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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Removing Spectral Diagnostics of Galactic and Stellar X-Ray Emission from Charged Exchange Recombination

Our research uses the electron beam ion trap (EBIT) at the Lawrence Livermore National Laboratory to study X-ray emission from the charge exchange (CX) of highly charged ions with neutral gases. The resulting data help to fill a void in existing experimental and theoretical understanding of this atomic physics process, and are needed to explain all or part of the observed X-ray emission from the soft X-ray background, stellar winds, the Galactic Center and Galactic Ridge, supernova ejecta, and photoionized nebulae. Appreciation of the astrophysical relevance of our work continues to grow with the publication of roughly a dozen papers in the past four years describing Chandra and XMM observations of geocoronal and heliospheric CX emission, the temporal variation of such emission and correlation with X-ray emission enhancements observed by ROSAT, the theoretical spatial distribution of that emission, and CX emission around other stars. A similar number of papers were also published during that time describing CX emission from planets and comets. We expect that the launch of ASTRSE2, with its second-generation XRS microcalo- (with 6-eV resolution), will reveal even more clearly the contributions of CX to astrophysical emission. In our EBIT work we collected CX spectra from such ions as H-like and He-like Ne, Ar, and Fe. Our early measurements were made with a high-purity Ge detector, but during the second year we began operation of the first-generation XRS microcalorimeter (a twin of the XRS on ASTRO-E) and greatly improved the resolution of our measurements from roughly 150 eV (FWHM) with the Ge detectors to 10 eV with the XRS. We found that saturation of the XRS counting apparatus, which we described in our proposal as a potential concern, is not a problem for studying CX. During the course of our research, we expanded the number of injection gases permitted by the LLNL safety team, purchased and eventually operated an atomic H source, and clearly demonstrated the feasibility of our longer-range plan. For example, we successfully injected He into EBIT (not a small feat because of the difficulty of maintaining a good vacuum with He and avoiding electrical breakdown) to collect a H-like oxygen CX spectrum. The highest energy CX spectrum recorded with the XRS to date is that of the Ar K-shell emission. These measurements provided the first observation of the relative intensity ratios of resolved He-like singlet and triplet n=2->1 lines. We also carried out measurements of He-like Ne as a function of collision energy (i.e., ion temperature). Significant differences in the resulting x-ray spectra were noted. In all cases, the intensity of high-n H-like Lyman lines is significantly higher than current theoretical CX models predict.

Wargelin, Brad↗
NASA NTRS1986

The honeymoon effect in job performance - Temporal increases in the predictive power of achievement motivation

Correlations between a job performance criterion and personality measures reflecting achievement motivation and an interpersonal orientation were examined at three points in time after completion of job training for a sample of airline reservations agents. Although correlations between the personality predictors and performance were small and nonsignificant for the 3-month period after beginning the job, by the end of six and eight months a number of significant relationships had emerged. Implications for the utility of personality measures in selection and performance prediction are discussed.

Helmreich, Robert L.↗
DOE OSTI2025

A Novel Thermodynamical Predictor of Tropical High‐Cloud Area Coverage: Estimated Anvil‐Outflow Stability

The stability at the lapse-rate tropopause (LRT) was previously defined as the upper-tropospheric stability (UTS and SUT) but might underestimate the stability control on high-level clouds, since there is little direct connection between convective processes and the thermal stratification at the LRT. Here, a novel estimated anvil-outflow stability (EAS) based on the minimum stability in the upper troposphere is proposed. At the Manus site, the results show that small and large values of the LRT-based UTS and SUT both correspond to strong divergence and frequent occurrence of high-level ice clouds. In contrast, smaller EAS corresponds to stronger convective outflows to produce likely more high-level ice clouds, with a strong negative correlation. In tropics, EAS better explains the geographic distribution of high-level cloud coverage (HCC) and its temporal variations than UTS and SUT. With a strong linear correlation, EAS is likely a simple useful predictor of HCC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Natural marine cloud brightening in the Southern Ocean

Abstract. The number of cloud droplets per unit volume (Nd) is a fundamentally important property of marine boundary layer (MBL) liquid clouds that, at constant liquid water path, exerts considerable controls on albedo. Past work has shown that regional Nd has a direct correlation to marine primary productivity (PP) because of the role of seasonally varying, biogenically derived precursor gases in modulating secondary aerosol properties. These linkages are thought to be observable over the high-latitude oceans, where strong seasonal variability in aerosol and meteorology covary in mostly pristine environments. Here, we examine Nd variability derived from 5 years of MODIS Level 2-derived cloud properties in a broad region of the summer eastern Southern Ocean and adjacent marginal seas. We demonstrate latitudinal, longitudinal and temporal gradients in Nd that are strongly correlated with the passage of air masses over high-PP waters that are mostly concentrated along the Antarctic Shelf poleward of 60∘ S. We find that the albedo of MBL clouds in the latitudes south of 60∘ S is significantly higher than similar liquid water path (LWP) clouds north of this latitude.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Three-dimensional full-field velocity measurements in shock compression experiments using stereo digital image correlation

