Height of turbulent non-premixed jet flames at elevated pressure

Guiberti, Thibault F.; Boyette, Wesley R.; Roberts, William L.

doi:10.1016/j.combustflame.2020.07.010

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…Guiberti, Boyette, and Roberts [4] explore the Delitcharsios' model to obtain the flame height for subsonic jet flames at elevated pressure. The results show that the Delitcharsios' model predicts well around 20% of the flame height in these cases, so a range of two empirical constants are suggested to improve the predictions of the flame height equation.…”

Section: Jet Fire Geometrical Featuresmentioning

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 Geometrical Featuresmentioning

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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show abstract

“…For instance, different radiation and turbulence models, along with CFD simulations, were explored by Kashi and Bahoosh [12] to evaluate how firewalls or equipment around the flame can affect the radiation distribution and temperature profile of the jet fire. The Delichatsios' model was explored by Guiberti et al [9] to obtain the flame height for subsonic jet flames at elevated pressure; however, this model by itself predicts well around 20% of the flame height in these cases. Mashhadimoslem et al [19] explored two Artificial Neural Networks (ANN) to estimate the jet flame lengths and widths based on the mass flow rates and the nozzle diameters.…”

Section: Risk Assessment and Modelingmentioning

confidence: 99%

Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach

Pérez-Guerrero¹,

Ciprián-Sánchez²,

Palacios³

et al. 2022

Preprint

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Among the different kinds of fire accidents that can occur during industrial activities that involve hazardous materials, jet fires are one of the lesser-known types. This is because they are often involved in a process that generates a sequence of other accidents of greater magnitude, known as domino effect. Flame impingement usually causes domino effects, and jet fires present specific features that can significantly increase the probability of this happening. These features become relevant from a risk analysis perspective, making their proper characterization a crucial task. Deep Learning approaches have become extensively used for tasks such as jet fire characterization; however, these methods are heavily dependent on the amount of data and the quality of the labels. Data acquisition of jet fires involve expensive experiments, especially so if infrared imagery is used. Therefore, this paper proposes the use of Generative Adversarial Networks to produce plausible infrared images from visible ones, making experiments less expensive and allowing for other potential applications. The results suggest that it is possible to realistically replicate the results for experiments carried out using both visible and infrared cameras. The obtained results are compared with some previous experiments, and it is shown that similar results were obtained.

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Effects of pressure on the structure of partially premixed turbulent diffusion flames: Calculations using MMC-LES

Aldawsari

Galindo-Lopez

Cleary

et al. 2023

Combustion and Flame

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Height of turbulent non-premixed jet flames at elevated pressure

Cited by 11 publications

References 18 publications

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

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

Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach

Effects of pressure on the structure of partially premixed turbulent diffusion flames: Calculations using MMC-LES

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