Inverse tracing of fire source in a single room based on CFD simulation and deep learning

Inverse tracing of fire source location is important for investigation after the fire accidents. In this work, the CFD simulations and deep learning were combined to explore a more efficient and intelligent tool for fire investigation. Firstly, a CFD model for single room was built using FDS. Then abundant simulations were performed by varying the initial conditions to collect massive data including temperature distribution and smoke layer heights in the room. The dataset was divided into two parts for the training and validation of BP network, respectively. The inverse model and forward model with the same data set and neural network parameters were established, which saved the time in adjusting models and producing data and improves the efficiency of research. The shared data sets and neural network parameters did not affect the final prediction results. The accuracy of forward and inverse models is still excellent, reaching a high accuracy. In particular, the accuracy of the inverse model had been improved to more than 99% compared with previous studies. The accuracy of the forward model is more than 80%. Besides, the inverse model's robustness was also examined, the model is still valid when some input features are lost.

Automated summary

In this study, CFD simulations and deep learning were combined to develop a more efficient and intelligent tool for fire investigation. A CFD model for a single room was built and abundant simulations were conducted to collect temperature distribution and smoke layer height data. The dataset was divided into two parts for training and validation of the neural network, and both the inverse and forward models showed high accuracy, with the inverse model reaching over 99% accuracy. The robustness of the inverse model was also examined and validated.