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

Mechanika ◽  
2021 ◽  
Vol 27 (4) ◽  
pp. 277-284
Author(s):  
Egidijus VAIDOGAS
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Bayesian Analysis ◽  
Pressure Vessel ◽  
Prior Distribution ◽  
Pressure Vessels ◽  
Probabilistic Prediction ◽  
Gamma Model ◽  
Individual Fragment ◽  
Explosion Accidents

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.

scholarly journals Bayesian Processing of Data on Bursts of Pressure Vessels

2021 ◽  
Vol 50 (4) ◽  
pp. 607-626
Author(s):  
Egidijus Rytas Vaidogas
Keyword(s):  
Epistemic Uncertainty ◽  
Probability Distributions ◽  
Pressure Vessels ◽  
Post Mortem ◽  
Bayesian Approaches ◽  
Prior Distributions ◽  
Informative Prior ◽  
Individual Fragment ◽  
Explosion Accidents ◽  
Bayesian Prior

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).

scholarly journals Performance of parametric Bayesian Methods for estimating the survivor function in uncensored data using Monte-Carlo simulation

2021 ◽  
Vol 11 (3) ◽  
pp. 430-436
Author(s):  
Mohammed Elamin Hassan ◽  
Fakhereldeen Elhaj Esmial Musa
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Bayesian Method ◽  
The Other ◽  
Mean Square ◽  
Monte Carlo Simulation Study ◽  
Survivor Function

The paper aimed to investigate the performance of some parametric survivor function estimators based on Bayesian methodology with respect to bias and efficiency. A simulation was conducted based on Mote Carlo experiments with different sample sizes different (10, 30, 50, 75, 100). The bias and variance of mean square Error V(MSE) were selected as the basis of comparison. The methods of estimation used in this study are Maximum Likelihood, Bayesian with exponential as prior distribution and Bayesian with gamma as prior distribution. A Monte Carlo Simulation study showed that the Bayesian method with gamma as prior distribution was the best performance than the other methods. The study recommended that.

Dietary intake and health risk assessment of nitrate, nitrite, and nitrosamines: a Bayesian analysis and Monte Carlo simulation

2020 ◽  
Vol 27 (36) ◽  
pp. 45568-45580
Author(s):  
Malihe Moazeni ◽  
Zahra Heidari ◽  
Sahar Golipour ◽  
Leila Ghaisari ◽  
Mika Sillanpää ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Health Risk ◽  
Bayesian Analysis ◽  
Dietary Intake ◽  
Health Risk Assessment
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