سال انتشار: ۱۳۸۶
محل انتشار: هفتمین همایش انجمن هوافضای ایران
تعداد صفحات: ۷
F Bazdidi-Tehrani – Department of Mechanical Engineering, Iran University of Science and Technology, Tehran. Associate Professor, (corresponding author)
H Zeinivand – M.Sc. Candidate in energy conversion
A three-dimensional numerical simulation of reactive swirling CH4/air flame is performed to predict the flow behavior in a model gas turbine combustor. A Finite-Volume, non-staggered grid approach is employed to solve the governing equations. The second-order upwind scheme is applied for the space derivatives of the advection terms in all transport equations. The SIMPLEC algorithm is employed to handle the velocity and pressure coupling. The Eddy Dissipation Model is employed to model the reaction. Two turbulence models, namely the Renormalization Group Theory (RNG) k-ε model and the Reynolds Stress Model (RSM) are applied for the flow predictions. Comparisons of the present numerical predictions with the available experimental data reveal that both models predict the occurrence of inner and outer recirculation zones. Both models underestimate the intensity of axial and tangential velocities. The RSM model predicts a larger corner recirculation zone (CRZ) compared with the RNG k-ε model. The RNG k-ε model predicts that the swirling flow evolves into a more intense solid-body rotation-type downstream. Also, for a swirl number of 0.55, it does not describe the shape of reversed flow downstream as accurately as the RSM. However, The RNG k-ε model gives satisfactory results with less time consumption. In addition, it is shown that the Eddy Dissipation Model fails to predict the temperature distribution in the region near the annular air inlet, mainly because the reaction time scale is of the same magnitude as the mixing time scale or the Damkohler number is less than unity, which is opposite to the Eddy Dissipation Model assumptions. The RNG k-ε model predicts higher temperatures compared with the RSM, mainly because of a more intense reversed flow prediction.