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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Third-Party Supplier Risk Re-Classification Using Multi-Model Semantic Voting and External Web Augmentation

Risk decisions in many third-party risk management (TPRM) workflows rely on static inherent risk questionnaires (IRQ). These static forms provide a snapshot of the vendor from the business users’ perspective, as these requests are processed without cross-referencing for evidence. Consequently, responses can be misinformed or embellished with inaccuracies, thereby masking the vendor’s true risk to the enterprise. This paper presents a multi-stage verification framework to augment IRQs with web evidence and a deterministic ensemble of large language model assessors to reclassify risk. In a case study of 100 submissions previously misclassified as low risk, the proposed framework correctly identified 76% of the cases as high risk, while the existing workflow identified none. McNemar’s continuity corrected statistics of 74 were obtained with a two sided p-value of 2.65 × 10-23, indicating a significantly more effective workflow compared to the legacy model.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Risk Model for EM Decision Support Toolsets

The Government Office of Accountability (GAO) has published several reports identifying the need for the Department of Energy (DOE) Office of Environmental Management (EM) to address mounting costs for DoE's cleanup program. DOEEM could greatly benefit from independent decision tool-sets/models that would allow them to evaluate options and inform business decision at the enterprise level considering site-specific life cycle costs and system plans. Program goals: Develop a tool-set that enables EM to independently evaluate alternatives, assess outcomes from different contracting strategies, and inform critical decisions for the enterprise. Project goals: Adaption of a Risk Model for integration with complimentary tool-sets for project level decision making. Operational Events: Discrete event model that evaluates operational variables (e.g. capacity, throughput, maintenance constraints) and identify bottlenecks. Identifying and Bounding Project Risk: Identify risks that impact confidence in meeting goals (e.g. production). Life cycle Cost: Evaluate impacts of staffing levels, inventory, and capital investments on life cycle costs. Methods and approach: Monte Carlo Analysis is being executed to generate results: Input derives from Risk Register Data; Tied to Projected and Target Schedules; Incorporates float duration within the model. Assumes associated risk mitigation actions being completed within a timeline of five fiscal years. Metrics include: Confidence in Meeting Production Goal; Confidence in Safety Standards; Confidence in Continuous Operation; Other Metrics can be added as appropriate regarding specific site needs. The adapted risk model can be used as a stand alone decision tool or can be integrated complementary tool-sets (i.e. process and cost models) for project-specific decisions. These support tool-sets can then be integrated with others for site- and complex- level evaluations. Future work includes designing an adaptable and modular framework that would allow integration of multiple tool-sets for holistic and/or targeted evaluation of alternative strategies for decision making that could lead to risk and cost reduction across the enterprise.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Vind: A Blockchain-Enabled Supply Chain Provenance Framework for Energy Delivery Systems

Enterprise-level energy delivery systems (EDSs) depend on different software or hardware vendors to achieve operational efficiency. Critical components of these systems are typically manufactured and integrated by overseas suppliers, which expands the attack surface to adversaries with additional opportunities to infiltrate into EDSs. Due to this reason, the risk management of the EDS supply chain is crucial to ensure that we are knowledgeable about the vulnerabilities in software and hardware components that comprise any critical part, quantifiable risk metrics to assess the severity and exploitability of the attack, and provide remediation solutions that can influence a prioritized mitigation plan. There is a need to realize cyber supply chain risk management for industrial control systems’ hardware, software, and computing and networking services associated with bulk electric system (BES) operations. This article proposes a blockchain-based cyber supply chain provenance platform (“Vind”) for EDSs to realize data provenance in a cyber supply chain ecosystem.

Bandara, Eranga↗
DOE OSTI2026

Commercial integration of advanced nuclear energy with Artificial Intelligence (AI): Possible implications

