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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nuclear Power Fault Diagnostics and Preventative Maintenance Optimization

Operation and maintenance costs for nuclear power plants are very large. Reactors are starting to shut down even after their operating licenses have been extended, because they are not price competitive compared to other energy sources. The nuclear industry is witnessing early closure of nuclear power plants due to economic reasons despite excellent safety records. Therefore, it is imperative to reduce costs to prevent these early closures. This paper showcases recent research into advanced fault diagnostics techniques and preventative maintenance optimization to reduce these maintenance costs. This report focuses on the condensate and feedwater system for both pressurized and boiling water reactor systems. The computerized maintenance management system, which contains the plant’s digital record of all the corrective- and preventative-maintenance work orders, was used as a ground truth to locate potential faults and label the process data as healthy or faulty. Various feature extraction techniques were utilized to further differentiate the faults from the healthy data. Support vectors machines were used to categorize other test sets of process data as healthy or faulty through a cross validation procedure. Similar faults were not found within this system leading to preventative maintenance optimization. Unnecessary amounts of preventative maintenance lead to inflated maintenance costs. This paper summarizes the steps for preventative maintenance optimization from component health determination to recommendation for action. This optimization was completed for condensate pumps, condensate booster pumps, and the respective motors that drive them.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Incorporation of Thermal Hydraulic Models for Thermal Power Dispatch into a PWR Power Plant Simulator

This report describes the development, modeling, and results of a generic pressurized water reactor power plant simulator that incorporates coupled electrical and thermal power dispatch to an industrial process located approximately one kilometer from the nuclear power plant. The simulator is a commercial PWR simulator that has been modified to include thermal power dispatch as described in past milestone reports [ , ]. The commercial PWR simulator is a generic simulator available from GSE SYSTEMS® (Sykesville, MD, USA) that is built using RELAP5-HDTM Real-Time Solution and in-house software developed by GSE Systems. This generic PWR (GPWR) simulator performs real-time simulation of the complete power plant from the reactor neutronics to the electricity generation and distribution. All primary, secondary, and auxiliary systems are modeled including all control logic in order to provide the most accurate representation of actual nuclear power plant (NPP) operation, and the simulator results have been rigorously verified by an actual NPP operating at approximately 1 GWe. This report is a continuation of worked performed in previous years, and supplemental information from previous reports is included in the appendix for reference.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

U.S. Efforts in Support of Examinations at Fukushima Daiichi- November 2020 Meeting Notes with Updated Information Requests

Much is still not known about the end-state of core materials in each unit that was operating on March 11, 2011 at the Fukushima Daiichi Nuclear Power Station (Daiichi). Information obtained from Daiichi is required to inform Decontamination and Decommissioning (D&D) activities, improving the ability of the Tokyo Electric Power Company Holdings, Incorporated (TEPCO Holdings) to characterize potential hazards and to ensure the safety of workers involved with cleanup activities. This information also has important implications for the safety and operation of U.S. commercial nuclear power plants. This document summarizes results from the Fiscal Year 2021 (FY2021) U.S. effort to review Daiichi information and extract insights to enhance the safety of existing and future nuclear power plant designs. This U.S. effort, which was initiated in 2014 by the Department of Energy Office of Nuclear Energy (DOE-NE), is completed by a group of experts in reactor safety and plant operations that identify examination needs and evaluate recent Daiichi examination data to address these needs. Since its inception, annual reports were issued that document significant safety insights being obtained in areas of special emphasis: system and component performance, radionuclide surveys and sampling, debris end-state location, combustible gas effects, and plant operations and maintenance. In addition to reducing uncertainties related to severe accident modeling progression, these insights have and continue to be used to update guidance for severe accident prevention, mitigation, and emergency planning. Reduced uncertainties in modeling the events at Daiichi improve the realism of reactor safety evaluations that inform future D&D activities. A key aspect of prior U.S. efforts, the updated list of information requests, is included in this FY2021 report to ensure that they are transmitted to organizations within Japan. This report also continues to emphasize how information obtained from the affected reactors at Daiichi has been and will continue to be used to update severe accident management strategies and reduce uncertainties in systems analysis code models. In addition, recommendations are included that would expand the use of this information to provide insights regarding maintenance, radiation protection, design, and siting activities for existing and new reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Attack Surface Analysis of the Digital Twins interface with Advanced Sensor and Instrumentation Interfaces: Cyber Threat Assessment and Attack Demonstration for Digital Twins in Advanced Reactor Architectures

