PSAM 16 - Session T22 Overview

Session Chair: Jeffrey Julius (jjulius@jensenhughes.com)

Paper 1 CE253

Lead Author: César Queral Co-author(s): Sergio Courtin, sergio.courtin@upm.es Rafael Iglesias Marcos Cabezas Alberto Garcia-Herranz Julia Herrero-Otero Enrique Meléndez, ema@csn.es Rafael Mendizábal, rmsanz@csn.es Miguel Sánchez-Perea, msp@csn.es

On the use of SPAR-CSN models for identifying DEC-A sequences. First ideas.
The Fukushima accident has brought, among others, an increment and improvement of the safety requirements for the nuclear power plants. One important requirement established in many countries is that the so-called Design Extension Conditions (DEC) need to be fully considered in the assessment of current and new advanced nuclear power plants. This safety improvement of NPPs focuses on the conditions of multiple failures of the safety system, included in the currently available guidances widely in the world (e.g., as proposed by IAEA SSR 2/1). The associated applications and practices begin to emerge, given that the topic of DEC is being advanced rapidly both nationally and internationally. Nevertheless, these practices are still not comprehensive, in particular, regarding the interface with the plant design basis, its role in the Defence-in-Depth, selection of requirements, impact on operating limits and conditions and/or selection of DEC sequences to be included in the analyses. As for the selection of DEC sequences, most if not all, the current practices/guidances indicate that ”the selection of events to be analysed shall be justified on the basis of deterministic and probabilistic arguments and of engineering judgement” and that “the selection process shall take into account all those events or combinations of events that cannot be considered extremely unlikely with a high degree of confidence and that might give rise to accident conditions more severe than those considered in design basis accidents”. Thus, there is a broad claim for the use of the PSA models as a tool for the DEC identification process. The Spanish Regulatory Body (CSN), in collaboration with the Universidad Politécnica de Madrid (UPM), has been assembling its own generic standardized model (SPAR-CSN) for 3-loop PWR-WEC designs for different purposes and uses. The present paper aims at enlarging the scope of initially foreseen applications to the goal of designing a suitable PSA-based methodology for DEC sequences. The paper will show the first ideas and provisions foreseen with this aim. Key Words: Design Extension Conditions (DEC), PSA, Standardized PSA models, DEC-A selection, mPSA based

On the use of SPAR-CSN models for identifying DEC-A sequences. First ideas.

The Fukushima accident has brought, among others, an increment and improvement of the safety requirements for the nuclear power plants. One important requirement established in many countries is that the so-called Design Extension Conditions (DEC) need to be fully considered in the assessment of current and new advanced nuclear power plants. This safety improvement of NPPs focuses on the conditions of multiple failures of the safety system, included in the currently available guidances widely in the world (e.g., as proposed by IAEA SSR 2/1). The associated applications and practices begin to emerge, given that the topic of DEC is being advanced rapidly both nationally and internationally. Nevertheless, these practices are still not comprehensive, in particular, regarding the interface with the plant design basis, its role in the Defence-in-Depth, selection of requirements, impact on operating limits and conditions and/or selection of DEC sequences to be included in the analyses. As for the selection of DEC sequences, most if not all, the current practices/guidances indicate that ”the selection of events to be analysed shall be justified on the basis of deterministic and probabilistic arguments and of engineering judgement” and that “the selection process shall take into account all those events or combinations of events that cannot be considered extremely unlikely with a high degree of confidence and that might give rise to accident conditions more severe than those considered in design basis accidents”. Thus, there is a broad claim for the use of the PSA models as a tool for the DEC identification process. The Spanish Regulatory Body (CSN), in collaboration with the Universidad Politécnica de Madrid (UPM), has been assembling its own generic standardized model (SPAR-CSN) for 3-loop PWR-WEC designs for different purposes and uses. The present paper aims at enlarging the scope of initially foreseen applications to the goal of designing a suitable PSA-based methodology for DEC sequences. The paper will show the first ideas and provisions foreseen with this aim. Key Words: Design Extension Conditions (DEC), PSA, Standardized PSA models, DEC-A selection, mPSA based

Paper CE253 | Download the paper file. | Download the presentation pdf file.

