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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

POTATO: A Unified Assessment System for HL-LHC Outer Tracker Module Productions

The CMS detector s silicon tracker will be replaced in preparation for the upcoming High-Luminosity LHC (HL LHC) upgrade. The inner tracker will include a new pixel detector while the outer tracker (OT) will feature 5,592 pixel strip (PS) and 7,608 strip strip (2S) sensor modules with production distributed across 7 sites worldwide, requiring standardized grading and testing. One testing apparatus the burn-in box replicates the temperature cycling conditions that successful modules must withstand. The Phase-II Outer Tracker Analyzer of Test Outputs (POTATO) standardizes module evaluation and grading among testing sites. Given burn-in box test output, POTATO analyzes the results and assigns a letter grade to a module and its sub-components. POTATO's grading process must adapt to evolving standards for module evaluation; hence, we detail work on POTATO's database and grading functionality that will facilitate the unified assessment of Phase-II OT module assembly and testing worldwide. Once completed, POTATO will allow technicians to interact with a database of all graded modules and efficiently assess the global module assembly effort.

Clark, Olivia J. [U. Tennessee, Knoxville]↗
DOE OSTI2021

Automated Classification of Plasma Regions Using 3D Particle Energy Distributions

We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is urn:x-wiley:21699380:media:jgra56742:jgra56742-math-0001%. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

In this work, we propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.

36 MATERIALS SCIENCE↗
DOE OSTI2026

United States Nuclear Power Reactor Used Nuclear Fuel Database and Applications

The Unified Database (UDB) within STANDARDS serves as the foundational data infrastructure for managing the United States' spent nuclear fuel inventory of 315,111 discharged assemblies totaling 91,036 metric tons of heavy metal. The database organizes this complex inventory through over 200 interconnected tables structured into eight primary attribute categories, supporting integrated analyses across storage, transportation, and disposal domains. Data enters the UDB through the GC-859 Nuclear Fuel Data Survey, which transitioned to web-based collection in 2023, improving data quality through real-time validation. The UDB enables automated generation of input files for nuclear safety analyses, reducing preparation time from weeks to hours while maintaining traceability. Applications include national inventory reporting, Certificate of Compliance assessments, and facility optimization. The three-tier distribution model balances accessibility with security requirements for federal agencies, national laboratories, and research organizations. The UDB provides essential data infrastructure as spent fuel management transitions from site-specific to integrated national campaigns.

Stefanovic, Peter↗
DOE OSTI2020

Draft genome of multiple resistance donor plant Sinapis alba: An insight into SSRs, annotations and phylogenetics

Sinapis alba is a wild member of the Brassicaceae family reported to possess genetic resistance against major biotic and abiotic stresses of oilseed brassicas. However, the resistance nature of S. alba was not exploited generously due to the unavailability of usable genome sequences in public databases. Therefore, the present study was conducted to assemble the first draft genome from raw whole genome shotgun sequences with annotation and develop simple sequence repeat markers for molecular genetics and marker-assisted breeding. Results The raw genome sequences had 96x coverage on the Illumina platform with 170 Gbp data. The developed assembly by SOAPdenovo2 has ~459 Mbp genome size covered in 403,423 contigs with an average size of 1138.04 bp. The assembly was BLASTX with Arabidopsis thaliana which showed 32.9% positive hits between both plants. The top hit species distribution analysis showed the highest similarity with A. thaliana. A total of 809,597 GO level annotations were recorded after BLASTX results, and 34,012 sequences were annotated with different enzyme codes grouped under seven classes. The gene prediction tool AUGUSTUS identified 113,107 probable genes with an average size of 684 bp. The biochemical pathway annotation assigned 16,119 potential genes to 152 KEGG maps and 1751 enzyme codes. The development of potential SSRs from the de-novo assembly yielded 70731 unique primer pairs. Out of 159 randomly selected SSR markers for validation, 149 successfully amplified in S. alba. However, 10 SSR markers did not amplify during the validation experiment. Conclusion The annotated genome assembly with a large number of SSRs was developed in the present study. To the best of our knowledge, this is the first report of S. alba genome assembly development, annotation, and SSRs mining to date. The data presented here will be a very important resource for future crop improvement programs, especially for resistant breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Control-Agnostic Beam Instrumentation with Redis at the Core

Redis isn’t a database — it’s our protocol. Fermilab’s RedisAdapter provides a high-performance, control-system-agnostic bridge between digitized beam data and downstream consumers such as ACNET and EPICS. It forms the foundation of three new software components deployed across MicroTCA-based digitizers: GMMDM, a runtime for memory-mapped data movement from Zynq-based platforms; GRAFE, a front end for Redis-to-ACNET presentation; and GREFE, an EPICS IOC front end. Together, these tools enable modular, standardized instrumentation pipelines. Precision timing is handled via White Rabbit PPS distribution, allowing nanosecond-scale synchronization across crates. This architecture, originally prototyped in Booster BPM systems, is now deployed on modern hardware and designed to meet the performance, modularity, and scalability requirements of the PIP-II era.

