Surface Density Function statistics in hydrogen-air flames for different turbulent premixed combustion regimes

Nilanjan Chakraborty, Markus Klein, Dana Alwazzan, Hong G. Im

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The choice of reaction progress variable (RPV) (Formula presented.) on the statistical behavior of the surface density function (SDF) and the strain rates, which govern the evolution of SDF, have been analyzed using a detailed chemistry Direct Numerical Simulation (DNS) database of freely propagating statistically planar (Formula presented.)-air flames with an equivalence ratio of 0.7. The DNS database consists of three cases spanning the corrugated flamelets (CF), thin reaction zones (TRZ) and broken reaction zones (BRZ) regimes of premixed turbulent combustion. For this analysis, the RPV is defined based on the mass fractions of H, O and HO. The mean variations of the SDF and the flame displacement speed, (Formula presented.), have also been found to be dependent on the choice of the RPV. The progressive weakening (strengthening) of the preferential alignment of the RPV gradient with the most extensive (compressive) principal strain rate with increasing Karlovitz number leads to changes in the behaviors of normal and tangential strain rates from one combustion regime to another. The differences in displacement speed statistics for different choices of RPV affect the behaviors of the normal strain rate due to flame propagation and curvature stretch. The relative thickening/thinning of the reaction layers of the major species has been explained in terms of the statistics of effective normal strain rate experienced by the (Formula presented.)-isosurfaces for different choices of RPVs.
Original languageEnglish (US)
Pages (from-to)1988-2002
Number of pages15
JournalCombustion Science and Technology
Volume190
Issue number11
DOIs
StatePublished - Jun 11 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Part of the work presented in this study was sponsored by competitive research funding from King Abdullah University of Science and Technology (KAUST). The work made use of computational resources at KAUST Supercomputing Laboratory and ARCHER at Engineering and Physical Sciences Research Council (EPSRC).

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