Investigation of Turbulence Models Applied to Premixed Combustion Using a Level-Set Flamelet Library Approach

2004 
Most of the common modeling approaches to premixed combustion in engineering applications are either based on the assumption of infinitely fast chemistry or the flamelet assumption with simple chemistry. The level-set flamelet library approach (FLA) has shown great potential in predicting major species and heat release, as well as intermediate and minor species, where more simple models often fail. In this approach, the mean flame surface is tracked by a level-set equation. The flamelet libraries are generated by all external code, which employs a detailed chemical mechanism. However a model for the turbulent flame speed is required, which, among other considerations, depends on the turbulence intensity, i.e., these models may show sensitivity to turbulence modeling. In this paper, the FLA model was implemented in the commercial CFD program Star-Cd, and applied to a lean premixed flame stabilized by a triangular prism (bluff body). The objective of this paper has been to investigate the impact on the mean flame position, and hence on the temperature and species distribution, using three different turbulent flame speed models in combination with four different turbulence models. The turbulence models investigated are: the standard k-epsilon model, a cubic nonlinear k-e model, the standard k-omega model and the shear stress transport (SST) k-omega model. In general, the computed results agree well with experimental data for all computed cases, although the turbulence intensity is strongly underestimated at the downstream position. The use of the nonlinear k-epsilon model offers no advantage over the standard model, regardless of flame speed model. The k-omega based turbulence models predict the highest turbulence intensity with the shortest flame lengths as a consequence. The Muller flame speed model shows the least sensitivity to the choice of turbulence model. (Less)
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    18
    References
    6
    Citations
    NaN
    KQI
    []