Application case study of AP1000 automatic depressurization system (ADS) for reliability evaluation by GO-FLOW methodology

Muhammad, Hashim; Yoshikawa, Hidekazu; Matsuoka, Toshifumi; Yang, Ming

doi:10.1016/j.nucengdes.2014.06.040

Cited by 24 publications

(13 citation statements)

References 9 publications

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“…Based on the mission phase of the system, the process of the task can be divided into five time points (Hashim et al, , 2014a The data for each operator are shown in Table 1. According to the BN transformation procedure for the GO-FLOW model, the GO-FLOW model is mapped into the BN, as shown in Fig.…”

Section: Case Studymentioning

confidence: 99%

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A modified GO-FLOW methodology with common cause failure based on Discrete Time Bayesian Network

Fan

Wang

Liu

et al. 2016

Nuclear Engineering and Design

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Section: Case Studymentioning

confidence: 99%

“…The calculation results of operators 23 and 24 are shown in Fig. 21 and compared to the results of references Hashim et al (2013Hashim et al ( , 2014a.…”

Section: Case Studymentioning

confidence: 99%

A modified GO-FLOW methodology with common cause failure based on Discrete Time Bayesian Network

Fan

Wang

Liu

et al. 2016

Nuclear Engineering and Design

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“…Furthermore, each probability of top event is determined by using fault tree analysis. Data of component failure is adopted AP1000 data which was published and IAEA generic data [8][9][10][11][12]. Finally, the total probability of event sequences are calculated.…”

Section: Kne Energymentioning

confidence: 99%

“…The analysis is done by using event tree and each probability of top event is calculated by using fault tree analysis. Failure rate of component used in calculation is based on AP1000 data that is published by Westinghouse and data from IAEA Tecdoc [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Sequence Analysis After Core Damage To Determine Safety Level Of The Ap1000

Tjahyani¹

2016

KEn

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In safety analysis, one of important parameters is core damage as this occasion can cause fission products to release. In that regard, all possible sequences afterward must be analyzed in order to ensure that all events have been considered, because each sequence has different consequence. The objective of this research is to determine the probability of event sequences after core damage so safety level of AP1000 could be known. The AP1000 reactor is chosen as the research object because currently many units are under construction. In this research the accident sequences were analyzed by using event tree, and the probability of top event was calculated by fault tree analysis. Meanwhile, the failure rates of component or operator action were collected from IAEA documents and also published documents of the AP1000 from Westinghouse Inc. The analysis results show that probability of event sequences which causes fission product release is ranging from 10 -2 to 10 -26 and the total probability is 3, 48 x 10 -2. Based on this analysis, it can be concluded that the AP1000 has high safety level because the probability of event sequences leading to fission product release is small. Moreover, if these results are joined with core damage probability then probability of fission product release would be less than 10 -9.

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“…The GO-FLOW model can be easily developed from operators, which denote particular functional logical gates. Muhammad et al [22] performed reliability evaluation with GO-FLOW method to determinate the influence of automatic depressurization system in dynamic reliability of passive core cooling system of AP1000. Common cause failure issues in the reliability analysis and the procedure of analysis has been treated by GO-FLOW method [23,24].…”

Section: Introductionmentioning

confidence: 99%

Reliability evaluation of auxiliary feedwater system by mapping GO-FLOW models into Bayesian networks

Liu

et al. 2016

ISA Transactions

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Application case study of AP1000 automatic depressurization system (ADS) for reliability evaluation by GO-FLOW methodology

Cited by 24 publications

References 9 publications

A modified GO-FLOW methodology with common cause failure based on Discrete Time Bayesian Network

A modified GO-FLOW methodology with common cause failure based on Discrete Time Bayesian Network

Sequence Analysis After Core Damage To Determine Safety Level Of The Ap1000

Reliability evaluation of auxiliary feedwater system by mapping GO-FLOW models into Bayesian networks

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