TY - GEN
T1 - Comparison of finite-rate chemistry and flamelet/progress-variable models II
T2 - AIAA Aerospace Sciences Meeting, 2018
AU - Yang, Suo
AU - Wang, Xingjian
AU - Yang, Vigor
AU - Sun, Wenting
N1 - Funding Information:
This work was funded partly by NASA (Grant NNX15AU96A), and partly by the William R.T. Oakes Endowment of the Georgia Institute of Technology.
PY - 2018
Y1 - 2018
N2 - In this study, simulations using both the finite-rate chemistry (FRC)-LES and the flamelet/progress-variable (FPV)-LES approaches are conducted for a piloted partially premixed methane/air flame with high turbulence intensity. The two models have different spatial distributions of both time-averaged quantities and instantaneous flame field. For both axial and radial profiles of time-averaged statistics, the FPV-LES approach provides overall better prediction than FRC-LES, primarily due to the unity effective Lewis number under high turbulence intensity. To properly apply FRC in LES, a better transport model covering a broad range of turbulence intensity is required. In contrast, for conditional statistics, in which the effects of transport modeling are largely removed, the FRC-LES approach provides overall better predictions than FPV-LES for all quantities at most locations and mixture fractions.
AB - In this study, simulations using both the finite-rate chemistry (FRC)-LES and the flamelet/progress-variable (FPV)-LES approaches are conducted for a piloted partially premixed methane/air flame with high turbulence intensity. The two models have different spatial distributions of both time-averaged quantities and instantaneous flame field. For both axial and radial profiles of time-averaged statistics, the FPV-LES approach provides overall better prediction than FRC-LES, primarily due to the unity effective Lewis number under high turbulence intensity. To properly apply FRC in LES, a better transport model covering a broad range of turbulence intensity is required. In contrast, for conditional statistics, in which the effects of transport modeling are largely removed, the FRC-LES approach provides overall better predictions than FPV-LES for all quantities at most locations and mixture fractions.
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U2 - 10.2514/6.2018-1427
DO - 10.2514/6.2018-1427
M3 - Conference contribution
AN - SCOPUS:85044437591
SN - 9781624105241
T3 - AIAA Aerospace Sciences Meeting, 2018
BT - AIAA Aerospace Sciences Meeting
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
Y2 - 8 January 2018 through 12 January 2018
ER -