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Journal of Combustion
Volume 2016, Article ID 2572035, 12 pages
Research Article

Numerical Analysis of Turbulent Combustion in a Model Swirl Gas Turbine Combustor

1Center of Flow Simulation (CFS), Department of Mechanical and Process Engineering, Düsseldorf University of Applied Sciences, Josef-Gockeln-Str. 9, 40474 Düsseldorf, Germany
2Laboratory of Turbomachinery, Helmut Schmidt University, Holstenhofweg 85, 22008 Hamburg, Germany
3Engineering Gas Turbines, MAN Diesel and Turbo SE, Steinbrinkstr. 1, 46145 Oberhausen, Germany

Received 30 November 2015; Revised 28 July 2016; Accepted 17 August 2016

Academic Editor: Hong G. Im

Copyright © 2016 Ali Cemal Benim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Turbulent reacting flows in a generic swirl gas turbine combustor are investigated numerically. Turbulence is modelled by a URANS formulation in combination with the SST turbulence model, as the basic modelling approach. For comparison, URANS is applied also in combination with the RSM turbulence model to one of the investigated cases. For this case, LES is also used for turbulence modelling. For modelling turbulence-chemistry interaction, a laminar flamelet model is used, which is based on the mixture fraction and the reaction progress variable. This model is implemented in the open source CFD code OpenFOAM, which has been used as the basis for the present investigation. For validation purposes, predictions are compared with the measurements for a natural gas flame with external flue gas recirculation. A good agreement with the experimental data is observed. Subsequently, the numerical study is extended to syngas, for comparing its combustion behavior with that of natural gas. Here, the analysis is carried out for cases without external flue gas recirculation. The computational model is observed to provide a fair prediction of the experimental data and predict the increased flashback propensity of syngas.