Large eddy simulations of a buoyant turbulent line flame using conditional source-term estimation (CSE)

Abstract

The objective of this paper is to assess the capabilities of the Conditional Source-term Estimation (CSE) approach applied to a laboratory scaled turbulent buoyant flame without extinction. CSE is coupled with the Large Eddy Simulation (LES) solver, FireFOAM. Tabulated detailed chemistry is included. Radiation is treated using the optically thin model using four different implementations and the effect of subgrid scale (sgs) Turbulence-Radiation Interactions (TRI) is considered. Predictions of time-averaged temperatures and corresponding root mean square (rms) are compared with the experimental measurements at several locations. Further, flame shape, unconditional and conditional species mass fractions, axial velocity and mixture fraction are also examined for qualitative analysis. The predicted temperatures are in good agreement with the experimental data, except very close to the fuel inlet. The predicted temperatures increase more slowly compared to the experimental data in the first 0.09 m above the burner. The temperature rms is in reasonable agreement with experimental data with the peak being overpredicted by approximately 17%. The predicted flame height closely matches the experimental value. The temperature predictions are consistent with previously published results. Best predictions are obtained when the effect of sgs TRI is included. For this flame, the optically thin assumption is found to be valid, in agreement with previous investigations. The calculated radiative fraction is found to be close to the experimental value, but the postprocessed heat release rate is larger than the experimental finding. This first LES-CSE study provides a good foundation for further developments, for example more detailed radiation models, for more complex fire cases.

Keywords:

• LES
• CSE
• buoyant
• turbulent
• flame

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The present study was made possible thanks to the funding provided by the Natural Sciences and Engineering Research Council of Canada (NSERC). Computational resources are provided by Digital Research Alliance of Canada. Computations are performed on the Niagara cluster managed by the SciNet HPC Consortium at the University of Toronto.