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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Yucatan Peninsula Energy Assessment: Pathways for a Clean and Sustainable Power System

This report begins with an overview of the status of the power sector in Mexico's Yucatán peninsula, including key challenges, followed by policy and program solutions that pave the way for a cleaner, more sustainable power sector in the Yucatan. This work is intended to provide information to the states of the Yucatán peninsula - Yucatán, Campeche, and Quintana Roo - for prioritization and future technical assistance from the 21st Century Power Partnership (21CPP) program, in Mexico including data gathering, capacity building, modelling and analysis, program and policy setting to help the states and the region overall best utilize its resources to meet its electricity needs while implementing energy efficiency measures towards a more sustainable, clean, reliable system with lower prices. This report is also available in Spanish: https://www.nrel.gov/docs/fy21osti/81142.pdf.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Energy Assessment of the Yucatan Peninsula: Pathways to a Clean and Sustainable Energy System; Evaluación Energética de la Península de Yucatán: Vías para un Sistema Energético Limpio y Sustentable

Quintana Roo - for prioritization and future technical assistance from the 21st Century Power Partnership (21CPP) program, in Mexico including data gathering, capacity building, modelling and analysis, program and policy setting to help the states and the region overall best utilize its resources to meet its electricity needs while implementing energy efficiency measures towards a more sustainable, clean, reliable system with lower prices.; Este trabajo tiene como objetivo brindar información a los estados de la península de Yucatán - Yucatán, Campeche y Quintana Roo - para priorizar y dar asistencia técnica futura del programa 21st Century Power Partnership (21CPP), en México, incluyendo recolección de datos, desarrollo de capacidades, modelación y análisis, y el establecimiento de programas y políticas para ayudar a los estados y la región en general a utilizar mejor sus recursos, para satisfacer sus necesidades de energía eléctrica, implementando también medidas de eficiencia energética para obtener un sistema más sustentable, limpio y confiable con precios más bajos. This report is also available in English: https://www.nrel.gov/docs/fy21osti/79680.pdf.

21CPP↗
DOE OSTI2021

Analysis Program Annual Progress Report (FY2020)

During fiscal year 2020 (FY 2020), the U.S. Department of Energy Vehicle Technologies Office (VTO) funded analysis projects supportive of VTO’s goals to pursue early stage research in vehicle and mobility system technologies to reduce petroleum dependence, increase energy reliability and security, improve transportation affordability, and promote economic growth. VTO analysis projects result in a foundation of data, analytical models, and applied analyses that provide insights into critical transportation energy problems and assist in research investment prioritization and portfolio planning. This document presents a brief overview of VTO analysis efforts and progress for projects funded in FY 2020. Each of the progress reports includes project objectives, approach, and highlights of the technical results that were accomplished during the fiscal year.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Technical Resilience Navigator

The Technical Resilience Navigator (TRN) is a systematic approach to identifying vulnerabilities with energy and water systems, and prioritizing solutions that reduce risk. The ultimate outcome of the TRN is a set of actionable resilience solutions that address the site's most important gaps in resilience and enhance the ability to maintain mission continuity. The TRN is designed to step users through this planning process, providing a framework to: assign roles and responsibilities; collect and document information and data; document key inputs and outputs for each of the TRN modules (Site Level Planning, Baseline Development, Risk Assessment, Solution Development, and Solution Prioritization); document prioritized list of resilience solutions; and track progress through the entire process. Currently "software as a service" at the listed website.

Rotondo, Julia↗
DOE OSTI2023

Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model’s historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However, for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Alternative Analysis and Prioritization of Department of Energy “TBD” Materials with No Identified Disposition Pathway

The Department of Energy (DOE) complex manages a significant inventory of excess nuclear materials for which disposition pathways have not been identified, commonly referred to as "To Be Determined" (TBD) items. The Disposition Pathways Program, initiated in FY2018, provides a standardized framework for identifying viable disposition pathways for these materials. In 2023, the program undertook a comprehensive review and systematic analysis of the remaining TBD material groups, updating the assessment from the 2020 TBD Study using the 2022 fiscal year-end Nuclear Material Inventory Assessment (NMIA) as the primary data source. This effort aimed to utilize quantitative analysis to down-select from a wide range of potential disposition options and prioritize the remaining pathways to facilitate informed, risk-based decision-making for future programmatic funding and execution. This paper details the alternative analysis methodology used for screening and prioritization, highlighting the key criteria, ranking process, and resultant recommendations. The study successfully narrowed fifty-two potential disposition options down to nineteen, providing a focused path forward for addressing a longstanding challenge within the DOE complex.

