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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impact of representative ground motion level on seismic PSA with the boundary between overestimation and underestimation

One commonly used approach in seismic probabilistic safety assessment (PSA) is the discrete method. This method follows the standard PSA framework and can be applied to various models, such as multi-unit models, while reducing computational costs using standard software. However, due to the inability to subdivide intervals infinitely, the discrete method approximates with a finite number of subintervals. In practice, different numbers of subintervals are applied, and the representative ground motion level is selected based on expert judgment. When employing a smaller number of subintervals, it is important to take caution to prevent underestimation. This study analyzes the impact of the representative ground motion level on seismic risk. It confirms that underestimation can occur with a small number of subintervals depending on the representative ground motion level. This study also proposes a method for determining the boundary of underestimation and overestimation. The method is demonstrated through examples, providing a mathematical foundation for selecting appropriate representative ground motion levels. By avoiding underestimation, this research helps prevent the oversight of significant risk contributors and enhances the understanding of seismic risk.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

The missing correlation between the potential rate impacts of rooftop solar and the timing of state net metering policy revisions

Residential solar photovoltaic (PV) output in most states is credited at the retail electricity rate, a policy commonly known as net metering. Twelve states have replaced net metering with alternative rate structures that reduce PV adopter bill savings. Proponents of these revisions argue that net metering increases the electricity rates of customers without PV. Here, we analyze the degree to which the timelines of net metering revisions have correlated with potential electricity rate impacts. We estimate that potential rate impacts at the end of 2023 were less than 1% of typical customer bills in 37 of 44 states that have offered net metering. There are no statistically significant differences in average or median estimated rate impacts between states that have and have not revised net metering. Nine of the states that had revised net metering did so when estimated impacts were less than 1% of typical customer bills. Many states have retained net metering into higher PV deployment levels with increased risk of potential rate impacts. Only two states—California and Hawaii—retained net metering beyond estimated rate impacts of 5%, and both have revised net metering. These findings do not suggest a clear, consistent link between net metering revision timelines and potential rate impacts. The timing and nature of net metering revisions are ultimately policy decisions based on state-level priorities and considerations.

14 SOLAR ENERGY↗
DOE OSTI2025

The Missing Correlation Between the Potential Rate Impacts of Rooftop Solar and the Timing of State Net Metering Policy Revisions

Data supporting the article “The Missing Correlation Between the Potential Rate Impacts of Rooftop Solar and the Timing of State Net Metering Policy Revisions” (https://www.nlr.gov/docs/fy25osti/93543.pdf). Residential solar photovoltaic (PV) output in most states is credited at the retail electricity rate, a policy commonly known as net metering. Twelve states have replaced net metering with alternative rate structures that reduce PV adopter bill savings. Proponents of these revisions argue that net metering increases the electricity rates of customers without PV. Here, we analyze the degree to which the timelines of net metering revisions have correlated with potential electricity rate impacts. We estimate that potential rate impacts at the end of 2023 were less than 1% of typical customer bills in 37 of 44 states that have offered net metering. There are no statistically significant differences in average or median estimated rate impacts between states that have and have not revised net metering. Nine of the states that had revised net metering did so when estimated impacts were less than 1% of typical customer bills. Many states have retained net metering into higher PV deployment levels with increased risk of potential rate impacts. Only two states-California and Hawaii-retained net metering beyond estimated rate impacts of 5%, and both have revised net metering. These findings do not suggest a clear, consistent link between net metering revision timelines and potential rate impacts. The timing and nature of net metering revisions are ultimately policy decisions based on state-level priorities and considerations.

14 SOLAR ENERGY↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2024

Efficient Hybrid Attack Graph Generation for Cyber-Physical System Resilience Experimentation (Final Project Report)

HAGEN project has developed theory, algorithms, and capabilities to assist cyber physical system modelers and operators to perform system and device-level vulnerability assessment, risk assessment, impact assessment, and mitigation planning. The project generates hybrid attack graphs for Cyber-Physical System (CPS) resilience experimentation at desired scale and speed. The project will produce composite attack datasets, algorithms, and demonstrable prototypical tools, and a library of high-impact attack sequences for a given CPS of interest. This report provided overall summary of research and development performed between FY22-24.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Fundamentals of wildlife dosimetry and lessons learned from a decade of measuring external dose rates in the field

