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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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At least 1,171 records · Page 65

All sources NASA NTRS DOE OSTI

DOE OSTI2026

First High-Throughput Evaluation of Dark Matter Detector Materials

In this work we perform the first high-throughput search and evaluation of materials that can serve as excellent low-mass dark matter detectors. Using properties of close to 1000 materials from the Materials Project database, we project the sensitivity in dark matter parameter space for experiments constructed from each material, including both absorption and scattering processes between dark matter and electrons. Using the anisotropic materials in the dataset, we further compute the level of daily modulation in interaction rates and the resulting directional sensitivities, highlighting materials with prospects to detect the dark matter wind. Our methods provide the basic tools for the data-driven design of dark matter detectors, and our findings lay the groundwork for the next generation of highly optimized direct searches for dark matter as light as the keV scale. This represents a major step in the application of results from condensed matter physics to dark matter search design.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

On the compatibility of established methods with emerging artificial intelligence and machine learning methods for disaster risk analysis

Abstract There is growing interest in leveraging advanced analytics, including artificial intelligence (AI) and machine learning (ML), for disaster risk analysis (RA) applications. These emerging methods offer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on “learning” through datasets. There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. These existing methods are generally accepted by the risk community and are grounded in use across various risk application areas. The next frontier in RA with emerging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consideration of usefulness, trust, explainability, and other factors. This article leverages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI‐based method for disaster RA applications. This article utilizes empirical evidence from expert perspectives to support key insights on those methods and the compatibility of those methods. This article will be of interest to researchers and practitioners in risk‐analytics disciplines who leverage AI/ML methods.

Mathematical Methods In Social Sciences↗

DOE OSTI2021

Omics-Lethal Human Viruses, Influenza A Experiment IM102

The purpose of this experiment was to evaluate the host response to wild-type Influenza A/Anhui/1/2013 (H7N9; "AH1-WT") virus and mutant viruses NS1-103F/106M ("AH1-F/M") and partially ferret-adapted ("AH1-691") infection. Sample data was obtained from mouse lung tissue and processed for mRNA, miRNA, proteomics, metabolomics, and lipidomics expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset download each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A data-driven method to estimate contamination from light ion beam transmutation at colliders

Collisions of relativistic light ions such as oxygen, neon, and magnesium, have been proposed as a way to examine the system-size dependence of dynamics typically associated with the quark-gluon plasma produced in collisions of heavier ions such as xenon, gold, or lead. Recent efforts at both the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) have produced large datasets of proton-oxygen, oxygen-oxygen, and neon-neon collisions, catalyzing intense interest in experimental backgrounds associated with light ion collisions. In particular, electromagnetic dissociation of light ions while they are circulating in a collider can result in beam contamination that is difficult to simulate precisely. Here we propose a data-driven method for evaluating the potential impact of beam contaminants on physics analyses. The method exploits the time-dependence and smaller size of contaminant ion species to define control regions that can be used to quantify potential contamination effects. A simple mode

Accelerator Physics (physics.acc-ph)↗

DOE OSTI2025

PHASE: Personalized Head-based Automatic Simulation for Electromagnetic properties in 7T MRI

Accurate and individualized human head models are becoming increasingly important for electromagnetic (EM) simulations. These simulations depend on precise anatomical representations to realistically model electric and magnetic field distributions, particularly when evaluating Specific Absorption Rate (SAR) within safety guidelines. State of the art simulations use the Virtual Population due to limited public resources and the impracticality of manually annotating patient data at scale. Here, this paper introduces Personalized Head-based Automatic Simulation for EM properties (PHASE), an automated open-source toolbox that generates high-resolution, patient-specific head models for EM simulations using paired T1-weighted (T1w) magnetic resonance imaging (MRI) and computed tomography (CT) scans with 14 tissue labels. To evaluate the performance of PHASE models, we conduct semi-automated segmentation and EM simulations on 15 real human patients, serving as the gold standard reference. The PHASE model achieved comparable global SAR and localized SAR averaged over 10 grams of tissue (SAR-10g), demonstrating its potential as a promising tool for generating large-scale human model datasets in the future. The code and models of PHASE toolbox have been made publicly available: https://github.com/hrlblab/PHASE.

