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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

FY2021 Progress Report on BISON Metallic Fuel Model Development and V&V Using EBR-II Legacy Data

In this report, the activities and achievements made by Argonne National Laboratory for the Nuclear Energy Advanced Modeling and Simulation (NEAMS) BISON code metallic fuel validation and verification project in FY2021 are summarized. The cladding degradation model based on the FCCI/CCCI wastage calculations has been developed and implemented into BISON. A comprehensive evaluation of the cladding degradation model was performed based on FIPD data of the IFR experiment X447. BISON objects were also developed to enable direct use of time-varying cladding outer surface temperature profile as temperature boundary conditions, which proved to provide more accurate temperature predictions for the metallic fuel pins irradiated in EBR-II. Additionally, a new BISON object was implemented to enable direct comparison between BISON predicted data and FIPD-based post-irradiation examination (PIE) results, which would significantly facilitate BISON metallic fuel verification and validation (V&V) activities. These new BISON-FIPD integration features were used in the establishment of a low-burnup fuel swelling evaluation framework as demonstration. The framework was successfully used to evaluate current fuel swelling models based on the IFR experiment X423.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A Weakly Supervised Machine Learning Procedure for Magnet Quench Diagnostics

Voltage taps remain the standard and reliable diagnostic tool for detecting quenches in superconducting magnets. However, they identify a quench only at the time of voltage rise and do not provide information on earlier physical precursors. In this work, we investigate whether acoustic emission data can reveal precursor activity that occurs before conventional voltage detection using machine learning techniques. We introduce an event selection method and a weakly supervised machine learning procedure to learn data-driven criteria for identifying potential acoustic precursors to quenches. Two Convolutional Neural Network (CNN) architectures are trained: one on acoustic sensor events from our selection procedure and one on the Fast Fourier Transforms (FFTs) of these events. Both networks are trained iteratively using confidence-weighted loss functions to associate certain subsets of training data with a precursor label. We evaluate the performance of these models by examining the time distribution of events classified as potential precursors relative to the quench onset. Results indicate that the proposed approach can possibly distinguish acoustic emission events occurring closer to the quench from earlier acoustic activity during ramping, suggesting the potential for flagging quench precursors in acoustic data.

Khan, Maira [Fermilab] (ORCID:0009000891602387)↗
NASA NTRS2000

A natural basis for efficient brain-actuated control

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.

NASA Discipline Space Human Factors↗
NASA NTRS2018

Birth of a Bipolar Active Region in a Small Coronal Hole

We report on an the emergence of an anemone active region in a very small coronal hole (about 120'' across), beginning at approximately 19:00 UT on March 3, 2016. The emergence was initially observed by an amateur astronomer (RW) in an H-alpha movie from the Global Oscillation Network Group (GONG); it attracted the attention of the observer because there was no active region at the site of the H-alpha brightening. To examine the region in detail, we use data from the Solar Dynamics Observatory (SDO), provided by the Atmospheric Imaging Assembly (AIA) in wavelengths 193 Å, 211 Å, 304 Å, and 94 Å, and with the Helioseismic and Magnetic Imager (HMI). Data analysis and calibration activities such as scaling, rotation so that north is up, and removal of solar rotation are accomplished with SunPy. The emergence seen in HMI data begins with the appearance of a bipole close to the center of the coronal hole, followed by the appearance and growth of a coronal anemone in AIA-EUV images; as the emergence proceeds, the anemone fills the coronal hole in AIA wavelengths. The interchange reconnection of the emerging closed field with ambient open field builds the anemone and shifts the coronal hole from one side of the anemone to the other. Eventually, a sunspot forms in one foot of the emerging field. To date, studies of observations with SDO of anemone emergence in on-disk coronal holes are rare. This bipolar flux emergence episode fits well with the standard picture for the production of coronal-anemone regions.

