scholarly journals A Methodology for the Development of a Reliability Database for an Advanced Reactor Probabilistic Risk Assessment

2016 ◽  
Author(s):  
Dave Grabaskas ◽  
Acacia J. Brunett ◽  
Matthew Bucknor
Keyword(s):  
Risk Assessment ◽  
Failure Modes ◽  
National Laboratory ◽  
Argonne National Laboratory ◽  
Probabilistic Risk Assessment ◽  
Quality Of Data ◽  
Reliability Data ◽  
Water Reactors ◽  
High Level ◽  
Probabilistic Risk

GE Hitachi Nuclear Energy (GEH) and Argonne National Laboratory are currently engaged in a joint effort to modernize and develop probabilistic risk assessment (PRA) techniques for advanced non-light water reactors. At a high level the primary outcome of this project will be the development of next-generation PRA methodologies that will enable risk-informed prioritization of safety- and reliability-focused research and development, while also identifying gaps that may be resolved through additional research. A subset of this effort is the development of a reliability database (RDB) methodology to determine applicable reliability data for inclusion in the quantification of the PRA. The RDB method developed during this project seeks to satisfy the requirements of the Data Analysis element of the ASME/ANS Non-LWR PRA standard. The RDB methodology utilizes a relevancy test to examine reliability data and determine whether it is appropriate to include as part of the reliability database for the PRA. The relevancy test compares three component properties to establish the level of similarity to components examined as part of the PRA. These properties include the component function, the component failure modes, and the environment/boundary conditions of the component. The relevancy test is used to gauge the quality of data found in a variety of sources, such as advanced reactor-specific databases, non-advanced reactor nuclear databases, and non-nuclear databases. The RDB also establishes the integration of expert judgment or separate reliability analysis with past reliability data. This paper provides details on the RDB methodology, and includes an example application of the RDB methodology for determining the reliability of the intermediate heat exchanger of a sodium fast reactor. The example explores a variety of reliability data sources, and assesses their applicability for the PRA of interest through the use of the relevancy test.

Combining RAVEN, RELAP5-3D, and PHISICS for Fuel Cycle and Core Design Analysis for New Cladding Criteria

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Andrea Alfonsi ◽  
George L. Mesina ◽  
Angelo Zoino ◽  
Nolan Anderson ◽  
Cristian Rabiti
Keyword(s):  
Risk Assessment ◽  
National Laboratory ◽  
Nuclear Regulatory Commission ◽  
Probabilistic Risk Assessment ◽  
Reactor Physics ◽  
Idaho National Laboratory ◽  
Accident Scenario ◽  
Regulatory Commission ◽  
Fuel Reload ◽  
Probabilistic Risk

The Nuclear Regulatory Commission (NRC) has considered revision of 10-CFR-50.46C rule (Borchard and Johnson, 2013, “10 CFR 50.46c Rulemaking: Request to Defer Draft Guidance and Extension Request for Final Rule and Final Guidance,” U.S. Nuclear Regulatory Commission, Washington, DC.) to account for burn-up rate effects in future analysis of reactor accident scenarios so that safety margins may evolve as dynamic limits with reactor operation and reloading. To find these limiting conditions, both cladding oxidation and maximum temperature must be cast as functions of fuel exposure. To run a plant model through a long operational transient to fuel reload is computationally intensive, and this must be repeated for each reload until the time of the accident scenario. Moreover for probabilistic risk assessment (PRA), this must be done for many different fuel reload patterns. To perform such new analyses in a reasonable amount of computational time with good accuracy, Idaho National Laboratory (INL) has developed new multiphysics tools by combining existing codes and adding new capabilities. The parallel highly innovative simulation INL code system (PHISICS) toolkit (Rabiti et al., 2016, “New Simulation Schemes and Capabilities for the PHISICS/RELAP5-3D Coupled Suite,” Nucl. Sci. Eng., 182(1), pp. 104–118; Alfonsi et al., 2012, “PHISICS Toolkit: Multi-Reactor Transmutation Analysis Utility—MRTAU,” PHYSOR 2012 Advances in Reactor Physics Linking Research, Industry, and Education, Knoxville, TN, Apr. 15–20.) for neutronic and reactor physics is coupled with the reactor excursion and leak analysis program—three-dimensional (RELAP5-3D) (The RELAP5-3D© Code Development Team, 2014, “RELAP5-3D© Code Manual Volume I: Code Structure, System Models, and Solution Methods,” Rev. 4.2, Idaho National Laboratory, Idaho Falls, ID, Technical Report No. INEEL-EXT-98-00834.) for the loss of coolant accident (LOCA) analysis and reactor analysis and virtual-control environment (RAVEN) (Alfonsi et al., 2013, “RAVEN as a Tool for Dynamic Probabilistic Risk Assessment: Software Overview,” 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, Sun Valley, ID, May 5–9, pp. 1247–1261.) for the probabilistic risk assessment (PRA) and margin characterization analysis. For RELAP5-3D to process a single sequence of cores in a continuous run required a sequence of restarting input decks, each with different neutronics or thermal-hydraulic (TH) flow region and culminating in an accident scenario. A new multideck input processing capability was developed and verified for this analysis. The combined RAVEN/PHISICS/RELAP5-3D tool is used to analyze a typical pressurized water reactor (PWR).

scholarly journals A Methodology for the Integration of a Mechanistic Source Term Analysis in a Probabilistic Framework for Advanced Reactors

2016 ◽  
Author(s):  
Dave Grabaskas ◽  
Acacia J. Brunett ◽  
Matthew Bucknor
Keyword(s):  
Source Term ◽  
National Laboratory ◽  
Argonne National Laboratory ◽  
Primary System ◽  
Separate Analysis ◽  
Event Sequences ◽  
Safety And Reliability ◽  
Detailed Assessment ◽  
Water Reactors ◽  
High Level