The integration of advanced nuclear technologies (both fission and fusion) with artificial intelligence (AI) presents unprecedented national security challenges and opportunities. As fusion energy approaches commercial viability alongside advanced Small Modular Reactors (SMRs), their integration with AI and Artificial General Intelligence (AGI) systems could fundamentally transform the global energy and AI landscapes — two pillars of national security. This document briefly examines how AI could accelerate nuclear energy development and deployment while altering existing power structures, a lot could be done to deepen the discussions. Simultaneously, it observes how nuclear-powered AI may expedite advances toward AGI and beyond. These issues are deeply interconnected and thus need to be examined as a whole and more comprehensively than what’s being summarized here. For instance, AI-powered autonomous operation of nuclear facilities could reduce human error but introduce new cybersecurity vulnerabilities and uncertainties. Further investigation would also address how AI-enhanced nuclear technologies might complicate proliferation concerns through advanced fuel cycle management, nuclear materials production and safeguard. The strategic advantage gained by first entities achieving successful AI-nuclear integration could reshape global and national security framework. Timely analysis of these implications may be crucial for policymakers seeking to harness these technologies' benefits while effectively mitigating their potential risks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Use of Operating Agreements and Energy Storage to Reduce Photovoltaic Interconnection Costs: Conceptual Framework

This report explores one integrated technical and process concept designed to manage interconnection costs and streamline interconnection timelines to support near-term renewable energy deployment. We describe a new agreement between renewable energy developers and utilities, informed by the technical analysis. The agreement defines the operational parameters for a renewable energy system, with the goal of reducing risk and cost to all parties. This work provides a foundation upon which other states and utilities may build proof of concept. This report is supported by a technical analysis that is detailed in a companion report, "Use of Operating Agreements and Energy Storage to Reduce Photovoltaic Interconnection Costs: Technical and Economic Analysis" (McLaren et al. 2022).

14 SOLAR ENERGY↗
DOE OSTI2023

Risk Analysis of Various Design Architectures for High Safety-significant Safety-related Digital Instrumentation and Control Systems of Nuclear Power Plants during Accident Scenarios

This report documents the plus-up activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing advanced risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems to support system design decisions and diversity and redundancy applications, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the LWRS-developed framework instructs nuclear vendors and utilities on how to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). Adding diversity within system or components is the main means to eliminate and mitigate CCFs, but diversity also increases plant complexity and errors and may not address all sources of systematic failures. How to optimize the diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in HSSSR DI&C systems of NPPs and supporting relevant design optimization, the framework provides: ? An integrated best-estimate, risk-informed capability to address new technical digital issues quantitatively, accurately, and efficiently in plan modernization progress, such as software CCFs in HSSSR DI&C systems of NPPs ? A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to efficiently predict and prevent risk in the early design stage of DI&C systems ? Technical bases and risk-informed insights to assist U.S. Nuclear Regulatory Commission (NRC) and industry to address and fulfill the risk-informed alternatives for evaluation of CCFs in HSSSR DI&C systems of NPPs ? An integrated risk-informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The plus-up research and development efforts of this project in FY 2022 are focused on methodology improvement of software CCF modeling and estimation, prevention analysis, importance analysis and risk analysis of various design architectures of HSSSR DI&C systems. This work greatly enhances the capability of the LWRS-developed framework for the risk assessment and design optimization of safety-critical DI&C systems. It should be noted that all the analyses are performed for the demonstration of the LWRS-developed framework, not for the evaluation of relevant systems. Results are obtained based on very limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Integrating Cybersecurity with System Operations and Restoration

This presentation covers the interaction of the discipline of system operations with the discipline of cybersecurity. First, a common mental model for risk - both cybersecurity and all-hazards - is presented, followed by a discussion of high-level management strategies for different kinds of cyber harm facing system operators, based on the consequences and frequencies of the harm. The next section covers the importance of cybersecurity for a system operator organization and explains some general concepts to understand the relationships. Finally the role of system operators in the security of the grid as a larger system of systems is discussed over the framework of a resilience event.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Ushering in the New Age of Laboratories: Smart Labs in Practice; Preprint

