# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
## 4 Flashover Occurrence Prediction
The following researches include real time applications techniques to predict certain situations related to confined fires. A small world network model to predict real-time fire spread on board naval vessels was developed by [PERSON] et al. [10]. Despite the fact that the purpose of this research is not the flashover prediction, it was taken in account to evaluate the fire compartment and decide if it will be crucial for fire spread to other enclosure. To determinate the occurrence of flashover the pyrolysis rate in the compartment was evaluated and the duration of the fully developed phase is calculated using empirical formulas for a fuel limited combustion and ventilated limited combustion. Finally, full-scale experiments in a multi-compartment fire container with mechanical ventilation and CFD simulation were conducted to validate the model and determinate the probability of spreading a fire in a specified configuration naval vessel. Despite pyrolysis rate is considered to predict the occurrence of flashover it cannot be considered for predicting these situations with TIC. Related to TIC [PERSON] et al., proposed a method [56] for fire heading in smoke-filled indoor environments using thermal imaginary designed for SAFFiR (Shipboard Autonomous Firefighting Robot). It is based on a previous study [11] in which it was used a probabilistic classification for fire, smoke, their thermal reflections and hot objects using Bayesian theory. The key point is the fire and smoke classification, a clustered-based image technique is used to discriminate the two classes foreground (objects) and background, resulting in fast computation during real-time implementation. Despite this method being used inside an enclosure, another approach classifying smoke from an outside compartment with a thermal camera could provide some relevant information about the fire development. In a recent study [57] Conditional Generative Adversarial Networks were used to predict rapid fire growth (flashover) in real time. There are some visual signals that can warn firefighters about a possible flashover, like dark smoke, high heat and rollover. A standard body camera was used to analyse the colour video stream. Very dark fire and smoke patterns were enhanced applying generative adversarial neural networks. Previously image-to-image conversion techniques were applied using conditional adversarial networks in order to obtain a thermal image from a regular colour image. The neural network training and test was carried out using 30 and 10 videos provided by two different fire departments. As a result flashover was predicted as early as 55 s before it occurred. While this technology was developed from a colour image authors remarks it can be applied over thermal images from TIC's to predict flashover situations. Currently training and test datasets from real situations are limited. Only is possible analysed a few cases with simple configurations like fire containers. CFD software could be an interesting tool to generate datasets with different configurations and fuels in order to train neural networks from TIC's images. Table 6 provides an overview of the references related with fire prediction cited in this section. The reader can detect at a glance if the reference is related with flashover phenomenon, which are the most important techniques used in the research, the software used for the simulations, infrared sensors and TIC employment.