PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Fernando Ferrante Co-author(s): Ken Kiper, kiperkl@westinghouse.com Carroll Trull, trullca@westinghouse.com Matt Degonish, degonimm@westinghouse.com

Development of Good Practices in the Implementation of Common Cause Failure in PRA Models
Modeling Common Cause Failure (CCF) quantitatively in Probabilistic Risk Assessment (PRA) models using parametric approaches has become significantly complex and challenging for risk-informed decision-making (RIDM) purposes, as the state-of-practice now includes an extensive consideration of CCF modeling. Different approaches are needed for modeling CCF in PRA models, depending on the specialized topic (e.g., support system initiating event, inter-system, functional dependency). How to model dependencies appropriately is a critical aspect, as different topics may be better addressed via different solutions (e.g., CCF basic event derived via parametric modeling versus direct inclusion of dependencies in the PRA logic structure). For example, the distinction between “inter-system” and “intra-system” CCF is artificial and potentially misleading, as different types of dependencies can be misinterpreted under each term. A better approach to distinguishing how dependencies and CCF need to be handled, regardless of such artificial definitions, would better serve the PRA community. In addition, risk-informed applications that impose a specific condition on the baseline PRA model (such as the consideration of changes in CCF basic events dure to a failure or degradation of a component in an individual CCF group) can raise the impact of CCF in RIDM (including lower contributors in baseline PRA models). The current approach of using CCF parameters reflected in baseline CCF modeling may not be completely appropriate for such applications, to the extent that better approaches may be needed. An investigation of CCF data gathering, development of CCF input parameters, estimation of CCF probabilities, and their inclusion in PRA modeling was performed which included a survey of a small subset of modern PRA models. The impacts and insights from CCF in base PRA models are derived from the surveys as well as a review of the detailed PRA models themselves. This is done to canvas the state-of-practice in this area, since multiple decades have now passed since the original development of the CCF tools and methods (as will be discussed in later sections). The overall intent is to provide a better context for understanding CCF within RIDM applications as currently implemented, not just as a separate, complex, technical issue. To this end, a potential path forward with the development of a suggested good practice framework that is anchored in current, feasible approaches while taking into account the existing landscape of PRA and RIDM. Potential solutions for further improvement in areas where CCF can challenge RIDM implementation are suggested, which can be further explored. The good practices use existing CCF data and standard CCF methods but address technical areas where the state-of-practice can benefit from additional considerations.

Development of Good Practices in the Implementation of Common Cause Failure in PRA Models

Modeling Common Cause Failure (CCF) quantitatively in Probabilistic Risk Assessment (PRA) models using parametric approaches has become significantly complex and challenging for risk-informed decision-making (RIDM) purposes, as the state-of-practice now includes an extensive consideration of CCF modeling. Different approaches are needed for modeling CCF in PRA models, depending on the specialized topic (e.g., support system initiating event, inter-system, functional dependency). How to model dependencies appropriately is a critical aspect, as different topics may be better addressed via different solutions (e.g., CCF basic event derived via parametric modeling versus direct inclusion of dependencies in the PRA logic structure). For example, the distinction between “inter-system” and “intra-system” CCF is artificial and potentially misleading, as different types of dependencies can be misinterpreted under each term. A better approach to distinguishing how dependencies and CCF need to be handled, regardless of such artificial definitions, would better serve the PRA community. In addition, risk-informed applications that impose a specific condition on the baseline PRA model (such as the consideration of changes in CCF basic events dure to a failure or degradation of a component in an individual CCF group) can raise the impact of CCF in RIDM (including lower contributors in baseline PRA models). The current approach of using CCF parameters reflected in baseline CCF modeling may not be completely appropriate for such applications, to the extent that better approaches may be needed. An investigation of CCF data gathering, development of CCF input parameters, estimation of CCF probabilities, and their inclusion in PRA modeling was performed which included a survey of a small subset of modern PRA models. The impacts and insights from CCF in base PRA models are derived from the surveys as well as a review of the detailed PRA models themselves. This is done to canvas the state-of-practice in this area, since multiple decades have now passed since the original development of the CCF tools and methods (as will be discussed in later sections). The overall intent is to provide a better context for understanding CCF within RIDM applications as currently implemented, not just as a separate, complex, technical issue. To this end, a potential path forward with the development of a suggested good practice framework that is anchored in current, feasible approaches while taking into account the existing landscape of PRA and RIDM. Potential solutions for further improvement in areas where CCF can challenge RIDM implementation are suggested, which can be further explored. The good practices use existing CCF data and standard CCF methods but address technical areas where the state-of-practice can benefit from additional considerations.

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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

PSAM 16 Conference Paper Overview

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