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HRP's Healthcare Spin-Offs Through Computational Modeling and Simulation Practice Methodologies

Spaceflight missions expose astronauts to novel operational and environmental conditions that pose health risks that are currently not well understood, and perhaps unanticipated. Furthermore, given the limited number of humans that have flown in long duration missions and beyond low Earth-orbit, the amount of research and clinical data necessary to predict and mitigate these health and performance risks are limited. Consequently, NASA's Human Research Program (HRP) conducts research and develops advanced methods and tools to predict, assess, and mitigate potential hazards to the health of astronauts. In this light, NASA has explored the possibility of leveraging computational modeling since the 1970s as a means to elucidate the physiologic risks of spaceflight and develop countermeasures. Since that time, substantial progress has been realized in this arena through a number of HRP funded activates such as the Digital Astronaut Project (DAP) and the Integrated Medical Model (IMM). Much of this success can be attributed to HRP's endeavor to establish rigorous verification, validation, and credibility (VV&C) processes that ensure computational models and simulations (M&S) are sufficiently credible to address issues within their intended scope. This presentation summarizes HRP's activities in credibility of modeling and simulation, in particular through its outreach to the community of modeling and simulation practitioners. METHODS: The HRP requires all M&S that can have moderate to high impact on crew health or mission success must be vetted in accordance to NASA Standard for Models and Simulations, NASA-STD-7009 (7009) [5]. As this standard mostly focuses on engineering systems, the IMM and DAP have invested substantial efforts to adapt the processes established in this standard for their application to biological M&S, which is more prevalent in human health and performance (HHP) and space biomedical research and operations [6,7]. These methods have also generated substantial interest by the broader medical community though institutions like the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) to develop similar standards and guidelines applicable to the larger medical operations and research community. DISCUSSION: Similar to NASA, many leading government agencies, health institutions and medical product developers around the world are recognizing the potential of computational M&S to support clinical research and decision making. In this light, substantial investments are being made in computational medicine and notable discoveries are being realized [8]. However, there is a lack of broadly applicable practice guidance for the development and implementation of M&S in clinical care and research in a manner that instills confidence among medical practitioners and biological researchers [9,10]. In this presentation, we will give an overview on how HRP is working with the NIH's Interagency Modeling and Analysis Group (IMAG), the FDA and the American Society of Mechanical Engineers (ASME) to leverage NASA's biomedical VV&C processes to establish a new regulatory standard for Verification and Validation in Computational Modeling of Medical Devices, and Guidelines for Credible Practice of Computational Modeling and Simulation in Healthcare.

Mulugeta, Lealem↗

A Risk Assessment Framework for Cyber-Physical Security in Distribution Grids with Grid-Edge DERs

Integration of inverter-based distributed energy resources (DERs) is reshaping the landscape of distribution grids to fulfill the socioeconomic, environmental, and sustainability goals. Addressing the technological challenges of DER grid integration requires an adaptive communication layer for efficient DER management and control. This transition has given rise to a cyberphysical system (CPS) architecture within the distribution system, causing new vulnerabilities for cyberphysical attacks. To better address potential threats, this paper presents a comprehensive risk assessment framework for cyberphysical security in distribution grids with grid-edge DERs. The framework incorporates a detailed CPS model accounting for dynamic DER characteristics within the distribution grid. It identifies vulnerabilities in DER communication systems, models attack scenarios, and addresses communication latency crucial for inverter control timescales. Subsequently, the quantification of attack impacts employs an attack probability model including both the vulnerability and criticality of cyber components. The proposed risk assessment framework was validated through testing on the modified IEEE 13-node and 123-node test feeders.

cyberattack↗

Systems Analysis of In-Space Manufacturing Applications for the International Space Station and the Evolvable Mars Campaign

Maintenance logistics support is a significant challenge for extended human operations in space, especially for missions beyond Low Earth Orbit (LEO). For missions to Mars (such as NASA's Evolvable Mars Campaign (EMC)), where timely resupply or abort in the event of emergency will not be possible, maintenance logistics mass is directly linked to the Probability of Loss of Crew (P(LoC)), and the cost of driving down risk is an exponential increase in mass requirements. The logistics support strategies that have maintained human operations in LEO will not be effective for these deep space missions. In-Space Manufacturing (ISM) is a promising technological solution that could reduce logistics requirements, mitigate risks, and augment operational capabilities, enabling Earth- independent human spaceflight. This paper reviews maintenance logistics challenges for spaceflight operations in LEO and beyond, and presents a summary of selected results from a systems analysis of potential ISM applications for the ISS and EMC. A quantitative modeling framework and sample assessment of maintenance logistics and risk reduction potential of this new technology is also presented and discussed.