Methods for determining the radiation dose received by exposed biota require major improvements to reduce uncertainties and increase precision. We share our experiences in attempting to quantify external dose rates to free-ranging wildlife using GPS-coupled dosimetry methods. The manuscript is a primer on fundamental concepts in wildlife dosimetry in which the complexities of quantifying dose rates are highlighted, and lessons learned are presented based on research with wild boar and snakes at Fukushima, wolves at Chornobyl, and reindeer in Norway. GPS-coupled dosimeters produced empirical data to which numerical simulations of external dose using computer software were compared. Our data did not support a standing paradigm in risk analyses: Using averaged soil contaminant levels to model external dose rates conservatively overestimate the dose to individuals within a population. Following this paradigm will likely lead to misguided recommendations for risk management. The GPS-dosimetry data also demonstrated the critical importance of how modeled external dose rates are impacted by the scale at which contaminants are mapped. When contaminant mapping scales are coarse even detailed knowledge about each animal’s home range was inadequate to accurately predict external dose rates. Importantly, modeled external dose rates based on a single measurement at a trap site did not correlate to actual dose rates measured on free ranging animals. These findings provide empirical data to support published concerns about inadequate dosimetry in much of the published Chernobyl and Fukushima dose-effects research. Furthermore, our data indicate that a huge portion of that literature should be challenged, and that improper dosimetry remains a significant source of controversy in radiation dose-effect research.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

System Safety Risk Analysis Models (SSRAM)

This presentation provides a high level overview of the System Safety Risk Analysis (SSRAM) methodology and motivation.

Clark, Andrew Jordan [Sandia National Laboratories↗
DOE OSTI2024

The Screening Tool for Industrial Resilience: Risk-Informed Decision Making to Support Resilience Planning

The Screening Tool for Industrial Resilience (STIR) helps small and medium-sized manufacturing plants manage risk to their production. Developed by the Pacific Northwest National Laboratory under direction and funding from the Department of Energy’s Office of Manufacturing and Energy Supply Chains, the STIR helps manufacturers enhance their resilience to a variety of disruptive events, both natural and human-caused, that could interrupt normal operations. This report provides an overview of the STIR’s risk-informed, high-level approach to resilience planning, which identifies potential solutions that could enhance site resilience based on calculated major risk drivers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Marine Energy Technology Development Risk Management Framework

Over the past decades, the global marine energy industry has suffered a number of serious technological and commercial setbacks. To help reduce the risks of industry failures and advance the development of new technologies, the U.S. Department of Energy (DOE) and the National Renewable Energy Laboratory (NREL) developed a Marine Energy Risk Management Framework in 2015, with this revision published in 2024. This risk management framework shall be utilized on all DOE Water Power Technologies Office (WPTO) projects that require system testing in the open water. By addressing uncertainties, the Marine Energy Risk Management Framework increases the likelihood of successful development of marine energy converter technology. It covers projects of any technology readiness level technology performance level (TPL) and all risk types (e.g. technological risk, regulatory risk, commercial risk) over the development cycle. This risk framework is not a substitute for other risk management procedures that may be required for marine operations, such as installations at sea, hoisting and rigging, safe diver operations, and other safety requirements. This risk framework is intended to meet DOE's risk management expectations for marine energy technology research and development efforts from WPTO. It also provides an overview of other relevant risk management tools and documentation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Editorial: Water supply sustainability and challenges in Asian megadeltas under global change

Globally, riverine deltas comprise barely 1% of land area, but they support the livelihoods of more than 500 million people (Moorhouse et al., 2021). The Asian megadeltas (e.g., Indus, Bengal, Mekong, Red River, and Yangtze) and their riverine floodplains are the most dynamic economic and environmental systems in the world. These low-lying coastal landforms support surface water bodies and highly productive aquifer systems. However, water resources of the Asian megadeltas are extremely vulnerable to geogenic (e.g., arsenic) and anthropogenic (e.g., bacteria) contamination, increased water salinisation from rising sea levels (Figure 1), and chemicals (e.g., fertilizers, pesticides) from land-use practices (Mukherjee et al., 2024) – all of which are threatening the sustainability of water supply and food security in the region. This Research Topic collection features five original articles that explore water chemistry including arsenic and bacteriological contamination, water salinisation, water-energy-food nexus in irrigated agriculture, and groundwater depletion resulting from irrigation, rapid urbanization, and climate change. Although the Research Topic collection does not include case studies from all the Asian Megadeltas shown in Figure 1, the relevance of multi-hazard driven water risks to all deltaic and river floodplain environments around the world is well-illustrated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Risk Assessment Information System Compendium of Ecological Screening Benchmarks for Chemicals and Radionuclides (2025) (Volume I – Text, Radionuclides for all Media, Chemicals for Air and Biota)

