PSAM 16 - Session M11 Overview

PSAM 16 Conference Session M11 Overview

Session Chair: Craig Primer (craig.primer@inl.gov)

Paper 1 MA57

Lead Author: Shuhei Matsunaka

Seismic Probabilistic Risk Analysis of Transmission Systems for Kashiwazaki-Kariwa NPS using Deaggregation Hazard taking account of Non-Specified Source Faults
In the 2011 Fukushima Dai-ichi nuclear power plants accident, power transmission facilities were damaged due to the earthquake, and the emergency diesel generators failed to perform their intended function due to the subsequent tsunami, resulting in the station blackout (SBO) scenario. Although the seismic reliability of offsite AC power supply has been evaluated in the conventional Seismic Probabilistic Risk Assessment (SPRA), in the United States, the generic fragility has been applied in the typical practical evaluation. Previous Kashiwazaki-Kariwa (KK) NPS SPRA has incorporated the plant-specific fragility of seismically weakest onsite power generation components (i.e., ceramic insulator), but it could be a non-conservative evaluation. Although there are evaluation examples of the transmission system, there are some assumptions and limitations that may become key in actual plant evaluation (such as insufficient consideration of non-specified source faults). In this study, the seismic reliability of the offsite power supply for the actual plant was evaluated in detail by calculating the frequency of the loss of offsite power (LOOP) using deaggregation 3-D hazard for each faults, modeling the transmission system as Boolean logic, and considering non-specified source faults. Compared with the conventional method (i.e., generic fragility in the United States and representative fragility by onsite facilities), the degree of conservative/optimistic were quantitatively confirmed respectively. As a plant characteristic of KK NPS, there are 3 power supply routes containing 5 lines (one route of which has a substantially different direction). Therefore, earthquakes of source faults that are close to one power transmission route (e.g., switching station) but far from the power plant is likely to be supplied from the other power transmission route. In this study, the effectiveness of transmission route redundancy was evaluated, and potential risk insights were gained by reviewing the positional relationship of the source faults, the power plant, and the transmission facilities including transmission network and switching station. Furthermore, since the importance of earthquakes of non-specified source faults was estimated, in addition to the conventional evaluation using logarithmic linear approximation based on the Gutenberg-Richter law, sensitivity analysis was also performed using the method of moments and other probability distribution forms including Generalized Extreme Value (GEV) distribution and Log-Pearson Type III distribution.

Seismic Probabilistic Risk Analysis of Transmission Systems for Kashiwazaki-Kariwa NPS using Deaggregation Hazard taking account of Non-Specified Source Faults

In the 2011 Fukushima Dai-ichi nuclear power plants accident, power transmission facilities were damaged due to the earthquake, and the emergency diesel generators failed to perform their intended function due to the subsequent tsunami, resulting in the station blackout (SBO) scenario. Although the seismic reliability of offsite AC power supply has been evaluated in the conventional Seismic Probabilistic Risk Assessment (SPRA), in the United States, the generic fragility has been applied in the typical practical evaluation. Previous Kashiwazaki-Kariwa (KK) NPS SPRA has incorporated the plant-specific fragility of seismically weakest onsite power generation components (i.e., ceramic insulator), but it could be a non-conservative evaluation. Although there are evaluation examples of the transmission system, there are some assumptions and limitations that may become key in actual plant evaluation (such as insufficient consideration of non-specified source faults). In this study, the seismic reliability of the offsite power supply for the actual plant was evaluated in detail by calculating the frequency of the loss of offsite power (LOOP) using deaggregation 3-D hazard for each faults, modeling the transmission system as Boolean logic, and considering non-specified source faults. Compared with the conventional method (i.e., generic fragility in the United States and representative fragility by onsite facilities), the degree of conservative/optimistic were quantitatively confirmed respectively. As a plant characteristic of KK NPS, there are 3 power supply routes containing 5 lines (one route of which has a substantially different direction). Therefore, earthquakes of source faults that are close to one power transmission route (e.g., switching station) but far from the power plant is likely to be supplied from the other power transmission route. In this study, the effectiveness of transmission route redundancy was evaluated, and potential risk insights were gained by reviewing the positional relationship of the source faults, the power plant, and the transmission facilities including transmission network and switching station. Furthermore, since the importance of earthquakes of non-specified source faults was estimated, in addition to the conventional evaluation using logarithmic linear approximation based on the Gutenberg-Richter law, sensitivity analysis was also performed using the method of moments and other probability distribution forms including Generalized Extreme Value (GEV) distribution and Log-Pearson Type III distribution.

Paper MA57 | | Download the presentation pdf file.