Owens, Andrew C.↗

A Privacy-Preserving Cyber Threat Intelligence Sharing System

Cyber Threat Intelligence (CTI) is a key resource for developing defensive strategies against potential cyber adversaries. Entities typically access CTI through open-source platforms, national agencies, or specialized commercial services. However, the bi-directional exchange of CTI is hindered by organizational trust boundaries, which complicate the sharing processes between entities and CTI providers. Centralized CTI services benefit from receiving suspicious cyber observables such as IP addresses, domain names, and email addresses from various entities. The aggregation allows for the correlation of widespread adversarial activities to enhance the alert and response mechanisms across the network of involved parties. Despite these benefits, openly sharing such observables incurs potential legal, regulatory, and reputational risks for the disclosing entities.This paper introduces a system designed to facilitate the secure exchange of cyber observables across trust boundaries without compromising the anonymity of the sharing entities. Here, we propose an architecture that leverages common web protocols alongside zero-knowledge proofs to authenticate members while maintaining anonymity. Additionally, we outline a privacy model tailored for STIX (Structured Threat Information eXpression) cyber observables to minimize the risk of inadvertently disclosing private information. Through our threat models, we assess the privacy implications of our proposed system and demonstrate its potential to enhance collaborative cyber defense efforts without exposing entities to undue risk.

BBS+ Signatures↗

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↗

Experimental Space Shuttle Orbiter Studies to Acquire Data for Code and Flight Heating Model Validation

In an experimental study to obtain detailed heating data over the Space Shuttle Orbiter, CUBRC has completed an extensive matrix of experiments using three distinct models and two unique hypervelocity wind tunnel facilities. This detailed data will be employed to assess heating augmentation due to boundary layer transition on the Orbiter wing leading edge and wind side acreage with comparisons to computational methods and flight data obtained during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM during STS-119 reentry. These comparisons will facilitate critical updates to be made to the engineering tools employed to make assessments about natural and tripped boundary layer transition during Orbiter reentry. To achieve the goals of this study data was obtained over a range of Mach numbers from 10 to 18, with flight scaled Reynolds numbers and model attitudes representing key points on the Orbiter reentry trajectory. The first of these studies were performed as an integral part of Return to Flight activities following the accident that occurred during the reentry of the Space Shuttle Columbia (STS-107) in February of 2003. This accident was caused by debris, which originated from the foam covering the external tank bipod fitting ramps, striking and damaging critical wing leading edge heating tiles that reside in the Orbiter bow shock/wing interaction region. During investigation of the accident aeroheating team members discovered that only a limited amount of experimental wing leading edge data existed in this critical peak heating area and a need arose to acquire a detailed dataset of heating in this region. This new dataset was acquired in three phases consisting of a risk mitigation phase employing a 1.8% scale Orbiter model with special temperature sensitive paint covering the wing leading edge, a 0.9% scale Orbiter model with high resolution thin-film instrumentation in the span direction, and the primary 1.8% scale Orbiter model with detailed thin-film resolution in both the span and chord direction in the area of peak heating. Additional objectives of this first study included: obtaining natural or tripped turbulent wing leading edge heating levels, assessing the effectiveness of protuberances and cavities placed at specified locations on the orbiter over a range of Mach numbers and Reynolds numbers to evaluate and compare to existing engineering and computational tools, obtaining cavity floor heating to aid in the verification of cavity heating correlations, acquiring control surface deflection heating data on both the main body flap and elevons, and obtain high speed schlieren videos of the interaction of the orbiter nose bow shock with the wing leading edge. To support these objectives, the stainless steel 1.8% scale orbiter model in addition to the sensors on the wing leading edge was instrumented down the windward centerline, over the wing acreage on the port side, and painted with temperature sensitive paint on the starboard side wing acreage. In all, the stainless steel 1.8% scale Orbiter model was instrumented with over three-hundred highly sensitive thin-film heating sensors, two-hundred of which were located in the wing leading edge shock interaction region. Further experimental studies will also be performed following the successful acquisition of flight data during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM on STS-119 at specific data points simulating flight conditions and geometries. Additional instrumentation and a protuberance matching the layout present during the STS-119 boundary layer transition flight experiment were added with testing performed at Mach number and Reynolds number conditions simulating conditions experienced in flight. In addition to the experimental studies, CUBRC also performed a large amount of CFD analysis to confirm and validate not only the tunnel freestream conditions, but also 3D flows over the orbiter acreage, wing leading edge, and controlurfaces to assess data quality, shock interaction locations, and control surface separation regions. This analysis is a standard part of any experimental program at CUBRC, and this information was of key importance for post-test data quality analysis and understanding particular phenomena seen in the data. All work during this effort was sponsored and paid for by the NASA Space Shuttle Program Office at the Johnson Space Center in Houston, Texas.

