Using an artificial neural network to predict flame spread across electrical wires

Abstract

There is currently a global-scale transition from fossil fuel energy technologies toward increasing use of electrically driven energy technologies, especially transportation and heat, fueled by renewable energy sources, which is making fire safety in electrically powered systems increasingly important. The work presented here provides a coherent understanding of flame spread parametric trends and associated fire safety issues in electrical systems for structural, transportation, and space applications. This understanding was obtained through the use of an artificial neural network (ANN) that was trained to predict the flame spread rate along "laboratory"wires of different sizes and compositions (copper, nichrome, iron, and stainless-steel tube cores and HDPE, LDPE, and ETFE insulation sheaths) and exposed to different ambient conditions (varying flows, pressure, oxygen concentration, orientation, and gravitational strength). For these predictions, a comprehensive database of 1200 data points was created by incorporating flame spread rate results from both in-house experiments (400 data points) and external experiments from other sources (800 data points). The predictions from the ANN show that it is possible to merge various data sets, including results from horizontal, inclined, vertical, and microgravity experiments, and obtain unified predictive results. While these initial results are very encouraging with an overall average error rate of 14%, they also show that future improvements to the ANN could still be made to increase prediction accuracy.