Adams, Mitzi↗
NASA NTRS1990

The Variation of Solar Fe 14 and Fe 10 Flux over 1.5 Solar Activity Cycles

A new source of data on the solar output, namely limb flux from the one- and two-million degree corona is presented. This parameter is derived from data obtained at the National Solar Observatory at Sacramento Peak with the 40 cm coronagraph of the John W. Evans Solar Facility and the Emission Line Coronal Photometer. The limb flux is defined to be the latitude-averaged intensity in millionths of the brightness of disk center from an annulus of width 1.1 minutes centered at a height of 0.15 solar constant above the limb of emission from lines at 6374A (Fe X) or 5303A (Fe XIV). Fe XIV data have been obtained since 1973 and Fe X since 1984. Examination of the Fe XIV data shows that there is ambiguity in the definition of the last two solar activity minima, which can affect the determination of cycle rise times and lengths. There is an indication that a constant minimum or basal corona may exist at solar minimum. Cycle 22 has had a much faster onset than Cycle 21 and has now overtaken Cycle 21. The rise characteristics of the two cycles were very similar up until Jul. to Aug. 1989, at which time a long-term maximum occurred in Fe X and Fe XIV, which could possibly be the solar maximum. Another maximum is developing at the current time. Cycle 21 was characterized in Fe XIV by at least 4 major thrusts or bursts of activity, each lasting on the order of a year and all having similar maximum limb fluxes which indicates that coronal energy output is sustained over periods in which the sunspot number declines significantly. Dramatic increases in the limb fluxes occur from minimum to maximum, ranging from factors of 14 to 21 in the two lines. Two different techniques to predict the epoch of solar maximum have been applied to the Fe XIV data, resulting in estimates of April 1989 (plus or minus 1 mo) and May 1990 (plus or minus 2 mos).

Altrock, Richard C.↗
DOE OSTI2019

Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators

The use of machine learning (ML) models toward development of validated structure–property relationships for two fundamental properties of activated inorganic scintillators for high energy radiation detection, namely the light yield (LY) and the decay time constant, is explored. The ML models are built on easily accessible proxies of materials—interchangeably referred to as features, descriptors or fingerprints—that are carefully selected on the basis of a physical understanding of the scintillation mechanism. Our study indicates that the developed physics-based ML models employing kernel ridge regression (KRR) and AdaBoost algorithm applied on top of a decision tree-based regression are able to “learn” the underlying design rules in a multi-dimensional feature space and thereby enable reasonably accurate predictions of the two target properties on unseen compounds (i.e., on a held-out test set). For instance, within a set of twenty-five cerium- or europium-doped scintillator materials, our analysis reveals a strong correlation between the average ionic part of the dielectric constant and the LY, irrespective of the specific chemistry of the compounds, indicating that the average ionic part of the dielectric constant is a particularly relevant descriptor toward prediction of the LY. Our results also demonstrate that, despite the use of small training datasets, the developed models are able to quickly distinguish high performing chemistries from those with relatively poor performance and therefore can play a crucial role in screening of new compounds with an attractive combination of targeted properties. Finally, the present study provides necessary motivation for future efforts involving ML models with relatively large training datasets, vast feature space explorations, and experimental design in search of promising novel scintillator chemistries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

IRDFF-II: A New Neutron Metrology Library

High quality nuclear data is the most fundamental underpinning for all neutron metrology applications. This paper describes the release of version II of the International Reactor Dosimetry and Fusion File (IRDFF-II) that contains a consistent set of nuclear data for fission and fusion neutron metrology applications up to 60 MeV neutron energy. The library is intended to support: a) applications in research reactors; b) safety and regulatory applications in the nuclear power generation in commercial fission reactors; and c) material damage studies in support of the research and development of advanced fusion concepts. The paper describes the contents of the library, documents the thorough verification process used in its preparation, and provides an extensive set of validation data gathered from a wide range of neutron benchmark fields. The new IRDFF-II library includes 119 metrology reactions, four cover material reactions to support self-shielding corrections, five metrology metrics used by the dosimetry community, and cumulative fission products yields for seven fission products in three different neutron energy regions. In support of characterizing the measurement of the residual nuclei from the dosimetry reactions and the fission product decay modes, the present document lists the recommended decay data, particle emission energies and probabilities for 68 activation products. It also includes neutron spectral characterization data for 29 neutron benchmark fields for the validation of the library contents. Additional six reference fields were assessed (four from plutonium critical assemblies, two measured fields for thermal-neutron induced fission on 233U and 239Pu targets) but not used for validation due to systematic discrepancies in C/E reaction rate values or lack of reaction-rate experimental data. Another ten analytical functions are included that can be useful for calculating average cross sections, average energy, thermal spectrum average cross sections and resonance integrals. The IRDFF-II library and comprehensive documentation is available online at www-nds.iaea.org/IRDFF/. Evaluated cross sections can be compared with experimental data and other evaluations at www-nds.iaea.org/exfor/endf.htm. The new library is expected to become the international reference in neutron metrology for multiple applications.

