Experimental and computational analysis of vertical jet fires of methane in normal and sub-atmospheric pressures

Rengel, B.; Àgueda, Alba; Pastor, Elsa; Casal, Joaquim; Planas, Eulàlia; Hu, Lu; Palacios, Adriana

doi:10.1016/j.fuel.2019.116878

Cited by 22 publications

(6 citation statements)

References 48 publications

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“…3.2Flame Height. Combined with the research of domestic and foreign scholars, 45 this paper defines the geometric characteristics of the flame as follows (Figure 4): Through visual image processing, the jet flame height curve of three different-diameter nozzles can be obtained (Figure 5). It can be seen from Figure 5 that first, the height of the jet fire flame increases, then remains stable for a short time, and finally decreases slowly.…”

Section: Resultsmentioning

confidence: 99%

“…Combined with the research of domestic and foreign scholars, this paper defines the geometric characteristics of the flame as follows (Figure ): (i) flame height, L F , the distance from the nozzle to the flame tip, (ii) flame width, W F , the average width occupied by the flame transversely, and (iii) flame area, A F , isothermal contour or visual flame surface.…”

Section: Resultsmentioning

confidence: 99%

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Experimental Research on the Combustion Characteristics of Transformer Oil Jet Fires in Oil-Filled Equipment under Heat

et al. 2021

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Under the action of heat, the jet fire of oil-filled equipment in substations produces intense heat radiation and flame impact, causing serious harm to the personnel and equipment. Therefore, this paper has carried out a series of oil-filled equipment transformer oil jet fire experiments with different nozzle diameters to study the characteristics of transformer oil jet flame combustion. The experiment measured the mass loss rate, axial centerline temperature of the fire plume, and radiant heat flux and processed the flame visualization image. The results show that the combustion process of the transformer oil jet fire can be divided into three stages. The functional relationship between the flame height and mass flow and the functional relationship between the flame temperature and axial height are derived. A prediction model of the radiant heat flux of the transformer oil jet fire for the oil-filled equipment of the substation was established. The research results are of great significance for improving the fire protection design of substations and improving fire management and control capabilities.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Experimental Research on the Combustion Characteristics of Transformer Oil Jet Fires in Oil-Filled Equipment under Heat

et al. 2021

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“…various studies. [27][28][29] In FLACS software, CASD acts as a preprocessing for FLACS simulation, performing geometric models, grids, and scenario inputs. Flowvis, which acts as post-processing, visualizes the simulation results.…”

Section: Monitor Point Settingmentioning

confidence: 99%

“…FLACS is one of the CFD simulators that can simulate release, spread, fire, explosion, etc., and has high reliability such as being used in various studies 27–29 . In FLACS software, CASD acts as a pre‐processing for FLACS simulation, performing geometric models, grids, and scenario inputs.…”

Section: Cfd Modeling and Simulationmentioning

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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“…Over the years, a significant portion of semiexperimental correlations have also been created that could forecast the jet flame shape and heat flux emissions. [1,3,12,[13][14][15][16][19][20][21][22][23][24][25][26][27][28][29][30][31][32] One of the most common methods used nowadays for large-scale jet fire radiation predictions is the use of numerical solution methods, which demand computational time and hardware infrastructure. [32][33][34] Hyperspectral photography, which Wang et al pioneered to improve the prediction of combustion parameters, was used to gather information about the fire.…”

Section: Introductionmentioning

confidence: 99%

Machine learning modelling and evaluation of jet fires from natural gas processing, storage, and transport

et al. 2023

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Jet fires and their repercussions play a significant role in catastrophic incidents that typically have a cascading impact in process industries. Several hydrocarbon experiments from 19 papers were incorporated into the current endeavour to develop simulations of jet flames using machine learning (ML) models. Dimensionless characteristics have been used as output and input variables, including mass flow rates, fuel density, jet flame length, and heat release fluxes. When training three layers of the multi‐layer feedforward neural network (MLFFNN) method, a Bayesian regularization backpropagation approach was adopted and evaluated with the radial based functions (RBF) algorithm. Through an optimization procedure, the first and second hidden layers of the MLFFNN have been optimized to include 10 and five neurons, respectively. The RBF algorithm with 40 neurons in a single layer has been computed using the same method. The best mean square error (MSE) validation results for RBF and MLFFNN were 0.006 and 0.0002, respectively, for 40 and 100 epochs. The MLFFNN and RBF models' respective regression statistical analysis outputs were 0.9949 and 0.9645. The ML method has been identified as a potentially useful technique for precisely predicting the geometrical and radiative characteristics of jet flames.

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Experimental and computational analysis of vertical jet fires of methane in normal and sub-atmospheric pressures

Cited by 22 publications

References 48 publications

Experimental Research on the Combustion Characteristics of Transformer Oil Jet Fires in Oil-Filled Equipment under Heat

Experimental Research on the Combustion Characteristics of Transformer Oil Jet Fires in Oil-Filled Equipment under Heat

Damage mitigation method studies through simulation modeling of chemical accidents

Machine learning modelling and evaluation of jet fires from natural gas processing, storage, and transport

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