Ventilation is the first line of defense against airborne hazards produced during research activities in laboratories. A vital component to maintaining healthy, safe, indoor air quality, laboratory ventilation systems are often victim to ineffective operation, posing a risk to an organization's most important asset - the researchers. Furthermore, system inefficiencies can lead to up to 50% wasted energy. By providing a framework to improve the safety and energy efficiency through optimized ventilation and operations, the Smart Labs Toolkit guides laboratory stakeholders through a straight-forward, holistic approach to achieving a dynamic Smart Labs program. A Smart Labs program employs a combination of physical, administrative, and management techniques to plan, assess, optimize, and manage high-performance laboratories. Grounded in the Smart Labs methodology, the National Renewable Energy Laboratory (NREL) implemented a successful Smart Labs program to oversee the design, construction, maintenance, and operations of its laboratories. To accomplish this effort, NREL's key stakeholders created an internal partnership to align NREL's existing laboratories with Smart Labs principles and solidify organizational roles for the safe and efficient operation of laboratory assets. The program provides the groundwork for decarbonization strategies centered around building operation. This paper outlines best practices employed by NREL to develop a cross-cutting Smart Labs team, garner managerial support, and effectively communicate of goals around safety and energy. Strategies include specific Smart Labs best practices, such as implementing a Laboratory Ventilation Risk Assessment - a systematic process for identifying risk due to airborne hazards and informing dynamic, demand-based ventilation to optimize safety and efficiency.

decarbonization↗
DOE OSTI2021

Hydrogen Component Leak Rate Quantification for System Risk and Reliability Assessment through QRA and PHM Frameworks: Preprint

The National Renewable Energy Laboratory's (NREL) Hydrogen Safety Research and Development (HSR&D) program in collaboration with the University of Maryland's Systems Risk and Reliability Analysis Laboratory (SyRRA) are working to improve reliability and reduce risk in hydrogen systems. This approach strives to use quantitative data on component leaks and failures, together with Prognosis and Health Management (PHM), and Quantitative Risk Assessment (QRA) to identify at-risk components, reduce component failures and downtime, and predict when components require maintenance. Hydrogen component failures increase facility maintenance cost, facility downtime, and reduce public acceptance of hydrogen technologies, ultimately increasing facility size and cost because of potentially overly conservative requirements. Leaks are a predominant failure mode for hydrogen components. However, uncertainties in the amount of hydrogen emitted from leaking components and the frequency of those failure events limit the understanding of the risks that they present under real-world operational conditions. NREL has deployed a test fixture, the Leak Rate Quantification Apparatus (LRQA), to quantify the mass flow rate of leaking gases from medium and high-pressure components that have failed while in service. Quantitative hydrogen leak rate data from this system could ultimately be used to better inform risk assessment and Regulation Codes and Standards (RCS). Parallel activity explores the use of PHM and QRA techniques to assess and reduce risk, thereby improving safety and reliability of hydrogen systems. The results of QRAs could further provide a systematic and science-based foundation for the design and implementation of RCS, as in the latest versions of the NFPA 2 code for gaseous hydrogen stations. Alternatively, data-driven techniques of PHM could provide new damage diagnosis and health-state prognosis tools. This research will help end users, station owners and operators, and regulatory bodies move towards risk-informed preventative maintenance versus emergency corrective maintenance, reducing cost and improving reliability. Predictive modelling of failures could improve safety and affect RCS requirements such as setback distances at liquid refuelling sites. The combination of leak rate quantification research, PHM, and QRA can lead to better informed models enabling data-based decision to be made for hydrogen system safety improvements.

codes and standards↗
DOE OSTI2024

Fusion Energy Research at Idaho National Laboratory: Experimentation and Simulation to Support Safety and Rapid Technology Development

Research into fusion energy is growing rapidly, responding to a call for sustainable sources of energy to replace fossil fuels and mitigate climate change. Within the United States, at least, researchers are also responding to the “Bold Decadal Vision” proposed by the White House, seeking to have a commercially relevant fusion pilot plant deployed within a decade. Before this can become a reality, many Fusion Science & Technology (FS&T) gaps remain. For over 45 years, Idaho National Laboratory has been at the forefront of addressing these FS&T gaps in the context of fusion safety and technology via the operation of world-leading experimental facilities within the Safety and Tritium Applied Research (STAR) Facility. Here, INL focuses on the tritium fuel cycle, conceptual system design studies, risk assessment, waste management, and materials safety. Modeling and Simulation (M&S) has also been a component of this portfolio of research, but, early on, focused on individual systems. Since 2019, active development and research on integrated whole device modeling tools based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework has been undertaken. This has culminated in a MOOSE-based version of the Tritium Migration and Analysis Program (TMAP), an INL code historically focused on tritium permeation and trapping within fusion systems. More recently, INL Laboratory Directed Research and Development funds have been used to create the Fusion ENergy Integrated multiphys-X (FENIX) code focused on scrape-off layer plasma physics and the first wall of a magnetically confined fusion device. This talk will focus on an overview of INL activities in the FS&T research area, with a particular focus on recent M&S activities and results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Recommendations for Distributed Energy Resource Access Control