Ramsey, Catherine [Savannah River National Laborat↗
DOE OSTI2023

Prevalence of Self-Reported Bothersome Tinnitus in U.S. Army Soldiers From January 1, 2015, Through September 30, 2019

Purpose: Tinnitus is a symptom of auditory dysfunction or injury and can be a precursor to permanent hearing loss. Tinnitus can interfere with communication, sleep, concentration, and mood; when this occurs, it is referred to as bothersome tinnitus. Annual hearing surveillance in the U.S. Army includes screening for bothersome tinnitus. Estimating the prevalence of self-reported bothersome tinnitus can facilitate prioritization of prevention and education efforts. The objective of this study was to examine Army hearing conservation data to estimate the prevalence of self-reported bothersome tinnitus as a function of age, hearing, sex, service component, and pay grade. Method: The study employed a cross-sectional, retrospective design. Records from 1,485,059 U.S. Army Soldiers retrieved from the Defense Occupational and Environmental Health Readiness System–Hearing Conservation were analyzed. Descriptive statistics and multinomial logistic regression analysis were used to estimate the prevalence of bothersome tinnitus and its associations with Soldiers' demographic characteristics. Results: The estimated prevalence of self-reported bothersome tinnitus was 17.1% between January 1, 2015, and September 30, 2019; 13.6% of the Soldiers reported “bothered a little” and 3.5% reported “bothered a lot.” Proportionally, the prevalence of self-reported bothersome tinnitus was higher for males, older Soldiers, and Reserve Component Soldiers. For every 1-year increase in age, the odds of self-reporting “bothered a little” tinnitus relative to “bothered not at all tinnitus” would be expected to increase by 2.2% (2.1%, 2.3%), and the odds of self-reporting “bothered a lot” tinnitus relative to “bothered not at all” tinnitus would be expected to increase by 3.6% (3.5%, 3.7%). Conclusions: The self-reported prevalence of bothersome tinnitus in the U.S. Army (17.1%) is substantially higher than that of the general population, where prevalence of bothersome tinnitus is estimated at 6.6%. Examination of bothersome tinnitus among Soldiers is an important step toward optimizing prevention, education, and intervention efforts.

Audiology & Speech-Language Pathology↗
DOE OSTI2024

Linking Threat Agents to Targeted Organizations: A Pipeline for Enhanced Cybersecurity Risk Metrics

In this study, we present a methodology leveraging Large Language Models (LLMs) to transform Cybersecurity Threat Intelligence (CTI) narratives into actionable insights for individual organizations. Our approach automates the extraction of machine-readable adversary SKRAM (Skills, Knowledge, Resources, Authorities, and Motivation) attributes from open-source reports, extending LLM utility beyond typical interactions. This innovation enables precise, automated assessments of cybersecurity risks posed by various adversaries. Using a chain-of-thought and multi-shot prompting strategy, our methodology advances the automation of cybersecurity feature extraction for new machine-learning models that predict the risk of adversary targeting. This approach is refined using a substantial dataset of over 150 analyst-validated threat reports and synthetic organizational data from 900 companies. Here, by bootstrapping the training data with a rule-based heuristic over synthetic data, we have developed a high-accuracy machine-learning model that allows entities to dynamically prioritize threats and defensive actions.

Cyber Threat Intelligence↗
DOE OSTI2022

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

42 ENGINEERING↗
DOE OSTI2020

FY2019 Analysis Program Annual Progress Report

The VTO Analysis Program supports mission-critical technology, economic, and interdisciplinary analyses to assist in prioritizing VTO technology investments and to inform research portfolio planning. These efforts provide essential vehicle and market data, modeling and simulation, and integrated and applied analyses, using the unique capabilities, analytical tools, and expertise resident in the national laboratory system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Analysis Program 2022 Annual Progress Report

The VTO Analysis Program supports mission-critical technological, economic, and interdisciplinary analyses to assist in prioritizing VTO technology investments and to inform research portfolio planning. These efforts provide essential vehicle and market data, modeling and simulation, and integrated and applied analyses, using the unique capabilities, analytical tools, and expertise resident in the DOE’s national laboratory system. VTO Analysis projects also demonstrate additional capabilities and expertise provided by research partnerships that may include academia, the private sector, and non-profit organizations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Analysis Program (2023 Annual Progress Report)

