An improved numerical heat transfer model considering pyrolysis effect is proposed to predict thermal performance of heat-resistant fabric subjected to radiant heat flux. The model incorporates the heat-induced change...An improved numerical heat transfer model considering pyrolysis effect is proposed to predict thermal performance of heat-resistant fabric subjected to radiant heat flux. The model incorporates the heat-induced changes in fabric thermophysical properties. The new model has been validated with data from modified Radiant Protective Performance (RPP) tests of flame-resistant cotton fabrics. Comparison with experimental data shows that the predictions of mass loss rates and temperature profiles within the charring material and skin simulant are in reasonably good agreement with the experiments. Results from the numerical model contribute to a better understanding of the heat transfer process within flame-resistant fabrics under high heat flux conditions, and also to establish a systematic method for analyzing heat transfer in other fibrous materials applications.展开更多
This paper is to report a prediction model for thermal protective performance of multilayer fabrics based on Matlab neural network toolbox. Then a back propagation (BP) neural network model is developed to predict the...This paper is to report a prediction model for thermal protective performance of multilayer fabrics based on Matlab neural network toolbox. Then a back propagation (BP) neural network model is developed to predict thermal protective performance of multilayer fabrics for firefighters. The network consists of twelve input nodes, six hidden nodes, and one output node. The inputs are weight, thickness, density of warp and weft, limited oxygen index (LOI), and heat conductivity of each-layer fabric. Thermal protective performance (TPP) rating of multilayer fabrics is the output. In this paper, the data from the experiments are used as learning information for the neural network to develop a reliable prediction model. Finnally the model performance is verified, and the proposed model can be applied to predict the thermal protective performance of multilayer fabrics for firefighters.展开更多
文摘An improved numerical heat transfer model considering pyrolysis effect is proposed to predict thermal performance of heat-resistant fabric subjected to radiant heat flux. The model incorporates the heat-induced changes in fabric thermophysical properties. The new model has been validated with data from modified Radiant Protective Performance (RPP) tests of flame-resistant cotton fabrics. Comparison with experimental data shows that the predictions of mass loss rates and temperature profiles within the charring material and skin simulant are in reasonably good agreement with the experiments. Results from the numerical model contribute to a better understanding of the heat transfer process within flame-resistant fabrics under high heat flux conditions, and also to establish a systematic method for analyzing heat transfer in other fibrous materials applications.
文摘This paper is to report a prediction model for thermal protective performance of multilayer fabrics based on Matlab neural network toolbox. Then a back propagation (BP) neural network model is developed to predict thermal protective performance of multilayer fabrics for firefighters. The network consists of twelve input nodes, six hidden nodes, and one output node. The inputs are weight, thickness, density of warp and weft, limited oxygen index (LOI), and heat conductivity of each-layer fabric. Thermal protective performance (TPP) rating of multilayer fabrics is the output. In this paper, the data from the experiments are used as learning information for the neural network to develop a reliable prediction model. Finnally the model performance is verified, and the proposed model can be applied to predict the thermal protective performance of multilayer fabrics for firefighters.