Shock compression plate impact experiments conventionally rely on point-wise velocimetry measurements based on laser-based interferometric techniques. This study presents an experimental methodology to measure the free surface full-field particle velocity in shock compression experiments using high-speed imaging and three-dimensional (3D) digital image correlation (DIC). The experimental setup has a temporal resolution of 100 ns with a spatial resolution varying from 90 to 200 μm/pixel. Experiments were conducted under three different plate impact configurations to measure spatially resolved free surface velocity and validate the experimental technique. First, a normal impact experiment was conducted on polycarbonate to measure the macroscopic full-field normal free surface velocity. Second, an isentropic compression experiment on Y-cut quartz–tungsten carbide assembly is performed to measure the particle velocity for experiments involving ramp compression waves. To explore the capability of the technique in multiaxial loading conditions, a pressure shear plate impact experiment was conducted to measure both the normal and transverse free surface velocities under combined normal and shear loading. The velocities measured in the experiments using digital image correlation are validated against previous data obtained from laser interferometry. Numerical simulations were also performed using established material models to compare and validate the experimental velocity profiles for these different impact configurations. The novel ability of the employed experimental setup to measure full-field free surface velocities with high spatial resolutions in shock compression experiments is demonstrated for the first time in this work.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula

Symbiotic associations between bacteria and leguminous plants lead to the formation of root nodules that fix nitrogen needed for sustainable agricultural systems. Symbiosis triggers extensive genome and transcriptome remodeling in the plant, yet an integrated understanding of the extent of chromatin changes and transcriptional networks that functionally regulate gene expression associated with symbiosis remains poorly understood. In particular, analyses of early temporal events driving this symbiosis have only captured correlative relationships between regulators and targets at mRNA level. Here, we characterize changes in transcriptome and chromatin accessibility in the model legume Medicago truncatula, in response to rhizobial signals that trigger the formation of root nodules. We profiled the temporal chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) dynamics of M. truncatula roots treated with bacterial small molecules called lipo-chitooligosaccharides that trigger host symbiotic pathways of nodule development. Using a novel approach, dynamic regulatory module networks, we integrated ATAC-seq and RNA-seq time courses to predict cis-regulatory elements and transcription factors that most significantly contribute to transcriptomic changes associated with symbiosis. Regulators involved in auxin (IAA4-5, SHY2), ethylene (EIN3, ERF1), and abscisic acid (ABI5) hormone response, as well as histone and DNA methylation (IBM1), emerged among those most predictive of transcriptome dynamics. RNAi-based knockdown of EIN3 and ERF1 reduced nodule number in M. truncatula validating the role of these predicted regulators in symbiosis between legumes and rhizobia. Our transcriptomic and chromatin accessibility datasets provide a valuable resource to understand the gene regulatory programs controlling the early stages of the dynamic process of symbiosis. The regulators identified provide potential targets for future experimental validation, and the engineering of nodulation in species is unable to establish that symbiosis naturally.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1972

Temporal evolution of velocity structures in the solar wind

Generally poor correlations were obtained of solar wind flow speed measurements at one point in the ecliptic plane with measurements at the same point 28 days (one solar rotation) earlier or with measurements at other points in the ecliptic plane separated by 50 deg or more in solar longitude. This is evidence that either the flow speed is a very sensitive function of solar latitude or that temporal processes typically alter the speed of the wind emanating from particular solar regions on a time scale of about 4 days. From a measure of the persistence of the flow speed at the orbit of the earth, it appears that the temporal explanation is more likely to be the correct one.

Gosling, J. T.↗
NASA NTRS2001

Seasonal variations of snow depth on Mars

Using topography collected over one martian year from the Mars Orbiter Laser Altimeter on the Mars Global Surveyor (MGS) spacecraft, we have measured temporal changes in the elevation of the martian surface that correlate with the seasonal cycle of carbon dioxide exchange between the surface and atmosphere. The greatest elevation change (1.5 to 2 meters) occurs at high latitudes ( above 80 degrees ), whereas the bulk of the mass exchange occurs at lower latitudes (below 75 degrees N and below 73 degrees S). An unexpected period of sublimation was observed during northern hemisphere autumn, coincident with dust storms in the southern hemisphere. Analysis of MGS Doppler tracking residuals revealed temporal variations in the flattening of Mars that correlate with elevation changes. The combined changes in gravity and elevation constrain the average density of seasonally deposited carbon dioxide to be 910 +/- 230 kilograms per cubic meter, which is considerably denser than terrestrial snow.