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Paper 2 SA151

Lead Author: Tatsuya Sakurahara Co-author(s): Zahra Mohaghegh, zahra13@illinois.edu

Risk-informed Analysis for Advanced Nuclear Power Reactors: Pipe Reliability Case Study and Lessons Learned
To facilitate the design and licensing of advanced nuclear power reactors, it is imperative to conduct risk-informed analysis prior to, or in parallel with, technology developments. Significant efforts have been dedicated to the developments of Probabilistic Risk Assessment (PRA) and the establishment of the risk-informed decision-making framework for advanced reactors, such as the Licensing Modernization Project (LMP), development of Title 10 of the Code of Federal Regulations, Part 53 and other regulatory guidance by the Nuclear Regulatory Commission (NRC), as well as the issuance of the ASME/ANS Non-LWR Probabilistic Risk Assessment Standard (RA-S-1.4-2021). In this realm, the authors’ team has participated in an International Atomic Energy Agency (IAEA) Coordinated Research Project, “Methodology for Assessing Pipe Failure Rates in Advanced Water-Cooled Reactors,” 2018-2021. This presentation summarizes the research findings and lessons learned from the authors’ activities under this IAEA CRP, aimed at advancing the pipe failure rate analysis methodologies for advanced reactors. Based on the outcomes and insights from the IAEA project and other research activities by the authors’ team, the current research needs for methodological developments for the risk-informed analyses of advanced reactors are discussed. One of the key methodological challenges is that a design-specific experiential database is often limited or not available for advanced reactors, while the applicability and relevancy of the experiential data from the existing fleet to advanced reactors may be questionable due to differences in design principles, physical conditions, and operation and maintenance procedures. Additionally, the lack of consensus, validated, and peer-reviewed phenomenological models unique to the advanced reactor designs can be another challenge. This paper discusses possible research paths and examples of methodological advancements from the authors’ research activities in the IAEA project to alleviate these methodological challenges. Acknowledgment: Part of this work was conducted by the International Atomic Energy Agency (IAEA) in the frame of the Coordinated Research Project I31030 on "Methodology for Assessing Pipe Failure Rates in Advanced Water-Cooled Reactors," 2018-2021.

Risk-informed Analysis for Advanced Nuclear Power Reactors: Pipe Reliability Case Study and Lessons Learned

To facilitate the design and licensing of advanced nuclear power reactors, it is imperative to conduct risk-informed analysis prior to, or in parallel with, technology developments. Significant efforts have been dedicated to the developments of Probabilistic Risk Assessment (PRA) and the establishment of the risk-informed decision-making framework for advanced reactors, such as the Licensing Modernization Project (LMP), development of Title 10 of the Code of Federal Regulations, Part 53 and other regulatory guidance by the Nuclear Regulatory Commission (NRC), as well as the issuance of the ASME/ANS Non-LWR Probabilistic Risk Assessment Standard (RA-S-1.4-2021). In this realm, the authors’ team has participated in an International Atomic Energy Agency (IAEA) Coordinated Research Project, “Methodology for Assessing Pipe Failure Rates in Advanced Water-Cooled Reactors,” 2018-2021. This presentation summarizes the research findings and lessons learned from the authors’ activities under this IAEA CRP, aimed at advancing the pipe failure rate analysis methodologies for advanced reactors. Based on the outcomes and insights from the IAEA project and other research activities by the authors’ team, the current research needs for methodological developments for the risk-informed analyses of advanced reactors are discussed. One of the key methodological challenges is that a design-specific experiential database is often limited or not available for advanced reactors, while the applicability and relevancy of the experiential data from the existing fleet to advanced reactors may be questionable due to differences in design principles, physical conditions, and operation and maintenance procedures. Additionally, the lack of consensus, validated, and peer-reviewed phenomenological models unique to the advanced reactor designs can be another challenge. This paper discusses possible research paths and examples of methodological advancements from the authors’ research activities in the IAEA project to alleviate these methodological challenges. Acknowledgment: Part of this work was conducted by the International Atomic Energy Agency (IAEA) in the frame of the Coordinated Research Project I31030 on "Methodology for Assessing Pipe Failure Rates in Advanced Water-Cooled Reactors," 2018-2021.