A digital twin is a virtual representation of a physical system or object using real-time data that can predict and analyze how the system or object performs. This relatively new technology can be applied to the field of nuclear power generation, to aid in the design and development of new nuclear power plants and reduce operation costs using predictive maintenance and other data analytical methods. While there are already companies utilizing simulation software to train operators and technicians in the nuclear industry, some are now transitioning to utilizing their existing technology, software, and methods to develop digital twin solutions for the next generation of nuclear power plants, offering their services to utilities and government organizations around the world.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Flexible Nuclear Energy for Clean Energy Systems

As part of the Nuclear Innovation: Clean Energy Future initiative, this report describes flexibility in nuclear systems, the value it can bring, and international experiences surrounding flexible nuclear energy. Flexible nuclear energy for this report is defined as “The ability of nuclear energy generation to economically provide energy services at the time and location they are needed by end-users. These energy services can include both electric and non-electric applications utilizing both traditional nuclear power plants and advanced integrated systems.” Flexibility in nuclear systems is enabled by three main mechanisms: core ramping, integrated energy systems with multiple byproducts, and thermal storage. The value of this flexibility on a $\$$/MW and a $\$$/MWh basis can be estimated through a combination of physics and economics modeling. International agencies describe their experiences in operating flexible nuclear systems and describe their plans for increasing nuclear flexibility in a clean energy system, likely with a high penetration of variable renewable energy.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Informing Plant Asset Reliability and Availability Through AI-Driven Analysis of Operator Logs

The availability and reliability of nuclear power plant (NPP) structures, systems, and components (SSCs) are critical parameters for NPP safety. Tracking these parameters is necessary but costly and labor-intensive, requiring the collection and evaluation of SSC event data such as shutdowns, startups, and failures. To show how these events are needed for the parameters an example is given: one measure of reliability is based on the number of equipment failure events and the number of run hours (i.e., the time from a startup event to a shutdown event). Here, this work investigates using artificial intelligence (AI) to mine NPP operator log entry texts for SSC event data. Four AI approaches were explored for identifying these events, including natural language processing (NLP) methods, generative AI, generative AI combined with NLP, and topic modeling. A key challenge addressed with all four approaches is the brevity of operator log entries. Among these four a neural network–based NLP method was shown to be the most promising for this application, achieving F1 scores of 86.0% for shutdowns, 92.2% for startups, and 80.4% for failures on a subject-matter-expert-curated dataset from NPP operator logs, compared to a baseline of 66.6% for a random classifier. This shows that NLP methods can perform better than generative AI. Additionally, the NLP methods combined with generative AI were shown to perform better than generative AI alone. Generative AI was most successful at providing the background information for the NLP methods to use. This work demonstrates the potential to use AI to automate parameter collection from NPP operator log entries and other records.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrating PCTRAN with AI-Driven Host-Intrusion Detection and Secured Container Systems for Advanced Malware Analysis (Summer Internship Report)

This study presents a solution for enhancing the security of the Personal Computer Transient Analyzer (PCTRAN) PC-based Nuclear Power Plant Simulator by integrating the software with an artificial intelligence (AI)-driven host-intrusion detection system (HIDS), in addition to a secured container system, for malware analysis. PCTRAN is a Windows XP-based software package that has the ability to simulate a variety of accident and transient conditions for nuclear power plants (NPPs). It offers a high-resolution replica of the Nuclear Steam Supply System (NSSS) and displays the status of important parameters allowing for operator interaction. By including AI-driven HIDS for the NSSS, the framework can identify security threats in real-time, ensuring the integrity of the nuclear simulation environment. Additionally, the secured container system offers the ability to isolate and analyze malware, preventing potential threats from affecting core systems. The integration process involves extensive testing and validation in order to ensure accuracy, reliability, and compliance with security policies. This framework sets a new precedent for secure simulation and training in NPP operations, and offers insight for future advancements in cybersecurity.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Demonstration of the Human and Technology Integration Guidance for the Design of Plant-Specific Advanced Automation and Data Visualization Techniques

Nuclear power continues to be a safe, reliable, and carbon-free electricity generating source for the United States, though the cost of operating and maintaining the current United States nuclear power plant fleet has become uncompetitive with other sources. This gap is attributed to the advent of new digital instrumentation and control technologies that other electricity generating industries are currently leveraging to streamline work and greatly reduce operating, maintenance, and support costs. Digital instrumentation and control systems and control room modernization offers significant opportunities to reduce operating and maintenance costs to ensure the continued operation of the existing United States light-water reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Data Analytics Methods to Measure Plant Outage Resilience

