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At least 271 records · Page 15

Consequence Based Framework for Deployment of Cloud Solutions in the Digital Energy Transition

This study proposes a framework for evaluating cloud computing deployment in the electric sector, focusing on the digital transition of energy systems. It assesses the implications of cloud technology adoption, particularly in terms of security, operational resilience, and efficiency. The paper introduces a framework for consequence-driven applied risk analysis, enabling utilities to prioritize and mitigate potential threats effectively, and responsibly deploy cloud applications. It also discusses the shared responsibility model in cloud computing, highlighting the need for collaborative security efforts. The research aims to provide utilities with a strategic assessment tool for cloud adoption, emphasizing the importance of security culture in enhancing cloud computing's role in critical infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Fluorescent amplification for next generation sequencing (FA-NGS) library preparation

BACKGROUND: Next generation sequencing (NGS) has become a universal practice in modern molecular biology. As the throughput of sequencing experiments increases, the preparation of conventional multiplexed libraries becomes more labor intensive. Conventional library preparation typically requires quality control (QC) testing for individual libraries such as amplification success evaluation and quantification, none of which occur until the end of the library preparation process. RESULTS: In this study, we address the need for a more streamlined high-throughput NGS workflow by tethering real-time quantitative PCR (qPCR) to conventional workflows to save time and implement single tube and single reagent QC. We modified two distinct library preparation workflows by replacing PCR and quantification with qPCR using SYBR Green I. qPCR enabled individual library quantification for pooling in a single tube without the need for additional reagents. Additionally, a melting curve analysis was implemented as an intermediate QC test to confirm successful amplification. Sequencing analysis showed comparable percent reads for each indexed library, demonstrating that pooling calculations based on qPCR allow for an even representation of sequencing reads. To aid the modified workflow, a software toolkit was developed and used to generate pooling instructions and analyze qPCR and melting curve data. CONCLUSIONS: We successfully applied fluorescent amplification for next generation sequencing (FA-NGS) library preparation to both plasmids and bacterial genomes. As a result of using qPCR for quantification and proceeding directly to library pooling, the modified library preparation workflow has fewer overall steps. Therefore, we speculate that the FA-NGS workflow has less risk of user error. The melting curve analysis provides the necessary QC test to identify and troubleshoot library failures prior to sequencing. While this study demonstrates the value of FA-NGS for plasmid or gDNA libraries, we speculate that its versatility could lead to successful application across other library types.

59 BASIC BIOLOGICAL SCIENCES↗

SMART Deliverable 6.1.2a: Application of the ORION tool to the IBDP Carbon Storage Site

Forecasting and managing potential induced seismic activity is one of the challenges facing commercialscale geologic carbon sequestration (GCS), as well as other geologic energy extraction and byproduct disposal technologies. Historically, the process to develop robust, science-based forecasts of induced seismicity has required an integrated effort from experts in seismology, geomechanics, and reservoir engineering to manage data, develop and evaluate models of subsurface processes, and to calibrate and interpret the results from a range of models to understand site behavior relative to prescribed standards and in the context of uncertainty in geologic characterization data, forecasting models, and operational scenario uncertainty. The Operational Forecasting of Induced Seismicity (ORION) toolkit is an open-source, observation-based forecasting toolkit that is being co-developed by two U.S. DOE-funded initiatives: the National Risk Assessment Partnership (NRAP) and the Scienceinformed Machine Learning for Accelerating Real Time Decisions in Subsurface Applications (SMART) Initiative. ORION is designed to provide functionality to support decision making about seismic hazard analysis and risk management for GCS stakeholders ranging from the public to site operators to expert seismologists. The tool, which is written as open-source code in the Python programming language, is composed of a desktop graphical user interface (GUI) and an underlying forecasting engine. The forecasting engine uses available reservoir properties, well and fluid injection scenario details, and observed seismic catalog data as inputs to produce a set of temporal and spatio-temporal seismic forecasts.

