PSAM 16 Conference Paper Overview
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Lead Author: Sai Zhang Co-author(s): Zhegang Ma, Zhegang.Ma@inl.gov
Christopher Hunter, Christopher.Hunter@nrc.gov
| Developing Component-Specific Prior Distributions for Common Cause Failure Alpha Factors |
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| Common cause failures (CCFs) have been recognized as significant risk contributors, ever since the early launching of probabilistic risk assessments (PRAs) for commercial nuclear power plants. A CCF database system was developed and maintained by U.S. Nuclear Regulatory Commission (NRC) and Idaho National Laboratory (INL) for the U.S. commercial nuclear power industry. CCF parameter estimations have been periodically updated using up-to-date failure data based on Bayesian update method. Historically, various generic prior distributions were developed using different date ranges of failure data available at the time for CCF alpha factor model parameter estimations. An INL report (INL/EXT-21-43723) updated generic prior distributions for CCF alpha factors, using the failure data from 1997 to 2015. These generic CCF priors were used in the recent 2020 CCF parameter estimations. Since 2021, NRC/INL have been striving to expand the existing work on generic priors and conduct a case study to evaluate the effect of component-type-based (i.e., component-specific) priors on the current alpha factors. This paper presents the development of component-specific CCF prior distributions for five component categories: pump, valve, strainer, generator, and all else. The same method of developing 2015 generic priors along with the same set of failure data (1997-2015) as those used in INL/EXT-21-43723 were used in this report for ease of comparing component-specific prior results with the 2015 generic priors. |
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Author and Presentation Info
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Lead Author Name: Sai Zhang (Sai.Zhang@inl.gov)
Bio: Dr. Sai Zhang works as a probabilistic risk and reliability analyst at Idaho National Laboratory, USA. Her research interests include probabilistic risk assessment, risk-informed analyses and applications, risk-cost optimization, and multi-criterion benefit evaluation for nuclear power plants. She has been a key investigator on a variety of U.S. Nuclear Regulatory Commission- and Department of Energy-sponsored projects including computational support for risk applications, Standardized Plant Analysis Risk (SPAR) modeling, risk-informed analyses for enhanced resilient plant systems, flood barrier testing strategy development, and exploration of artificial intelligence/machine learning in nuclear operating experience. She holds a Ph.D. degree in nuclear engineering from Tsinghua University, China.
Country: United States of America
Company: Idaho National Laboratory
Job Title: Probabilistic Risk and Reliability Analyst
Bio: Dr. Sai Zhang works as a probabilistic risk and reliability analyst at Idaho National Laboratory, USA. Her research interests include probabilistic risk assessment, risk-informed analyses and applications, risk-cost optimization, and multi-criterion benefit evaluation for nuclear power plants. She has been a key investigator on a variety of U.S. Nuclear Regulatory Commission- and Department of Energy-sponsored projects including computational support for risk applications, Standardized Plant Analysis Risk (SPAR) modeling, risk-informed analyses for enhanced resilient plant systems, flood barrier testing strategy development, and exploration of artificial intelligence/machine learning in nuclear operating experience. She holds a Ph.D. degree in nuclear engineering from Tsinghua University, China.
Country: United States of America
Company: Idaho National Laboratory
Job Title: Probabilistic Risk and Reliability Analyst