A Computer Program for Evaluating the Alpha Factor Model Parameters Using the Bayesian Operation

Title
A Computer Program for Evaluating the Alpha Factor Model Parameters Using the Bayesian Operation
Author
제무성
Keywords
common cause failure; Multiple Greek Letter model; α-factors model; Bayesian; parameter uncertainty; data analysis
Issue Date
2014-06
Publisher
Techno-Info Comprehensive Solutions (TICS)
Citation
, Page. 1-8
Abstract
The assessment of common cause failure (CCF) is necessary for reducing the uncertaintyduring the process of probabilistic safety assessment. A basic unavailability assessment method is anapproach for the quantitative analysis of CCF modeling using Bayesian probability, in which theestimation of parameters is more accurate by combining the failure information from system,component and cause level. This study describes the CCF evaluation program which has beendeveloped for assessing the α-factor common cause failure parameters. Examples are presented todemonstrate the calculation process and necessary databases are presented. As a result, the posteriordistributions for α-factors model parameter are obtained using the conjugate family distributions aswell as general distributions for conducting a numerical estimation.Due to the fact that CCF is one of the significant factors to affect both core damage frequency andlarge early release frequency, the appropriate evaluation for the relevant parameters is essential,though there are rare the CCF data. In the previous study, the Multiple Greek Letter model (MGL) hadbeen used for modeling the common cause failures in the OPR 1000 reactors. In the future modelingfor the reactors, the α-factors approach might be employed for simulating the common cause failuresas well as it will be quantified using the computer program developed by the C# language. The mainoperation to quantify the α-factors parameters is Bayesian which combines the prior distribution andthe likelihood function to produce the posterior distribution. It is expected that this program mightcontribute to enhancing the quality of probabilistic safety assessment and to reducing common causefailure uncertainty.
URI
http://www.psam12-org.meetingsandconferences.com/proceedings/paper/paper_302_1.pdfhttps://repository.hanyang.ac.kr/handle/20.500.11754/70799
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > NUCLEAR ENGINEERING(원자력공학과) > Articles
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