Paper SA151 | |

Name: Tatsuya Sakurahara (sakurah2@illinois.edu)

Bio: Tatsuya Sakurahara is a Research Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign (UIUC) and is the Chief Scientist in the Socio-Technical Risk Analysis (SoTeRiA) Laboratory, directed by Dr. Zahra Mohaghegh. He is involved in large-scale PRA projects, developing methodologies and computational platforms to advance PRA for commercial nuclear power plants and advanced reactors. Sakurahara holds a Ph.D. in Nuclear Engineering (2018) from UIUC. His Ph.D. research focused on developing the Integrated PRA methodology to increase the realism of risk estimation for nuclear power plants. His Ph.D. research contributed to advanced techniques for uncertainty analysis, importance measures, and simulation-informed common cause failure modeling. Sakurahara received a BS in Environment and Energy Systems (2011) and an MSc in Nuclear Engineering and Management (2013) from the University of Tokyo, Japan.

Country: USA
Company: University of Illinois at Urbana-Champaign
Job Title: Research Assistant Professor

Paper 3 MA193

Lead Author: Tatsuya Sakurahara Co-author(s): Sari Alkhatib, sarifa2@illinois.edu; Mohammad Albati, malbati2@illinois.edu; Seyed Reihani, sreihani@illinois.edu; Ernie Kee, erniekee@illinois.edu; Zahra Mohaghegh, zahra13@illinois.edu; Terry L. von Thaden, vonthade@illinois.edu; Richard Kesler, rkesler2@illinois.edu; Farzaneh Masoud, fmasoud2@illinois.edu; Brian Ratté, bdratte@STPEGS.COM; Mary Anne Billings, mabillings@STPEGS.COM

Academia-Industry Collaboration to Advance Fire Probabilistic Risk Assessment of Nuclear Power Plants
This presentation reports on the academia-industry project supported by the U.S. Department of Energy. This project aims to improve the operational efficiency of Nuclear Power Plants (NPPs) by enhancing the realism of the Fire Probabilistic Risk Assessment (PRA). In previous work by the Socio-Technical Risk Analysis (SoTeRiA) Laboratory at the University of Illinois at Urbana-Champaign, the Fire PRA realism associated with fire progression and damage modeling and the modeling of interactions between fire progression and manual suppression was advanced by developing an Integrated PRA (I-PRA) methodological framework. This latest academia-industry project has advanced the Fire I-PRA methodological framework, focusing on the current Fire PRA challenges in the nuclear industry, and scaled up Fire I-PRA to a full-scope plant. This project has been conducted in three phases. Phase I developed a streamlined approach to perform a more efficient screening of fire zones and ignition sources in Fire PRA. The advanced screening approach was mapped to the NUREG/CR-6850 procedure to demonstrate that it can seamlessly merge with the current Fire PRA procedure. The implementation of the advanced screening has been shown by case studies using two fire zones at South Texas Project Nuclear Operating Company (STPNOC). In Phase II, the Multi-Compartment Analysis (MCA) for Plant Analysis Units (PAUs) involving transient ignition sources was enhanced. A methodological and computational platform called “SoTeRiA-Fire” was developed to automate the Fire Scenario Selection and Analysis (FSS) supporting MCA. The SoTeRiA-Fire platform can help reduce resources required for Fire PRA implementation by (a) automating the pre-processing of input data, execution of a fire model, and post-processing of outputs for various tasks in the FSS and (b) providing a mechanism to gradually increase the realism of FSS while screening out insignificant scenarios. The applicability and practical values of the SoTeRiA-Fire platform have been demonstrated by case studies for two PAUs at the STPNOC plant. Phase III performed experimental validation of an agent-based fire crew simulation developed by the UIUC team. The previous research demonstrated that the fire crew simulation could improve the realism of the non-suppression probability estimation compared to the data-driven non-suppression curve currently used by the nuclear industry; however, the validation of the fire crew simulation needed to be evaluated. The fire crew performance tests were conducted at the Illinois Fire Service Institute to generate validation data. This research is the first effort to validate the probabilistic simulation of the fire crew performance at NPPs using the test data.

