Pressure effects on soot formation and evolution in turbulent jet flames

Dezhi Zhou, Shufan Zou, Wesley R. Boyette, Thibault F. Guiberti, William L. Roberts, Suo Yang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


In this study, two series of pressurized turbulent jet sooting flames at 1, 3, and 5 bar with either fixed jet velocity or fixed Reynolds number are simulated to study the pressure effects on soot formation and evolution. Through a radiation flamelet progress variable approach with a conditional soot subfilter probability density function (PDF) model to consider the turbulence-chemistry-soot interactions, quantitatively good agreements are achieved for soot volume fraction (SVF) predictions compared with the experimental data, regardless different turbulent intensities and residence times. SVF source terms are then discussed to show the pressure effects on nucleation, condensation, surface growth, and oxidation at different axial positions in these flames. It is found that surface growth and oxidation increase by about three orders of magnitude from 1 to 5 bar, while nucleation and condensation only increase within one order of magnitude. The stronger SVF scaling on pressure than measured data is found to be attributed to the inaccurate surface growth and oxidation scaling on pressure. Further analysis indicates that (i) the uncertainty of C2H2 prediction at elevated pressures is likely a major reason for the too strong surface growth scaling; and (ii) taking account of pressure effects in the conditional subfilter PDF modeling for turbulence-soot-chemistry interactions is likely a key to improve oxidation prediction. The results in this study open up the possibilities for improving future turbulent sooting flame modeling by improving C2H2 chemistry and turbulence-chemistry-soot modeling at elevated pressures.

Original languageEnglish (US)
Article number015105
JournalPhysics of Fluids
Issue number1
StatePublished - Jan 1 2023

Bibliographical note

Funding Information:
S. Yang gratefully acknowledges the faculty start-up funding from the University of Minnesota and the grant support from NSF CBET 2038173. The authors acknowledge Mr. Anders Vaage for part of the RFPV database generation. Part of the simulation was conducted on the computational resources from the Minnesota Supercomputing Institute (MSI).

Publisher Copyright:
© 2023 Author(s).


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