Steinkamp, Derek [Fermilab] (ORCID:000900027228626↗
DOE OSTI2023

THE POTENTIAL FOR REUSE OF PRODUCED WATER IN THE CRITICAL MINERALS SUPPLY CHAIN IN THE POWDER RIVER BASIN OF WYOMING AND MONTANA

The Powder River Basin (PRB) Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) project is part of the Department of Energy sponsored CORE-CM initiative focused on domestic production of critical minerals and promoting economic development in traditionally fossil fuel producing basins. An important part of this project is regional assessment of waste streams and development of basinal strategies for waste stream reuse within the critical minerals (CM) supply chain. The availability of water for all parts of the CM supply chain is of particular concern in the arid mountain west. In the Wyoming PRB, average annual production of produced water (PW) by the oil and gas industry is more than 16 billion gallons (2015-2022). Through published data and engagement with industry partners, we conducted a preliminary investigation of PW in the PRB to understand volumes, quality, and the potential for use in the CM supply chain. PW was assessed for potential as a direct feedstock and for use in CM processing of a variety of conventional and unconventional feedstocks, including ore from the Bear Lodge Alkaline Complex, coal, and coal byproducts. Preliminary findings suggest that PRB PW contains significantly lower concentrations of rare earth elements (REE) and lithium than would be considered economic at this time. For example, concentrations of REE+Sc measured in PRB PW are <1 ppb (DE-EE0007603), and only seven PRB PW samples reported in the USGS PW database had lithium concentrations ≥50 ppm. However, enrichment of CM in residual brine and sludge from PW evaporation and solidification treatment ponds may yield concentrations of interest over a pond lifetime and should be considered in future studies. Coal bed methane (CBM) PW accounted for 42% of all PW in the Wyoming PRB in 2022. Compared to non-CBM PW, PRB CBM water quality generally meets standards allowing surface discharge and reuse in local industries such as livestock. CBM PW may help to fill water needs within the CM supply chain. Understanding the distribution of CBM PW that is discharged versus reused will help develop basinal reuse strategies.

Jackson, Lily↗
DOE OSTI2023

NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes

Abstract The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100 000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Resilience Assessment Framework For Electric Distribution Systems Performance Under Extreme Conditions

The devastating impact of extreme weather-related events is increasingly evident on power grids, especially on distribution grids. The severity of their potential impact calls for 1) developing a suitable resilience assessment framework to capture the system performance and 2) assessing relevant mitigative strategies to lessen the impact of such events. This paper proposes a framework to identify grid vulnerabilities using the energy-at-risk concept to select, disconnect and isolate grid portions due to a resilience event. The proposed framework mainly consists of two steps; i) processing the utility's available infrastructure, i.e., a network model, possible switching combinations, and outage information for those combinations, as a graph-based database, and ii) implementing a novel optimal switching algorithm leveraging database and grid simulated metrics. These switching actions are generated to implement load curtailment in a rolling manner during anticipated grid scarcity conditions. In this study, a test case is created using two taxonomy feeders and is simulated against an extreme temperature event, e.g., a long, relatively cold, and prolonged freeze peak, thereby creating stress on the grid. It is demonstrated that the proposed framework allows utilities to predict the energy-at-risk during such resilience events and design suitable outage management strategies.

Poudel, Shiva↗
DOE OSTI2022

Data augmentation for disruption prediction via robust surrogate models

The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student t process regression. We apply Student t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Global transpiration data from sap flow measurements: the SAPFLUXNET database

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.