This document summarizes the progress of VTO Analysis projects supported during the fiscal year 2023. The VTO Analysis Program supports mission-critical technological, economic, and interdisciplinary analyses to assist in prioritizing VTO technology investments and to inform research portfolio planning. These efforts provide essential vehicle and market data, modeling and simulation, and integrated and applied analyses, using the unique capabilities, analytical tools, and expertise resident in the DOE’s national laboratory system. VTO Analysis projects also demonstrate additional capabilities and expertise provided by research partnerships that may include academia, the private sector, and non-profit organizations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Hydrological connectivity: a review and emerging strategies for integrating measurement, modeling, and management

This review synthesizes methods for measuring, modeling, and managing hydrologic connectivity, offering pathways to improve practices and address environmental challenges (e.g., climate change) and sustainability. As a key driver of water movement and nutrient cycling, hydrologic connectivity influences flood mitigation, water quality regulation, and biodiversity conservation. However, traditional field-based methods (e.g., dye tracing), indirect measurements (e.g., runoff analysis), and remote sensing techniques (e.g., InSAR) often struggle to capture the complexity of catchment-scale interactions. Similarly, modeling approaches—including process-based and percolation theory-based models, graph theory, and entropy-based metrics—face limitations in fully representing these interconnected processes. Both modeling and measurement techniques are constrained by inadequate spatial and temporal coverage, high data demands, computational complexity, and difficulties in representing subsurface connectivity. Subsequently, we critique current management practices that prioritize isolated variables (e.g., streamflow, sediment transport) over system-wide strategies and emphasize the need for adaptive, connectivity-based approaches in water resource planning and restoration. Moving forward, we highlight the importance of interdisciplinary collaboration, technological innovations (e.g., AI-driven modeling, real-time monitoring), and integrated frameworks to improve connectivity measurement, modeling, and adaptive management to restore fragmented hydrologic networks. This integrated approach sets the stage for transformative water resource management, fostering proactive policy development and stakeholder engagement.

Dwivedi, Dipankar↗
DOE OSTI2025

Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke

Exposure modeling is critical in environmental epidemiology and human health but may face challenges (e.g., skewed data, unequal error, context-insensitive validation, and computational demands). Modeling decisions reflect the intended use of the models and the values that modelers prioritize. We aimed to provide a conceptual framework and machine learning (ML) modeling protocols that address these issues. With 500m-gridded hourly PM 2.5 and O 3 levels in Illinois before, during, and after the 2023 Canadian wildfire season as a motivating example, we conducted modeling experiments to evaluate modeling methods, guided by three domains we propose based on theories of science: 1) Data Diversity, leveraging open and citizen science data to enhance inclusivity, parsimony, and representativeness; 2) Equitable Accuracy, ensuring fairly distributed uncertainties across subpopulations; and 3) Sustainable Modeling, balancing accuracy with reducing computational demands to promote accessibility for under-resourced researchers. Here, we found that ML with publicly available data can achieve high accuracy. Depending on methods, performance may vary substantially, even with identical input data. Large but skewed data may reduce performance. Misuse of cross-validation protocols can underestimate prediction error; although we observed R 2 s of ∼98 %, the modeled estimates varied significantly, indicating the need for careful model validation. By using new modeling protocols including representativeness-considered training and validation data and a new loss function, we achieved high agreement between estimates and ground-based measurements (e.g., R 2 = ∼90 % for PM 2.5 ; ∼80 % for O 3 ), equally distributed errors across sociodemographic strata and urban–rural divides, and reduction in computation time—from several weeks or months to a few days.

Exposure assessment↗
DOE OSTI2023

The VST ATLAS quasar survey I: Catalogue of photometrically selected quasar candidates