58 GEOSCIENCES↗

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

Assessment of Survey Results from Advanced Reactor Industry Domain Experts on Nonlinear Soil-Structure Interaction Analysis Software Verification and Validation

The seismic load case has a significant impact on the design and cost of nuclear power plants. Given the need to substantially reduce cost, advanced reactor designers are looking to leverage numerical tools that enable seismic analysis of an integrated vessel/support/structure/soil system. Modern nonlinear analysis tools provide a solution, capturing dynamic coupling between components (including soil-structure interaction (SSI)) concurrent with nonlinear behavior in one or more parts of the system. Although these methods have a rich history of technical development and implementation, software quality assurance (SQA) following nuclear industry standards remains a significant burden for those wishing to adopt such methods for advanced reactor design and licensing. To reduce the SQA burden, this project will develop the guidance for SQA verification and validation (V&V) of coupled nonlinear SSI analysis tools, to include a test matrix of software features and test problems to support commercial grade dedication (CGD). The guidance is intended to be technology-neutral: supporting designers of all advanced reactors. To maximize the value of the project to reactor designers, the project team performed focused surveys of and interviews with advanced reactor designers. Additionally, the project surveyed members of the broader industry involved in reactor design and licensing. The aggregated industry feedback consists of written survey responses, live polling responses, focused interviews, and informal feedback provided after outreach presentations, collectively referred to as “survey results”. Herein, the survey results are reported, reviewed, and assessed to inform follow-on project activities. The survey results confirmed the familiarity of the industry with the coupled nonlinear SSI analysis. They also affirm the project need based upon the expressed intent to (1) incorporate nonlinear features and (2) pursue the coupled nonlinear SSI analysis to reduce seismic demands and construction costs. All reactor designers indicated their intent to incorporate two or more nonlinear features, and all reactor designer respondents opined that the coupled nonlinear SSI analysis would allow for the optimization of the reactor design and construction. However, the programmatic challenges, whether real or perceived, present a significant barrier to reactor designers. The barrier most commonly identified by the reactor designer population was regulatory risk, with 70% citing this as a reason not to pursue the approach. The perceived regulatory risk identified by the reactor designers underscores the importance of regulator engagement and dialogue in this project. Additionally, half of the reactor designers identified cost and lack of guidance as a deterrent. The survey results also provide insights to tailor specific aspects of the guidance document and test matrix. The project plans to prepare both a formal referenceable guidance document and a collaborative, web-based test matrix and problem set. The project will focus on more complete test problem definitions for the prioritized nonlinear features over shallower problem descriptions for a larger set of features. The nonlinearities prioritized as high based upon survey feedback include fluid-structure interaction and seismic isolation and energy dissipation devices. The nonlinearities prioritized as intermediate include nonlinear geomaterials, nonlinear concrete, nonlinear steel, and interface/contact nonlinearity. Deep embedment and the associated nonlinear phenomena are assigned the lowest priority based upon survey feedback.

42 ENGINEERING↗

Adapting the Technology Performance Level Integrated Assessment Framework to Low-TRL Technologies Within the Carbon Capture, Utilization, and Storage Industry, Part I

With the urgent need to mitigate climate change and rising global temperatures, technological solutions that reduce atmospheric CO 2 are an increasingly important part of the global solution. As a result, the nascent carbon capture, utilization, and storage (CCUS) industry is rapidly growing with a plethora of new technologies in many different sectors. There is a need to holistically evaluate these new technologies in a standardized and consistent manner to determine which technologies will be the most successful and competitive in the global marketplace to achieve decarbonization targets. Life cycle assessment (LCA) and techno-economic assessment (TEA) have been employed as rigorous methodologies for quantitatively measuring a technology's environmental impacts and techno-economic performance, respectively. However, these metrics evaluate a technology's performance in only three dimensions and do not directly incorporate stakeholder needs and values. In addition, technology developers frequently encounter trade-offs during design that increase one metric at the expense of the other. The technology performance level (TPL) combined indicator provides a comprehensive and holistic assessment of an emerging technology's potential, which is described by its techno-economic performance, environmental impacts, social impacts, safety considerations, market/deployability opportunities, use integration impacts, and general risks. TPL incorporates TEA and LCA outputs and quantifies the trade-offs between them directly using stakeholder feedback and requirements. In this article, the TPL methodology is being adapted from the marine energy domain to the CCUS domain. Adapted metrics and definitions, a stakeholder analysis, and a detailed foundation-based application of the systems engineering approach to CCUS are presented. The TPL assessment framework is couched within the internationally standardized LCA framework to improve technical rigor and acceptance. It is demonstrated how stakeholder needs and values can be directly incorporated, how LCA and TEA metrics can be balanced, and how other dimensions (listed earlier) can be integrated into a single metric that measures a technology's potential.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The examination of the spatial and contextual disparities of determinant factors of adult obesity among communities in Chicago