The Risk Assessment Information System Ecological Benchmark Tool compiles screening level benchmark values for assessing potential ecological risks posed by chemical and radionuclide contaminants. Drawing from a wide array of federal, state, and international sources, it consolidates benchmarks across environmental media, including soil, water, sediment, air, and biota, and presents values for a broad range of receptors and exposure pathways. The tool includes both effect-based and no-effect thresholds, with media- and organism-specific guidance derived from peer-reviewed literature and regulatory frameworks. By integrating diverse benchmark types into a unified reference, the tool supports consistent, scientifically grounded evaluations in ecological risk assessments and environmental management practices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Adaptive Grid Redistribution for a 1D Model of Turbulence and Clouds

In global atmospheric models, resolving stratocumulus (Sc) in the vertical is computationally expensive. However, Sc appear only under special meteorological conditions. Therefore, there is motivation to refine the vertical grid levels adaptively. In order to facilitate the possibility of parallelization on graphical processing units, our grid adaptation method prescribes the number of vertical levels a priori. Then grid levels are relocated toward altitude ranges in need of refinement. Because the method relocates existing grid levels, rather than adding extra levels, there is a risk of creating regions with overly coarse grid spacing, that is, voids in the grid mesh. To prevent such voids from forming, a simple method is developed to impose a maximum grid spacing. To decide where to place enhanced resolution, the authors develop an empirical mesh refinement criterion. It refines grid spacing near the ground, near strong temperature gradients, and within clouds. Our grid adaptation method is implemented in a single-column model and evaluated on four test cases: decaying stratocumulus, developing shallow cumulus, a quasi-stationary stratocumulus deck, and the diurnal cycle of a dry boundary layer. In the stratocumulus cases, mesh refinement leads to improvements in both the time evolution of fields and their time averages. The other two cases show smaller differences.

Carstensen, Steffen [Univ. of Wisconsin, Milwaukee↗
DOE OSTI2024

Alternatives to NIST Cf-252 Iirradiations for Transfer Calibration of S-32 Neutron Monitors

Gas-flow proportional counting systems are used by the Radiation Metrology Laboratory (RML) at Sandia National Laboratories for reactor fluence monitoring with the 32 S(n,p) 32 P reaction. Calibration of these systems has traditionally been accomplished by fluence-transfer irradiations at the NIST 252 Cf facility. Such calibrations have become increasingly difficult as the NIST 252 Cf source decayed to unusable levels. To minimize the risk to the testing programs from an inability to properly calibrate these systems, the RML has developed two alternative calibration techniques: 1) development and implementation of certified 32 P sources for activity calibrations and subsequent calculation of neutron fluence, and 2) direct counting of non-certified reactor-irradiated sulfur pellets by liquid scintillation counting to determine 32 P activity for the subsequent calibration of gas-flow proportional counters. Preliminary comparisons show that the several calibration methods are capable of overall uncertainties within about 5 percent.

Vehar, David W.↗
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 OSTI2026

Using Machine Learning to Understand Electric and Hybrid Vehicles Ownership in Burdened and Nonburdened Communities

Transitioning to electric and hybrid vehicles (EHVs) for all communities is a pivotal step toward sustainable transportation and environmental conservation. This paper aims to understand the adoption of EHVs, focusing on burdened communities (BCs) in the United States. The EHV ownership-based analysis combines two datasets—behavioral data from the Puget Sound Regional Travel Survey integrated with BCs (Justice40) data covering transportation insecurity, environmental burden, social vulnerability, health vulnerability, and climate and disaster risk burden. After creating this unique database, descriptive analysis and modeling are used to analyze the data and predict EHV ownership in the future. Specifically, we use a new method that combines particle swarm optimization (PSO) with a stacking model named PSO-Stacking, which incorporates heterogeneous base learners of machine learning and deep learning. PSO applies a customized objective function to select the optimal hyperparameters for heterogeneous learners within the stacking model, effectively addressing challenges such as multicollinearity, data imbalance, nonlinearity, and overfitting. The proposed solution covers more accurate results than standard benchmark models for EHV ownership in BCs and non-BCs. In addition, the results of the PSO-Stacking method are explained using the local interpretable model-agnostic explanations technique. Results show a negative correlation between the BCs indicators, that is, higher transportation insecurity associated with lower EHV ownership. Furthermore, BCs have higher future climate risk scores, diesel particulate matter levels, and PM2.5 in the air than non-BCs because of higher conventional vehicle ownership. These communities are at higher risk and can benefit from electrification, EV infrastructure, and EV policies to address environmental challenges.