IAEA IRDFF↗
DOE OSTI2017

Nanoquinacrine caused apoptosis in oral cancer stem cells by disrupting the interaction between GLI1 and β catenin through activation of GSK3β

Presences of cancer stem cells (CSCs) in a bulk of cancer cells are responsible for tumor relapse, metastasis and drug resistance in oral cancer. Due to high drug efflux, DNA repair and self-renewable capacity of CSCs, the conventional chemotherapeutic agents are unable to kill the CSCs. CSCs utilizes Hedgehog (HH-GLI), WNT-β catenin signalling for its growth and development. GSK3β negatively regulates both the pathways in CSCs. Here, we have shown that a nano-formulated bioactive small molecule inhibitor Quinacrine (NQC) caused apoptosis in oral cancer stem cells (OCSCs; isolated from different oral cancer cells and oral cancer patient derived primary cells) by down regulating WNT-β catenin and HH-GLI components through activation of GSK3β. NQC activates GSK3β in transcriptional and translational level and reduces β catenin and GLI1 as well as downstream target gene of both the pathways Cyclin D1, C-Myc. The transcription factor activity of both the pathways was also reduced by NQC treatment. GSK3β, β catenin and GLI1 interacts with each other and NQC disrupts the co-localization and interaction between β catenin and GLI1 in OCSCs in a dose dependent manner through activation of GSK3β. Thus, data suggest NQC caused OCSCs death by disrupting the crosstalk between β catenin and GLI1 by activation of GSK3β. - Graphical abstract: Schematic representation of inhibition of HH-GLI and WNT-β catenin cross talk by NQC through GSK3β. Diagram showing HH-GLI and WNT-β catenin pathway, where GLI1 and β catenin and other components were co-localized in a complex and co-ordinately regulate the downstream target genes. NQC activates GSK3β, which further phosphorylates and activates the β catenin and GLI1. Activated β catenin and GLI1 were degraded by proteosomal degradation pathway and unable to translocate into nucleus as a result growth of cells inhibited. - Highlights: • Nano-formulated quinacrine (NQC) activates GSK3β in oral cancer stem cells (OCSCs) in vitro and ex vivo. • NQC induces apoptosis in OCSCs by inhibiting WNT-β catenin and HH-GLI cascade in vitro and ex vivo. • NQC disrupts the interaction and co-localization between GLI1 and β catenin. • NQC-mediated disruption of the crosstalk between GLI1 and β catenin is GSK3β dependent.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195

Chronic active multiple sclerosis (MS) lesions have a rim of activated microglia/macrophages (m/M) leading to ongoing tissue damage, and thus represent a potential treatment target. Activation of this innate immune response in MS has been visualized and quantified using PET imaging with [ 11C]-(R)-PK11195 (PK). Accurate identification of m/M activation in chronic MS lesions requires the sensitivity to detect lower levels of activity within a small tissue volume. We assessed the ability of kinetic modeling of PK PET data to detect m/M activity in different central nervous system (CNS) tissue regions of varying sizes and in chronic MS lesions. Ten patients with MS underwent a single brain MRI and two PK PET scans 2 hours apart. Volume of interest (VOI) masks were generated for the white matter (WM), cortical gray matter (CGM), and thalamus (TH). The distribution volume (VT) was calculated with the Logan graphical method (LGM-VT) utilizing an image-derived input function (IDIF). The binding potential (BPND) was calculated with the reference Logan graphical method (RLGM) utilizing a supervised clustering algorithm (SuperPK) to determine the non-specific binding region. Masks of varying volume were created in the CNS to assess the impact of region size on the various metrics among high and low uptake regions. Chronic MS lesions were also evaluated and individual lesion masks were generated. The highest PK uptake occurred the TH and lowest within the WM, as demonstrated by the mean time activity curves. In the TH, both reference and IDIF based methods resulted in estimates that did not significantly depend on VOI size. However, in the WM, the test-retest reliability of BPND was significantly lower in the smallest VOI, compared to the estimates of LGM-VT. These observations were consistent for all chronic MS lesions examined. In this study, we demonstrate that BPND and LGM-VT are both reliable for quantifying m/M activation in regions of high uptake, however with blood input function LGM-VT is preferred to assess longitudinal m/M activation in regions of relatively low uptake, such as chronic MS lesions.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

