Experimental study of jet fire radiation and a new approach for optimizing the weighted multi-point source model by inverse methods

Quezada, L.A.; Pagot, Paulo Roberto; França, Francis; Pereira, Fernando M.

doi:10.1016/j.firesaf.2020.102972

Cited by 18 publications

(1 citation statement)

References 24 publications

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“…The Single Point Source model relies on the assumption that the radiation intensity emitted from the source is isotropic, but on the other hand, the Weighted Multi Point Source model considers that the radiation emanates from N point sources distributed along the main axis of the flame. In Quezada, Pagot, França, and Pereira [23], they propose a new version of the inverse method for optimizing the Weighted Multi Point Source model parameters. The proposed new optimized weighting coefficients and the new correlation for the radiative heat transfer rate showed improved results for predicting the radiative heat transfer from the experiments.…”

Section: Jet Fire Thermal Radiationmentioning

confidence: 99%

Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods

Pérez-Guerrero¹,

Palacios²,

Ochoa-Ruiz³

et al. 2022

Preprint

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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 the flame's 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.

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Section: Jet Fire Thermal Radiationmentioning

confidence: 99%