# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
## 1 Introduction
Sometimes this is not possible due to the rapid response multitasking nature of the emergency and the corresponding stress that these situations generate for the firefighter Attempting a prevention of the flashover phenomenon described in this paper, the firefighter handling the TIC normally tries to monitor the temperature of the hot gas layer to detect changes in the environment. However, this method is not very accurate because flashover depends on multiple factors such as compartment configuration, fuel and ventilation, among others, and most importantly, the human factor. Another reason is that temperature acquisition with a TIC does not work in the same way when a smoke layer is present. This is because the temperature shown by the camera is the reflection temperature and not the inside temperature of the smoke layer. For instance, carbon particles or water vapour can reflect the visible light interfering in the image acquisition process. On the other hand due to the long wave of infrared light it is not easily reflected by smoke layer particles. In view of the previous discussion, it is worthwhile mentioning the potential for using AI to predict this phenomenon. For this reason, this paper reviews work which has applied AI in similar situations. Through AI techniques, which focus on the study of intelligent agents, a device can take actions that maximise the chances of successfully completing a task based on environment  
Figure 3: Enclosure effects on heat transfer in a fire container. (**a**) Schematic overview: Blue arrows indicate heat transfer, which is not used in the fire process; ‘rad’ refers to radiation, ‘cond’ refers to conduction and ‘conv’ refers to convection. The curved arrow refers to the mass flow rate of combustible gases [5]. (**b**) Thermal image of enclosure effects in a fire container during a firefighting training at San Vicente Fire Station, Alicante, Spain. perception. Nowadays AI techniques, such as flame detection [8; 9], fire spread [10], fire and smoke classification [11], flashover occurrence [12; 13], thermal interface location in a single compartment fire [14] or temperature and velocity profiles [15] among others, have been proven to predict certain fire-related situations. Different approaches have been used to research fire prediction, the most commonly adopted approach is artificial neural networks (ANN). ANN are computing systems usually used to find complex relationships between a source (input) and a target(output). Furthermore, research about fire science including this technology can be found. For example, in image recognition, they might learn to identify images containing fire by analysing example images and using the results to detect fire in other images [16]. Related to image dataset, convolutional neural networks (CNN) seems to be more effective to find highly accuracy patterns. It is noteworthy to mention that it is very difficult to find a useful data set of real flashover emergencies. To address this interesting point, this paper reviews research that used synthetic data obtained from simulations. The main contribution of this manuscript is to conduct a review of the literature related to the modelling and simulation of enclosure fires with respect to flashover phenomenon. This is very important to be aware of the transcendence of the phenomenon as well as to analyze how technological innovation can contribute to predict it. The outline of this paper is as follows. Firstly, research related with flashover modelling and simulation techniques is reviewed. Secondly, the cutting edge technology for comparing synthetic images with real images from thermal imaging cameras is revised. Third, the models that have been developed to predict flashover are reviewed. Fourth, we include a results and discussion section, where the key points of each section are treated. Conclusions and future lines of work can be consulted in the last section.