Carbon Dioxide↗
DOE OSTI2025

Vortical interactions in turbulent thermoacoustic systems

This study examines the dynamics of vortical interactions and their implications for mitigating thermoacoustic instability in a turbulent combustor. The regions of intense vortical interactions are identified as vortical communities in the network space of weighted directed vortical networks constructed from two-dimensional experimental velocity data. One can expect vortical interactions in the combustor to be strongest near the moment of vortex shedding, as the shed vortices gradually weaken due to dissipation while convecting downstream. However, we show that, during the state of thermoacoustic instability, there is a non-trivial consistent phase lag of approximately 52° between the shedding of the coherent structures from the backward-facing step and the time instant when the vortical interactions attain their local maximum value. We explain this phase lag by investigating the correlation between acoustic pressure fluctuations, spatio-temporal dynamics of coherent structures and vortical interactions in the reaction field of the combustor. We also show the aperiodic variation of vortical interactions during the states of combustion noise and aperiodic epochs of intermittency. Furthermore, the spatio-temporal evolution of pairs of vortical communities with the maximum inter-community interactions provides insight into explaining the critical regions detected in the reaction field during the states of intermittency and thermoacoustic instability, also identified in previous studies. As a result, we further show that the most efficient suppression of thermoacoustic instability via air microjet injection is achieved when steady air jets are introduced to disrupt the maximum inter-community interactions present during the state of thermoacoustic instability.

Sahay, Ankit [Indian Institute of Technology Madra↗
DOE OSTI2023

Tunable momentum pair creation of spin excitations in dipolar bilayers

Here we study the temporal growth and spatial propagation of quantum correlations in a two-dimensional bilayer realizing a spin-1/2 quantum XXZ model with couplings mediated by long-range and anisotropic dipolar interactions. Starting with an initial state consisting of spins with opposite magnetization in each of the layers, we predict a dynamic instability that results, at short times, in the creation of correlated pairs of excitations at specific momenta at exponentially fast rates and entanglement between spatially separated modes. The momentum structure of the created pairs can be controlled via the dipolar orientation, the layer separation, or the dipolar couplings. The predicted behavior remains observable at very low filling fractions, making it accessible in state-of-the-art experiments with Rydberg atoms, magnetic atoms, and polar molecule arrays.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Anticipating Optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning

Radiofrequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed to provide insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon numbers and proximity.

FSO availability↗
DOE OSTI2025

Enhancing Short-Range Weather Forecasts through Temporal Variation Encoding: A Multiperiod Embedding Approach

Machine learning (ML) techniques have emerged as promising approaches to improve regional weather forecast accuracy and reliability through data-driven methods. We propose a novel ML-based weather forecasting model, the Multiperiod Embed Net (MPENet). A key distinguishing feature of MPENet is its explicit utilization of the inherent cyclic nature in weather dynamics, unlike the autoregressive strategies commonly used in other ML weather forecasting approaches. Critical cyclic structures are identified via Fourier analyses of dynamic time series. Cyclicity in the convolutional representation is achieved by transforming one-dimensional time series of meteorological variables into two-dimensional tensors based on identified periods. This approach enables the model to leverage intrinsic weather patterns, enhancing regional forecast performance. To demonstrate the effectiveness of MPENet, we conduct a comparative analysis with Nvidia’s FourCastNet. Both models are trained on High-Resolution Rapid Refresh (HRRR) data from 2015 to 2022, over a 192 km × 192 km region in Tennessee. The comparisons are performed locally at two specific locations known to have different weather dynamics due to orographic effects: Crossville, on the relatively flat Cumberland Plateau with fewer topographic airflow disruptions, and Oak Ridge, in the ridge-and-valley region, where airflow is heavily influenced by surrounding valleys and mountains. Our results indicate that FourCastNet achieves strong accuracy at very short lead times, while MPENet maintains competitive skill and shows advantages in capturing temporal evolution over longer periods. Cross-correlation analyses of MPENet and FourCastNet predictions with the HRRR data suggest that encoding critical cyclicity into the network architecture leads to improvements in the forecasting skill.

Artificial intelligence↗
DOE OSTI2022

Variable Daily Autocorrelation Functions of High-Frequency Seismic Data on Mars

Abstract High-frequency seismic data on Mars are dominated by wind-generated lander vibrations, which are radiated partially to the subsurface. Autocorrelation functions (ACFs) of seismic data on Mars filtered between 1 and 5 Hz show clear phases at ∼1.3, ∼2.6, and ∼3.9 s. Daily temporal changes of their arrival times (dt/t) correlate well with the daily changes of ground temperature, with ∼5% daily variation and ∼50 min apparent phase delay. The following two mechanisms could explain the observations: (1) the interference of two predominant spectral peaks at ∼3.3 and ∼4.1 Hz, assumed to be both lander resonance modes, generate the apparent arrivals in the ACFs; (2) the interference of the lander vibration and its reflection from an interface ∼200 m below the lander generate the 3.3 Hz spectral peak and ∼1.3 s arrival in the ACFs. The driving mechanism of the resolved dt/t that most likely explains the ∼50 min delay is thermoelastic strain at a near-surface layer, affecting the lander–ground coupling and subsurface structures. The two outlined mechanisms suggest, respectively, up to ∼10% changes in ground stiffness at 1–5 Hz and ∼15% velocity changes in the top ∼20 m layer. These are upper bound values considering also other possible contributions. The presented methodology and results contribute to analysis of ACFs with limited data and the understanding of subsurface materials on Mars.

