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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

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↗
DOE OSTI2022

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↗
DOE OSTI2020

Quantile-dependent expressivity of postprandial lipemia

“Quantile-dependent expressivity” describes an effect of the genotype that depends upon the level of the phenotype (e.g., whether a subject’s triglycerides are high or low relative to its population distribution). Prior analyses suggest that the effect of a genetic risk score (GRS) on fasting plasma triglyceride levels increases with the percentile of the triglyceride distribution. Postprandial lipemia is well suited for testing quantile-dependent expressivity because it exposes each individual’s genotype to substantial increases in their plasma triglyceride concentrations. Ninety-seven published papers were identified that plotted mean triglyceride response vs. time and genotype, which were converted into quantitative data. Separately, for each published graph, standard least-squares regression analysis was used to compare the genotype differences at time t (dependent variable) to average triglyceride concentrations at time t (independent variable) to assess whether the genetic effect size increased in association with higher triglyceride concentrations and whether the phenomenon could explain purported genetic interactions with sex, diet, disease, BMI, and drugs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Test–retest reliability for a social discounting of personal information task

Increasing cybercrime rates means identifying potential victims is critically important. Social discounting tasks show that individuals share less personally identifying information as social distance increases. However, the test–retest reliability and uniqueness of this measure is unclear. The current study assessed social discounting for personally identifying information (SDPII), delay discounting, risk taking, and personality at two measurement waves 30 days apart for 64 undergraduate students. Test–retest reliability was statistically significant for the SDPII and all other measures, replicating previous studies. SDPII rates were not significantly correlated with other measures during both measurement waves, showing discriminant validity. SDPII rates were lower than those reported in a previous study but were still well described by a hyperbolic discounting function, suggesting replicability across studies. Furthermore, the high test–retest reliability, uniqueness, and replicability of the SDPII suggests that it may quantitatively identify cybercrime victimization. Future research should test which measure or combination of measures can accurately predict scam and cybercrime victimization to inform data-based interventions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Validating a Dynamic PWR Safety and Security Model?

Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2023

Safety Benefits Assessment for Accident Tolerant Fuels in Consideration of Steam Generator Tube Degradation Using Dynamic Event Tree Analysis

Accident tolerant fuel (ATF) is expected to delay or prevent core damage by providing additional coping time under accidents involving loss of core cooling. The effect of extended coping time may vary depending on the plant response to accidents. Age-related component degradation that deteriorates plant performance over time could have an impact on the actual advantages of ATF. The potential safety benefits of two near-term ATF candidates, including Cr-coated Zr cladding and FeCrAl cladding, are assessed for a 2-in. loss-of-coolant accident with failed high-pressure safety injection using the dynamic event tree (DET) approach considering possible stress corrosion cracking of steam generator (SG) tubing under aging. The DET approach allows likelihood quantification of accident sequences leading to core damage, including stochastic variation of system response and human actions during accident mitigation. The safety benefits of the selected ATF claddings in terms of additional coping time and the core damage frequency reduction rate under specified accident situations were quantitatively estimated. The results show that the deployment of the two selected ATF claddings is expected to lead to longer coping times and lower core damage frequency due to the wider safety margin to peak cladding temperature they provide. The safety advantages would be greater as SG tube degradation proceeds. Thus, the two ATF candidates would lead to less severe consequences in terms of likelihood of core damage and susceptibility to the SG tube degradation than UO 2 -Zr fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Challenges in Demonstrating Safety Criteria Satisfaction for External Events under a Risk-Informed Performance-Based Framework

The NRC is currently developing 10 CFR Part 53 that would establish a technology-inclusive regulatory framework for use by applicants for new commercial advanced nuclear reactors. The new draft rule language brings new explicit regulatory requirements of the quantitative health objectives (QHOs) as a constraint on plant risk and safety criteria for the plant design, including external hazards. Historically, light water reactors (LWRs) have demonstrated satisfaction of the QHOs through the use of surrogates in the form of CDF/LERF. However, these risk surrogates are not broadly applicable for all advanced reactor types, and for advanced reactors where external events are likely to be the dominant contributor to plant risk, a direct calculation of the QHOs in terms of offsite consequence is likely necessary. While the detailed analysis of low frequency events provides insights that can help justify alternative treatments to past conservatisms, the findings are dependent on the quality and confidence associated with the analyses. The assessment of external hazards presents a unique challenge, as their potential frequency of occurrence, especially of largemagnitude events, is inherently uncertain given the long return periods in question. This paper evaluates the QHOs and their potential role as safety criteria in evaluating external event risk as it applies to advanced reactors to aid in the development of consistent and appropriate analysis methodologies.