Cybersecurity for internet - connected Distributed Energy Resources (DER) is essential for the safe and reliable operation of the US power system. Many facets of DER cybersecurity are currently being investigated within different standards development organizations, research communities, and industry committees to address this critical need. This report covers DER access control guidance compiled by the Access Controls Subgroup of the SunSpec/Sandia DER Cybersecurity Workgroup. The goal of the group was to create a consensus - based technical framework to minimize the risk of unauthorized access to DER systems. The subgroup set out to define a strict control environment where users are authorized to access DER monitoring and control features through three steps: (a) user is identified using a proof-of-identity, (b) the user is authenticated by a managed database, (c) and the user is authorized for a specific level of access. DER access control also provides accountability and nonrepudiation within the power system control environment that can be used for forensic analysis and attribution in the event of a cyber-attack. This paper covers foundational requirements for a DER access control environment as well as offering a collection of possible policy, model, and mechanism implementation approaches for IEEE 1547-mandated communication protocols.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Security Enhancement of Network Constraint Grid-Edge Energy Management System

Network constrained grid edge energy management system (EMS) provides economic solution for active and reactive power dispatch of distributed energy resources (DERs) at the grid edge level. Grid edge EMS ensures secure interconnection of a circuit segment to the distribution system by maintaining grid code requirements (e.g. IEEE 1547–2018). Grid edge EMS is dependent on communication to receive load measurement, which brings a risk of unobservable false data injection attacks (FDIAs). To mitigate the risk, this paper proposes a framework to enhance resilient operation of grid edge EMS by detecting the unobservable FDIAs on loads and replacing them with forecasted values. In this work, a two-step detection algorithm is proposed. In first step, conventional residual based algorithm is deployed. Autoencoder (AE) based data driven mechanism is included in second step to detect the presence of unobservable FDIAs. After ensuring the presence of FDIA, its specific location is detected by checking the maximum residue values till the predefined threshold value is reached. Detected false data injected loads are then replaced with forecasted load values following long-short term memory (LSTM) based forecast to ensure resilient performance of grid edge EMS in the presence of attacks. This proposed security enhancement framework for grid edge EMS is evaluated in IEEE 13 bus system with three integrated DERs. Numerical simulation shows the validation of the proposed framework by reducing voltage violation in real operation of grid edge EMS.

cyber attack detection↗
DOE OSTI2026

Reimagining How Flood Warnings Can Inform Decision‐Making and Community Actions

Society faces increasingly severe flood hazards, intensifying demand for flood early warning systems (FEWS) that deliver accurate and actionable information. However, most existing FEWS remain prediction‐centric, treating decision‐making as a downstream consumer of hazard forecasts while offering limited support for uncertainty interpretation, risk communication, and real‐world response. This Perspective presents a vision and blueprint for a novel inland FEWS‐decision‐making (FEWS‐DM) framework that repositions decision‐making as an equal partner in the forecasting process—not a passive recipient of its outputs. The framework is built on three tightly coupled, co‐evolving thrusts: Physical Science (T1), which advances flood prediction with quantified uncertainty informed by decision relevance; Human Science (T2), which incorporates psychology, behavior, and cultural and institutional context; and Decision Science (T3), which unifies physical predictions and human factors through principled, utility‐based decision support with end‐to‐end uncertainty management. Rather than treating T1 as a solved problem, FEWS‐DM recognizes that forecast development itself must be shaped by decision needs through continuous bidirectional feedback. We identify key scientific, behavioral, and operational challenges limiting such integration and discuss the enabling role of AI, while emphasizing human‐centered design and community feedback as essential for building trust and improving flood risk management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

