Complex-chemistry direct numerical simulation (DNS) data obtained earlier from lean hydrogen-air flames associated with corrugated flame (case A), thin reaction zone (case B), and broken reaction zone (case C) regimes of turbulent burning are analysed to directly assess capabilities of the flamelet approach to predict mean concentrations of species in a premixed turbulent flame. The approach consists in averaging dependencies of mole fractions, reaction rates, temperature, and density on a single combustion progress variable c, which are all obtained from the unperturbed laminar flame. For this purpose, four alternative definitions of c are probed and two probability density functions (PDFs) are adopted, i.e. either an actual PDF extracted directly from the DNS data or a presumed β-function PDF obtained using the DNS data on the first two moments of the c(x, t)-field. Results show that the mean density and mean mole fractions of H2, O2, and H2O are well predicted using both PDFs for each c, although the predictive capabilities are little worse in case C. In cases A and B, the use of the actual PDF and the fuel-based c also offers an opportunity to well predict mean mole fractions of O and H, whereas the mean mole fraction of OH is slightly underestimated. In the highly turbulent case C, the same approach performs worse, but still appears to be acceptable for evaluating the mean radical concentrations. The use of the β-function PDFs or another combustion progress variable yields substantially worse results for these radicals. When compared to the mean mole fractions, the mean rate of product creation, i.e. the source term in the transport equation for the mean combustion progress variable, is worse predicted even for a quantity (species concentration or temperature) adopted to define c and using the actual PDF. Consequently, turbulent burning velocity is not predicted either.
|Original language||English (US)|
|Number of pages||13|
|Journal||Combustion and Flame|
|State||Published - Sep 15 2020|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: ANL gratefully acknowledges the financial support provided by CERC. VAS gratefully acknowledges the financial support provided by ONERA and by the Grant of the Ministry of Education and Science of the Russian Federation (Contract No. 14.G39.31.0001 of 13.02.2017). WS, FEHP, and HGI were sponsored by King Abdullah University of Science and Technology (KAUST). Computational resources for the DNS calculations were provided by the KAUST Supercomputing Laboratory.