Real-Time prediction of pool fire burning rates under complex heat transfer effects influenced by ullage height: A comparative study of BPNN and SVR

Abstract

This research utilizes machine learning methods to forecast the complex, non-linear thermal phenomena, along with heat transfer mechanisms, that influence the burning rate of pool fires, especially with changes in ullage height. Experiments involving pool fires were systematically designed and carried out, incorporating different diameters and ullage heights. Heptane was used as the representative alkane fuels. A dataset containing more than 70,000 sets of data was created as a training dataset for training the Backpropagation Neural Network (BPNN) and Support Vector Regression (SVR) models. During the optimization of machine learning model parameters, this study is based on Particle Swarm Optimization (PSO) with the principle of intelligent optimization to efficiently and accurately screen and optimize the key parameters of the model. The combustion duration, pool dimensions, and non-dimensional ullage height were input into a machine-learning model to predict the burning rate. By comparing against experimental data, the model was found to be able to predict the dynamic evolution of the burning rate of the pool fire in a real-time manner. The SVR model demonstrates greater predictive accuracy in comparison to the BPNN model, and the relative prediction error remains within ± 20 %, which fully proves its effectiveness and generalization ability in the prediction of pool fire burning rate. The insights gained will offer substantial scientific backing for enhanced fire monitoring systems, while highlighting the capability of advanced machine learning methodologies to predict the intricate, real-time thermal dynamics and heat transfer characteristics of burning liquid fuels.