Geochemistry & Geophysics↗
DOE OSTI2023

A spatially regularized detector for emergent/re-emergent disease outbreaks

Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Study on Application of Distributed Network of Sensors with List Mode for NMAC Literature Review

Nuclear material accounting and control (NMAC) for nuclear security detects, deters, and resolves questions related to unauthorized removal (i.e. theft) or misuse of nuclear material. NMAC also serves as a key insider threat mitigation measure and aids in recovery of nuclear material that is missing. Effective nuclear security depends on NMAC for timely and accurate information about nuclear material types, quantities, and locations. Bulk nuclear material processing facilities, however, present unique challenges for effective NMAC due to the presence of large quantities of material in-process and the accumulation of residual material holdup within process equipment. These holdup accumulations can obscure accurate physical inventory taking and complicate efforts to resolve NMAC irregularities at the facility level. Bulk material monitoring systems often rely on material balance calculations and indirect measurement techniques, which may mask protracted theft of smaller amounts of nuclear material. These monitoring limitations have generated increased interest in continuous monitoring technologies, including distributed non-destructive assay (NDA) sensor networks capable of providing real-time or near-real-time measurement of material movement and accumulation within bulk processing environments. Recent advancements in distributed networks of NDA radiation detectors and sensing technologies provide an opportunity to address these limitations. Although such distributed sensor networks have been implemented in select facilities for IAEA Safeguards applications, their potential for supporting NMAC functions specifically tailored to nuclear security objectives remains largely unexplored. Furthermore, emerging list-mode data acquisition technologies have reached high technology readiness levels, enabling time-correlated detection of nuclear events across multiple temporal scales. These capabilities provide enhanced opportunities for accurate holdup measurement, continuous process monitoring, and improved detection of material theft or misuse over time. The increasing global expansion of civil nuclear power and development of related bulk material processing facilities, including those supporting high-assay low-enriched uranium (HALEU) and other advanced reactor fuel fabrication, further increases the need for advanced measurement and monitoring strategies for NMAC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2007

TRMM-TMI Satellite Observed Soil Moisture and Vegetation Density (1998-2005) Show Strong Connection with El Nino in Eastern Australia

Spatiotemporal patterns in soil moisture and vegetation water content across mainland Australia were investigated from 1998 through 2005, using TRMMITMI passive microwave observations. The Empirical Orthogonal Function technique was used to extract dominant spatial and temporal patterns in retrieved estimates of moisture content for the top 1-cm of soil (theta) and vegetation moisture content (via optical depth tau). The dominant temporal theta and tau patterns were strongly correlated to El Nino/Southern Oscillation (ENSO) in spring (3 = 0.90), and to a progressively lesser extent autumn, summer and winter. The Indian Ocean Dipole (IOD) index also explained part of the variation in spring 8 and z. Cluster analysis suggested that the regions most affected by ENS0 are mainly located in eastern Australia. The results suggest that the drought conditions experienced in eastern Australia since 2000 an clearly expressed in these satellite observations have a strong connection with ENSO patterns.

Liu, Yi↗
NASA NTRS2009

DSN Data Visualization Suite

The DSN Data Visualization Suite is a set of computer programs and reusable Application Programming Interfaces (APIs) that assist in the visualization and analysis of Deep Space Network (DSN) spacecraft-tracking data, which can include predicted and actual values of downlink frequencies, uplink frequencies, and antenna-pointing angles in various formats that can include tables of values and polynomial coefficients. The data can also include lists of antenna-pointing events, lists of antenna- limit events, and schedules of tracking activities. To date, analysis and correlation of these intricately related data before and after tracking have been difficult and time-consuming. The DSN Data Visualization Suite enables operators to quickly diagnose tracking-data problems before, during, and after tracking. The Suite provides interpolation on demand and plotting of DSN tracking data, correlation of all data on a given temporal point, and display of data with color coding configurable by users. The suite thereby enables rapid analysis of the data prior to transmission of the data to DSN control centers. At the control centers, the same suite enables operators to validate the data before committing the data to DSN subsystems. This software is also Web-enabled to afford its capabilities to international space agencies.

Bui, Bach X.↗