Chen, Ben↗
DOE OSTI2023

LDRD23-0184: Resilience and Hazard Risk Assessment to Prioritize Security Operations for Decisions and Impacts (RHAPSODI)

Recent examples provide a significant concern for the resilience of the U.S. electric grid and represent a need for enhanced decision-making to address an increasingly wide range of complex system interactions and potential consequences. In response, this LDRD project produced a proof-of-concept evaluation called the Resilience and Hazard Assessment to Prioritize Security Operations for Decisions and Impacts (RHAPSODI) methodology as an agile and flexible analytic framework capable of addressing multiple, diverse threats to desired electric grid performance. After empirically grounding needs for the future of U.S. electric grid resilience, this project employed the systems-theoretic process analysis (STPA) to develop a systems engineering risk model. The results of a completed feasibility study of a notional high voltage transmission system demonstrate an improved ability to incorporate both spatial (e.g., geographically distributed) and temporal (e.g., dynamic and time-dependent) elements of security risk to the gird. The success of this LDRD project provides the foundation for further evolution of the systems engineering risk model for the grid; derivation of quantitative approaches to evaluate risk and resilience performance; facilitation of agile experimenting and grid sensitivity to a range of vulnerabilities; and development of tools to assist decision-makers in enhancing U.S. electrical grid resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Conservative Estimation of Tail Probabilities from Limited Sample Data

Several sparse-sample uncertainty quantification (UQ) methods are compared for conservative but not overly conservative estimation of small tail probabilities involving responses that lay beyond specified thresholds in the tails of probability distributions. Sixteen very differently shaped distributions (or probability density functions, PDFs) and tail probability magnitudes ranging from 10 -5 to 10 -1 are considered in order for the study to be relevant to a wide range of risk analysis and quantification of margins and uncertainty (QMU) problems. The emphasis of the study is on limited data regimes ranging from N = 2 to 20 samples, reflective of most experimental and some expensive computational situations. Relatively simple sparse-sample UQ methods tested for this regime involve statistical tolerance interval "Equivalent Normal and related "Ensemble of Normals" and "Superdistribution (SD) approaches. (The independently derived SD is effectively equivalent to the Bayesian posterior predictive distribution given the assumptions of the derivation.) The performance of the methods was generally improved for N ≥ 5 samples with a generalized Jackknife resampling technique, which determines a tail probability estimate by averaging estimates from smaller sub-samples. Several quantitative metrics for method conservatism and accuracy of tail probability estimation are used to assess and rank the methods' performance over many random trials for each test PDF and probability magnitude. A variant of Bootstrap resampling was also tried, but did not significantly improve tail probability estimates in most cases. Detailed results are presented from over 100-million tests over the above factors that provide useful granular information on which methods or combination of methods perform best in various areas of the factor space.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Building a framework to genetically characterize “feather spots” and understand demographic impacts of solar energy sites on migratory bird populations