NASA GLOBE Clouds: Matching your sky observations to multiple satellite data

Have you ever wondered how your view of the sky compares to satellite data? The NASA GLOBE Clouds team at NASA Langley Research Center in Hampton, VA, matches ground-cloud observations taken by citizen scientists through the GLOBE Program to geostationary satellites as well as to Terra, Aqua, and CALIPSO satellites. The result is the NASA GLOBE Clouds satellite match table sent in a NASA personalized email summarizing both ground-cloud and satellite observations. The table contains ground-cloud and satellite values of total cloud cover and cloud cover by height (low, mid, high), cloud opacity and cloud phase. The satellite match table also includes images from the respective satellites centered on the latitude and longitude of the ground-cloud report entered through the GLOBE Program. Participants can receive satellite flyover notifications for their location through the GLOBE Observer app or through the satellite flyover schedule. This presentation will engage the audience through a live demonstration showing how to access GLOBE Clouds data visualization tools and view corresponding satellite imagery and data. We will also discuss best practices suggested by classroom teachers and undergraduate professors in incorporating active learning and satellite data analysis.

Marilé Colón Robles↗
NASA NTRS2000

Role of muscle pulleys in producing eye position-dependence in the angular vestibuloocular reflex: a model-based study

It is well established that the head and eye velocity axes do not always align during compensatory vestibular slow phases. It has been shown that the eye velocity axis systematically tilts away from the head velocity axis in a manner that is dependent on eye-in-head position. The mechanisms responsible for producing these axis tilts are unclear. In this model-based study, we aimed to determine whether muscle pulleys could be involved in bringing about these phenomena. The model presented incorporates semicircular canals, central vestibular pathways, and an ocular motor plant with pulleys. The pulleys were modeled so that they brought about a rotation of the torque axes of the extraocular muscles that was a fraction of the angle of eye deviation from primary position. The degree to which the pulleys rotated the torque axes was altered by means of a pulley coefficient. Model input was head velocity and initial eye position data from passive and active yaw head impulses with fixation at 0 degrees, 20 degrees up and 20 degrees down, obtained from a previous experiment. The optimal pulley coefficient required to fit the data was determined by calculating the mean square error between data and model predictions of torsional eye velocity. For active head impulses, the optimal pulley coefficient varied considerably between subjects. The median optimal pulley coefficient was found to be 0.5, the pulley coefficient required for producing saccades that perfectly obey Listing's law when using a two-dimensional saccadic pulse signal. The model predicted the direction of the axis tilts observed in response to passive head impulses from 50 ms after onset. During passive head impulses, the median optimal pulley coefficient was found to be 0.21, when roll gain was fixed at 0.7. The model did not accurately predict the alignment of the eye and head velocity axes that was observed early in the response to passive head impulses. We found that this alignment could be well predicted if the roll gain of the angular vestibuloocular reflex was modified during the initial period of the response, while pulley coefficient was maintained at 0.5. Hence a roll gain modification allows stabilization of the retinal image without requiring a change in the pulley effect. Our results therefore indicate that the eye position-dependent velocity axis tilts could arise due to the effects of the pulleys and that a roll gain modification in the central vestibular structures may be responsible for countering the pulley effect.

NASA Discipline Neuroscience↗
DOE OSTI2025

A Perspective on Data and Privacy for AI in Healthcare [Industrial and Governmental Activities]

As large language models continue to push the bounds of AI model size, they are also being trained on unprecedented volumes of data. While individual hospitals are estimated to produce petabytes of data per year, only a small fraction is currently being used for developing AI models. Additionally, with such data resources available, healthcare is well-positioned to benefit from the current trends in AI. Moreover, the inherently multi-modal and longitudinal nature of clinical data – from omics to imaging to unstructured notes – provides a fertile ground for the development and application of cutting-edge architectures like foundation models.

Gounley, John [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2019

Analysis of Multimodal Wearable Sensor Data to Characterize Social Groups and Influence in Organizations

This document reports the results of a 4-month study analyzing data provided to Los Alamos National Laboratory (LANL) by the performer teams for the Multimodal Objective Sensing to Assess Individuals with Context (MOSAIC) project. The MOSAIC performer teams monitored hundreds of volunteers from several organizations in their daily activities, using wearable sensors to capture data about those activities. The project also collected “ground truth” data on individual behavior and psychological state using survey instruments throughout the study. The LANL MOSAIC analysis team approached this data with the goal of capturing and analyzing social dynamics within organizations. We focused on two key analysis objectives: identifying and characterizing groups as the evolve over time, and identifying and modeling patterns of social influence.

99 GENERAL AND MISCELLANEOUS↗