Every 18 or 24 months nuclear power plants (depending on plant configuration, pressurized or boiling water reactor respectively) undergo a period of outage where the plant is taken offline and a large number of maintenance and surveillance activities (that cannot be performed while plant is running) are performed in typically 2–3 weeks. Planning of a plant outage is very challenging since all the activities are required to be performed in the shortest amount of time given available resources (typically contractor crews hired for the duration of the outage). Consequently, plant outages can be costly due the actual loss of power generation and crew costs and, because of it, there is a need to maximize resource usage in the outage planning phase and reduce the risk of outage delays. This paper is addressing these needs by providing a set of analytical methods designed to analyze plant outage schedule and identify critical elements based on available resources (time and crews). These methods are based on natural language processing and optimization algorithms. In this respect, two classes of methods have been developed: one that focuses on the time resource and how variability in the time to complete outage tasks may impact outage delays, and one that minimizes the risk of outage delays by integrating available resources to assess when daily activities should be performed.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Model-Based Diagnostics and Mitigation of Cyber Threats

The report summarizes key tasks performed to develop a toolkit for detecting cyber-attack events in instrumentation and control (I&C) systems of nuclear power plants. The toolkit connects the state-of-the-art GPWR Simulator with the RELAP5 code providing best-estimate nuclear steam supply system (NSSS) analyses, via the application programming interface (API), and allows users to introduce potential cyber-attack scenarios into power plant operational simulation. This summary for the project reflects topical reports submitted during the project as well as a journal paper published in 2022. The focus areas of the summary include: (1) modeling I&C systems for the AP1000 Generation III+ nuclear plant and GPWR simulator, (2) simulation and monitoring of plant response to cyber-attack events, (3) API structure for the toolkit interfacing the GPWR simulator and RELAP5 code, and (4) restructuring of the three-loop NSSS software of GPWR to model the two-loop AP1000 structure. Discussed in some details are (a) the attack tree analysis assessing the susceptibility of the AP1000 I&C system, resulting in reactor trips, in terms of the attack possibility and component sensitivity and (b) realistic estimation of the time to steam generator trip due to cyber-intrusions in the GPWR Simulator. Finally, sample demonstrations of the cyber-security tool kit, in the form of the GPWR-RELAP5 API, are summarized.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Analysis of Loss-of-Offsite-Power Events: Update

Loss-of-offsite power (LOOP) can have a negative impact on a nuclear power plant’s ability to achieve and maintain safe shutdown conditions. LOOP event frequencies and times required for subsequent restoration of offsite power are important inputs to plant probabilistic risk assessments. This report presents a statistical and engineering analysis of LOOP frequencies and durations at U.S. commercial nuclear power plants. The data used in this study were based on the operating experience during calendar years 1987–2020, while the most recent 15-year data (i.e., from 2006–2020) were used for most analyses in this report. LOOP events during critical operation that did not result in a reactor trip are not included. Frequencies and durations were determined for four LOOP event categories: plant-centered, switchyard-centered, grid-related, and weather related. Highly significant decreasing trends in the LOOP occurrence rates were identified for All-LOOPs during critical operation (p-value = 0.001) and switchyard-centered LOOPs during critical operation (p-value = 0.002) for the most recent 10-year period (2011–2020). Adverse trends in LOOP durations continue for switchyard-centered LOOPs (p-value = 0.005), All-LOOPs (p-value = 0.019), as well as All-LOOPs during shutdown operation (p-value = 0.003). Statistical tests show the LOOP counts are not uniformly distributed across the 12 months, and variation among the months exists for plant-centered LOOPs (p-value = 0.028), grid-related LOOPs (p-value = 0.021), All-LOOPs (p-value = 0.003), and All-LOOPs during critical operation (p-value = 0.008). The engineering analysis of LOOP data showed for the period of 2006–2020, the equipment failure events were dominated by failures of circuits and relay; human errors have been less frequent and occurred primarily in maintenance; and weather events were dominated by tornadoes and lightning.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Advancements in Development and Testing of Thermal Power Dispatch Simulators

