A COCAP Program for Estimating the Common Cause Failure Parameters

The CCF(Common Cause Failure) parameter data provided by US NRC, however, is currently the CCF parameter data of Alpha-Factor Model (AFM). Thus, the CCF parameters used for the CCF analysis in Korea have used MGL parameters converted form AFM parameters to MGL parameter. In this case, the PSA researchers have not performed the Bayesian updating for reducing uncertainty of the MGL parameters due to the fact that conversions of these CCF parameters cannot know the MGL parameters distribution. For this reason, the MGL parameter distribution has been used popularly to perform PSA for Korean NPPs. In this study, therefore, the Bayesian-based CCF parameters distribution estimation program using α-factors parameter data has been developed for evaluating the CCF parameters and applied for the system of the reference plant to obtain the uncertainty data of the parameters in MGL model. The common cause failures in nuclear power plants are one of the significant factors to affect both the values of Core Damage Frequency and Large Early Release Frequency. Several investigators have made many efforts to attempt to reduce the common cause failure uncertainty. In this study a methodology using the Bayesian operation has been developed and applied for reducing the uncertainty of the common cause failures. The Bayesian-based CCF parameters estimation program called COCAP has also been developed for evaluating the common cause failures parameters. It is shown that COCAP program can obtain the parameter distributions for various common cause failure models for both the non-staggered and the staggered tests. It is also shown that this program might contribute to assessing the safety measures such as core damage frequency and large early release frequency