We present the VST ATLAS Quasar Survey, consisting of ~1229 000 quasar (QSO) candidates with 16 < g < 22.5 over ~4700 deg 2 . The catalogue is based on VST ATLAS+NEOWISE imaging surveys and aims to reach a QSO sky density of 130 deg –2 for $z$ < 2.2 and ~30 deg –2 for $z$ > 2.2. To guide our selection, we use X-ray/UV/optical/MIR data in the extended William Herschel Deep Field (WHDF) where we find a g < 22.5 broad-line QSO density of 269 ± 67 deg –2 , roughly consistent with the expected ~196 deg –2 . We find that ~25 per cent of our QSOs are morphologically classed as optically extended. Overall, we find that in these deep data, MIR, UV, and X-ray selections are ~70–90 per cent complete while X-ray suffers less contamination than MIR and UV. MIR is however more sensitive than X-ray or UV to $z$ > 2.2 QSOs at g < 22.5 and the S X (0.5 – 10 keV) > 1 X 10 -14 ergs cm –2 s –1 limit of eROSITA. We adjust the selection criteria from our previous 2QDES pilot survey and prioritize VST ATLAS candidates that show both UV and MIR excess, also selecting candidates initially classified as extended. We test our selections using data from DESI (which will be released in DR1) and 2dF to estimate the efficiency and completeness, and we use ANNz2 to determine photometric redshifts. Applying over the ~4700 deg 2 ATLAS area gives us ~ 917 000 $z$ < 2.2 QSO candidates of which 472 000 are likely to be $z$ < 2.2 QSOs, implying a sky density of ~100 deg –2 , which our WHDF analysis suggests will rise to at least 130 deg –2 when eROSITA X-ray candidates are included. At $z$ > 2.2, we find ~310() 000 candidates, of which 169 000 are likely to be QSOs for a sky density of ~36 deg –2 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Leveraging 13C-Labeling to Assign Molecular Formulas to Unknown Yeast Metabolites

Mass spectrometry analyses have identified tens of thousands of unknown small molecule-associated peaks in different biological specimens. Notably, even the simplest and best studied organisms like Escherichia coli and Saccharomyces cerevisiae yield thousands of unknown peaks. A key question is how many of these reflect actual novel endogenous metabolites. To explore this, Mahieu and Patti used complete 13 C -labeling in E. coli to credential peaks as biological. This reduced the number of unknowns by more than 90%. Here, we carry out similar uniform 13 C-labeling in the Baker’s yeast S. cerevisiae and two less-studied bioenergy-relevant yeasts Rhodotorula toruloides (lipid producer) and Issatchenkia orientalis (organic acid producer). Identification of unknown metabolite peaks and their molecular formulas is facilitated through software tailored for 13 C labeling data and resulting knowledge of carbon atom count. A classification model evaluates the plausibility of each candidate formula, with peaks lacking plausible candidate formulas unlikely to reflect metabolite molecular ions. This approach prioritizes about one hundred candidate abundant unknown metabolites with logical molecular formulas. Most of these are species-specific rather than conserved across yeasts, and more are found in the nonmodel yeasts than S. cerevisiae. Thus, 13 C-labeling data on unknown metabolites highlights the potential for discovering new metabolites and pathways in nonmodel yeasts.

Carbon↗
DOE OSTI2025

Integrated multi-omic characterizations of the synapse reveal RNA processing factors and ubiquitin ligases associated with neurodevelopmental disorders

The molecular composition of the excitatory synapse is incompletely defined due to its dynamic nature across developmental stages and neuronal populations. To address this gap, we apply proteomic mass spectrometry to characterize the synapse in multiple biological models including the fetal human brain and hiPSC-derived neurons. To prioritize the identified proteins, we develop an orthogonal multi-omic screen of genomic, transcriptomic, interactomic, and structural data. This data-driven framework identifies proteins with key molecular features intrinsic to the synapse, including characteristic patterns of biophysical interactions and cross-tissue expression. The multi-omic analysis captures synaptic proteins across developmental stages and experimental systems, including 493 synaptic candidates supported by proteomics. We further investigate three such proteins that are associated with neurodevelopmental disorders – the CUL3 E3 ubiquitin ligase, the DDX3X and YBX1 nucleic-acid binding proteins – by mapping their networks of physically interacting synapse proteins or transcripts. Our study demonstrates the potential of an integrated multi-omic approach to systematically and more comprehensively resolve the synaptic architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Global characterization of biosynthetic gene clusters in non-model eukaryotes using domain architectures

Abstract The majority of pharmaceuticals are derived from natural products, bioactive compounds naturally synthesized by organisms to provide evolutionary advantages. Although the rich evolutionary history of eukaryotic algal species implicates a high potential for natural product-based drug discovery, it remains largely untouched. This study investigates 2762 putative biosynthetic gene clusters (BGCs) from 212 eukaryotic algal genomes. To analyze a vast set of structurally diverse BGCs, we employed comparative analysis based on the vectorization of biosynthetic domains, referred to as biosynthetic domain architecture (BDA). By characterizing core biosynthetic machineries through BDA, we identified key BDAs of modular BGCs in diverse eukaryotes and introduced 16 candidate modular BGCs with similar BDAs to previously validated BGCs. This study provides a global characterization of eukaryotic algal BGCs, offering an alternative to laborious manual curation for BGC prioritization.

59 BASIC BIOLOGICAL SCIENCES↗