The lack of data on the impact of utility-scale solar facilities on avian species and populations adds to the cost of siting and operation. As much as 32 percent of the avian biological material (feathers and carcasses) recovered from solar facilities remain unidentified, because they often take the form of “feather spots”. Feather spots are remains of impacted animals that can be separated into two broad categories: 1) those remains that may be visually identified to a species, or 2) those that cannot be visually identified to a species due to degradation from the environment and/or scavenger activity (listed as “unknown”). Even when feather spots can be identified to species, they cannot be visually assigned to particular breeding populations. In some cases, it is unknown whether multiple feather spots represent single or multiple individuals. This project’s objectives were to: 1. Use a developed, genetic-based technique to identify and determine the species, population of origin, and number of individuals found in feather spots recovered from solar facilities. 2. Implement collected data and resulting analyses to develop a publicly accessible web-based decision-making tool that can be used by the solar industry, regulators and other stakeholders to inform siting, mitigation, and conservation management efforts. 3. Establish a not-for-profit fee-for-service center at UCLA to ensure collection and identification of feather spots continue after the project period of performance. During the Project Period, we proposed to establish a pipeline for collecting, transporting, and storing of avian biological material collected at solar facilities and the collection and identification of feather spots to species and individual. We proposed the development of a genetic-based framework that would recover viable DNA from feather spots, amplify this DNA (i.e., make millions of copies of the original DNA), and use it to match the resulting sequences to a national database of known species of birds. The result would be the identification of feathers spots that were previously unidentified, and the incorporation of these samples into a larger database that included all samples recovered from solar facilities. The resulting report (below) details the result of this work and its alignment with proposed activities. We proposed the use of the data collected to assess the comparative risk to specific species or populations of species from solar facilities. For some species, we have already identified genomic markers of specific breeding populations and developed “genoscapes,” maps of unique genetic variation across the full breeding range of a species. We used these (previously and newly developed) genoscapes to probabilistically link a feather spot to the specific breeding populations from which it originated (assignment probabilities range from 75%-100% depending on species and population groups). For those species without genoscapes, we developed a vulnerability and susceptibility estimate that determines the relative local and regional risk to populations that are in geographic proximity to solar facilities, using citizen science data (Breeding Bird Survey (BBS) and eBird). These two feather spot processing pipelines (see Figure 1 below) provide quantitative estimates as to the numbers of individuals from a given population of origin that are affected by solar facilities, and ultimately can reduce costs to the consumer by reducing the industry costs associated with mitigation and siting strategies for future solar energy development.

14 SOLAR ENERGY↗
DOE OSTI2023

Multi-dimensional resilience: A quantitative exploration of disease outcomes and economic, political, and social resilience to the COVID-19 pandemic in six countries

The COVID-19 pandemic has highlighted a need for better understanding of countries’ vulnerability and resilience to not only pandemics but also disasters, climate change, and other systemic shocks. A comprehensive characterization of vulnerability can inform efforts to improve infrastructure and guide disaster response in the future. In this paper, we propose a data-driven framework for studying countries’ vulnerability and resilience to incident disasters across multiple dimensions of society. To illustrate this methodology, we leverage the rich data landscape surrounding the COVID-19 pandemic to characterize observed resilience for several countries (USA, Brazil, India, Sweden, New Zealand, and Israel) as measured by pandemic impacts across a variety of social, economic, and political domains. We also assess how observed responses and outcomes (i.e., resilience) of the COVID-19 pandemic are associated with pre-pandemic characteristics or vulnerabilities, including (1) prior risk for adverse pandemic outcomes due to population density and age and (2) the systems in place prior to the pandemic that may impact the ability to respond to the crisis, including health infrastructure and economic capacity. Our work demonstrates the importance of viewing vulnerability and resilience in a multi-dimensional way, where a country’s resources and outcomes related to vulnerability and resilience can differ dramatically across economic, political, and social domains. This work also highlights key gaps in our current understanding about vulnerability and resilience and a need for data-driven, context-specific assessments of disaster vulnerability in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Production Agency Quality Assurance Management Execution Strategy [Thesis]

There are multiple federal directives that Los Alamos National Laboratory (LANL) must follow for the proper implementation of quality assurance, strategic planning, and execution of manufacturing and surveillance operations. Recent assessments identified that manufacturing in all processing areas is dynamic due to influencing scope changes, design modifications, funding adjustments, and staff attrition. In response, this project was started to develop a comprehensive PAQ execution strategy to support the success of LANL’s manufacturing mission. This project’s method for developing an updated architecture was to create an integrated, flexible, reliable, and agile strategic process. The execution plan has five deliverables: 1) an integrated schedule view of PAQ work scope, 2) a resource management plan that aligns with the required scope, 3) a metrics monitoring dashboard, 4) a project management plan for sustaining NAP 401.1A implementation, and 5) a risk management plan. The research design is a mixed-method approach focused on data collection for each deliverable. The methodology entails qualitative and quantitative research, metrics monitoring, data analysis, and the creation of a dashboard. The qualitative methods used include literature reviews and interviews. The quantitative methods include resource, financial, schedule and survey data analysis. Preliminary and final results were peer reviewed by subject matter experts both within the ALDWP organization and deployed support.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Regulatory Treatment of Low Frequency External Events under a Risk-Informed Performance-Based Licensing Pathway: Safety Criteria Evaluation

