Development of correlations and artificial neural network models to predict second-degree burn time for thermal-protective fabrics

Abstract

Firefighters and operators working near industrial furnaces or ovens usually encounter extreme environmental conditions. Heat transfer through fabrics exposed to such high heat flux exposures causes skin burn. There are various parameters which govern heat transfer and second degree burn time in such situations. In this work, six dimensionless parameters associated with heat transfer through fabrics exposed to various flame and radiant heat flux exposures are obtained. Correlations for dimensionless second degree burn time are obtained in terms of other non-dimensional parameters. These separate correlations for each type of exposure can be used to obtain thermal protective performance (TPP) of any fabric exposed to different type of heat conditions, provided that fabric material, fabric geometric parameters, air gap between fabric and skin, and intensity of heat exposure are known. Artificial neural network (ANN) approach is also used to predict TPP of clothing. Multi-layer feed-forward back-propagation networks are developed for different type of exposures. Results shows that both correlations and ANN approach used in the study are promising. ANN model predictions are found to agree better with experimental observations as compared to the outcome of developed correlations. Importance of various dimensionless parameters obtained using dimensional analysis are also emphasized.