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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were retrieved and downloaded from PubMed as of April 2024 using three Medical Subject Headings (MeSH) words: “opioid,” “cardiovascular,” and “women.” Machine Learning of Language Toolkit (MALLET) was employed for the implementation of LDA. BioBERT was used for document embedding in BERTopic. Eighteen was selected as the optimal topic number for MALLET and 23 for BERTopic. ChatGPT-4-Turbo was integrated to interpret and compare the results. The short descriptions created by ChatGPT for each topic from LDA and BERTopic were highly correlated, and the performance accuracies of LDA and BERTopic were similar as determined by expert manual reviews of the abstracts grouped by their predominant topics. The results of the t-SNE (t-distributed Stochastic Neighbor Embedding) plots showed that the clusters created from BERTopic were more compact and well-separated, representing improved coherence and distinctiveness between the topics. Our findings indicated that AI algorithms could augment both traditional and contemporary topic modeling techniques. In addition, BERTopic has the connection port for ChatGPT-4-Turbo or other large language models in its algorithm for automatic interpretation, while with LDA interpretation must be manually, and needs special procedures for data pre-processing and stop words exclusion. Therefore, while LDA remains valuable for large-scale text analysis with resource constraints, AI-assisted BERTopic offers significant advantages in providing the enhanced interpretability and the improved semantic coherence for extracting valuable insights from textual data.

Research & Experimental Medicine↗
DOE OSTI2026

Predictive analytics to direct clinical attention to complex patients with elevated suicide risk: enhancement of the Veterans Health Administration REACH VET model

Suicide is a major public health concern, particularly among Veterans. The U.S. Department of Veterans Affairs Veterans Health Administration (VHA) employs the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET) model to prioritise high-risk patients for targeted clinical attention. REACH VET 1.0 (RV 1.0) was developed on 2008–2011 data. To reflect changes in clinical practice and populations, VHA updated it to REACH VET 2.0 (RV 2.0). This study describes its development and validation. RV 2.0 used longitudinal data from 7,248,170 VHA patients (4,967 suicide deaths) in 2018–2019, with 650 time-varying demographic, clinical and area-level predictors derived from a 2-year lookback (2016–2019). An ensemble of Elastic-Net logistic regression models was trained on 2018 data and evaluated monthly at the population level in 2019, focusing on the top 0.1% intervention risk tier. Analyses assessed model discrimination, suicide detection, risk concentration, subgroup consistency (sex, age and race/ethnicity) and performance relative to RV 1.0 using the same percentile-based risk strata. RV 2.0 outperformed RV 1.0 across all risk strata, with better discrimination (C-statistic 0.76 vs 0.69) and consistent performance across demographic subgroups. Within the top 0.1% of predicted risk, RV 2.0 identified more deaths, higher suicide rates and greater mortality risk concentration both when averaged across the 12 monthly 2019 test sets (5.6 vs 3.6; 83.6 vs 53.7 per 100,000 person-years; 21.0 vs 14.1) and when annualised for 2019 (67 vs 43; 2.7% vs 1.7%; 1,003 vs 644 per 100,000 person-years; 26.7 vs 17.1). RV 2.0 improves suicide risk stratification among Veterans, demonstrating better performance and consistent prediction across subgroups and highlighting the need for regular model updates and evaluation.

Peluso, Alina [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2025

PSA 2025 DPRA for Cyber Optimization

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Evaluating defense options should include quantitative evaluation of overall effectiveness to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation and have difficulty with time dependent scenarios. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related integrity is a requirement set by the U.S. Nuclear Regulatory Commission. But companies are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want reliability analysis while optimizing cost, which requires more than safety modeling methods. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with timing and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues such as state-base explosion found in Markov-based tools. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quantifying Investment Risk: Analysis of the Purchase Decision of a Nuclear Power Plant

Cost overruns are an ill-fated part of the deployment history of nuclear power plants (NPPs) in the United States, and yet studies increasingly show the important role nuclear technologies must play in decarbonizing the U.S. economy. Paradoxically, then, a key piece of a coherent decarbonization strategy depends on attracting investor action to a purchase where historical cost overruns have been sizeable. To address this challenge, this study aims to develop a financial model that quantifies risk of cost overruns in the decision-making process for purchasing advanced reactor concepts. Using the concept of Value at Risk (VaR), the model is built to evaluate financial risk nuclear construction with the aim to identify risk mitigation strategies. The objective is to identify strategies to mitigate cost-risk challenges and to assess the potential reduction in investor risk exposure. The paper presents the initial development and preliminary verification of the financial risk analysis model. The development of this model involved a comprehensive approach to estimating financial risk over the operating life of NPP that stems from construction uncertainties. By utilizing net present value (NPV) with discounted cash flows, the model captures the complex interconnections of project costs, construction timelines, revenue, and uncertainties. Verification of the model involved testing historical data from previous reactor construction projects against the construction project of Vogtle 3 and 4. The results of this paper present the comparison of the preconstruction cost overrun prediction with the current cost estimates from a nearly complete Vogtle 3 and 4.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Responsible Adoption of Artificial Intelligence (AI) in Electric Grid Operations

The future of the grid will be powered by AI—or undermined by it. Artificial intelligence is rapidly reshaping grid operations, improving fault detection, forecasting accuracy, and real-time optimization. As AI systems move closer to operational decision loops, however, they introduce new consequence pathways: expanded attack surfaces, model integrity risks, regulatory exposure, and human-automation challenges. This talk presents a consequence-driven framework for deploying AI responsibly in the electric grid. Attendees will gain practical strategies to strengthen resilience, boost reliability, and deploy AI securely — ensuring the grid of the future is not only smarter but safer.