There has been recent progress in the development of risk-informed performance-based (RIPB) design and licensing approaches, which leverage the insights gained from detailed risk assessments and utilize performance-based metrics to permit flexibility and innovation. The RIPB approaches have included the probabilistic treatment of external hazards, including low frequency events that are beyond the design basis. A preceding study conducted in FY21 noted that there are certain challenges to the probabilistic treatment of low frequency external events, which are primarily the result of uncertainty in the hazard curve and the associated plant response to rare, severe events. The current work expands on the previous study by examining the regulatory safety criteria contained within draft 10 CFR Part 53 and associated draft RIPB seismic design guidance. The evaluation identified potential challenges in demonstrating satisfaction of the safety criteria for low frequency external events, with specific difficulties associated with demonstrating compliance with cumulative safety criteria metrics, such as the quantitative health objectives (QHOs). Non-LWRs are expected to utilize the direct calculation of offsite consequence, rather than use surrogates, for comparison to the QHOs, which can be particularly challenging as the previously identified uncertainties are compounded by uncertainties in the response of the neighboring population. The current work details this evaluation and key findings. The central recommendation from this effort is that it is necessary to develop an approach for demonstrating compliance with the safety criteria in draft Part 53 that addresses the key challenges while providing flexibility to applicants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Applied Risk Analysis for Guiding Homeland Security Policy

Risk analysis methods may be qualitative, semi-quantitative, or quantitative; adopt probabilistic and statistical theories; and implement concepts from core disciplines including operations research, reliability engineering, systems engineering, and applied mathematics. These methods continue to develop and evolve and have successfully been applied to address various homeland security mission challenges in recent years. The objective of this book is to: 1) highlight the role of risk analysis for informing homeland security policy decisions, and 2) describe case studies from academia, government, and industry that apply risk analysis methods for addressing challenges within each of the DHS missions.

national security, risk assessment, risk managemen↗
DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Data Quality Methodology (4.2.1)

SECARB-USA Deliverable 4.2.1 utilizes the outputs from the Needs Assessment (Subtask 2.1) to develop a data quality methodology. Many types of data are needed to evaluate a site for technical and financial viability. Several inventories of data types have been produced (for example NETL, 2010). The objective here is to organize the data types so that the needs met are specified. From this cross index (Table 1 in the Appendix), it will be possible in future tasks to (1) determine, on a site-specific basis, how much of each data type is required at each stage of a project to meet the need, and conversely (2) to further specify and define the data collection methods applied such that the data are tailored to fit that need. Derivative tables can then be developed to semi-quantitively evaluate the extent to which need is critical for early go/no decision points, or if it is more important than average, requiring faster or larger capitalization and spend to meet the need. In an additional step, the current availability of data for a site can be semi-quantitively assessed. From the table of data criticality and the table of data availability, site-specific cost for meeting the data needs can be determined, and allow a pre-permit spend estimated for a portfolio of projects. All of the data needs to evaluate a site are somewhat interconnected. We used criterion (2) above to determine if the data collection design would have to be modified to meet the need; if this was commonly true a linkage was shown. The application of this cross index to sites in the subsequent tasks will demonstrate that the demand for data types varies site-to-site and project-to-project. Examples of factors to be considered are the complexity of the geology, the injection goals such as rate and duration of the injection, and the types of risk and risk tolerance of key stakeholders. In future tasks the team will compare the demand for data with existing data availability. This will, in turn, determine when data needs to be acquired to support project development. For example, in a project area with complex structure, 3-D seismic data may be needed earlier and more urgently than in an area with simple rock body geometries. In some locations, a 3-D seismic survey has already been collected and can be purchased. In other locations the project developer will need to collect these data. For another example, a project near an urban area or near to a park may generate earlier and more substantive public concern than a site that is developed in mined lands. A calculation using the derivative from table 1 will show the different investment needs. Project cost will vary corresponding to data criticality and data availability.

42 ENGINEERING↗