2.1.3.404 - WEC Array Power Management and Output Simulation Tool

An array of wave energy converter (WEC) devices has variations in power output due to the chaotic nature of the waves. Eliminating or mitigating the power fluctuations is important for reducing the integration impacts of WEC plants in both distribution and transmission grids, and in standalone isolated power systems. Reduced variability of WEC-generated power in combination with energy storage or power management control at each WEC and at the array level will help increasing hosting capacity of distribution feeders for this type of variable renewable generation, and minimization of electric losses. To reduce risks and risk perception, gain key stakeholder acceptance, and enable developers to design effective and compatible energy plants with arrays of WEC devices, it requires the use of modeling tools to simulate the resource environment, device dynamics, utility power system response, as well as the development of an interface for an array controller. The project will create a publicly accessible numerical modeling framework to empower the wave energy sector to design projects of various scales (kW-100s MW), which are optimized on a plant performance basis and are compatible with different power systems and wave conditions. The framework will integrate with WEC-Sim, a wave environment model (SWAN-FUNWAVE), as well as established relevant electrical analysis tools, to model the grid system and interconnection, and optimize power output and power management for the WEC array.

POWER TRANSMISSION AND DISTRIBUTION,TIDAL AND WAVE↗
DOE OSTI2021

A Risk-Informed Performance-Based Methodology to Manage Fire Protection Systems in Nuclear Facilities

Fire protection systems (FPSs) and features are installed in U.S. Department of Energy (DOE) Hazard Category 1, 2, or 3 nuclear facilities to protect property (maximum possible fire loss thresholds), life, and nuclear safety (i.e., structures, systems, and components). These FPSs and features are designed and maintained in accordance with the prescriptive guidance provided in applicable building codes and National Fire Protection Association codes and standards. Management, operations, and maintenance activities of FPSs involve significant effort. A DOE facility’s documented safety analysis or other safety basis document could also rely on FPSs to provide either a safety significant or safety class function to mitigate fire hazards and minimize radiological consequences. In some cases, the designation of safety significant or safety class may be determined to provide a layer of defense-in-depth to minimize nuclear safety risks independent of the fire risk. DOE standards allow the use of performance-based design alternatives developed by the fire industry but do not consider the defense-in-depth layers of protection provided in DOE facilities to prevent or mitigate the risks associated with unintended release of radioactive materials into the environment. Pacific Northwest National Laboratory developed a decision-making methodology tailored for DOE non-reactor nuclear facilities to manage FPSs and features by integrating nuclear safety risk insights into a performance-based analysis. This risk-informed, performance-based (RIPB) methodology can be used to provide the technical basis for classifying an FPS as safety class and safety significant, tailoring administrative controls (e.g., technical safety requirements), and ranking the importance of FPSs to prioritize maintenance, upgrades, and replacement activities. The RIPB methodology is a graded approach to inform DOE facility owners and Fire Protection Program managers of the most risk-significant FPSs and equipment, and those systems would be cost-beneficial to relax rigor if there is a need to re-design the FPS coverage or deviate from DOE and National Fire Protection Association standards for those systems that would be less significant. This paper describes the framework used to develop the RIPB methodology and the outcome of implementing this methodology in a use-case nuclear facility. This paper also discusses the impact to DOE policies and standards and the safety margins and defense-in-depth measures credited in nuclear safety assessments in a facility’s documented safety analysis and the benefits of implementing an RIPB methodology in lieu of a prescriptive method to comply with fire protection requirements.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning

This project report presents the results from the AU31 project that is entitled “Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning” performed between FY2021 and FY2022. The technical objective of this project is to enhance risk analysis of accidental release of radioactive materials and provide novel risk forecasting and capabilities for assessing unplanned hazardous chemical releases for the U.S. Department of Energy (DOE). The approach includes a cross-platform finite element analysis of airborne chemical release, machine learning (ML) for simulation and forecasting, and a big data approach to data management, transformation and analysis capable of handling petabyte-scale unstructured and structured data. The outcome of these findings were assembled and accessible as a final output via an intuitive web hosted geospatial-based user interface for demonstration and general dissemination. These new results also provide a mechanism of combining this knowledge in a risk analysis framework tool. The geospatial analysis and output can utilize 5 years of qualified meteorological data to illustrate projected plume footprints that are generated from computational aspects of this study. Specifically, we show the development of a geospatial risk analysis capabilities to respond to and plan for accidental release of radioactive materials and to provide a risk forecasting tool for assessing hazardous chemical releases for the DOE. The focus of this effort is germane to common practices in risk analysis; and it is highly relevant to the timely and transparent release of information related to materials at risk including hazardous materials and chemical vapors and rapid assessment of the potential impacts on the workforce at active sites across the DOE complex.

42 ENGINEERING↗