The issue of adult obesity has multiple complexes contributing factors and is becoming a significant public health concern worldwide, including in the neighborhoods of Chicago. This study utilized data on nineteen demographic, environmental, socioeconomic, and behavioral characteristics of community neighborhoods in Chicago to analyze the interplay and impact of these complex factors, which is essential for understanding and addressing the issue. The analysis revealed significant geographic variations in the prevalence of adult obesity across Chicago neighborhoods, with associations of these patterns found significant in 17 out of 19 determinant factors studied. Notably, strong associations were found between obesity and the percentage of the White population, the quality of sidewalks and walkability, the economic hardship index, and the unemployment rate. Identifying high-risk adult obesity communities and understanding the multifaceted contributing factors is crucial for developing evidence-based interventions and policy initiatives to reduce obesity and create healthier, more equitable urban neighborhoods for a city such as Chicago and beyond.

60 APPLIED LIFE SCIENCES↗

Cumulative Effects Analysis for Wind Energy Development: Current Practices, Challenges, and Opportunities (IEA Wind White Paper)

The increasing global deployment of wind energy has given rise to concerns about potential adverse effects on certain wildlife species and habitats. The United States and European nations use environmental impact assessments (EIAs) to evaluate the environmental effects of wind energy and inform wind energy planning, siting, and operational processes. A key component of the EIA is the cumulative effects analysis/assessment (CEA). CEAs consider the effects of a proposed development in the context of past, present, and future developments, as well as other (non-wind) activities. However, practitioners worldwide have struggled to implement cost-effective and consistent processes for CEAs. Further, there is no widely accepted scientific methodology to assess cumulative effects. As wind energy deployment continues to expand, developing a consistent and scientifically based approach to CEAs may provide a more comparable across assessments and cost-effective means of reducing risk during siting, operations, and decommissioning/repowering, while minimizing regulatory hurdles. This technical report evaluates the current state of CEA practices, covering both land-based and offshore wind energy development. It focuses on impacts from the preconstruction, construction, and operational phases of the wind farm, which are the phases where most research currently exists. Emerging research addresses impacts from the perspective of life cycle assessments (LCAs), including the impacts of manufacturing and preconstruction (May et al. 2020). The technical report also summarizes CEA processes and guidelines, analysis approaches, and current challenges. Finally, it highlights opportunities for further research and coordination, and includes a geographically organized CEA information resource bank.

17 WIND ENERGY↗

Economic Analysis of an Electric Thermal Energy Storage System Using Solid Particles for Grid Electricity Storage

As renewable power generation becomes the mainstream new-built energy source, energy storage will become an indispensable need to complement the uncertainty of renewable resources to firm the power supply. When phasing out fossil-fuel power plants to meet the carbon neutral utility target in the midcentury around the world, large capacity of energy storage will be needed to provide reliable grid power. The renewable power integration with storage can support future carbon-free utility and has several significant impacts including increasing the value of renewable generation to the grid, improving the peak-load response, and balancing the electricity supply and demand. Long-duration energy storage (10–100 hours duration) can potentially complement the reduction of fossil-fuel baseload generation that otherwise would risk grid security when a large portion of grid power comes from variable renewable sources. Current energy storage methods based on pumped storage hydropower or batteries have many limitations. Thermal energy storage (TES) has unique advantages in scale and siting flexibility to provide grid-scale storage capacity. A particle-based TES system has promising cost and performance for the future growing energy storage needs. This paper introduces the system and components required for the particle TES to be technically and economically competitive. A technoeconomic analysis based on preliminary component designs and performance shows that the particle TES integrated with an efficient air-Brayton combined cycle power system can provide power for several days by low-cost, high-performance storage cycles. It addresses grid storage needs by enabling large-scale grid integration of intermittent renewables like wind and solar, thereby increasing their grid value. The design specifications and cost estimations of major components in a commercial scale system are presented in this paper. The cost model provides insights for further development and cost comparison with competing technologies.

