Improving Heat Flux Predictions for Directional Flame Thermometers by Incorporating Convective Effects

Abstract

The directional flame thermometer (DFT) is a device used to measure radiative heat flux in fire scenarios. In many DFT applications, radiation is the dominant mode of heat transfer and convection does not significantly affect radiative heat flux predictions. However, at higher air velocities, neglecting convection causes error in heat flux predictions. This study explores the error caused by ignoring forced convection in heat flux predictions and proposes a method to reduce it. To accomplish this, a forward heat transfer model is used to generate synthetic temperature data for the DFT given radiative and convective conditions. Using the forward model and an existing inverse model, this study shows that assuming natural convection is the only mode of convective heat transfer leads to significant error in the predicted radiative heat flux. To reduce this error, this work proposes a framework for recovering the heat transfer coefficient and radiative heat flux in forced convection conditions. The framework utilizes an additional thermocouple above the plate of the DFT. The framework performed poorly for simulations in which the temperature of the air stream and that of the thermocouple was lower than 15$^{\circ }$C. Omitting simulations where this condition holds, on average, using the framework under constant heat flux and constant characteristic air velocity conditions reduces the error in the predicted heat flux by a factor of 12 compared to when the heat flux is predicted assuming that natural convection is the only mode of convection. A simple experimental test was conducted to validate the computational framework.