Flexible plant operations and generation (FPOG) offer nuclear power plants (NPPs) the chance to leverage alternative, non-electric revenue streams while ensuring their continued role as reliable, clean, and constant sources of baseload electrical power. The excess thermal energy generated from NPPs during periods of low electricity demand can be channeled as raw materials to numerous industrial processes via a thermal power dispatch (TPD) system. Hydrogen production via high-temperature steam electrolysis (HTSE) is an optimal use case based on technical and economic feasibility. Researchers at Idaho National Laboratory (INL) have conducted previous works that developed and implemented TPD system models within the GSE Solutions Generic Pressurized Water Reactor (GPWR) simulator to support human-in-the-loop (HITL) scenario-based evaluations. The first part of this report documents modifications made to the GPWR TPD model and HMI from the previous iteration in line with a new Sargent and Lundy (S&L) TPD design with an automatic control system. The was done in collaboration with Westinghouse using their three-loop pressurizer water reactor (W3LPWR) simulator which contains an industrial grade automatic control system for the TPD. This was installed in the Human Systems Simulation Laboratory (HSSL) at INL. The second part of the report documents findings from an all-hands-on-deck integration and verification workshop that was conducted in the HSSL over several days. The research team comprised INL human factors and TPD experts, a nuclear engineer from GSE Solutions who implemented the revised TPD model for GPWR, the human-machine interface (HMI) prototyping and human factors team from the University of Idaho, and personnel with operations experience with pressurized water reactors. The workshop provided time and expertise to conduct the final activities to bring the operations, HMI, and simulator into a functional state. The goals of the integration and verification workshop were: 1. to install the revised GPWR TPD model into the HSSL 2. verify the TPD HMI prototype was functional 3. integrate the HTSE Simulink model to GPWR. 3. Issues were identified for resolution, but overall the workshop accomplished its goal to integrated and verify the majority of the intended functional. Future work will resolve the identified issues and use the integrated simulation to support an evaluation and demonstration in the next fiscal year.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Analysis of Loss-of-Offsite-Power Events: 2021 Update

Loss-of-offsite power (LOOP) can have a negative impact on a nuclear power plant’s ability to achieve and maintain safe shutdown conditions. LOOP event frequencies and times required for subsequent restoration of offsite power are important inputs to plant probabilistic risk assessments. This report presents a statistical and engineering analysis of LOOP frequencies and durations at U.S. commercial nuclear power plants. The data used in this study were based on the operating experience during calendar years 1987–2021, while the most recent 15-year data (i.e., from 2007–2021) were used for most analyses in this report. LOOP events during critical operation that did not result in a reactor trip are not included. Frequencies and durations were determined for four LOOP event categories: plant-centered, switchyard-centered, grid-related, and weather-related. These categories (and the All-LOOPs group which contains all LOOPs without regarding of the four categories) could be further grouped by whether a LOOP event occurred during critical operation, during shutdown operation, or during all operations. The following decreasing trends in the LOOP occurrence rates were identified for the most recent 10-year period (2012–2021): All-LOOPs during critical operation, switchyard-centered LOOPs during critical operation, and grid-related LOOPs during critical operation. Adverse trends in LOOP durations continue for switchyard-centered LOOPs during all operations, All-LOOPs during all operations, and All-LOOPs during shutdown operation for the 1997–2021 period. Statistical tests show the LOOP counts for the period of 2007–2021 are not uniformly distributed across the 12 months, and variation among the months exists for grid-related LOOPs during all operations, All-LOOPs during all operations, and All-LOOPs during critical operation. The engineering analysis of LOOP data showed for the period of 2007–2021, the equipment failure events were dominated by failures of relay and other; human errors have been less frequent and occurred primarily in maintenance and switching; and weather were dominated by tornadoes then by lightning and hurricane. Weather was the cause for 45% of LOOPs for the last fifteen years (2007– 2021) but only for 20% of LOOPs for the previous 20 years (1987–2006) .

