Uncertainty techniques in liquefied natural gas (LNG) dispersion calculations

Siuta, Dorota; Markowski, A.; Mannan, M. Sam

doi:10.1016/j.jlp.2012.07.020

Cited by 31 publications

(21 citation statements)

References 4 publications

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“…: Sonlanma noktası mesafesi (mi), 1, 2 : Sabitler (kılavuz ek tabloları [14]), : Buharlaşma hızı (Ib/min) veya toplam salınan miktarın onda biri, : Toplam dökülen miktar (Ib), : Ortam sıvı faktörü (Liquid Factor Ambient), : Yoğunluk faktörü (Density Factor), : Sabitler (kılavuz ek tabloları [14]), ℎ : Kaçak delik alanı (in 2 ), : Tank basıncı (Ib/in 2 , psia), : Tank sıcaklığı ( o C), : Gaz faktörü, 1, 2 : Sabitler (kılavuz ek tabloları [14]), : Serbest kalan yanabilen madde miktarı (Ib), : Yanabilen maddenin yanma ısısı, : TNT (trinitro toluen) yanma ısısı, 1, 2 : Sabitler (kılavuz ek tabloları [14]), .…”

Section: Sembollerunclassified

Determination of explosion, fire and toxic emission physical effect areas

Çetinyokuş¹

2017

Pamukkale J Eng Sci

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Section: Sembollerunclassified

Determination of explosion, fire and toxic emission physical effect areas

Çetinyokuş¹

2017

Pamukkale J Eng Sci

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“…Uncertainties also occur in the model of heat radiation in the case of pool fire, e.g. in calculating the pool shape, height, and extent [36]. The models of fatality due to the direct impact of the debris, the model of the probability of damage caused by chemical-physical phenomena and many others can also be considered uncertain.…”

Section: The Uncertainties Associated With the Models Of The Probabilmentioning

confidence: 99%

“…In general, the uncertainties could be taken into account at least by considering that the parameters of the model are distributed according to given probability distributions. For example, for the case of Gaussian Plum Model, Siuta et al (2013) [36] suggested using triangular distributions for wind velocity u. However, more sophisticated distributions can also be used if the relevant data are known.…”

Section: The Uncertainties Associated With the Models Of The Probabilmentioning

confidence: 99%

Fatality risk estimation for industrialized urban areas considering multi-hazard domino effects triggered by earthquakes

Celano

Dolšek

2021

Reliability Engineering & System Safety

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The rapid expansion of the built environment has resulted in the coexistence of industrial facilities and urban centres. Following recent major earthquakes throughout the world, it has become clear that multi-hazard domino effects can significantly increase the risk of fatalities, environmental problems and losses. This complex phenomenon is not yet well understood. In this paper, the problem is treated by decomposing it into several subproblems which are described by simplified probabilistic models. These models are then coupled with the Monte Carlo method to estimate the annual probability of fatality for an individual who is continuously standing in a location of interest and to estimate fatality risk maps for an area of interest. Emphasis is placed on considering multi-hazard domino effects, which can be triggered within an industrial area due to the damage caused by earthquakes. Thus it is considered that fatalities can be caused: a) as a direct consequence of seismic damage to a unit b) as a direct physical and/or chemical consequence due to the loss of containment of hazardous material, and c) as a consequence of domino triggered by physical and chemical events such as fire, explosion, and toxic dispersion. The capabilities of the proposed methodology are demonstrated by calculating fatality risk maps for a hypothetical industrialized urban area. It is shown that disregarding multi-hazard domino effects in the estimation of fatality risk could lead to significant underestimation of the fatality risk in an industrialized urban area. Thus, it is necessary to account for multi-hazard domino effects. However, different teams of engineers can enhance the models for the probability of fatality due to various phenomena, which will improve the accuracy of the proposed methodology.

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“…In this study, the Monte Carlo simulation approach was used to quantify the DFTs. The Monte Carlo simulation is mainly used to represent the aleatory uncertainty which is related to the stochastic distribution of the physical parameters in models [16]. The key in using the Monte Carlo simulation method is to generate random numbers to determine the failure timing and failure sequence.…”

Section: Characteristics Of a Dynamic Fault Treementioning

confidence: 99%

“…Bayesian networks also enable fast and accurate calculations [15]. Analyses using binary decision diagrams and fuzzy theory methods are also available [16,17].…”

Section: Introductionmentioning

confidence: 99%

Application of Dynamic Fault Tree Analysis to Prioritize Electric Power Systems in Nuclear Power Plants

Baek

Heo

2021

Energies

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Because the scope of risk assessments at nuclear power plants (NPPs) is being extended both spatially and temporally, conventional, or static fault trees might not be able to express failure mechanisms, or they could be unnecessarily conservative in their expression. Therefore, realistic assessment techniques are needed to adequately capture accident scenarios. In multi-unit probabilistic safety assessment (PSA), fault trees naturally become more complex as the number of units increases. In particular, when considering a shared facility between units of the electric power system (EPS), static fault trees (SFTs) that prioritize a specific unit are limited in implementing interactions between units. However, dynamic fault trees (DFTs) can be available without this limitation by using dynamic gates. Therefore, this study implements SFTs and DFTs for an EPS of two virtual NPPs and compares their results. In addition, to demonstrate the dynamic characteristics of the shared facilities, a station blackout (SBO), which causes the power system to lose its function, is assumed—especially with an inter-unit shared facility, AAC DG (Alternate AC Diesel Generator). To properly model the dynamic characteristics of the shared EPS in DFTs, a modified dynamic gate and algorithm are introduced, and a Monte Carlo simulation is adopted to quantify the DFT models. Through the analysis of the DFT, it is possible to confirm the actual connection priority of AAC DG according to the situation of units in a site. In addition, it is confirmed that some conservative results presented by the SFT can be evaluated from a more realistic perspective by reflecting this.

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Uncertainty techniques in liquefied natural gas (LNG) dispersion calculations

Cited by 31 publications

References 4 publications

Determination of explosion, fire and toxic emission physical effect areas

Determination of explosion, fire and toxic emission physical effect areas

Fatality risk estimation for industrialized urban areas considering multi-hazard domino effects triggered by earthquakes

Application of Dynamic Fault Tree Analysis to Prioritize Electric Power Systems in Nuclear Power Plants

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