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DOE OSTI2023

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Refinement and Exploration

This report documents activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2023 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 technical basis to support effective, and secure DI&C technologies for digital upgrades/designs. A risk assessment-informed framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk informed capability to quantitatively estimate the safety margin obtained from plant modernization, especially for safety-related DI&C systems, (2) support and supplement existing 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, (4) assure the long-term safety and reliability of safety-related DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the LWRS-developed framework provides a means to address relevant technical issues by: (1) defining a risk informed analysis process for DI&C upgrade that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying 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 and deployment of advanced DI&C technologies in nuclear power plants (NPPs). Adding diversity within a system or components is the primary means to eliminate and mitigate CCFs, but diversity also increases system complexity and may not address all sources of systematic failures. Optimization of 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 safety-related DI&C systems of NPPs and supporting relevant design optimization, the proposed framework provides: (a) A best-estimate, risk informed capability to address new technical digital issues quantitatively, focusing on software CCFs in safety-related DI&C systems of NPPs; (b) A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to predict and prevent risk in the early design stage of DI&C systems; (c) Technical bases and risk informed insights to assist users address the risk informed alternatives for evaluation of CCFs in safety-related DI&C systems of NPPs; and (d) A 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 research and development efforts of this project in FY 2023 are focused on refining current methods on software CCF modeling and estimation and exploring additional innovative approaches to risk assessment of DI&C systems to enable a more comprehensive and complete assessment of various safety-related DI&C design architectures. The primary audience of this report are DI&C designers, engineers, and probabilistic risk assessment (PRA) practitioners. This includes stakeholders, such as the nuclear utilities and regulators who consider the deployment and upgrade of DI&C systems, DI&C software developers and reviewers, and cybersecurity specialists. It should be noted that all the analyses are performed for the demonstration of the methodology, not for the evaluation of an actual digital control system. Results are obtained based on limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Studies on the Vitrification Potential of High Risk Spent Nuclear Fuels

This research explored vitrification of U-Zr nuclear fuel that exhibits an explosion hazard when processed by common nitric acid dissolution. Glass frits were chosen based on properties that had been previously measured as well as being demonstrated successfully in production at the Defense Waste Process Facility (DWPF). Zirconium and Aluminum metal powders were chosen to represent the spent fuels after literature demonstrated that the potential phase that causes the issue for explosive reaction in the effluent stream is tied to the Zirconium-III phase field. DSC measurements were carried out on various simulants and waste forms to better understand thermal behavior of these materials. Zirconium (Zr) was tested with both nitric acid and the new effluent flowsheet that is under development by the SRNL Chemical Process Control (CPC) group. The nitric acid test did show the expected exothermic behavior as well as how the behavior is affected by particle size of the powders used. In contrast, the effluent samples did not show an exothermic peak. However, the concentration of metal in the samples was orders of magnitude lower than in the nitric acid testing and further testing of the effect of concentration on the exothermicity that could be measured confirmed a dampened signal. Tests were run that included frit, both DWPF Frit 510 and Iron Phosphate frit, exhibiting complex curves that included multiple endothermic and exothermic peaks during heating and cooling. The final crucible samples appear similarly to the bulk glasses pointing to these peaks being a part of the dissolution of the simulant into the base glasses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Basin & Range Investigation for Developing Geothermal Energy

Hidden geothermal systems represent a potentially prolific energy resource that could support critical U.S. public and government energy priorities. Basin and Range Investigations for Developing Geothermal Energy (BRIDGE) addressed some the challenges associated with hidden system exploration by prioritizing cost-effective exploration early on through strategic workflow and informed decision-making that mitigates early risk and shifts resources to later exploration stages (e.g., drilling). Sandia National Laboratories partnered with U.S. Navy Geothermal Office, Geologic Geothermal Group, and independent consultants, with additional collaboration with U.S. Geological Survey and private industry. The primary tool of the BRIDGE project was to deploy a regional-scale airborne electromagnetic method to investigate the shallow resistivity structure in areas with high prospectivity. This was followed up at several prospects by a multidisciplinary exploration approach, including additional geologic, geophysical and geochemical studies. A central tenet to the BRIDGE methodology is that zones of low resistivity frequently occur over geothermal systems in the Basin and Range, and when paired with other data constraints, imaging these zones can enable discovery of these systems. In addition to exploring greenfield areas (i.e., Grover Point), the BRIDGE project also flew HTEM resistivity surveys over known geothermal systems including those with established power plants (Don A. Campbell and Salt Wells) and prospects that are known to the literature but remain undeveloped, at least in part, due to a lack of understanding on the location of their producible reservoirs. BRIDGE produced a comprehensive set of data from prospects identified in the Nevada Play Fairway Analysis along with conceptual models for top ranking prospects, wherein all of the observations are used to inform an interpreted model of the system. These models present a range of possible system parameters such as temperature and size, and they are further informed by system analogues in the Basin and Range province and elsewhere. The results of this work leave space for further exploration that may now occur at prospects ‘down the list’ rather than distribution exploration resources evenly across all prospects.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Quantitative Risk Assessment for Hydrogen-Powered Locomotive Refueling