58 GEOSCIENCES↗
DOE OSTI2026

Millimeter-wave Detections of Symbiotic Stars in SPT and ACT Data

We present the results of a joint targeted search of candidate symbiotic stars at millimeter wavelengths using the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). Candidates are selected from the New Online Database of Symbiotic Variables, restricting to objects that are within either the SPT-3G or ACT~DR6 footprint, covering most of the southern hemisphere and up to a declination of $+20^\circ$. Forced photometry on the 828 candidate symbiotic star locations in SPT and ACT data results in 31 unique objects detected with more than a $3σ$ significance using two frequency bands: 18 confirmed and 13 suspected symbiotic stars. We provide the SPT and ACT 95/98, 150, and 220~GHz light curves, along with optical and infrared light curves from 2016--2026, as well as spectral energy distributions, physical parameters from the literature, and brief summaries regarding the nature of each individual object. Using Herschel SPIRE data from 2013, we place upper limits on millimeter flux for CN Cha near the beginning of the optical rise in its 2012/2013 nova, which suggests a strong variability and lag at millimeter wavelengths and results in a rare observance of a Galactic millimeter slow transient. In addition, we provide coadded thumbnails and light curves for the remaining 797 candidate symbiotic stars that did not pass our detection thresholds. Millimeter-wave emission from symbiotic stars is primarily a combination of free-free emission of the ionization region and optically thick blackbody emission of the cooler dust components of the system. When combined with contemporaneous multi-wavelength observations, millimeter-wave observations can be used to test binary models of symbiotic stars and provide insight on the geometry and physical properties of these systems.

Tandoi, C. [Illinois U., Urbana, Astron. Dept.] (O↗
DOE OSTI2022

BASS. XXIX. The Near-infrared View of the Broad-line Region (BLR): The Effects of Obscuration in BLR Characterization

Virial black hole (BH) mass (M BH ) determination directly involves knowing the broad-line region (BLR) clouds' velocity distribution, their distance from the central supermassive BH (R BLR ), and the virial factor (f). Understanding whether biases arise in M BH estimation with increasing obscuration is possible only by studying a large (N > 100) statistical sample of obscuration-unbiased (hard) X-ray-selected active galactic nuclei (AGNs) in the rest-frame near-infrared (0.8–2.5 μm) since it penetrates deeper into the BLR than the optical. We present a detailed analysis of 65 local Burst Alert Telescope (BAT) selected Seyfert galaxies observed with Magellan/FIRE. Adding these to the near-infrared BAT AGN spectroscopic survey database, we study a total of 314 unique near-infrared spectra. While the FWHMs of Hα and near-infrared broad lines (He i, Paβ, Paα) remain unbiased to either BLR extinction or X-ray obscuration, the Hα broad-line luminosity is suppressed when N H ≳ 10 21 cm –2 , systematically underestimating M BH by 0.23–0.46 dex. Near-infrared line luminosities should be preferred to Hα until N H < 10 22 cm –2 , while at higher obscuration a less-biased R BLR proxy should be adopted. We estimate f for Seyfert 1 and 2 using two obscuration-unbiased M BH measurements, i.e., the stellar velocity dispersion and a BH mass prescription based on near-infrared and X-ray, and find that the virial factors do not depend on the redshift or obscuration, but some broad lines show a mild anticorrelation with M BH . Furthermore, our results show the critical impact obscuration can have on BLR characterization and the importance of the near-infrared and X-rays for a less-biased view of the BLR.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Data augmentation for disruption prediction via robust surrogate models

The goal of this work is to generate large statistically representative datasets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student-t process regression. We apply Student-t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via coloring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics, and classic machine learning clustering algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Hydrogen Component Reliability Database (HyCReD)

The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety reliability for hydrogen facilities by integrating risk reduction methodologies and component reliability data taxonomies that support hydrogen infrastructure failure rate analysis.

availability↗
DOE OSTI2025

Investigation of divertor detachment induced through neon seeding and density ramp on HL-3

A new self-consistent 1D scrape-off layer model has been recently developed in BOUT++ framework, named SD1D, which includes equations for various particle species (e.g. main plasma, neutrals and impurities) and couples open databases like ADAS and AMJUEL. It is able to quickly and effectively simulate divertor detachment experiments. In this work, a typical detachment experiment (shot #6270) on HL-3 with neon seeding is simulated using the SD1D code. It is found that the target electron temperature and the target ion saturation current in the simulations are consistent with experimental results measured by Langmuir probes on the target plate. The variation of D α radiation intensity in the divertor is qualitatively similar to the measured D α signal. Following the experimental validations, different upstream densities are set in the simulations to study the impurity distribution under different plasma density conditions. It is found that increasing upstream density can be helpful for the control of the neon radiation front (closer to the target). In this work we also compare two detachment regimes in simulations. Based on the same initial experimental parameters (shot #6270) on HL-3, a scan of upstream density and a scan of neon seeding rate are carried out respectively. It is found that the role of atomic and molecular processes is different in the two detachment regimes. The current density roll-over is ascribed to a drop in the divertor ion source, and the variation of D α radiation intensity via different excitation channels is associated with the relevant collisional reaction sources.

