A priori DNS study of applicability of flamelet concept to predicting mean concentrations of species in turbulent premixed flames at various Karlovitz numbers

摘要

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 beta-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 H-2, O-2, 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 beta-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. (C) 2020 The Combustion Institute. Published by Elsevier Inc. All rights reserved.