Hydrogen powered locomotives are being explored to reduce emissions in rail applications. The risks of operations like refueling should be understood to ensure safe environments for workers and members of the public. Sensitivity analyses were conducted using HyRAM+ to identify major drivers of risk and compare effects of system parameters on individual risk. The consequences of jet fires from full-bore leaks dominated the risk, compared to explosions or smaller leaks. Pipe size, leak detection capability, and leak frequencies of system components greatly affected risk while overpressure modeling parameters and ambient conditions had little effect. The effects of personal protective equipment (PPE) materials on individual risk were quantified by reducing the individual’s exposure time or absorbed thermal dose. PPE only showed a risk reduction in low-risk cases. This study highlighted target areas for risk mitigation, including leak detection equipment and component maintenance, and indicated that the minimal effects of other parameters on risk may not justify prescriptive requirements for refueling operators.

08 HYDROGEN↗

DOE OSTI2025

Grid Communications: Digital Assurance and Supply Chain Challenges and Emerging Regulation Session Two

The TADA Grid Communications Workshops are designed to strengthen cybersecurity and digital assurance across the energy sector by focusing on secure deployment and management of grid communications technologies. These workshops bring together state energy offices, utilities, and technology suppliers to explore the intersection of communications infrastructure, supply chain risks, and emerging regulatory requirements. Participants will apply Cyber-Informed Engineering (CIE) principles to reduce risks in communications systems, engage with INL’s procurement guidance, and explore future tools. Through scenario-based exercises and peer exchange, attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their grid communications projects. The workshops also help participants navigate evolving regulatory frameworks such as FEOC rules in the OBBB, NERC CIP-013, and NDAA 2024, while identifying compliance gaps in mixed-technology environments. A key outcome is the formation of a practitioner network with ongoing access to INL expertise and resources, fostering long-term resilience in the digital energy ecosystem. This is Session 2 of 3 (Full Version).

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Grid Communications: Cybersecurity and Supply Chain Challenges and Emerging Regulation Session Three

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Grid Communications Supply Chain & Emerging Regulation Challenges Session 1

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2025

Risk-Aware Reinforcement Learning Framework for User-Centric O-RAN

The evolution of Open Radio Access Networks (O-RAN) presents an opportunity to enhance network performance by enabling dynamic orchestration of configuration and optimization parameters (COPs) through online learning methods. However, leveraging this potential requires overcoming the limitations of traditional cell-centric RAN architectures, which lack the necessary flexibility. On the other hand, despite their recent popularity, the practical deployment of online learning frameworks, such as Deep Reinforcement Learning (DRL)-based COP optimization solutions, remains limited due to their risk of deteriorating network performance during the exploration phase. In this article, we propose and analyze a novel risk-aware DRL framework for user-centric RAN (UC-RAN), which offers both the architectural flexibility and COP optimization to exploit this flexibility. We investigate and identify UC-RAN COPs that can be optimized via a soft actor-critic algorithm implementable as an O-RAN application (rApp) to jointly maximize latency satisfaction, reliability satisfaction, area spectral efficiency, and energy efficiency. We use the offline learning on UC-RAN to reliably accelerate DRL training, thus minimizing the risk of DRL deteriorating cellular network performance. Results show that our proposed solution approaches near-optimal performance in just a few hundred iterations with a decrease in risk score by a factor of ten.

6G and beyond↗

DOE OSTI2024

Analysis of Historical Power Outages of the United States and the National Risk Index

Several works have been documented in the literature to study the societal effect of power outages and to analyze their correlation with the Social Vulnerability Index (SVI). However, the relationship between National Risk Index (NRI) and power outages is yet to be explored. This work analyzes the NRI indices such as Risk, Expected Annual Loss, Social Vulnerability, and Community Resilience with several resilience metrics such as event duration, impact duration, recovery duration, impact level, impact rate, recovery rate, recovery to impact ratio, and area under the outage curves to see the correlation of NRI indices with the resilience metrics. The results show that NRI indices such as Risk and Expected Annual Loss increase with the increase of event duration, impact duration, and recovery duration. All Other metrics are indifferent to the change in the Risk and EAL ratings. The results also show that there is no strong relationship between all the metrics and community resilience and social vulnerability. This work also performed the sensitivity analysis of the extreme event selection process. This sensitivity analysis reveals that the way of identifying extreme events has a significant impact on the evaluation of the events.

Bhusal, Narayan↗

DOE OSTI2024

Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems

As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.

distribution system↗

DOE OSTI2021

Pursuing sources of heterogeneity in modeling clustered population

Abstract Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity , that is, to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously. We develop an efficient algorithm and show that our approach can achieve both estimation and selection consistency. Simulation studies further demonstrate the effectiveness of our method under various practical scenarios. Three applications are presented, namely, an imaging genetics study for linking genetic factors and brain neuroimaging traits in Alzheimer's disease, a public health study for exploring the association between suicide risk among adolescents and their school district characteristics, and a sport analytics study for understanding how the salary levels of baseball players are associated with their performance and contractual status.

