PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

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Lead Author: Fernando Ferrante Co-author(s): Ali Mosleh mosleh@g.ucla.edu Enrique Lopez Droguett eald@g.ucla.edu Justin Hiller jhiller@ameren.com Sergio Cofré-Martel scofre@umd.edu

A Bayesian Method for Estimating Potential Impact of Increase in STI on Component Failure Rates
Extending the time interval between inspections of surveillance test intervals (STIs) for risk-informed applications such as the surveillance frequency control program (SFCP) in the U.S. includes guidance on addressing the potential impact of a component’s failure rate due to unseen or and in-progress failure mechanisms. The STI extension methods described in the Nuclear Energy Institute (NEI) guidance for SFPC (NEI 04-10) involve conservatively modeling STI-modified components in a probabilistic risk assessment (PRA) model to assess potential risk impacts. While the guidance in NEI 04-10 provides details in terms of addressing the overall impact, it also includes a step to account for a periodic reassessment of the overall program impact. For this step, NEI 04-10 provides two options for how a periodic reassessment may be performed in terms of incorporating revised STIs into the base PRA model. The first option is to use the original conservative data assumptions that were utilized in performing the initial STI assessment, while the second option is to utilize data collection and statistical analysis to show that the reliability of the components affected by the STI change has not been impacted (or has improved) from the revised STI frequency value. Because of the scarcity of failure data for some components, NEI 04-10 clarifies that the latter option may be limited due to data collection issues (insufficient evidence). Hence, the lack of a statistical method for the second option could limit the implementation of this step under SFCP to conservative assumptions under the first option. To these ends, a technical basis was sought in the work presented here to establish a framework for developing a statistical methodology for periodic re-assessments under the SFCP that could form the basis for a practical approach to be utilized under NEI 04-10. Bayesian updating, past plant-specific test/inspection, operational records, and failure mode assessment are considered in a general framework for how a relevant technical basis can be derived for further use. Actual plant data from a U.S. nuclear power plant employing the SFCP was leveraged to support the development of a mathematical framework. It is expected that this framework can be applied under further piloting by considering practical aspects of its use with a PRA model currently supporting a SFCP, as well as broader industry data utilization to further calibrate its inputs. Ultimately, this effort represents an initial formal investigation into a basis for future practical use, in an area that was not previously explored with mathematical rigor.

A Bayesian Method for Estimating Potential Impact of Increase in STI on Component Failure Rates

Extending the time interval between inspections of surveillance test intervals (STIs) for risk-informed applications such as the surveillance frequency control program (SFCP) in the U.S. includes guidance on addressing the potential impact of a component’s failure rate due to unseen or and in-progress failure mechanisms. The STI extension methods described in the Nuclear Energy Institute (NEI) guidance for SFPC (NEI 04-10) involve conservatively modeling STI-modified components in a probabilistic risk assessment (PRA) model to assess potential risk impacts. While the guidance in NEI 04-10 provides details in terms of addressing the overall impact, it also includes a step to account for a periodic reassessment of the overall program impact. For this step, NEI 04-10 provides two options for how a periodic reassessment may be performed in terms of incorporating revised STIs into the base PRA model. The first option is to use the original conservative data assumptions that were utilized in performing the initial STI assessment, while the second option is to utilize data collection and statistical analysis to show that the reliability of the components affected by the STI change has not been impacted (or has improved) from the revised STI frequency value. Because of the scarcity of failure data for some components, NEI 04-10 clarifies that the latter option may be limited due to data collection issues (insufficient evidence). Hence, the lack of a statistical method for the second option could limit the implementation of this step under SFCP to conservative assumptions under the first option. To these ends, a technical basis was sought in the work presented here to establish a framework for developing a statistical methodology for periodic re-assessments under the SFCP that could form the basis for a practical approach to be utilized under NEI 04-10. Bayesian updating, past plant-specific test/inspection, operational records, and failure mode assessment are considered in a general framework for how a relevant technical basis can be derived for further use. Actual plant data from a U.S. nuclear power plant employing the SFCP was leveraged to support the development of a mathematical framework. It is expected that this framework can be applied under further piloting by considering practical aspects of its use with a PRA model currently supporting a SFCP, as well as broader industry data utilization to further calibrate its inputs. Ultimately, this effort represents an initial formal investigation into a basis for future practical use, in an area that was not previously explored with mathematical rigor.

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Author and Presentation Info

Lead Author Name: Fernando Ferrante (fferrante@epri.com)

Bio: Fernando Ferrante is a Principal Project Manager at the Electric Power Research Institute (EPRI) in the Risk and Safety Management group (RSM). Ferrante joined EPRI in 2017 as a Principal Technical Leader in RSM. He was promoted to Principal Project Manager within RSM in March 2021, gaining responsibility for direct oversight of RSM staff involved in human reliability, fire risk assessment, external flooding PRA, along with RIDM framework activities. Dr. Ferrante held positions as a risk analyst at the U.S. Nuclear Regulatory Commission and senior engineer at the Defense Nuclear Facilities Safety Board. Dr. Ferrante holds a Bachelor of Science degree in Mechanical Engineering from University College London, in the United Kingdom, and a Doctor of Philosophy degree in Civil Engineering from Johns Hopkins University.

Country: ---
Company: Electric Power Research Institute
Job Title: Program Manager

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PSAM 16 Conference Paper Overview

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