Academia-Industry Collaboration to Advance Fire Probabilistic Risk Assessment of Nuclear Power Plants

This presentation reports on the academia-industry project supported by the U.S. Department of Energy. This project aims to improve the operational efficiency of Nuclear Power Plants (NPPs) by enhancing the realism of the Fire Probabilistic Risk Assessment (PRA). In previous work by the Socio-Technical Risk Analysis (SoTeRiA) Laboratory at the University of Illinois at Urbana-Champaign, the Fire PRA realism associated with fire progression and damage modeling and the modeling of interactions between fire progression and manual suppression was advanced by developing an Integrated PRA (I-PRA) methodological framework. This latest academia-industry project has advanced the Fire I-PRA methodological framework, focusing on the current Fire PRA challenges in the nuclear industry, and scaled up Fire I-PRA to a full-scope plant. This project has been conducted in three phases. Phase I developed a streamlined approach to perform a more efficient screening of fire zones and ignition sources in Fire PRA. The advanced screening approach was mapped to the NUREG/CR-6850 procedure to demonstrate that it can seamlessly merge with the current Fire PRA procedure. The implementation of the advanced screening has been shown by case studies using two fire zones at South Texas Project Nuclear Operating Company (STPNOC). In Phase II, the Multi-Compartment Analysis (MCA) for Plant Analysis Units (PAUs) involving transient ignition sources was enhanced. A methodological and computational platform called “SoTeRiA-Fire” was developed to automate the Fire Scenario Selection and Analysis (FSS) supporting MCA. The SoTeRiA-Fire platform can help reduce resources required for Fire PRA implementation by (a) automating the pre-processing of input data, execution of a fire model, and post-processing of outputs for various tasks in the FSS and (b) providing a mechanism to gradually increase the realism of FSS while screening out insignificant scenarios. The applicability and practical values of the SoTeRiA-Fire platform have been demonstrated by case studies for two PAUs at the STPNOC plant. Phase III performed experimental validation of an agent-based fire crew simulation developed by the UIUC team. The previous research demonstrated that the fire crew simulation could improve the realism of the non-suppression probability estimation compared to the data-driven non-suppression curve currently used by the nuclear industry; however, the validation of the fire crew simulation needed to be evaluated. The fire crew performance tests were conducted at the Illinois Fire Service Institute to generate validation data. This research is the first effort to validate the probabilistic simulation of the fire crew performance at NPPs using the test data.

Paper MA193 | |

Name: Tatsuya Sakurahara (sakurah2@illinois.edu)

Bio: Tatsuya Sakurahara is a Research Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign (UIUC) and is the Chief Scientist in the Socio-Technical Risk Analysis (SoTeRiA) Laboratory, directed by Dr. Zahra Mohaghegh. He is involved in large-scale PRA projects, developing methodologies and computational platforms to advance PRA for commercial nuclear power plants and advanced reactors. Sakurahara holds a Ph.D. in Nuclear Engineering (2018) from UIUC. His Ph.D. research focused on developing the Integrated PRA methodology to increase the realism of risk estimation for nuclear power plants. His Ph.D. research contributed to advanced techniques for uncertainty analysis, importance measures, and simulation-informed common cause failure modeling. Sakurahara received a BS in Environment and Energy Systems (2011) and an MSc in Nuclear Engineering and Management (2013) from the University of Tokyo, Japan.

Country: USA
Company: University of Illinois at Urbana-Champaign
Job Title: Research Assistant Professor

PSAM 16 Conference Session T22 Overview