonly used in detailed prediction of emissions for various fuel blends.

Keywords:

• Emissions
• Lean premixed combustion
• PDF combustion model
• RANS combustion models
• Reduced mechanisms

ACKNOWLEDGEMENTS

This technical effort was performed in support of the National Energy Technology Laboratory's ongoing research on the assessment of Turbo-Chemistry Models for Prediction of Fuel Composition Effects on GTC Emissions, under the RDS contract DE-AC26–04NT4181. The authors would like to thank Kent Castleton, Dan Maloney, and Geo Richards at NETL and Peyman Givi at the University of Pittsburgh for their invaluable support, insight, and guidance as well as and the combustion group at the Cornell University for the permission to utilize their PDF/RANS computer code.