PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

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

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.

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Author and Presentation Info

Presentation only, a full paper is not available.

Lead Author 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: United States of America
Company: University of Illinois at Urbana-Champaign
Job Title: Research Assistant Professor