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​​​​​​​​​​Enhancement of Legacy Probabilistic Risk Assessment Tools

 

G​oal

To enhance the legacy risk analysis tools and methods currently used in the nuclear power industry.

The use of legacy probabilistic risk assessment (PRA) tools is fundamental to nuclear power plant operations and the Reactor Oversight Program's regulatory infrastructure in the U.S. However, the application​​​​​ of legacy tools and methods has demonstrated that complexity, high modeling-resource requirements, and lengthy analysis times make the use of legacy PRA tools complicated.​

Outco​​me

Researchers will develop solutions to challenges associated with the legacy PRA tools. Based upon recent industry concerns and feedback, the project will focus on three high-priority areas: (1) quantification speed to support decision-making, (2) dependency modeling of human-related basic events, and (3) integration of multiple-hazard models for better risk insights.

Planned Major Accomplishments:​

  • 2022—provide solutions for the highest-priority challenges in PRA modeling, simulations, and analyses using traditional PRA tools and methods

  • 2024—complete advanced development of an enhanced modeling and maintenance approach for legacy PRA applications 

Related Reports 

 

 

Solutions for Enhanced Legacy Probabilistic Risk Assessment Tools and Methodologies Improving Efficiency of Model Development and Processing via Innovative Human Reliability Dependency Analysis, INL/RPT-23-71788269639This report focuses on the potential to improve model efficiencies, including quantification speed, via enhanced techniques for modeling human-action dependency analyses. In addition, this report explores opportunities to improve the underlying theoretical bases of a dependency analysis by investigating prospects for empirical data collection.This report focuses on the potential to improve model efficiencies, including quantification speed, via enhanced techniques for modeling human-action dependency analyses. In addition, this report explores opportunities to improve the underlying theoretical bases of a dependency analysis by investigating prospects for empirical data collection.4/3/2023 2:40:55 AMU.S. Department of Energy Office of Nuclear Energy This information was prepared as an account of work sponsored by an agency of the U.S. Government As the complexity of the PRA 188https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf
Enhancement of Industry Legacy Probabilistic Risk Assessment Methods and Tools, INL/EXT-21-64448234761Three areas were identified as most beneficial to address to maintain and improve the usefulness of the current practice legacy PRA tools: improved quantification speed, increased ability to efficiently model multi-hazard models, and improved modeling human action dependency in PRA.Three areas were identified as most beneficial to address to maintain and improve the usefulness of the current practice legacy PRA tools: improved quantification speed, increased ability to efficiently model multi-hazard models, and improved modeling human action dependency in PRA.9/29/2021 8:03:58 PMU.S. Department of Energy Office of Nuclear Energy This information was prepared as an account of work sponsored by an agency of the U.S. Government The need for research to address 510https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf
R&D Roadmap to Enhance Industry Legacy Probabilistic Risk Assessment Methods and Tools, INL/EXT-20-59202199538R&D Roadmap to Enhance Industry Legacy Probabilistic Risk Assessment Methods and Tools, INL/EXT-20-59202R&D Roadmap to Enhance Industry Legacy Probabilistic Risk Assessment Methods and Tools, INL/EXT-20-592028/12/2020 3:36:25 PMAndrew Miller, Stephen Hess, and Curtis Smith U.S. Department of Energy Office of Nuclear Energy This information was prepared as an account of work sponsored by an agency of the 1062https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf

​For more information contact

Svetlana (Lana) Lawrence
Risk-Informed Systems Analysis, Pathway Lead
Idaho National Laboratory




PRA Fault Tree.png

An illustration of the PRA fault tree displayed in the SAPHIRE tool.

The figure is hard to read please provide a new one.​​