Wadhams, T. P.↗

Alfalfa Virtual Building Service: Software Engineering Best Practices Applied to Runtime Interaction with Building Energy Models

Buildings are active participants in increasingly complex energy systems. Building Energy Modeling (BEM) has a key role to play in planning and de-risking an equitable energy transition, with BEM-backed "virtual buildings" critical path for diverse applications that include workforce training tools, Hardware-in-the-Loop (HIL) experimentation to study equipment performance under a range of conditions, Control-Hardware-in-the-Loop (CHIL) experimentation to de-risk commercial control implementations at equipment through grid orchestration levels, and integration of dynamic load profiles into grid modeling tools for energy system experimentation at the urban scale. Modeling requirements vary across these applications, but many software engineering tasks do not. The Alfalfa Virtual Building Service (AVBS, see https://github.com/NREL/alfalfa/wiki) is an open-source web service that solves these common tasks robustly in one place, providing a foundational platform for power users to bootstrap their own applications. AVBS abstracts the specifics of runtime interaction with OpenStudio, Modelica, and Spawn of EnergyPlus models behind a unified REST API. Additionally, AVBS provides resources for cloud deployment and scaling to 100s of parallel simulations, a growing library of modular Operational Technology (OT) integrations for emulation of real-world interfaces, and scripts to automate the population of communities of virtual buildings from URBANopt, ResStock and ComStock.

building automation↗

Stochastic fracture generation and thermo-hydro-mechanical modeling in an equivalent continuum framework for enhanced geothermal systems

Enhanced geothermal systems (EGS) involve fracturing low permeability material to establish well connectivity and then injecting and circulating fluid into the fractured subsurface for geothermal power production. Changes in fracture aperture from contraction of the cooling matrix rock may alter network connectivity and risk thermal short-circuiting. Thermo-hydro-mechanical (THM) models are a useful tool to study these processes. However, as fracture networks are complex, and data may be limited, fracture networks in THM models are often stochastically generated. Given reliance on stochastic fracture networks and THM modeling to represent the subsurface and assess productivity of EGS, increased understanding of the influence of such statistically derived fracture networks on flow and heat transport in THM models is needed. Here, a new fracture process model is developed in the reactive transport code PFLOTRAN to stochastically generate fracture families and simulate changes in fracture aperture over time due to temperature changes of the rock matrix. Sixty-four different fracture networks ranging from well to poorly-connected, are modeled in PFLOTRAN with and without mechanical processes (THM vs TH). Results indicate that for well-connected fracture networks, thermal short-circuiting is less of a concern due to the abundance of available alternative flowpaths. For poorly-connected fracture networks, inclusion of mechanical processes showed steep thermal drawdown coincident with increase in fracture aperture along developing colder flowpaths, demonstrating the risk of thermal short-circuiting. Simulations with additional, larger fractures engineered to establish connectivity in a poorly-fractured subsurface, indicate that while stochastic variation of fracture orientation of the background network had limited influence, such variation in the engineered fractures significantly affected flow and heat transport.

Discrete fracture networks (DFN)↗

Credibility Assessment of Deterministic Computational Models and Simulations for Space Biomedical Research and Operations

Human missions beyond low earth orbit to destinations, such as to Mars and asteroids will expose astronauts to novel operational conditions that may pose health risks that are currently not well understood and perhaps unanticipated. In addition, there are limited clinical and research data to inform development and implementation of health risk countermeasures for these missions. Consequently, NASA's Digital Astronaut Project (DAP) is working to develop and implement computational models and simulations (M&S) to help predict and assess spaceflight health and performance risks, and enhance countermeasure development. In order to effectively accomplish these goals, the DAP evaluates its models and simulations via a rigorous verification, validation and credibility assessment process to ensure that the computational tools are sufficiently reliable to both inform research intended to mitigate potential risk as well as guide countermeasure development. In doing so, DAP works closely with end-users, such as space life science researchers, to establish appropriate M&S credibility thresholds. We will present and demonstrate the process the DAP uses to vet computational M&S for space biomedical analysis using real M&S examples. We will also provide recommendations on how the larger space biomedical community can employ these concepts to enhance the credibility of their M&S codes.

