Prediction of temperature and velocity profiles in a single compartment fire by an improved neural network analysis

A novel hybrid Artificial Neural Network (ANN) model, denoted GRNNFA, has been developed for fire studies. The major feature of the model is its ability to work in a noisy environment, which is usually the case with fire experiment data. The GRNNFA model is applicable in the determination of the location of the thermal interface in a single compartment fire. The performance of the GRNNFA has been proven to be comparable to that of the Computational Fluid Dynamics (CFD) model. In addition, the computational speed of the GRNNFA model is much faster than that of the CFD model. However, the original GRNNFA model is only capable of handling the training samples with scalar output. This shortcoming restricts the application area of the model. Hence, this paper presents a modification of the original GRNNFA model for multi-dimensional prediction problems. It also demonstrates the first application of ANN techniques to predicting the velocity and temperature profiles at the center of the doorway in a single compartment fire. These profiles are commonly used to benchmark the performances of CFD models. They are employed in this study to evaluate the performance of the modified GRNNFA model. The predicted profiles are compared with the experimental results and the results simulated by the CFD model. These results show that the prediction errors of the GRNNFA model are less than those of the CFD model in actual application.