Improved far-field blast predictions from fast deflagrations, DDTs and detonations of vapour clouds using FLACS CFD

Hansen, Olav R.; Johnson, M. M.

doi:10.1016/j.jlp.2014.11.005

Cited by 61 publications

(42 citation statements)

References 10 publications

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“…By comparing the simulation results, the following observations can be made: The region exposed to very high pressures inside the process areas and at the hull deck is much larger if there is a DDT than for a deflagration only, that is, significantly worse damage inside and in the near‐field around the parts of the cloud that detonates can be expected if a DDT takes place. In the far‐field, for example, the accommodation structure, the explosion loading is very comparable for the deflagration case and the DDT case, and in some cases far‐field pressures in the direction of the explosion may even be lower if there is a DDT. The reason for this is that the blast energy is more directional along the explosion path for a deflagration, while the blast energy in the case of detonation is spread in all directions (same conclusion can be seen from the example in Figure , see ). …”

Section: Risk Assessment For the Fpso Process Areasupporting

confidence: 64%

“…In the far-field, for example, the accommodation structure, the explosion loading is very comparable for the deflagration case and the DDT case, and in some cases far-field pressures in the direction of the explosion may even be lower if there is a DDT. The reason for this is that the blast energy is more directional along the explosion path for a deflagration, while the blast energy in the case of detonation is spread in all directions (same conclusion can be seen from the example in Figure 1, see [11]). This second bullet is a very important observation, this means that if it is possible to dimension the primary structures of the process area and the hull to survive the extreme loads of a detonation, and critical escalation scenarios can be prevented, the challenges with regard to personnel safety at accommodation and muster areas will not get significantly worse for a DDT as long as these areas are a certain distance away from the explosion event.…”

Section: Ddt Scenariossupporting

confidence: 62%

“…Even if ignored in the past, strong methane dominated natural gas explosions may also undergo DDT. This was substantiated by Hansen and Johnson when reassessing large‐scale natural gas explosion tests in detail. A well‐defined CFD modeling approach was developed with the FLACS tool to predict consequences from detonation and DDT scenarios.…”

Section: Introductionmentioning

confidence: 78%

“…The DDT‐detection variable DPDX was included in the simulations in order to predict which scenarios that could be likely to experience DDT. If a DDT was predicted for a scenario, the actual scenario would be resimulated with DDT settings specified, in current guidelines for how to evaluate a scenario for DDT are given: DPDX < 0.5 DDT is not considered likely 0.5 < DPDX < 5 DDT may happen if hot spot region in flame front is significant* DPDX > 5 DDT is likely if hot spot region in flame front is significant* …”

Section: Risk Assessment For the Fpso Process Areamentioning

confidence: 99%

“…The DDT-detection variable DPDX was included in the simulations in order to predict which scenarios that could be likely to experience DDT. If a DDT was predicted for a scenario, the actual scenario would be resimulated with DDT settings specified, in [11] current guidelines for how to evaluate a scenario for DDT are given: Based on an evaluation of the FLACS DPDX-parameter (predicting DDT) all propane scenarios with estimated Q9 of 10,000 m 3 and higher were predicted to undergo DDT with the conservative ignition locations chosen. For the natural gas scenarios, only one or two of the 15 simulated cases were predicted to undergo DDT, for the smallest with Q9 cloud size around 5,000 m 3 the DDT indicators were not convincing enough to definitely conclude for a DDT, while for the larger cloud of 10,000 m 3 there was a clear indication of likely DDT.…”

Section: Explosion Studymentioning

confidence: 99%

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Impact of DDT on FPSO explosion risk assessment

Hansen¹,

Martini²,

Choi

et al. 2014

Process Safety Progress

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In recent explosion accidents on onshore petrochemical facilities, it has been concluded that a deflagration‐to‐detonation‐transition (DDT) took place, both with Liquefied Petroleum Gas (LPG) and gasoline vapor. DDT has also been observed in a number of large‐scale experiments. DDT leads to very high pressures (16–20 barg) and flame speeds (1,600–2,000 m/s) even outside congested regions and has a significant impact on the severity of the near‐field and far‐field explosion loads. It has not been generally accepted that DDT may be a threat at offshore petrochemical facilities and prediction tools have not been available. Thus, the potential effect of DDT is seldom considered. For Floating production, storage and offloading vessels (FPSOs), and even more Floating liquified natural gas vessels (FLNGs), due to larger dimensions and inventories, operators should be concerned that major explosion scenarios can lead to DDT phenomena. Recently, a DDT prediction parameter was implemented in the Computational Fluid Dynamics (CFD) model FLACS, and with model adjustments it is possible to calculate DDT and effect on explosion loads with good precision. A CFD‐based FPSO explosion study is presented evaluating DDT potential and impact. DDT will mainly be a concern for large cloud sizes, often larger than commonly considered for design accidental loads for an FPSO (10−4/year). Mitigation by separation walls is also evaluated. © 2014 American Institute of Chemical Engineers Process Saf Prog 34: 44–57, 2015