25 - ENERGY STORAGE↗
DOE OSTI2024

Endogenizing Probabilistic Resource Adequacy Risks in Deterministic Capacity Expansion Models

In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find an appropriate reserve level. The endogenous method may also use iterative re-optimizations to further improve solution quality, although these incremental benefits were modest in the system studied.

capacity expansion modeling↗
DOE OSTI2025

Machine Learning Models for Mapping Groundwater Pollution Risk: Advancing Water Security and Sustainable Development Goals in Georgia, USA

The widespread use of pesticides, such as atrazine and malathion, in agricultural systems raises significant concerns regarding the contamination of groundwater, which serves as a critical resource for drinking water. This study applies machine learning techniques to predict the concentrations of atrazine and malathion in groundwater across Georgia, USA, using 2019 data. A Random Forest classifier was employed to integrate various environmental and demographic factors, including pesticide application rates, precipitation, lithology, and population density, to predict pesticide contamination in groundwater. The models demonstrated high training accuracies of 100% and moderate average testing accuracy of 55% for atrazine and 60% for malathion across five iterations. The low test accuracy of the model, ranging from 50% to 75%, is likely due to overfitting, which can be attributed to the small dataset size and the complex nature of pesticide-contamination patterns, making it challenging for the model to generalize to unseen data. Feature importance analysis revealed that average pesticide usage emerged as the most influential factor for atrazine, while aquifer lithology and precipitation played crucial roles in both models. These results provide valuable insights into the dynamics of pesticide contamination, highlighting areas at greater risk of contamination. The findings underscore the importance of integrating environmental, geological, and agricultural variables for more effective groundwater management and sustainable agricultural practices, contributing to the protection of water resources and public health.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

System-Level Integration of Modular Language Models for Real-Time Risk Assessment in Third-Party Risk Management Systems

Large enterprises typically rely on dedicated teams to govern and implement security measures throughout their supply chains, ensuring compliance with enterprise security procedures. There is a significant reliance on Third-Party Risk Management (TPRM) platforms, which often require complete, highly structured information from potential vendors. The review and compliance assurance processes are time- and labor intensive, often requiring several rounds of review between the supply chain security risk management teams, business users, and potential vendors, leading to delays in the supply chain processing and consumer experience. Significant challenges in the risk management paradigm include handling unstructured data in various formats and providing real-time feedback to users to reduce the required review time. This paper presents a novel solution to these challenges. A modular multi-step system architecture is proposed using advances in language processing, specifically for unstructured responses and provides real-time feedback (i.e., 3 seconds) so that users can improve their responses before the TPSRM team review. This novel system architecture will increase information accuracy and significantly reduce time and labor during the review process.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

A ‘Nuclear Bomb’ or Just ‘a Joke’? Groundwater Models May Help Communicate Nuanced Risks to the Great Salt Lake

The Great Salt Lake entered the zeitgeist of environmental concern in 2022 when a coalition of scientists and activists warned in a highly publicized report that the lake might be just five years away from complete desiccation, a possibility one state official warned was tantamount to an “environmental nuclear bomb”. Shortly thereafter, an unpredicted and unprecedented pluvial winter resulted in an increase in inflow, temporarily halting the lake’s decline and prompting Utah’s governor to mock the dire prediction as “a joke”, an outcome that speaks to the tension between agenda-setting and trust-building that researchers face when sharing worst-case warnings, particularly those based on short-term variability. Here, we describe a robust relationship between the lake and groundwater in the surrounding region and demonstrate how coupled models can thus be used to improve lake elevation predictions, suggesting that while the situation may not be as dire as some have warned, the lake remains at long-term risk as a result of climate warming. We further suggest that efforts to communicate the risk of future desiccation should be informed by stochastic variability and guided by long-term fluctuations in the total water storage of the endorheic lake’s watershed.

Environmental Sciences & Ecology↗
DOE OSTI2025

Rising concerns of climate extremes and land subsidence impacts

A recent article in Reviews of Geophysics explores land subsidence drivers, rates, and impacts across the globe. It also discusses the need for improved process representations and the inclusion of the interplay among land subsidence and climatic extremes, including their effects in models and risk assessments. Here, we asked the lead author to explain the concept of land subsidence, its impacts, and future directions needed for improved mitigation.