27 ARPA - Advanced Research Projects Agency-Energy↗

Quantitative Risk Analysis of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants using IRADIC Technology

This report documents the activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2021 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 (IRADIC technology) 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) develop an advanced risk assessment technology to support transition from analog to DI&C technologies for nuclear industry, (3) assure the long-term safety and reliability of vital HSSSR DI&C systems, (4) 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 IRADIC technology is instructive for nuclear vendors and utilities 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 responding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the individual level, system level, and plant level, (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). In this report, an approach for performing software CCF analysis, given limited data, is developed and demonstrated using a case study of a highly redundant digital reactor trip system. Consequence analysis is also performed based on different accident scenarios. Results indicate that plant modernization including the improvement of HSSSR DI&C systems will make great benefits to plant safety by providing more safety margins to accident management. In addition, a novel approach is proposed in this report for the quantification of software hazards when sufficient operational and testing data available. The method incorporates software development quality as well as strong analysis techniques to identify and link software defects to potential failure modes. The approach includes both semantic and test-based analysis to detect failures that can exist in different stages of the software development life cycle. This method is applied to an advanced human system interface relevant to reactor trip safety developed from the APR 1400 design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Stakeholder-Engaged Structured Decision Making for the Los Alamos Legacy Cleanup Mission - 20501

The Los Alamos National Laboratory (LANL) environmental legacy cleanup program requires decisions to be made for environmental remediation, decommissioning and disposal or management of radioactive waste. This legacy cleanup program was established to address groundwater contamination, material disposal areas (MDAs) that have been used to dispose of radioactive and other waste material, and 'aggregate areas' that might produce radioactive or other chemical waste as a result of remediation activities. The LANL site is regulated for environmental concerns under the Resource Conservation and Recovery Act (RCRA). However, some parts of LANL, such as material disposal area G (MDA G), have disposed of radioactive waste under DOE Order 435.1, and are subject to other regulations. For example, decommissioning the remote-handled TRU material stored in 33 shafts at MDA G falls under EPA's 40 CFR 191. Collectively, the regulations are all aimed in the same direction of finding the best solution, either through constructs such as 'as low as reasonably achievable' (ALARA), considering balancing factors as opposed to only cost and human health risk, and, under EPA regulations such as RCRA and NEPA, evaluating impact from all chemicals and both human health and ecological endpoints. Despite the basic goals and objectives of the regulations or their guidance, the main challenge is in their implementation. Arguably perhaps, but really in principle, all of these (and similar) regulations are asking for a decision analysis to be performed. Implementation challenges encountered have included lack of understanding of decision analysis in the industry, lack of effective stakeholder engagement in the decision analysis process, and lack of appreciation of the need to separate value judgments from science, the latter leading to developing conservative, or protective, science-based models. Conservative models lead to poor solutions, lack of effective stakeholder engagement leads to long drawn out protracted approaches to finding a solution (which also might never be found with this approach), and lack of understanding of decision analysis and Bayesian statistics causes poor models to be built, which creates unfortunate situations of 'garbage in, garbage out' becoming the basis for decision making. Stakeholder-engaged structured decision making (SDM) is an approach to solving problems that relies on the theory of decision science to involve stakeholders in the decision-making process. This approach incorporates stakeholder values using a scientifically rigorous methodology that separates value judgments from science in a way that helps avoid the pitfalls of biased, protective, or conservative modeling. This approach has its foundation in Keeney's 1992 treatise on value-focused thinking [1]. Keeney advocated a paradigm shift in decision making based on the idea that the standard way of thinking about decisions is backwards. The standard approach of focusing first on identifying alternatives rather than on articulating values results in a reactive approach with the emphasis on mechanics and fixed choices instead of the core values that have meaning to stakeholders. This paradigm shift effectively engages all stakeholders in the decision-making process while using a values focused thinking approach that can lead to the identification of decision opportunities and the creation of better alternatives. The intent is to be proactive and generate solutions that are related directly to values and objectives as identified by stakeholders. There are, perhaps, two overarching reasons why SDM can be used to benefit LANL's environmental legacy cleanup. Some of LANL's remaining waste management and environmental management problems are challenging and complex (for example, the Cr and RDX plumes, and MDAs) and while the traditional approach has, arguably, worked well for relatively simple risk-based problems, it cannot, or should not, be applied to more complex problems if the most effective and efficient solutions are desired. The second reason is cost. This has perhaps become more critical since publication of the Government Accountability Office (GAO) reports that DoE's environmental liability is considered a high-risk concern for the nation [2]. The focus of SDM is on structuring solutions to decision risk problems by first addressing stakeholder and decision maker values and subsequently developing decision objectives and ways to measure those objectives, preference weighting across objectives, identifying decision alternatives that best achieve those values, and characterizing uncertainty in predictions of the measures. Because a complete decision model is created using SDM, it can be evaluated to find the main elements of the model that drive, or predict, the best solution. This approach creates complete decision models that are transparent, traceable, reproducible and technically defensible. The science behind SDM, or decision analysis, is well founded, yet it is not unusual to see ad hoc approaches to decision making implemented under various environmental regulations that are pertinent to the LANL site, including NEPA, RCRA and DOE Order 435.1. Such ad hoc approaches are often not transparent or traceable, and lack reproducibility and technical defensibility. The LANL legacy cleanup program has embarked on using SDM to address the complex problems that remain. Stakeholder meetings have been held, and a prototype version of the stakeholder value system has been developed. Further meetings are expected in the future to address specific project needs. This is a long-term endeavor considering the complex environmental problems faced by DOE EM in Los Alamos (EM-LA), and careful planning, consideration of stakeholder value systems, and engagement with stakeholders throughout the SDM process is expected to lead to a successful endpoint. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Economic Analysis of a Novel Thermal Energy Storage System Using Solid Particles for Grid Electricity Storage: Preprint

