# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
## 6 Conclusions and Future Work  
Regarding to flashover phenomenon modelling, real complex scenarios with different vents and fuels have not been evaluated. Reduced-scale experiments with a simple configuration are used to study this kind of situation. However, to determine the occurrence of flashover, not only do gas layer temperature and heat flux radiation at floor level need to be considered but also ventilation factor and HRR/time. For this reason, other configurations of the enclosure should be analysed. In conclusion, flashover is well simulated using different CFD software.  
The articles reviewed suggest that it is possible to obtain images from CFD software that are similar to a specific thermal imaging camera using its technical parameters. This should be taken into account for developing artificial intelligence technology based on TIC if CFD data want to be used to train ANN's. Programming and computer vision techniques can be used to conduct the mathematical calculations to achieve the final result.  
Some research regarding flashover prediction using ANN was identified but to the best of our knowledge, research that predicts this phenomenon with TIC's using datasets from CFD software in order to train ANN's have not been found. Furthermore, thermal image cameras may be useful devices to predict flashover by fire services using ANN. Considering the research analysed, new prediction models can be developed focusing on real-time thermal images.  
Taking into account the different areas studied and analysed in this review we conclude that flashover phenomenon is sufficient studied to be modelled and simulated using simple configurations. Furthermore, AI techniques have been used to predict its occurrence using sensors placed inside the enclosure. However, there are not any research which use data from CFD software to train ANN with flashover prediction purposes in TIC's.  
We envisage future research on predicting flashover phenomenon with TIC's using datasets made from CFD software to train ANN's. This technology can prevent dangerous situations by warning firefighters, who handle thermal cameras, in real-time. Gas layer temperature, heat flux radiation at floor level, ventilation factor and HRR/time in the enclosure should be factored in. CFD techniques may be used to train the ANN model, and real experiments in a fire container may be a useful way to validate the final model. Furthermore, the study of this phenomenon using AI can help to delve into certain aspects that may not have been considered previously.  
As future work, we plan to use other parameters from field models in order to create a dataset with thermal vision characteristics to train ANN models based on TIC. This will allow a breakthrough for the simulation and prediction of the flashover phenomenon.  
[PERSON] conceived of the presented idea and wrote the manuscript with support from [PERSON], [PERSON], [PERSON], [PERSON] All authors provided critical feedback and helped shape the research, analysis and manuscript. All authors discussed the challenges an opportunities, commented on the conclusions and future work and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Spanish Government TIN2017-89069-R grant supported with Feder funds. This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32 and the Lucentia AGI Grant.  
We would like to express our deepest appreciation to Alicante Province Fire Service for providing us support during this project.  
The authors declare no conflict of interest.