BOUT++↗
DOE OSTI2024

Existing evidence on the effects of climate variability and climate change on ungulates in North America: a systematic map

Abstract Background Climate is an important driver of ungulate life-histories, population dynamics, and migratory behaviors. Climate conditions can directly impact ungulates via changes in the costs of thermoregulation and locomotion, or indirectly, via changes in habitat and forage availability, predation, and species interactions. Many studies have documented the effects of climate variability and climate change on North America’s ungulates, recording impacts to population demographics, physiology, foraging behavior, migratory patterns, and more. However, ungulate responses are not uniform and vary by species and geography. Here, we present a systematic map describing the abundance and distribution of evidence on the effects of climate variability and climate change on native ungulates in North America. Methods We searched for all evidence documenting or projecting how climate variability and climate change affect the 15 ungulate species native to the U.S., Canada, Mexico, and Greenland. We searched Web of Science, Scopus, and the websites of 62 wildlife management agencies to identify relevant academic and grey literature. We screened English-language documents for inclusion at both the title and abstract and full-text levels. Data from all articles that passed full-text review were extracted and coded in a database. We identified knowledge clusters and gaps related to the species, locations, climate variables, and outcome variables measured in the literature. Review findings We identified a total of 674 relevant articles published from 1947 until September 2020. Caribou ( Rangifer tarandus ), elk ( Cervus canadensis ), and white-tailed deer ( Odocoileus virginianus ) were the most frequently studied species. Geographically, more research has been conducted in the western U.S. and western Canada, though a notable concentration of research is also located in the Great Lakes region. Nearly 75% more articles examined the effects of precipitation on ungulates compared to temperature, with variables related to snow being the most commonly measured climate variables. Most studies examined the effects of climate on ungulate population demographics, habitat and forage, and physiology and condition, with far fewer examining the effects on disturbances, migratory behavior, and seasonal range and corridor habitat. Conclusions The effects of climate change, and its interactions with stressors such as land-use change, predation, and disease, is of increasing concern to wildlife managers. With its broad scope, this systematic map can help ungulate managers identify relevant climate impacts and prepare for future changes to the populations they manage. Decisions regarding population control measures, supplemental feeding, translocation, and the application of habitat treatments are just some of the management decisions that can be informed by an improved understanding of climate impacts. This systematic map also identified several gaps in the literature that would benefit from additional research, including climate effects on ungulate migratory patterns, on species that are relatively understudied yet known to be sensitive to changes in climate, such as pronghorn ( Antilocapra americana ) and mountain goats ( Oreamnos americanus ), and on ungulates in the eastern U.S. and Mexico.

Malpeli, Katherine C. (ORCID:000000030780918X)↗
DOE OSTI2022

First-Principles Calculations of the Structural, Electronic, Optical, and Mechanical Properties of 21 Pyrophosphate Crystals

Pyrophosphate crystals have a wide array of applications in industrial and biomedical fields. However, fundamental understanding of their electronic structure, optical, and mechanical properties is still scattered and incomplete. In the present research, we report a comprehensive theoretical investigation of 21 pyrophosphates A 2 M (H 2 P 2 O 7 ) 2 •2H 2 O with either triclinic or orthorhombic crystal structure. The molecule H 2 P 2 O 7 is the dominant molecular unit, whereas A = (K, Rb, NH 4 , Tl), M = (Zn, Cu, Mg, Ni, Co, Mn), and H 2 O stand for the cation elements, transition metals, and the water molecules, respectively. The electronic structure, interatomic bonding, partial charge distribution, optical properties, and mechanical properties are investigated by first-principles calculations based on density functional theory (DFT). Most of these 21 crystals are theoretically investigated for the first time. The calculated results show a complex interplay between A, M, H 2 P 2 O 7 , and H 2 O, resulting in either metallic, half-metallic, or semi-conducting characteristics. The novel concept of total bond order density (TBOD) is used as a single quantum mechanical metric to characterize the internal cohesion of these crystals to correlate with the calculated properties, especially the mechanical properties. This work provides a large database for pyrophosphate crystals and a road map for potential applications of a wider variety of phosphates.

36 MATERIALS SCIENCE↗