Li, Yan↗

DOE OSTI2025

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Uncertainty Quantification in High-Low Dynamic System Coupling using RAVEN and TRANSFORM

This work demonstrates new functionality and applications stemming from the development of high-fidelity to low-fidelity (high-low) coupling for system simulations and to further explore the capabilities of the Risk Analysis Virtual Environment (RAVEN) in the performance of uncertainty quantification in this kind of high-low coupled system models. The work builds from previous work on high-low coupling that utilized COBRA-TF (CTF), the high-fidelity subchannel analysis code, with a low fidelity model built in ORNL’s TRANSFORM, the system analysis code, utilizing the Functional Mock-Up Interface (FMI). Steady-state and transient analysis examples using the high/low coupled models generated from CTF and TRANSFORM/FMI are investigated. The workflows for both steady-state and transient coupled simulations are described. A steady-state parameter sweep and uncertainty analysis of the primary flow rates and reactor power are demonstrated. Likewise, a transient pump trip and power ramp sensitivity studies are also demonstrated. This work elucidates some of the potential benefits and future needs of using RAVEN for high/low system coupling analysis of energy systems. It also shows some of the difficulties that can be encountered in coupling system simulations.

Williams, Wesley↗

DOE OSTI2023

Assessing Solar Cybersecurity: Questions for States to Ask Electric Utilities

To help State Energy Officials and Public Utility Commissions engaging investor-and consumer-owned utilities and the solar industry in topical discussions about their cybersecurity practices and priorities in general, and for interconnected solar systems, specifically, this report outlines discussions prompts for these discussions. By using the questions in this tool states can explore aspects of utilities’ cybersecurity risk management programs that target solar devices, systems, and communication pathways. Such discussions help states identify cybersecurity gaps and assist in emergency response planning, as well as general risk awareness.

14 SOLAR ENERGY↗

DOE OSTI2020

The Long-term Storage of ILW: Building Stores with the Appropriate Environmental Controls - 20294

In the UK, containers for the storage and disposal of intermediate level radioactive waste (ILW) typically use stainless steel due to its excellent mechanical properties and corrosion resistance. The period of interim storage before a disposal facility is available, however, is longer than available engineering experience with stainless steels in relevant environmental conditions. Additionally, after disposal the ability of ILW containers to withstand further periods of exposure to the environment is expected to provide substantial benefits to the operation of a disposal facility and the safety case underpinning its closure. For these reasons, although there have been no instances of failure of stainless steels in indoor storage conditions, a focused R and D programme has been carried out to identify the environmental envelope over which the material does not significantly degrade. In this study we summarise experimental and modelling studies carried out over the last decade to build a solid mechanistic understanding of the corrosion behaviour of stainless steels in indoor atmospheric conditions. We then identify suitable environmental regimes to minimise the risk of degradation processes and explore approaches to monitoring the storage conditions to gain confidence in the long-term performance of the system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Near-term transition and longer-term physical climate risks of greenhouse gas emissions pathways

Policy, business, finance and civil society stakeholders are increasingly looking to compare future emissions pathways across both their associated physical climate risks stemming from increasing temperatures and their transition climate risks stemming from the shift to a low-carbon economy. Herein, we present an integrated framework to explore near-term (to 2030) transition risks and longer-term (to 2050) physical risks, globally and in specific regions, for a range of plausible greenhouse gas emissions and associated temperature pathways, spanning 1.5–4 °C levels of long-term warming. By 2050, physical risks deriving from major heatwaves, agricultural drought, heat stress and crop duration reductions depend greatly on the temperature pathway. By 2030, transition risks most sensitive to temperature pathways stem from economy-wide mitigation costs, carbon price increases, fossil fuel demand reductions and coal plant capacity reductions. Considering several pathways with a 2 °C target demonstrates that transition risks also depend on technological, policy and socio-economic factors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Risk Analysis for Remote Operation of Microreactors

Microreactors are a subset of advanced nuclear reactors that can be factory fabricated, transportable, and self-regulating. They have the potential to be used in microgrids, rural and remote areas, or emergency response applications, replacing fossil fuel sources like diesel generators and enabling sustainable energy generation. In order to make microreactor operation cost-effective, it is likely that remote communications will be needed to reduce the number of personnel required to be on site. While remote operation of energy generation and other industrial control systems is common in other industries, it is not yet adopted in the nuclear community and has many perceived and actual risks. In this paper, the severity of the risks introduced by remote operations for microreactors are explored. The primary changes in the operations involve the addition of a remote communications network and a certification system for data and controls. These changes lend themselves to considerations of cyber risks, whether unintentional or adversarial, but the assessment considers not just cyber risks introduced, but also how physical and human factors-based risks will impact the remote operations system and change the overall risk profile. This initial assessment indicates that there are standard cyber and mitigation measures that can be put in place so the risk of doing remote operations does not dramatically increase compared to local operations. This evaluation is a critical step in the process of evaluating if remote operations of microreactors is a suitable solution to meet future sustainable grid needs

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