Aslam, Zeeshan [ORNL]↗
DOE OSTI2026

Evidence for a cytokine-sensitive network of iron-associated genes that protects pancreatic islets against ferroptosis

Background/Objectives: The micronutrient iron is closely connected to inflammation and is among the complex factors contributing to beta-cell failure in diabetes. High levels of dietary iron increase the risk of developing type 2 diabetes, and excessive iron uptake by beta-cells can cause oxidative stress and inhibit function. Elevated levels of proinflammatory cytokines in obese individuals, such as interleukin (IL)-1beta and IL-6, increase the risk of developing type 2 diabetes, and there is evidence that these low levels of circulating cytokines can lead to islet dysfunction. Methods: In this study, gene microarray and other data were analyzed for expression differences in islets treated for 48 h with 10 pg/mL IL-1beta + 20 pg/mL IL-6 as a model of low-grade inflammation versus untreated. Results: Three iron-associated genes were among the most cytokine-sensitive in the mouse genome: Hamp, Steap4, and Lcn2. These proteins are all involved with increasing/retaining cellular iron. We hypothesized that increased cellular iron would lead to increased susceptibility to ferroptosis. Surprisingly, 24 h pre-exposure to low-grade inflammation, which upregulates this iron-gene network, prevented subsequent erastin-induced ferroptosis. We also found that Steap4 overexpression reduced islet dysfunction caused by high-dose proinflammatory cytokines (10× low-dose), suggesting an overall protective effect. Steap4 overexpression also upregulated Hamp and Lcn2, suggesting Steap4 regulates these cytokine-sensitive iron genes.; in contrast, ferritin and ferroportin gene expression, which are not sensitive to cytokines, were unchanged. Conclusions: These data suggest an inflammation-induced network of genes involved in cellular iron uptake and retention plays a protective role in islets against oxidative stress and ferroptosis.

IL-1β↗
DOE OSTI2024

Application of Banking Scoring and Rating for Coherent Risk Measures in Electricity Systems ABSCORES

This project developed a framework for asset and system risk management that can be incorporated into current electricity system operations to improve economic efficiency and establish an Electric Assets Risk Bureau. We leveraged scoring and ratings from banking and financial institutions alongside current optimization methods in dispatching power systems to help system operators and electricity markets schedule resources. This approach is based on the observation that there are major discrepancies between the power scheduled by a system operator and the actual power generated/consumed. These discrepancies—exacerbated by unplanned contingencies (e.g., natural disasters)—are caused by multiple factors, including the different financial, environmental and risk preferences of power producers, consumers, and aggregators. We developed a framework that counteracts two failures in electricity system operations: imperfect information and missing markets for products. The technical approach included five tasks. Tasks 1 and 2 supported the development of risk scores at the asset level with historical data collected for this project. Tasks 3, 4, and 5 incorporated scoring into decision-making at the system level. The proposed effort achieved PERFORM's Program Objectives because the proposed outputs and algorithms do not exist in the electricity industry and are an innovative approach to managing risk. Since the acknowledged need to better assess and act upon risk profiles for grid assets has not been met by the industry, this project will also impact ARPA-E's Mission Areas, including improving energy efficiency and giving the U.S. a technological lead in advanced energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A decade of progress in understanding and managing legacy well integrity for geologic carbon storage

This study reviews a decade of research progress in legacy well integrity and risk management for geologic carbon storage (GCS) to commemorate the 20 th anniversary of the Intergovernmental Panel on Climate Change’s 2005 Special Report on Carbon Capture and Storage. In the past ten years, legacy well research has benefited from global efforts to constrain emissions from abandoned oil and gas wells, a continued focus on well materials performance in the presence of CO 2 -rich fluids, and practical experience gained through GCS implementation. Field measurements of abandoned well emissions show that leakage is not universal or catastrophic but forms a continuum of low-to-moderate fluxes that depend on isolation integrity and environmental attenuation. Materials research has constrained the conditions under which Portland cements exhibit self-sealing and non-sealing behaviors, and has identified the impact of geomechanical properties, non-uniform pathway apertures, multi-phase flow, and impurities in the CO 2 stream, on leakage pathways as important new areas for investigation. GCS projects at brownfield sites have inspired the creation of new workflows that integrate various tools and technologies to manage legacy well leakage risks. GCS implementation has also motivated a push towards scenario-based well modeling that directly informs permit applications. These advances inspire new research questions for the coming decade, particularly around the level of legacy well leakage risk that is environmentally acceptable and tolerable to stakeholders when sequestering millions of tonnes of CO 2 annually.

Carbon capture and storage↗