Deep learning↗

DOE OSTI2026

Data-driven method to estimate contamination from light ion beam transmutation at colliders

Collisions of relativistic light ions, such as oxygen, neon, and magnesium, have been proposed as a way to examine the system-size dependence of dynamics typically associated with the quark-gluon plasma produced in collisions of heavier ions such as xenon, gold, or lead. Recent efforts at both the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) have produced large datasets of proton-oxygen, oxygen-oxygen, and neon-neon collisions, catalyzing intense interest in experimental backgrounds associated with light-ion collisions. In particular, electromagnetic dissociation of light ions while they are circulating in a collider can result in beam contamination that is difficult to simulate precisely. Here we propose a data-driven method for evaluating the potential impact of beam contaminants on physics analyses. The method exploits the time dependence and smaller size of contaminant ion species to define control regions that can be used to quantify potential contamination effects. A simple model is used to illustrate the method and to study its robustness. Furthermore, this method can inform studies of recent LHC and RHIC data and could also be useful for future light-ion programs at the LHC and beyond.

Beam loss↗

DOE OSTI2023

Visualization and Decision Making Design Under Uncertainty

Uncertainty is an important aspect to data understanding. Without awareness of the variability, error, or reliability of a dataset, the ability to make decisions on that data is limited. However, practices around uncertainty visualization remain domain-specific, rooted in convention, and in many instances, absent entirely. Part of the reason for this may be a lack of established guidelines for navigating difficult choices of when uncertainty should be added, how to visualize uncertainty, and how to evaluate its effectiveness. Unsurprisingly, the inclusion of uncertainty into visualizations is a major challenge to visualization. As work concerned with uncertainty visualization grows, it has become clear that simple visual additions of uncertainty information to traditional visualization methods do not appropriately convey the meaning of the uncertainty, pose many perceptual challenges, and, in the worst case, can lead a viewer to a completely wrong understanding of the data. These challenges are the driving motivator for this special issue.

data models↗

DOE OSTI2026

Exploring Sustainability in Scientific Software through Code Quality & Test Coverage Metrics

Context: Scientific open-source software (SciOSS) plays a foundational role in research and engineering, yet its long-term sustainability has often been overlooked and remains a significant concern. Objective: This study investigates the long-term sustainability of SciOSS through code and test quality metrics. Method: We analyze CASS Software Portfolio projects, classifying them by sustainability and comparing their code structure, test coverage, and links between code quality and testing across the dataset. Results: Sustainable projects show higher, more consistent test coverage and clearer code-test correlations, while unsustainable ones show weaker patterns. Overall, test coverage is low in scientific software, and high complexity and coupling reduce testability. Conclusion: In this study, we present a practical, data-driven approach for assessing sustainability in scientific software, offering a foundation for evaluating long-term software health and supporting future efforts in quality assurance and sustainability monitoring.

Md mushfiqur rahman, Sheikh [University of Tenness↗

DOE OSTI2021

Omics-Lethal Human Viruses, Influenza A Experiment ICL103

The purpose of this experiment was to evaluate the human host response to Influenza A wild-type (H5N1) virus and mutant viruses. Sample data was obtained for human lung adenocarcinoma cell line (Calu-3) infected with WT Influenza A/Vietnam/1203/2004 (H5N1, VN1203) and mutant viruses PB2-K627E and NS1-trunc124 for mRNA, miRNA, proteomics, lipidomics, and metabolomics data analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset download each have a direct relationship to a primary sample submission corresponding to a specific Influenza A virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Omics-Lethal Human Viruses, Interferon-Stimulated Response Experiment IFNaHUH001

The purpose of this experiment was to evaluate the human host cellular response to treatment with and without interferon alpha/beta (IFNα/β) treatment. Sample time course data was obtained from human hepatoma carcinoma cells (HUH-7) expressing transcriptional activation protein VP30, stably transfected with the Ebola VP30 gene for enabling replication of VP30-deficient Ebola virus for transcriptome expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics dataset download have a direct relationship to a primary sample submission corresponding to a specific interferon response to viral infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Omics-Lethal Human Viruses, West Nile Experiment WLN002

The purpose of this experiment was to evaluate the host response to wild-type West Nile virus infectious clone (WNV-NY99 clone 382) and mutant WNV-E218A (WNV-NY99 382 E218A 2 nt) virus infection. Sample data was obtained from mouse popliteal draining lymph nodes for mRNA and miRNA expression analysis. See Experiment WLN003 for corresponding omics analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics dataset downloads have a direct relationship to a primary sample submission corresponding to a specific West Nile virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Overcoming the disconnect between energy system and climate modeling

Energy system models underpin decisions by energy system planners and operators. Energy system modeling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modeling has started to form that aims to better integrate energy system models with weather and climate models. In this work, we evaluate the disconnects between the energy system and climate modeling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modeling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision-making.

