Assessment of radiative heat transfer characteristics of a combustion mixture in a three-dimensional enclosure using RAD-NETT (with application to a fire resistance test furnace)

Using RAD-NETT, a neural network correlation of the non-gray radiative absorption properties of combustion gases (CO2 and H2O) and soot, the emissivity and hemispherical absorptivity of a combustion mixture to a boundary in a rectangular enclosure is determined. Results show that the both the emissivity and hemispherical absorptivity have a strong dependence on the mixture properties, as well as the medium temperature and wall temperature. The gray assumption with emissivity equal to absorptivity is generally inaccurate. The numerical model is used to analyze temperature and heat transfer data generated from a fire resistance test furnace. Results show that emission and reflection from the wall boundaries have a major effect of the radiative heat flux measurement in a test sample in a fire resistance test. Numerical results also demonstrate that the furnace was operating essentially in an isothermal condition. From the perspective of a compartment fire, numerical data show that soot emission and emission from the wall are essential in the initiation of flashover in a compartment fire.