Jet fire assessment in complex environments using computational fluid dynamics

Kashi, Eslam; Bahoosh, Mohsen

doi:10.1007/s43153-019-00003-y

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…However, it is possible to identify the optimal space for the location of equipment and structures, by evaluating the size and shape of the flames. Therefore, being able to predict the shape and proportions of a jet fire is extremely important from a risk management point of view [24]. Previous research has been done to characterize the main geometrical aspects of jet flames.…”

Section: Jet Fire Geometrical Featuresmentioning

confidence: 99%

“…The models mentioned previously lack consideration of the geometry of the system, which may make them unreliable when dealing with barriers or equipment, therefore CFD represent an alternative solution for evaluating both the jet fire's geometric features and thermal radiation. For instance, Kashi and Bahoosh [24] explored different radiation and turbulence models along with CFD simulations to evaluate how firewalls or equipment around the flame can affect the radiation distribution and temperature profile of the jet fire.…”

Section: Jet Fire Thermal Radiationmentioning

confidence: 99%

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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%

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

View full text Add to dashboard Cite

show abstract

“…Furthermore, the thermal radiation of fire incidents can be analyzed to monitor the heat transfer process to nearby objects. Therefore, fast and reliable tools to obtaining the shape and proportions of a jet fire, as well as their radiation properties is extremely important from a risk management point of view [12].…”

Section: Risk Assessment and Modelingmentioning

confidence: 99%

“…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.…”

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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“…14 However, with the advancements in computing technology, numerical model-based calculations have significantly improved, allowing for the implementation of 3D CFD simulations under various boundary and atmospheric conditions. [15][16][17][18] In addition, there are studies that evaluate quantitative risks by 3D CFD simulations and evaluate the effects of thermal radiation and structural damage caused by fires and explosions. [19][20][21][22] In this study, the severity of radiant thermal radiation and explosion over incidents involving fire and explosion accidents was simplified and expressed as severity indices, ranking their impact.…”

mentioning

confidence: 99%

Damage mitigation method studies through simulation modeling of chemical accidents

Oh,

Lee,

2024

Process Safety Progress

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In the chemical industry, when a fire occurs, a significant amount of energy is generated due to combustion, impacting other facilities within the plant and potentially leading to severe consequences through a domino effect. For decades, thermal radiation caused by flames has been calculated and predicted through simplified fire modeling. However, with advancements in computing technology, numerical model‐based calculations have greatly improved, allowing for a more realistic implementation that considers actual phenomena. In this study, accident data and 3D modeling information were utilized to conduct fire modeling and simulation based on actual incidents in chemical plants. Through the analysis of simulation results, the initial emergency evacuation distance was provided to minimize the damage caused by thermal radiation, and the final evacuation distance was presented using the probit function. In addition, the study evaluated the impact of generated thermal radiation and overpressure on structures and equipment, providing evidence regarding the potential for secondary incidents. Moreover, the research revealed that the impact of thermal radiation and overpressure decreases due to obstacles, offering insights into the selection of emergency evacuation routes. This study can contribute to supporting effective emergency evacuation strategies in chemical facilities.

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Jet fire assessment in complex environments using computational fluid dynamics

Cited by 10 publications

References 27 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

Damage mitigation method studies through simulation modeling of chemical accidents

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