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 the 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. This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames to extract main geometrical attributes, relevant for fire risk assessments. A comparison is made between traditional image processing methods and some state-of-the-art deep learning models. It is found that the best approach is a deep learning architecture known as UNet, along with its two improvements, Attention UNet and UNet++. The 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 Attention UNet and UNet++. 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.
翻译:喷气机的火灾相对较小,在工业工厂中可能发生的不同火灾事故中,喷气机的火灾影响最小;然而,它们通常涉及被称为多米诺效应的过程,导致更严重的事件,如爆炸或引发另一起火灾,使对此类火灾的分析成为风险分析的一个重要部分。这一研究工作探索了在另一种方法中应用深学习模型,这种方法使用喷气火的语义分割法来提取与火灾风险评估相关的主要几何特性。对传统图像处理方法与一些最先进的深度学习模型进行了比较。发现最佳方法是一个深层学习结构,称为UNet,以及两个改进,即UNet和UNet++。然后这些模型被用来将一组不同管道直径的垂直喷气火焰分开,以提取其主要的几何特性。 注意UNet在高空和火焰地区的近距离上都取得了最佳的总体性能,同时显示它与UNet++之间在统计学上非常显著的准确性值差异。 UNet在升平流模型和升平流模型之间取得了良好的性能。