Mulugeta, Lealem↗

Shuttle user analysis (study 2.2). Volume 3: Business risk and value of operations in space (BRAVO). Part 5: Analysis of GSFC Earth Observation Satellite (EOS) system mission model using BRAVO techniques

Cost comparisons were made between three modes of operation (expend, ground refurbish, and space resupply) for the Earth Observation System (EOS-B) to furnish data to NASA on alternative ways to use the shuttle/EOS. Results of the analysis are presented in tabular form.

Source record↗

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗

Integrated Medical Model (IMM) Optimization Version 4.0 Functional Improvements

The IMMs ability to assess mission outcome risk levels relative to available resources provides a unique capability to provide guidance on optimal operational medical kit and vehicle resources. Post-processing optimization allows IMM to optimize essential resources to improve a specific model outcome such as maximization of the Crew Health Index (CHI), or minimization of the probability of evacuation (EVAC) or the loss of crew life (LOCL). Mass and or volume constrain the optimized resource set. The IMMs probabilistic simulation uses input data on one hundred medical conditions to simulate medical events that may occur in spaceflight, the resources required to treat those events, and the resulting impact to the mission based on specific crew and mission characteristics. Because IMM version 4.0 provides for partial treatment for medical events, IMM Optimization 4.0 scores resources at the individual resource unit increment level as opposed to the full condition-specific treatment set level, as done in version 3.0. This allows the inclusion of as many resources as possible in the event that an entire set of resources called out for treatment cannot satisfy the constraints. IMM Optimization version 4.0 adds capabilities that increase efficiency by creating multiple resource sets based on differing constraints and priorities, CHI, EVAC, or LOCL. It also provides sets of resources that improve mission-related IMM v4.0 outputs with improved performance compared to the prior optimization. The new optimization represents much improved fidelity that will improve the utility of the IMM 4.0 for decision support.

risk assessment↗

PRA (Probabilistic Risk Assessments) Participation versus Validation

Probabilistic Risk Assessments (PRAs) are performed for projects or programs where the consequences of failure are highly undesirable. PRAs primarily address the level of risk those projects or programs posed during operations. PRAs are often developed after the design has been completed. Design and operational details used to develop models include approved and accepted design information regarding equipment, components, systems and failure data. This methodology basically validates the risk parameters of the project or system design. For high risk or high dollar projects, using PRA methodologies during the design process provides new opportunities to influence the design early in the project life cycle to identify, eliminate or mitigate potential risks. Identifying risk drivers before the design has been set allows the design engineers to understand the inherent risk of their current design and consider potential risk mitigation changes. This can become an iterative process where the PRA model can be used to determine if the mitigation technique is effective in reducing risk. This can result in more efficient and cost effective design changes. PRA methodology can be used to assess the risk of design alternatives and can demonstrate how major design changes or program modifications impact the overall program or project risk. PRA has been used for the last two decades to validate risk predictions and acceptability. Providing risk information which can positively influence final system and equipment design the PRA tool can also participate in design development, providing a safe and cost effective product.

DeMott, Diana↗

Hydrogen Equipment Enclosure Risk Reduction through Earlier Detection of Component Failures

Hydrogen component reliability and the hazard associated with failure rates is a critical area of research for the successful implementation and growth of hydrogen technology across the globe. The research team has partnered to quantify system risk reduction through earlier detection of hydrogen component failures. A model of hydrogen dispersion in a hydrogen equipment enclosure has been developed utilizing experimentally quantified hydrogen component leak rates as inputs. This model provides insight into the impact of hydrogen safety sensors and ventilation on the flammable mass within a hydrogen equipment enclosure. This model also demonstrates the change in safety sensor response time due to detector placement under various leak scenarios. The team looks to improve overall hydrogen system safety through an improved understanding of hydrogen component reliability and risk mitigation methods. This collaboration fits under the work program of IEA Hydrogen Task 43 Subtask E Hydrogen System Safety.

CFD↗

A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet

We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here, we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters. For example, based on the same ice sheet model and data set, the 99th percentile of the Antarctic ice sheet contribution to sea level rise in 2300 increases from 6.5 m to 13.1 m when we increase the number of calibrated parameters from three to 11. With previous calibration methods, it would be challenging to go beyond five parameters. Here, this work provides an important next step toward improving the uncertainty quantification of complex, computationally intensive and decision-relevant models.