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Numerical Investigation of Butterfly Valve Performance in Variable Valve Sizes, Positions and Flow Regimes

Reliability and efficiency of valves are necessary for precise control and sufficient heat-flow to heat application plants for the integrated energy systems of nuclear power plants (NPPs). Strategic Management Analysis Requirement and Technology (SMART) valves’ ability to control flow and assess environmental parameters stands out for these requirements. Their ability to sustain the downstream flow rate, prevent reverse flow, and maintain pressure in the heat transport loop is much more efficient with the integration of sensors and intelligent algorithms. For assessing valve performance and monitoring, mechanical design and operating conditions are two important parameters. In this study, the butterfly valves of three different sizes are simulated with water and steam using STAR-CCM+ in various flow regimes and positions to analyze performance parameters to strategize an automated control system for efficiently balancing the heat–transport network. Also, flow behavior is studied using velocity and pressure fields for valve–body geometry optimization. It can be observed, through performance parameters, that the valves are suitable for operation between 30° and 90° positions with significantly low loss coefficients and high flow coefficients, and the performance parameters follow a certain pattern in both water and steam flow in each scenario.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Human-System Interface Design Review Guidelines

The U.S. Nuclear Regulatory Commission (NRC) staff reviews the human factors engineering (HFE) aspects of nuclear power plants in accordance with the Standard Review Plan (NUREG-0800, Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition). The Human Factors Engineering Program Review Model (NUREG-0711, Revision 3, issued November 2012) contains detailed design review procedures. As part of the review process, the interfaces between plant personnel and the plant's systems and components are evaluated for conformance with HFE guidelines. This document, Human-System Interface Design Review Guidelines (NUREG-0700, Revision 3), provides the guidelines necessary to perform this evaluation. The review guidelines address the physical and functional characteristics of human-system interfaces (HSIs). Because these guidelines only address the HFE aspects of design and not other related considerations, such as instrumentation and control and structural design, they are referred to as HFE guidelines. In addition to the review of actual HSIs, the NRC staff can use the NUREG-0700 guidelines to evaluate a design-specific HFE guidelines document or style guide. The HFE guidelines are organized into four basic parts, which are divided into sections. Part I contains guidelines for the basic HSI elements: information displays, user-interface interaction and management, and analog displays and controls. These elements are used as building blocks to develop HSI systems to serve specific functions. Part II contains the guidelines for reviewing the following HSI systems: alarm system, safety parameter display system, group-view display system, soft control system, computer-based procedure system, automation system, and communication system. Part III provides guidelines for the review of workstations and workplaces. Part IV provides guidelines for the review of HSI support (i.e., maintainability of digital systems and degraded HSI and instrumentation and control conditions).

42 ENGINEERING↗
DOE OSTI2020

Risk-Informed ATF and FLEX Analysis for an Enhanced Resilient BWR Under Design-Basis and Beyond-Design-Basis Accidents

This report documents the activities performed by the Idaho National Laboratory (INL) during fiscal year (FY) 2020 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk-Informed System Analysis (RISA) Pathway, Enhanced Resilient Plant (ERP) Systems research. The purpose of the RISA Pathway research and development is to support plant owner-operator decisions with the aim to improve the economics, reliability, and maintain the high levels of safety of current nuclear power plants over periods of extended plant operations. The concept of ERP refers to the combinations of accident-tolerant fuel (ATF), optimal use of diverse and flexible coping strategy (FLEX), enhancements to plant components and systems, and the incorporation of augmented or new passive cooling systems, as well as improved fuel cycle efficiency. The objective of the ERP research effort is to use the RISA methods and toolkit in industry applications, including methods development and early demonstration of technologies, in order to enhance existing reactors’ safety features (both active and passive) and to substantially reduce operating costs through risk-informed approaches to plant design modifications to the plant and their characterization. One main focus of the FY 2020 efforts documented in this report was to extend the analyses conducted in FYs 2018 and 2019 for a pressurized water reactor (PWR) to a boiling water reactor (BWR). The same analysis process, risk analysis approaches, and analysis tools as in the previous work for PWR were used for a generic BWR with near-term ATF cladding (i.e., Iron-Chromium-Aluminum [FeCrAl] cladding and Chromium [Cr]-coated cladding) designs under the postulated station blackout (SBO) and medium loss-of-coolant (MLOCA) accident scenarios. In addition, a FLEX model was developed and incorporated into a generic BWR probabilistic risk assessment (PRA) model using the INL-developed software tool, Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE), to assess the risk impact from FLEX. The other main focus of the FY 2020 efforts was to advance analysis methods, including developing dynamic approach for FLEX human reliability analysis (HRA) using the INL-developed software tool, Event Modeling Risk Assessment using Linked Diagrams (EMRALD), as well as developing a multicriterion benefit evaluation (MCBE) method for evaluating costs and benefits of safety enhancements in nuclear power plants (NPPs). As a case study, the MCBE method was applied to evaluate the costs and benefits brought by FLEX implementation.

99 GENERAL AND MISCELLANEOUS↗