17 WIND ENERGY↗

DOE OSTI2020

Analysis of the Frequency of Extreme Precipitation Events at Los Alamos National Laboratory

The Environmental Protection and Compliance (EPC) Storm Water Permitting and Compliance Program is currently updating its stormwater compliance standards for the Los Alamos National Laboratory (LANL) Engineering Standards Manual (2015) and is addressing the required current meteorological data needed for storm water applications. Information on the frequency of extreme precipitation events at LANL is needed. In response to this request, LANL Meteorology & Air Quality (MAQ) was tasked to estimate the frequency of extreme precipitation events. An earlier study by Foley (2012) which estimated extreme precipitation rates at LANL for a set of duration periods and average recurrence intervals (return periods) was evaluated. However, it was determined that this study would not be helpful since its technical support documentation was unavailable and the meteorological data and the methodology employed were not well understood. This led to the need to develop a new peer-reviewed methodology. This report documents the development of the new methodology and the application of a temporally representative precipitation dataset from February 1990 through December 2018, monitored at the official LANL meteorological station, Technical Area (TA)-6, to develop the frequency of extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Omics-Lethal Human Viruses, West Nile Experiment WLN003

The purpose of this experiment was to evaluate the host response to wild-type West Nile virus infectious clone (WNV-NY99 clone 382) and mutant WNV-E218A (WNV-NY99 382 E218A 2 nt) virus infection. Sample data was obtained from mouse popliteal draining lymph nodes for proteomics, metabolomics, and lipidomics expression analysis. See Experiment WLN002 for corresponding transcriptome analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, and lipidomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific West Nile virus infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

FEW questions, many answers: using machine learning to assess how students connect food–energy–water (FEW) concepts

There is growing support and interest in postsecondary interdisciplinary environmental education which integrate concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts. This work tests a text classification machine learning model as a tool to assess student systems thinking capabilities using two questions anchored by the Food-Energy-Water (FEW) Nexus phenomena by answering two questions (1) Can machine learning models be used to identify instructor-determined important concepts in student responses? (2) What do college students know about the interconnections between food, energy and water, and how have students assimilated systems thinking into their constructed responses about FEW? Reported here are a broad range of model performances across 26 text classification models associated with two different assessment items, with model accuracy ranging from 0.755 to 0.992. Expert-like responses were infrequent in our dataset compared to responses providing simpler, incomplete explanations of the systems presented in the question. For those students moving from describing individual effects to multiple effects, their reasoning about the mechanism behind the system indicates advanced systems thinking ability. Specifically, students exhibit higher expertise for explaining changing water usage than discussing tradeoffs for such changing usage. This research represents one of the first attempts to assess the links between foundational, discipline-specific concepts and systems thinking ability. These text classification approaches to scoring student FEW Nexus Constructed Responses (CR) indicate how these approaches can be used, in addition to several future research priorities for interdisciplinary, practice-based education research. Development of further complex question items using machine learning would allow evaluation of the relationship between foundational concept understanding and integration of those concepts as well as more nuanced understanding of student comprehension of complex interdisciplinary concepts.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Dimensionless parameters for ballistic performance evaluation of ceramic-faced bicomponent targets against sharp-nosed projectiles

Two dimensionless parameters are proposed to give first-order approximations for the ballistic performance of ceramic-faced bicomponent armor plates impacted by conical-nosed steel projectiles. The current work collates expansive experimental data from Wilkins and Mayseless covering a broad range of material and mechanical properties, target construction, and projectile dimensions, the data is collapsed using the Hugoniot elastic limit of the ceramic strike face as a strength metric. We find that within datasets where experimental conditions are kept constant, a singular cubic curve can be generated, but variations in experimental conditions for the ballistic limit test between datasets can result in differences in the predictive curve coefficients.

36 MATERIALS SCIENCE↗

DOE OSTI2026

VA Community Determinants of Health Data Curation Documentation FY26-Q2

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Community Determinants of Health (EDH) Data project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1 km grid) to another (e.g., U.S. Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, U.S. Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1 km grids. Some economic data may only be available at the ZIP code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., U.S. Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Community Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

99 GENERAL AND MISCELLANEOUS↗