Earth science↗
DOE OSTI2026

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↗
DOE OSTI2025

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) Projects in Germany and the Netherlands—Review and Lessons Learned

Aquifer thermal energy storage (ATES) is a concept that can help to address heating and cooling needs through the use of the subsurface as a seasonal thermal energy storage (STES) system. Over 2800 ATES systems have been deployed with storage temperatures typically below 25 °C and only a few with higher temperatures (>40 °C), which would increase the energy density and utility of the stored thermal fluids. Until now, only a few high-temperature aquifer thermal energy storage (HT-ATES) projects have been initiated and are still in operation. These HT-ATES projects have encountered a range of technical and non-technical challenges. This study reviews ten such projects: four in Germany and six in the Netherlands. The non-technical issues include public acceptance, a lack of regulatory framework for these systems, managing overlapping uses of the subsurface, managing changes with the providers and off-takers of thermal energy, and obtaining financing to implement these projects. Common technical issues include geological factors such as incomplete characterization of the subsurface and reservoir heterogeneity; geochemical issues such as mineral scaling, corrosion, and biofouling; lower than expected thermal recovery; and issues with system design and reliability. This review highlights benefits and challenges faced by HT-ATES projects with the goal to use the lessons learned to improve the siting, design, development, and operation of such systems. Recommendations include improved initial subsurface site characterization, use of coupled process models to optimize system design and predict system performance, cascaded uses of stored thermal energy to better utilize the stored heat, monitoring networks to provide feedback on system performance, and expanded system scale to allow for continued operation even when maintenance of some system components is required. Techno-economic modeling and risk analysis could be used to optimize such HT-ATES project design and identify key factors that will affect sustained economic viability. In addition, design flexibility is important for these systems to allow for changing conditions regarding the supply and demand of thermal energy. Adopting these findings should improve the performance and reduce the risks for future HT-ATES projects worldwide.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2025

MRCI Task 2: Addressing Key Technical Challenges - Executive Summary Report

This report provides a high-level overview of Task 2 products that were developed through defining carbon storage systems, Precambrian basement structure and stress, developing regional technical collaboration, modeling and risk assessment. Subtask 2.1: Defining Sub-Regional CS/CCUS Systems Subtask 2.2: Defining Precambrian basement faulting/stress Subtask 2.3: Developing industrial partnerships and regional technical collaborations Subtask 2.4: Conducting regional/subregional analysis Subtask 2.5: Assessing and managing risk for potential commercial-scale storage complexes

MRCI,Technical Challenges↗
DOE OSTI2026

System Study: Reactor Core Isolation Cooling 1998-2024

This report presents an unreliability evaluation of the reactor core isolation cooling (RCIC) system at 28 U.S. commercial operating boiling water reactors. New Standardized Plant Analysis Risk (SPAR) models with the most recent SPAR parameter update results were used in this report. Demand, run hour, and failure data from calendar years 1998 to 2024 for selected components were obtained from the Institute of Nuclear Power Operations Industry Reporting and Information System. The unreliability results are trended for the most recent 10 year period and yearly estimates for system unreliability are provided for the entire active period. No statistically significant increasing or decreasing trends were identified in the RCIC results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

System Study: Emergency Power System 1998-2024

This report presents an unreliability evaluation of the emergency power system (EPS) at 93 U.S. commercial operating nuclear reactors. New Standardized Plant Analysis Risk (SPAR) models with the most recent SPAR parameter update results were used in this report. Demand, run hour, and failure data from 1998 to 2024 for selected components were obtained from the Institute of Nuclear Power Operations Industry Reporting and Information System. The unreliability results are trended for the most recent 10 year period while yearly estimates for system unreliability are provided for the entire active period. No statistically significant increasing or decreasing trends were identified in the industry-wide estimates of EPS system start-only unreliability, but a statistically significant decreasing trend was identified in the industry-wide estimates of EPS system 24-hour mission unreliability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

System Study: High-Pressure Coolant Injection 1998-2024

This report presents an unreliability evaluation of the high-pressure coolant injection (HPCI) system at 22 U.S. commercial operating boiling water reactors. New Standardized Plant Analysis Risk (SPAR) models with the most recent SPAR parameter update results were used in this report. Demand, run hour, and failure data from 1998 to 2024 for selected components were obtained from the Institute of Nuclear Power Operations Industry Reporting and Information System. The unreliability results are trended for the most recent 10 year period and yearly estimates for system unreliability are provided for the entire active period. No statistically significant increasing or decreasing trends were identified in the HPCI results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

System Study: Auxiliary Feedwater 1998-2024

This report presents an unreliability evaluation of the auxiliary feedwater (AFW) system at 62 U.S. commercial operating nuclear reactors. New Standardized Plant Analysis Risk (SPAR) models with the most recent SPAR parameter update results were used in this report. Demand, run hour, and failure data from calendar years 1998 to 2024 for selected components were obtained from the Institute of Nuclear Power Operations Industry Reporting and Information System. The unreliability results are trended for the most recent 10 year period and yearly estimates for system unreliability are provided for the entire active period. No statistically significant increasing or decreasing trends were identified in the industry-wide estimates of AFW system start-only unreliability, but a statistically significant decreasing trend was identified in the industry-wide estimates of AFW system 24-hour mission unreliability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