Study on the Number of Primary and Secondary Fragments Produced by Explosion of Horizontal Vessel

Li, Zijie; Sun, Defeng; Sun, Jiahui; Jiang, Juncheng

doi:10.1007/978-981-15-9121-1_28

Cited by 3 publications

(7 citation statements)

References 13 publications

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“…The data has been acquired from books and scientific journals. This data was repeatedly presented by Li et al[21]. The counts obtained by Sun et al are given in Table1.…”

supporting

confidence: 61%

“…Propagation of uncertainties expressed by Bayesian priors and posteriors through QRA models is fairly simple and transparent as compared with propagation of Fisherian confidence intervals [36]. Numbers of fragments counted after 259 BLEVE events [21,38] 3 The currently available methods of elicitation of the prior distributions related to PG and MD models allow to develop priors that express the analyst's attitude towards the situation of available knowledge on vessel fragmentation. This knowledge is relatively vague.…”

Section: Review Of Related Work and Available Datamentioning

confidence: 99%

“…Amount of data on specific aspects of these events is not sufficient for a Fisherian (classical, frequentist) statistical analysis in the context of QRA. For instance, the largest widely known set of data on fragmentation of vessels due to BLEVEs consists of approximately 250 records and represents statistically highly nonhomogeneous population of these pressure containers [21,38]. However, we think that limited data on vessel explosions is suitable for Bayesian analysis that forms the methodological basis of QRA [35].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Processing of Data on Bursts of Pressure Vessels

Vaidogas

2021

ITC

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Two alternative Bayesian approaches are proposed for the prediction of fragmentation of pressure vessels triggered off by accidental explosions (bursts) of these containment structures. It is shown how to carry out this prediction with post-mortem data on fragment numbers counted after past explosion accidents. Results of the prediction are estimates of probabilities of individual fragment numbers. These estimates are expressed by means of Bayesian prior or posterior distributions. It is demonstrated how to elicit the prior distributions from relatively scarce post-mortem data on vessel fragmentations. Specifically, it is suggested to develop priors with two Bayesian models known as compound Poisson-gamma and multinomial-Dirichlet probability distributions. The available data is used to specify non-informative prior for Poisson parameter that is subsequently transformed into priors of individual fragment number probabilities. Alternatively, the data is applied to a specification of Dirichlet concentration parameters. The latter priors directly express epistemic uncertainty in the fragment number probabilities. Example calculations presented in the study demonstrate that the suggested non-informative prior distributions are responsive to updates with scarce data on vessel explosions. It is shown that priors specified with Poisson-gamma and multinomial-Dirichlet models differ tangibly; however, this difference decreases with increasing amount of new data. For the sake of brevity and concreteness, the study was limited to fire induced vessel bursts known as boiling liquid expanding vapour explosions (BLEVEs).

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“…The data has been acquired from books and scientific journals. This data was repeatedly presented by Li et al[21]. The counts obtained by Sun et al are given in Table1.…”

supporting

confidence: 61%

Section: Review Of Related Work and Available Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian Processing of Data on Bursts of Pressure Vessels

Vaidogas

2021

ITC

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“…Until now, a probability distribution of the fragment number Nfrg was fitted in the classical statistical (Fisherian) format by means of the maximum entropy method [13,15,16]. However, this method yields a continuous exponential distribution with three parameters which are difficult to handle in the Bayesian format.…”

Section: Bayesian Estimation Of Fragment Numbers By Means Of Poisson-gamma Modelmentioning

confidence: 99%

“…Despite seemingly chaotic and unforeseeable nature of pressure vessels bursts, the fragmentation of vessels that undergo BLEVEs brinks out some regularity in terms of fragment numbers and fragmentation patters [13,14]. This relatively weak regularity allows to approximately predict the number of fragments generated by BLEVEs.…”

Section: Introductionmentioning

confidence: 99%

Explosions of Cylindrical Pressure Vessels Subjected to Fire: Probabilistic Prediction of a Number of Fragments

Vaidogas

2021

mech

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The aim of this study was to propose a procedure for a prediction of the number of fragments generated by fire induced explosions of cylindrical pressure vessels. The prediction is carried out in terms of probabilities of individual fragment numbers. The prevailing numbers of two to four fragments are considered. The fragment number probabilities are estimated by applying data on vessel fragmentations acquired in investigations of past explosion accidents. The pressure vessel explosions known as BLEVEs are considered. The Bayesian analysis is used for the estimation of the fragment number probabilities. This analysis is carried out on the basis of Poisson-gamma model. An approach to developing a gamma prior distribution for the average number of fragments per explosion accident is proposed. The assessment of the fragment number probabilities is carried out by propagating uncertainty related to the average number of fragments to uncertainty in the fragment number probabilities. The stochastic (Monte Carlo) simulation is used for this propagation. Findings of this study are viewed as a possibility to improve the assessment of risk posed by pressure vessel explosions.

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A model study of the effect of explosion fragment size on ejection hazard

Wang

Sun

2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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In order to investigate the impact mechanism of explosion fragment size on its collision probability, five typical fragments that often appear in industrial accidents were selected. Taking into account the rotation of the fragments during ejection, a correction factor for collision probability was proposed, and a modified model for fragment collision probability was established. The results of the case analysis show that the maximum ejection distance of the five types of fragments can reach 550-950 m, and the maximum possible ejection distance is distributed between 76-544 m. The ejection distance of CE-type fragments is relatively small, while that of PT-type fragments is relatively large. In addition, under the condition of considering the size of the fragments, increasing the size of the fragments can improve the collision probability. The impact of the fragment size on the collision probability is more significant within a close range of 60 m. When the distance between the fragment and the target exceeds 60 m, the impact of the fragment size on the collision probability can be ignored.

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Study on the Number of Primary and Secondary Fragments Produced by Explosion of Horizontal Vessel

Cited by 3 publications

References 13 publications

Bayesian Processing of Data on Bursts of Pressure Vessels

Bayesian Processing of Data on Bursts of Pressure Vessels

Explosions of Cylindrical Pressure Vessels Subjected to Fire: Probabilistic Prediction of a Number of Fragments

A model study of the effect of explosion fragment size on ejection hazard

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