As renewable power generation becomes the mainstream new-built energy source, energy storage will become an indispensable need to complement the uncertainty of renewable resources to firm the power supply. When phasing out fossil-fuel power plants to meet the carbon neutral utility target in the midcentury around the world, large capacity of energy storage will be needed to provide reliable grid power. The integration of renewable power and storage to support future carbon-free utility has several significant and positive impacts including expanding the renewable generation into the grid, improving the peak-load response, and balancing the electricity supply and demand. Long-duration energy storage (10–100 hours duration) can potentially complement the reduction of fossil-fuel baseload generation that otherwise would risk grid security when a large portion of grid power comes from variable renewable sources. Current energy storage methods based on pumped storage hydropower or batteries have many limitations. Thermal energy storage (TES) has unique advantages in scale and siting flexibility to provide grid-scale storage capacity. A particle-based TES system has promising cost and performance for the future growing energy storage needs. This paper introduces the system and components required for the particle TES to be technically and economically competitive. A technoeconomic analysis based on preliminary component designs and performance shows that the particle TES integrated with an efficient air-Brayton combined cycle power system can provide power for several days by low-cost, high-performance storage cycles. It addresses grid storage needs by enabling large-scale grid integration of intermittent renewables like wind and solar, thereby increasing their grid value. The design specifications and cost estimations of major components in a commercial scale system are presented in this paper. The cost model provides insights for further development and cost comparison with competing technologies.

27 ARPA - Advanced Research Projects Agency-Energy↗

Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML)

Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.

97 MATHEMATICS AND COMPUTING↗

Blueprint: Stakeholder-Specific Vulnerability Categorization Guidance

Vulnerability management is a process of discovering, analyzing, and handling new or reported security vulnerabilities in systems to prevent the systems from being exploited, to reduce risk, and to protect assets. For vulnerability analysis, handling, and response, the prioritization of organizational and analyst resources must precede. The Common Vulnerability Scoring System (CVSS) is a standard prioritization method that is used to rate the severity of security vulnerabilities in systems by assigning numerical severity scores, but it does not provide clear guidelines of how the numerical severity scores might inform decisions. The Stakeholder-Specific Vulnerability Categorization (SSVC) provides a method for prioritizing vulnerabilities based on the needs of the stakeholders involved in the vulnerability management process. Instead of the numerical scoring used in the CVSS, the SSVC focuses on contextual decision-making to determine how quickly and effectively an organization should respond to vulnerabilities. The main functionality of the SSVC accommodates the diversity of the stakeholders in the vulnerability management process, including finders, vendors, coordinators, deployers, and others. So, the SSVC should be designed to be used by any of these stakeholders, and it should be customizable to enable specific stakeholder decision models and risk appetites.