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Section: Risk Assessment For the Fpso Process Areasupporting

confidence: 64%

Section: Ddt Scenariossupporting

confidence: 62%

Section: Introductionmentioning

confidence: 78%

Section: Risk Assessment For the Fpso Process Areamentioning

confidence: 99%

Section: Explosion Studymentioning

confidence: 99%

See 3 more Smart Citations

Impact of DDT on FPSO explosion risk assessment

Hansen¹,

Martini²,

Choi

et al. 2014

Process Safety Progress

View full text Add to dashboard Cite

show abstract

Explosion pressure and duration prediction using machine learning: A comparative study using classical models with Adam‐optimized neural network

Idris,

Rusli,

Mohamed

et al. 2024

Can J Chem Eng

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The application of machine learning (ML) for the prediction of gas explosion pressure remains limited, and the prediction of the explosion duration is nearly non‐existent. A series of dispersion and subsequent explosion computational fluid dynamics (CFD) simulations were conducted to determine explosion pressure and duration values. These results were used to train classical ML models, that is, support vector regression (SVR), random forest (RF), and decision tree (DT) models. Additionally, a multi‐output Adam‐optimized artificial neural network (ANN) model was employed for performance comparison. All the models demonstrated respectable predictions for both parameters, while the RF model demonstrated the highest performance based on the metrics analyzed, followed by the DT model. The proposed gas volume and gas volume blockage ratio (gas‐VBR) emerged as the most crucial feature for predicting explosion pressure, while the monitoring point and gas‐VBR was the most important feature for explosion duration. It is recommended to consider the gas‐VBR feature in future studies rather than solely focusing on blockage ratio or obstacle location. The model proposed was compared with models from previous studies for predicting explosion pressure. The findings conclusively demonstrate that the multi‐output model outperforms the compared models, offering a notable advantage in its ability to predict both gas explosion pressure and duration.

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Numerical investigation of the in situ gas explosion fracturing and the enhancement of the penetration in coal seam boreholes

Qiao,

Zhang,

Yuan

et al. 2023

Energy Science & Engineering

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Permeability enhancement of low permeability coal seams is a key tool in coal mine gas extraction and utilization. Numerical investigations are used to analyze the geometric parameters of the drilled gas cavity and the effect of initial pressure on explosion parameters to explore the potential of original gas blasting in enhancing crack penetration in coal seams. On the basis of these analyses, the changes in characteristic parameters of the coal body under blasting pressure are examined. The results show that the maximum explosion pressure tends to be close to the theoretical explosion maximum when the L/Φ is 1.67–2.5, and the mechanical effect of the explosion pressure and surge velocity on the wall reaches the optimum in the experimental group when the L/Φ is 1.67. The initial pressure and the maximum explosion pressure demonstrate a linear positive correlation, and increasing the former can effectively enhance the force applied to the hole wall. During the gas explosion pressurization stage, the cracking range of coal under pressure is 0.05 m, but the duration of peak pressure can extend the range of cracking. The combined effect of the stress and pressure fields causes elastic energy storage in the coal body to fail in the radial region of the hole wall, but regains elastic energy storage as the pressure increases. The effect of fracturing on the radial coal seam permeability of the borehole wall before and after the fracturing effect is expanded by 19.6 times, proving that in situ gas explosion in boreholes can effectively improve the gas seepage characteristics of coal seams and increase the gas recovery rate. The simulations indicate that the application of in situ gas explosion fracturing and permeation technology is limited by the increase in maximum explosion pressure and the cumulative effect time of the peak pressure. This provides a theoretical basis for understanding the constraints of gas explosion fracturing and permeation technology.

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Improved far-field blast predictions from fast deflagrations, DDTs and detonations of vapour clouds using FLACS CFD

Cited by 61 publications

References 10 publications

Impact of DDT on FPSO explosion risk assessment

Impact of DDT on FPSO explosion risk assessment

Explosion pressure and duration prediction using machine learning: A comparative study using classical models with Adam‐optimized neural network

Numerical investigation of the in situ gas explosion fracturing and the enhancement of the penetration in coal seam boreholes

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