Thematic network for a Phebus FPT1 international standard problem (THENPHEBISP)

Clément, Bernard; Haste, T.; Elisabeth, Krausmann; Dickinson, S.; Gyenes, G.; Duspiva, J.; Rosa, F. De; Paci, Sandro; Martín-Fuertes, F.; Scholytssek, W.; Allelein, Hans-Josef; Güntay, S.; Ariën, B.; Marguet, Serge; Leskovar, Matjaž; Sartmadjiev, A.

doi:10.1016/j.nucengdes.2004.08.057

Cited by 30 publications

(15 citation statements)

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“…As a result, the International Phebus Fission Product program (Phebus) was launched in 1988 to study the degradation of a core reactor, the transport of fission products and their release into the reactor containment volume. [16][17][18][19][20][21][22] Its objective is to reduce the uncertainty in the speciation and quantity of fission products that may be released into the environment if a major nuclear power plant accident were to occur, releasing some fission products in the environment. One of the results of this program was that short chain organic iodides are formed such as CH 3 I and CH 2 I 2 , henceforth RI, under irradiation from the painted surfaces of the reactor containment and then partly destroyed by radiations through complex mechanisms involving air and steam radiolytic products such as OH and O radicals among others.…”

Section: Introductionmentioning

confidence: 99%

Kinetic study of the reaction of OH with CH2I2

Zhang¹,

Strekowski²,

Bosland³

et al. 2011

Phys. Chem. Chem. Phys.

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Flash photolysis (FP) coupled to resonance fluorescence (RF) was used to measure the absolute rate coefficients (k(1)) for the reaction of OH(X(2)Π) radicals with diiodomethane (CH(2)I(2)) over the temperature range 295-374 K. The experiments involved time-resolved RF detection of the OH (A(2)Σ(+)→X(2)Π transition at λ = 308 nm) following FP of the H(2)O/CH(2)I(2)/He mixtures. The OH(X(2)Π) radicals were produced by FP of H(2)O in the vacuum-UV at wavelengths λ > 120 nm. Decays of OH radicals in the presence of CH(2)I(2) are observed to be exponential, and the decay rates are found to be linearly dependent on the CH(2)I(2) concentration. The results are described by the Arrhenius expression k(1)(T) = (4.2 ± 0.5) × 10(-11) exp[-(670 ± 20)K/T] cm(3) molecule(-1) s(-1). The implications of the reported kinetic results for understanding the atmospheric chemistry of CH(2)I(2) are discussed.

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Section: Introductionmentioning

confidence: 99%

Kinetic study of the reaction of OH with CH2I2

Zhang¹,

Strekowski²,

Bosland³

et al. 2011

Phys. Chem. Chem. Phys.

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“…Probability risk analysis mostly deals with evaluation of components, systems and equipment failure probabilities, human error probabilities and quantification of accident scenarios, which includes modelling of physical processes (Moieni, 1994;Clément et al, 2005). The model is a computer assisted decision tool that combines all the previously mentioned probabilities.…”

Section: Risk Management In the Manufacturing Of Jet Enginesmentioning

confidence: 99%

Probabilistic risk analysis in manufacturing situational operation: application of modelling techniques and causal structure to improve safety performance.

Pereira

Lima

2015

Int. J. Prod. Manag. Eng.

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The use of probabilistic risk analysis in jet engines manufacturing process is essential to prevent failure. The objective of this study is to present a probabilistic risk analysis model to analyze the safety of this process. The standard risk assessment normally conducted is inadequate to address the risks. To remedy this problem, the model presented in this paper considers the effects of human, software and calibration reliability in the process. Bayesian Belief Network coupled to a Bow Tie diagram is used to identify potential engine failure scenarios. In this context and to meet this objective, an in depth literature research was conducted to identify the most appropriate modeling techniques and an interview were conducted with experts. As a result of this study, this paper presents a model that combines fault tree analysis, event tree analysis and a Bayesian Belief Networks into a single model that can be used by decision makers to identify critical risk factors in order to allocate resources to improve the safety of the system. The model is delivered in the form of a computer assisted decision tool supported by subject expert estimates.

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“…The decay power of the molten pool applied in our model is assumed to be lower than the decay heat in an intact core due to volatile fission products released during the core degradation process (PHEBUS, 2001;Clément and Haste, 2004;Clément et al, 2005). It was also assumed that 20% of the volatiles remain in the pool.…”

Section: Internal Heat Sources Decay Power Of the Poolmentioning

confidence: 99%