33 ADVANCED PROPULSION SYSTEMS↗

Portable and Cost-Effective Device for Reliable Detection of Counterfeit and Non-compliant Refrigerants in Diverse Applications

Counterfeit refrigerants pose significant challenges to safety, system reliability, and operational effectiveness due to their harmful contaminants or incompatible chemical compositions. Utilizing these noncompliant products can lead to reduced efficiency, equipment failures, and expensive repairs. Additionally, heightened demand for alternative refrigerants during the industry's transition has created supply gaps, enabling counterfeit products to proliferate. Accurate detection and analysis tools are therefore essential to verify refrigerant authenticity and ensure system integrity in diverse applications. This paper presents the development of a portable device designed for reliable identification and detailed analysis of refrigerant composition. By integrating precision gas sampling, controlled pressure regulation, and automated sensor technology, the device not only detects deviations from standard refrigerant properties but also provides a comprehensive composition breakdown. Pre-calibrated sensors measure the refrigerant gas to identify specific concentrations and contaminants, with an intuitive LED-based indicator system ensuring quick interpretation of results. The user-friendly interface enables operators to select refrigerant types for targeted testing, further enhancing accuracy and usability for field technicians. Comprehensive testing was conducted on mildly flammable A2L refrigerants, showcasing the device’s robustness and adaptability in analyzing composition and detecting discrepancies. The device demonstrated consistent accuracy across a range of refrigerant samples, affirming its reliability in diverse operational environments. Its design minimizes contamination risks during sampling and provides detailed composition results within 90 seconds, ensuring efficient and precise analysis. With a projected price point under $150, the proposed solution delivers affordability alongside its lightweight portability and straightforward operation. Unlike complex and costly alternatives, such as gas chromatography systems, this device provides an accessible option for technicians, customs personnel, and industry operators in need of quick and effective refrigerant verification. Compatible with both current formulations and emerging refrigerant technologies, the device addresses critical counterfeit detection needs across a range of applications. By delivering accurate composition analysis and counterfeit identification, this innovation enhances system performance, safety, and operational reliability in crucial industries.

Cheekatamarla, Praveen [ORNL] (ORCID:0000000248827↗

Failure Modes and Effects Analysis of Biorefinery Pathways

This talk provides an overview of failure modes and effects analysis (FMEA) development and implementation as a systematic criticality and risk assessment tool for biorefinery pathways within the FCIC. This supports a quality by design (QbD) approach, and this talk provides a high-level overview of the results for the FMEA evaluation focused on the generation of pine residue materials for high-temperature pyrolysis conversion. the FMEA interviews included two approaches. The first approach was based around the entire system of unit operations giving a wholistic system level view. The second approach used detailed interviews from individual unit operation within the system allowing for specific failures for individual system components. These two approaches provide different resolutions of information about the reliability and risk. The FMEA results focused on failures associated with meeting critical quality attributes (CQAs) identified for the high temperature conversion of loblolly pine residues and were supplemented with experimental data supporting process upsets and reliability also collected within the consortium. Estimations of risk scores for meeting each given CQA specification, identification of the impacts for not meeting a CQA specification, capturing causes associated with material attributes and process parameters for each failure, identification of current detection methods, and speculation of potential mitigation strategies for decreasing a failure’s risk score were gathered through the FMEA interviews, and were combined to understand the overall process risk metrics and where technology, process, and knowledge improvements are needed in order to de-risk emerging biorefineries.

09 BIOMASS FUELS↗

Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. Furthermore, the result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.

42 ENGINEERING↗

Phosphoproteomics Modifications in Women with Rheumatoid Arthritis─Application of Web-Based Software to Enhance Data Visualization

Individuals with rheumatoid arthritis (RA) are at increased risk of functional disability, cardiovascular disease, and obesity, all of which are influenced by dysregulated skeletal muscle. Here, this pilot study aims to identify phosphoproteomics changes in RA skeletal muscle and visualize modifications through development of a web-based app designed to promote user-friendly data interpretation and visualization. NanoLC–MS/MS analysis was performed on vastus lateralis biopsies from three women with RA and matched healthy controls. Differential analysis was performed using the Limma R package. Kinase substrate enrichment analysis (KSEA) predicted changes in kinase activity. RA muscle displayed 35 upregulated and 60 downregulated phosphosites, including the cytoskeletal proteins TTN (Ser33201, Ser33013, Ser20925), NEB (Ser2219, Thr254, Ser33013, Ser20925), FLNA (Ser1459), and LASP1 (Ser146). Compared to healthy controls, KSEA predicted decreased activity of several kinases in RA muscle, including PRKACA and CDKs. All such changes were visualized by use of our web-based app. Overall, phosphoproteome analysis reveals signaling alterations in RA skeletal muscle linked to cytoskeletal proteins, representing candidate disease biomarkers; these modifications can be explored through use of our web-based software.

phosphoproteomics↗