54 ENVIRONMENTAL SCIENCES↗

High-Burnup BWR LOCA Burst Analysis Using High-Fidelity Multiphysics Simulations

The US nuclear industry is looking to improve on the operating economics of the current fleet of light-water reactors (LWRs). One way of achieving this is by operating fuel to higher burnup. In pressurized water reactors (PWRs), relaxing the current burnup limit will allow for cycle length extensions and power uprates; in boiling water reactors (BWRs) it may allow for improved fuel utilization and reduced feed assemblies, as well as more efficient power uprates and increased capacity factors that will support the Administration’s Executive Order to facilitate 5 GW of power uprates at existing nuclear facilities. However, one of the key limitations to operating fuel to higher burnup is the risk of fuel fragmentation, relocation, and dispersal (FFRD). Recognizing the high interest in extending burnup limits, the US Nuclear Regulatory Commission (NRC) has issued Draft Regulatory Guide DG-1434, which defines an approach that would be acceptable to the NRC for addressing FFRD risk. The approach defined will require better understanding of the phenomena leading to FFRD as well as best-estimate simulation methods to understand FFRD risk in high-burnup cores. The Nuclear Energy Advanced Modeling and Simulation program is supporting the FFRD industry challenge problem through development of state-of-the-art, high-fidelity modeling and simulation LWR analysis capabilities; namely, the BISON fuel performance code and the VERA core simulator software. These tools, along with the US NRC TRACE system analysis code, have been utilized for analysis of FFRD risk in both PWR and BWR cores in recent years. The work documented in this report addresses the lack of high-fidelity research for BWRs and builds on a previous activity where the framework has been applied to Cycles 16 through 18 of Limerick Unit 1, a BWR/4, with introduction of 8 high-burnup lead use assemblies (HBLUAs) that were representative of the 8 HBLUAs loaded into Limerick Unit 2 in 2021. VERA was used in this previous activity to model rod-by-rod depletion in these cycles, and its solution was used to initialize a TRACE simulation of a large-break loss-of-coolant accident (LBLOCA) at the end of Cycle 18. In the work documented in this report, the TRACE model was improved by refining the core mesh and utilizing a new feature that allows for capturing the full 3D VERA power distribution in the model. This allows for a more detailed solution for setting BISON boundary conditions. Furthermore, the solutions from VERA and TRACE were used to set up and perform BISON simulations of about 1,000 rods sampled from the core, including all burnup levels. Utilizing two cladding burst models, it was shown that no fuel rods were predicted to burst during the postulated LBLOCA transient. Additionally, a sensitivity study was performed by artificially increasing linear heat rate during the postulated LBLOCA to identify parameters that correlate with rod burst susceptibility. Burnup, fission gas release, and hoop strain were all found to be positively correlated with rod burst susceptibility. Small-break loss-of-coolant accident (SBLOCA) analyses were also performed; these analyses predicted cladding temperature increases that were bounded by the LBLOCA cladding temperatures for all small break sizes studied for this plant. However, future refinements to the plant response assumptions during the SBLOCA could impact the predicted cladding response. Finally, a benchmark study was performed between CTF and TRACE for LOCA conditions to better qualify CTF for BWR LOCA modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

After the hurricane: Validating a resilience assessment methodology

With increasing utility grid outages in the United States, there is growing interest in assessing risk and developing mitigation strategies to reduce the impact of grid outages. Working with the U.S. Air Force, the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) developed a replicable energy resilience assessment methodology and investment decision tool to: (1) identify and score hazards and vulnerabilities at the site level; (2) analyze risks to energy infrastructure; and (3) identify and prioritize energy resilience investments. This work improves on existing resilience assessment methodologies and tools by combining a bottom-up, all-hazards assessment methodology with top-down geographic information system mapping capabilities to provide an innovative, dynamic tool for identifying and prioritizing actionable solutions. This process combines probabilistic forecasting with an iterative approach for continuously updating and reassessing risks to address temporal dynamism. Relationships among systems are modeled and visualized to estimate the effectiveness of resilience actions across multiple interdependent systems and inform financial priorities through cost-difficulty-impact trade-offs. The approach is validated in a case study at Tyndall Air Force Base (AFB) in Florida, which experienced a Category 5 hurricane in 2018. The risks and mitigation strategies identified pre-hurricane are compared with post-hurricane, realized impacts. The assessment effectively identifies risks and actions to increase site energy resilience, but the methodology can be enhanced though greater consideration of the interdependencies between the energy system and related systems like transportation, communication, and food/water systems, which impact the recovery of the energy system and the base.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