Journal
FIRE SAFETY JOURNAL
Volume 120, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.firesaf.2020.103103
Keywords
Thermal radiation; Pool fires; Gray gas models; Radiative heat
Funding
- CAPES
- CNPq [302686/2017-7]
- CNPq/Brazil (National Council for Scientific and Technological Development) [400472/2016-3, 205477/2018-6]
Ask authors/readers for more resources
The study found that the gray gas (GG) assumption is effective for modeling weakly-sooting fuels such as ethanol and heptane in fire simulations. Three different GG formulations were compared in Fire Dynamics Simulator (FDS), with the default FDS formulation and the weighted-sum-of-gray-gases formulation showing better performance.
The accuracy of the gray gas (GG) assumption for modeling the radiative transfer in fire simulations is studied for a closed compartment, an open environment, and a compartment with an opening. The liquid fuels used for each case are ethanol, heptane and methanol, respectively. Three different GG formulations, implemented on the solver Fire Dynamics Simulator (FDS), are tested and compared to experimental data. Transient, large eddy simulations (LES) are carried out in FDS, where the radiative transfer is solved coupled to the fluid flow and combustion processes. For each fire configuration, the GG formulations are compared to each other and to experimental data. The mean radiative heat flux computed in the simulations showed a good correspondence to measurements, especially for the ethanol and heptane flames, with average deviations ranging between 7% and 21%, which indicates that the GG assumption is adequate even for these weakly-sooting fuels. Among the GG models tested, the default formulation employed by FDS and the one based on a weighted-sum-of-gray-gases estimation of the medium emittance performed similarly, both providing in general more accurate results than the approach using the Planck-mean absorption coefficient.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available