Name: Shuhei Matsunaka (matsunaka-shuuhei@tepsys.co.jp)

Bio: I studied Nuclear Engineering at Osaka University in Japan and received master degree of engineering in 2011. I have been working on the development of design basis of external events for several years. I have also been studying Seismic PRA, Tsunami PRA, and Dynamic PRA.

Country: JPN
Company: TEPCO SYSTEMS CORPORATION
Job Title: Deputy Manager

Paper 2 SU171

Lead Author: Sung-yeop Kim Co-author(s): Yun Young Choi (choi930121@nims.re.kr) Soo-Yong Park (sypark@kaeri.re.kr)

Application of Deep Learning Models to Estimate Source Release of NPP Accidents
In the event of nuclear power plant (NPP) accident, estimation of source release should be performed quickly and accurately in order to support the decision of public protection. In case of Fukushima Dai-ichi NPP accident, even though System for Prediction of Environmental Emergency Dose Information (SPEEDI) has been developed and prepared, it was not used to support the decision making of public protection due to the lack of source term information which should be provided from the system. In order to overcome the limitation of existing methods in aspect of quick and accurate source term estimation, deep learning approach using various NPP safety parameters as the learning input and releases of radioactive materials as the learning output is applied in this study. It was tried to search and apply variety of deep learning models such as ANN with pre-assigned function, encoder model of Transformer followed by fully connected layer, and multi-stage Transformer, in order to find and develop an optimized deep learning model to estimate the source release of NPP accidents.

Application of Deep Learning Models to Estimate Source Release of NPP Accidents

In the event of nuclear power plant (NPP) accident, estimation of source release should be performed quickly and accurately in order to support the decision of public protection. In case of Fukushima Dai-ichi NPP accident, even though System for Prediction of Environmental Emergency Dose Information (SPEEDI) has been developed and prepared, it was not used to support the decision making of public protection due to the lack of source term information which should be provided from the system. In order to overcome the limitation of existing methods in aspect of quick and accurate source term estimation, deep learning approach using various NPP safety parameters as the learning input and releases of radioactive materials as the learning output is applied in this study. It was tried to search and apply variety of deep learning models such as ANN with pre-assigned function, encoder model of Transformer followed by fully connected layer, and multi-stage Transformer, in order to find and develop an optimized deep learning model to estimate the source release of NPP accidents.

Paper SU171 | Download the paper file. |

Name: Sung-yeop Kim (sungyeop@kaeri.re.kr)

Bio: PhD, Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST) Research Interest - Offsite consequence analysis - Multi-unit Level 3 PSA

Country: KOR
Company: Korea Atomic Energy Research Institute
Job Title: Senior Researcher

Paper 3 AL271

Lead Author: Ali Ayoub Co-author(s): Haruko M Wainwright, hmwainw@mit.edu Giovanni Sansavini, sansavig@ethz.ch

Closing the Planning-to-Implementation Gap in Nuclear Emergency Response: Lessons Learned and Methodological Advances
Post-accident mitigation and consequence analysis have been subjects of extensive research in the nuclear industry. Strict regulatory guidelines and radiation monitoring networks are usually in place to support the prompt implementation of protective actions (evacuation, sheltering, etc.) in case of emergency. Most of the emergency actions and Emergency Planning Zones (EPZ) are pre-planned based on presumptions, coupling accident scenarios with potential offsite radiological consequences (Probabilistic Risk Assessment and atmospheric transport and dispersion models). However, the Fukushima Daiichi nuclear accident has exposed the challenges in nuclear emergency responses, since the existing plans had to be adapted several times, and monitoring data as well as dispersion codes could not be used as planned, hence aggravating the situation. In this paper, we present a comprehensive review of existing international emergency response plans and guidelines. In addition, we investigate a list of well-documented atmospheric transport and dispersion codes that are typically used for emergency planning and guidance. We then present a retrospective analysis of the Fukushima disaster, debunking the decision delays, plan changes, and the failure in utilizing existing predictive models and monitoring networks. Finally, we present new strategies to help close the identified discrepancy between the plans and their practical implementation, based on lessons learned as well as recent advances in sensors and computational methods (real-time dispersion forecasts, emulators, and model-data integration).