GE Hitachi Nuclear Energy (GEH) and Argonne National Laboratory are currently engaged in a joint effort to modernize and develop probabilistic risk assessment (PRA) techniques for advanced non-light water reactors. At a high level, the primary outcome of this project will be the development of next-generation PRA methodologies that will enable risk-informed prioritization of safety- and reliability-focused research and development, while also identifying gaps that may be resolved through additional research. A subset of this effort is the development of PRA methodologies to conduct a mechanistic source term (MST) analysis for event sequences that could result in the release of radionuclides. The MST analysis seeks to realistically model and assess the transport, retention, and release of radionuclides from the reactor to the environment. The MST methods developed during this project seek to satisfy the requirements of the Mechanistic Source Term element of the ASME/ANS Non-LWR PRA standard. The MST methodology consists of separate analysis approaches for risk-significant and non-risk significant event sequences that may result in the release of radionuclides from the reactor. For risk-significant event sequences, the methodology focuses on a detailed assessment, using mechanistic models, of radionuclide release from the fuel, transport through and release from the primary system, transport in the containment, and finally release to the environment. The analysis approach for non-risk significant event sequences examines the possibility of large radionuclide releases due to events such as re-criticality or the complete loss of radionuclide barriers. This paper provides details on the MST methodology, including the interface between the MST analysis and other elements of the PRA, and provides a simplified example MST calculation for a sodium fast reactor.

scholarly journals Dynamic Probabilistic Risk Assessment Based Response Surface Approach for FLEX and Accident Tolerant Fuels for Medium Break LOCA Spectrum

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2490
Author(s):  
Asad Ullah Amin Shah ◽  
Robby Christian ◽  
Junyung Kim ◽  
Jaewhan Kim ◽  
Jinkyun Park ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Response Surface ◽  
Failure Modes ◽  
Probabilistic Risk Assessment ◽  
Operating Conditions ◽  
Cumulative Distribution ◽  
Surface Approach ◽  
Dynamic Probabilistic Risk Assessment ◽  
Safety Features ◽  
Probabilistic Risk

After the Fukushima Daiichi Accident, the safety features such as accident tolerant fuel (ATF) and diverse and flexible coping strategies (FLEX) for existing nuclear fleets are being investigated by the US Department of Energy under the Light Water Reactor Sustainability Program. This research is being conducted to quantify the risk-benefit of these safety features. Dynamic probabilistic risk assessment (DPRA)-based response-surface approach has been presented to quantify the FLEX and ATF benefits by estimating the risk associated with each option. ATFs with multilayered silicon carbide (SiC), iron-chromium-aluminum, and chromium-coated zirconium cladding were considered in this study. While these ATF candidates perform better than the current zirconium cladding (Zr), they may introduce additional failure modes in some operating conditions. The fuel failure analysis modules (FAMs) were developed to investigate ATF performance. The dynamic risk assessments were performed using RAVEN, a DPRA tool, coupled with RELAP5 and FAMs. A cumulative distribution function-based index provided a mean of comparing the benefits of safety enhancements. For medium break loss of coolant accidents, FLEX operational timing window for each fuel type was estimated. Among these ATF candidates, SiC-type ATF was the most beneficial candidate for an increased safety margin than Zr-based fuel and was found to complement FLEX strategies in terms of risk and coping time.

Advanced Small Modular Reactor Probabilistic Risk Assessment Framework

2014 ◽  
Author(s):  
Curtis Smith
Keyword(s):  
Risk Assessment ◽  
Quantitative Methods ◽  
3D Visualization ◽  
National Laboratory ◽  
Probabilistic Risk Assessment ◽  
Mitigation Strategies ◽  
Small Modular Reactor ◽  
Safety Case ◽  
Safety Models ◽  
Probabilistic Risk

A key area of the Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) use is in the development of methodologies and tools that will be used to predict the safety, security, safeguards, performance, and deployment viability of SMR systems starting in the design process through the operation phase. Recently, the Idaho National Laboratory (INL) set out to develop quantitative methods and tools and the associated analysis framework for assessing a variety of SMR risks. Development and implementation of SMR-focused safety assessment methods may require new analytic methods or adaptation of traditional methods to the advanced design and operational features of SMRs. The development of SMR-specific safety models for margin determination will provide a safety case that describes potential accidents, design options (including postulated controls), and supports licensing activities by providing a technical basis for the safety envelope. INL has proposed an approach to expand and advance the state-of-the-practice in PRA. Specifically we will develop a framework for applying modern computational tools to create advanced risk-based methods for identifying design vulnerabilities in SMRs. This framework will require the fusion of state-of-the-art PRA methods, advanced 3D visualization methods, and highperformance optimization. The approach has several defining attributes focused within three general areas: 1. Models – A single 3D representation of all key systems, structures, and components (SSCs) will be defined for a particular facility. We will be able to simulate — by understanding how each SSC interacts with other parts of the facility — the hazard-induced susceptibilities of each SSC. 2. Phenomena – An approach to effectively representing hazards and their effect on physical behavior at a facility will need to be determined. In many cases, multiple models of a specific phenomenon may be available, but this ensemble of models will need to be intelligently managed. 3. Integration – Any advanced risk-informed decision support approach will rely on a variety of probabilistic and mechanistic information. The safety, security, and economic drivers will need to be integrated in order to determine the effectiveness of proposed mitigation strategies. We will need to be able to manage all (important) hazards for all (important) scenarios all of the time the facility is in operation. The focus of the paper will be on discussing the features of the proposed advanced SMR PRA Framework and providing an status update of the development activities.

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