Visualisation of jet fires from hydrogen release

Deimling, L.; Weiser, Volker; Blanc, Andrew Le; Eisenreich, N.; Billeb, G.; Keßler, Armin

doi:10.1016/j.ijhydene.2010.05.068

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…It was already successfully applied to quasi stationary hydrogen jets [23,32]. It is especially useful for transient events, and delivers transient flame contours.…”

Section: Data Evaluationmentioning

confidence: 99%

Ignition of hydrogen jet fires from high pressure storage

Keßler

Schreiber

Wassmer

et al. 2014

International Journal of Hydrogen Energy

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“…It was already successfully applied to quasi stationary hydrogen jets [23,32]. It is especially useful for transient events, and delivers transient flame contours.…”

Section: Data Evaluationmentioning

confidence: 99%

Ignition of hydrogen jet fires from high pressure storage

Keßler

Schreiber

Wassmer

et al. 2014

International Journal of Hydrogen Energy

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“…Moreover, recent studies involving numerical analysis of jet fires [4] , [5] , [6] , [7] only analyzed these fire incidents from the context of simple geometry, and there is not enough study about a simulation of jet fire in a complex structure such as a chemical plant [3] .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an analysis of a jet fire from a pipe connected to a compressor under high pressure with a simulation methodology was conducted with respect to a fire accident that frequently occur in a chemical plant, and various variables such as forms of installations and tools, positional density, turbulence, atmospheric condition, obstacles, and wind effect were assessed for an analysis of the thermal effect using Computational Fluid Dynamics (CFD), which generates the virtually estimated result to be very similar to the actual result [3] , [7] , [8] .…”

Section: Introductionmentioning

confidence: 99%

Simulation and Damage Analysis of an Accidental Jet Fire in a High-Pressure Compressed Pump Shelter

Jang

Choi

2017

Safety and Health at Work

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BackgroundAs one of the most frequently occurring accidents in a chemical plant, a fire accident may occur at any place where transfer or handling of combustible materials is routinely performed.MethodsIn particular, a jet fire incident in a chemical plant operated under high pressure may bring severe damage. To review this event numerically, Computational Fluid Dynamics methodology was used to simulate a jet fire at a pipe of a compressor under high pressure.ResultsFor jet fire simulation, the Kemeleon FireEx Code was used, and results of this simulation showed that a structure and installations located within the shelter of a compressor received serious damage.ConclusionThe results confirmed that a jet fire may create a domino effect that could cause an accident aside from the secondary chemical accident.

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“…The Piper Alpha incident, the deadliest offshore oil industry disaster on record with 167 fatalities, resulted from fire and subsequent explosion caused by the release of pressurized hydrocarbons. , The consequence of a particular fire incident can be quantified in terms of a designated radiation effect distance ( L de ), with the designated distance in current work being chosen as the distance at which heat radiation intensity equals a threshold value of 12.5 KW/m 2 , which is sufficient to cause equipment damage, plastic melting, and blistering . . − …”

Section: Introductionmentioning

confidence: 99%

Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks

Sun

Wang

Zhu

et al. 2019

Ind. Eng. Chem. Res.

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This paper demonstrates the successful implementation of an artificial neural network to accurately predict the designated thermal radiation distance for jet fire, early pool fire, and late pool fire hazard consequence analysis. Specifically, integrated feedforward neural network models employing the backpropagation Levenberg–Marquardt algorithm were trained using data sets obtained through separate PHAST software simulations of 450 leak scenarios of 35 common flammable chemicals. For each fire model (jet, early, late pool), there are 11 input parameters spanning both chemical parameters and release conditions. Simulation data was randomly divided into 70% training, 15% validation, and 15% test sets to conduct cross-validation and provide an independent measure of predictive accuracy for the neural network models. Statistical values, namely, coefficient of determination (R 2) and mean-square error (MSE), are calculated to evaluate model regression performance. All three neural network predictive models achieved considerably accurate predictions of the logarithm format of the designated radiation effect distance. The models give an overall R 2 of 0.9930 and an MSE of 0.0022 for jet fire, an overall R 2 of 0.9909 and an MSE of 0.0016 for early pool fire, and an overall R 2 of 0.9899 and an MSE of 0.0015 for late pool fire.

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Visualisation of jet fires from hydrogen release

Cited by 16 publications

References 9 publications

Ignition of hydrogen jet fires from high pressure storage

Ignition of hydrogen jet fires from high pressure storage

Simulation and Damage Analysis of an Accidental Jet Fire in a High-Pressure Compressed Pump Shelter

Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks

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