Closing the Planning-to-Implementation Gap in Nuclear Emergency Response: Lessons Learned and Methodological Advances

Post-accident mitigation and consequence analysis have been subjects of extensive research in the nuclear industry. Strict regulatory guidelines and radiation monitoring networks are usually in place to support the prompt implementation of protective actions (evacuation, sheltering, etc.) in case of emergency. Most of the emergency actions and Emergency Planning Zones (EPZ) are pre-planned based on presumptions, coupling accident scenarios with potential offsite radiological consequences (Probabilistic Risk Assessment and atmospheric transport and dispersion models). However, the Fukushima Daiichi nuclear accident has exposed the challenges in nuclear emergency responses, since the existing plans had to be adapted several times, and monitoring data as well as dispersion codes could not be used as planned, hence aggravating the situation. In this paper, we present a comprehensive review of existing international emergency response plans and guidelines. In addition, we investigate a list of well-documented atmospheric transport and dispersion codes that are typically used for emergency planning and guidance. We then present a retrospective analysis of the Fukushima disaster, debunking the decision delays, plan changes, and the failure in utilizing existing predictive models and monitoring networks. Finally, we present new strategies to help close the identified discrepancy between the plans and their practical implementation, based on lessons learned as well as recent advances in sensors and computational methods (real-time dispersion forecasts, emulators, and model-data integration).

Paper AL271 | Download the paper file. | Download the presentation PowerPoint file.

Name: Ali Ayoub (aliayoub@mit.edu)

Bio: Currently a postdoctoral researcher at the Department of Nuclear Science and Engineering at MIT. I hold a PhD in Nuclear Engineering from the ETH Zürich. Research interest: risk analysis, nuclear safety, uncertainty quantification, probabilistic risk assessment, radioactive atmospheric transport and dispersion, prediction, resilience engineering, and risk communication.

Country: USA
Company: MIT
Job Title: Postdoctoral Associate

Paper 4 HI121

Lead Author: Eishiro Higo Co-author(s): Hiromichi Miura, h-miura@criepi.denken.or.jp Hiroyuki Yamada, yamada@criepi.denken.or.jp Masafumi Matsuyama, matsu@criepi.denken.or.jp Yukihiro Kirimoto, kirimoto@criepi.denken.or.jp

Identification of Superimposition Events Induced by a Combination of Seismic and Tsunami Impacts for Developing a Multi-Hazard Probabilistic Risk Assessment Method
Fukushima-Daiichi Nuclear Power Plant accident in March 2011 revealed the risk of the combinational hazards of seismic and tsunami at nuclear power plants (NPPs). For evaluation and improvement of the safety of NPPs against seismic and tsunami hazards, probabilistic risk assessment (PRA) methods have been developed for each hazard. However, each method focuses on the target single-hazard and ignores almost all impacts of the other hazard. For a precise risk assessment of the combination of the two hazards, a new PRA method needs to be developed that considers not only the impact of each hazard but also the impacts of their combination. This study identifies in general what kind of events can be induced by the combination of seismic and tsunami impacts at a typical NPP site faced on a coast. These events are classified based on their causes and occurring locations. How each event can be considered in PRA is briefly described.

Identification of Superimposition Events Induced by a Combination of Seismic and Tsunami Impacts for Developing a Multi-Hazard Probabilistic Risk Assessment Method

Fukushima-Daiichi Nuclear Power Plant accident in March 2011 revealed the risk of the combinational hazards of seismic and tsunami at nuclear power plants (NPPs). For evaluation and improvement of the safety of NPPs against seismic and tsunami hazards, probabilistic risk assessment (PRA) methods have been developed for each hazard. However, each method focuses on the target single-hazard and ignores almost all impacts of the other hazard. For a precise risk assessment of the combination of the two hazards, a new PRA method needs to be developed that considers not only the impact of each hazard but also the impacts of their combination. This study identifies in general what kind of events can be induced by the combination of seismic and tsunami impacts at a typical NPP site faced on a coast. These events are classified based on their causes and occurring locations. How each event can be considered in PRA is briefly described.

Paper HI121 | Download the paper file. |

Name: Eishiro Higo (higo3791@criepi.denken.or.jp)

Bio: Eishiro Higo is a Research Scientist in the Nuclear Risk Research Center (NRRC), the Central Research Institute of Electric Power Industry (CRIEPI), Japan. He is working on studies for multi-hazard PRA and multi-unit PRA. His most recent focus is on accident sequence analysis for multi-hazard (seismic and tsunami) PRA. His other research interests include natech risk assessment, disaster risk assessment, and risk communication. He received the B.E. degree in civil engineering and the M.Eng. degree in urban management from Kyoto University, Japan, in 2010 and 2012, respectively. He received the Ph.D. degree in civil engineering from the University of Waterloo, Ontario, Canada, in 2019. Dr. Higo is a member of the Atomic Energy Society of Japan (AESJ).

Country: JPN
Company: Central Research Institute of Electric Power Industry
Job Title: Research Scientist