Characterization of jet fire flame temperature zones using a deep learning-based segmentation approach

Abstract

Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants; however, they are usually involved in a process known as domino effect, that leads to more severe events, such as explosions or the initiation of another fire, making the analysis of such fires an important part of risk analysis. One such analysis would be the segmentation of different radiation zones within the flame, therefore this thesis presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem. A data set of propane jet fires is used to train and evaluate the different approaches. Different metrics are correlated to a manual ranking performed by experts to make an evaluation that closely resembles the expert’s criteria. Additionally, given the difference in the distribution of the zones and background of the images, different loss functions, that seek to alleviate data imbalance, are explored. The Hausdorff Distance and Adjusted Rand Index were the metrics with the highest correlation and the best results were obtained from training with a Weighted Cross-Entropy Loss. The best performing models were found to be the UNet architecture, along with its recent variations, Attention UNet and UNet++. These models are then used to segment a group of vertical jet flames of varying pipe outlet diameters to extract their main geometrical characteristics. Attention UNet obtained the best general performance in the approximation of both height and area of the flames, while also showing a statistically significant difference between it and UNet++. UNet obtained the best overall performance for the approximation of the lift-off distances; however, there is not enough data to prove a statistically significant difference between UNet and its two variations. The only instance where UNet++ outperformed the other models, was while obtaining the lift-off distances of the jet flames with 0.01275 m pipe outlet diameter. In general, the explored models show good agreement between the experimental and predicted values for relatively large turbulent propane jet flames, released in sonic and subsonic regimes; thus, making these radiation zones segmentation models, a suitable approach for different jet flame risk management scenarios.