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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Variability Timescale and Spectral Index of Sgr A* in the Near Infrared: Approximate Bayesian Computation Analysis of the Variability of the Closest Supermassive Black Hole

Sagittarius A* (Sgr A* ) is the variable radio, near-infrared (NIR), and X-ray source associated with accretion onto the Galactic center black hole. We present an analysis of the most comprehensive NIR variability data set of Sgr A* to date: eight 24 hr epochs of continuous monitoring of Sgr A* at 4.5 μm with the IRAC instrument on the Spitzer Space Telescope, 93 epochs of 2.18 μm data from Naos Conica at the Very Large Telescope, and 30 epochs of 2.12 μm data from the NIRC2 camera at the Keck Observatory, in total 94,929 measurements. A new approximate Bayesian computation method for fitting the first-order structure function extracts information beyond current fast Fourier transformation (FFT) methods of power spectral density (PSD) estimation. With a combined fit of the data of all three observatories, the characteristic coherence timescale of Sgr A* is b 243 57 82 t = - + minutes (90% credible interval). The PSD has no detectable features on timescales down to 8.5 minutes (95% credible level), which is the ISCO orbital frequency for a dimensionless spin parameter a = 0.92. One light curve measured simultaneously at 2.12 and 4.5 μm during a low flux-density phase gave a spectral index αs = 1.6 ± 0.1 (F µ n s n -a ). This value implies that the Sgr A* NIR color becomes bluer during higher flux-density phases. The probability densities of flux densities of the combined data sets are best fit by log-normal distributions. Based on these distributions, the Sgr A* spectral energy distribution is consistent with synchrotron radiation from a nonthermal electron population from below 20 GHz through the NIR.

accretion↗
DOE OSTI2025

Converting sWeights to probabilities with density ratios

The use of machine learning approaches continues to have many benefits in experimental nuclear and particle physics. One common issue is generating training data which is sufficiently realistic to give reliable results. Here we advocate using real experimental data as the source of training data and demonstrate how one might subtract background contributions through the use of probabilistic weights which can be readily applied to training data. The sPlot formalism is a common tool used to isolate distributions from different sources. However, the negative sWeights produced by the sPlot technique can cause training problems and poor predictive power. This article demonstrates how density ratio estimation can be applied to convert sWeights to event probabilities, which we call drWeights. The drWeights can then be applied to produce the distributions of interest and are consistent with direct use of the sWeights. This article will also show how decision trees are particularly well suited to convert sWeights, with the benefit of fast prediction rates and adaptability to aspects of experimental data such as the data sample size and proportions of different event sources. We also show that a density ratio product approach in which the initial drWeights are reweighted by an additional converter gives substantially better results.

Glazier, Derek I. [Univ. of Glasgow, Scotland (Uni↗
NASA NTRS2001

Tentative Identification of Interstellar CS by UV Absorption in the C-X Transition

Current chemical models of translucent clouds imply that sulfur is depleted with respect to its average abundance in diffuse clouds as determined by Morton (1975 ApJ,197, 85) by factors exceeding 10. Existing gigahertz emission observations yield sizable uncertainties in the column densities to background stars, and attempts to measure column densities of CS in the lines-of-sight to various stars through the absorption in the A-X (0,0) electronic transition near 257.6 nm have been unsuccessful. CS dissociates primarily through discrete absorptions into predissociating states. In analogy with CO the strongest of these is the C-X band found near 140.0 nm. We report the tentative identification of the C-X band of CS in the interstellar spectra of HD 24398 (zeta Per), HD 148184 (chi Oph), and HD 154368. The estimated column densities will be compared to the predictions of prevailing chemical models.

Smith, Andrew↗
NASA NTRS2012

Infrared and X-Ray Spectroscopy of the Kes 75 Supernova Shell Characterizing the Dust and Gas Properties

We present deep Chandra observations and Spitzer Space Telescope infrared (IR) spectroscopy of the shell in the composite supernova remnant (SNR) Kes 75 (G29.7-0.3). The remnant is composed of a central pulsar wind nebula and a bright partial shell in the south that is visible at radio, IR, and X-ray wavelengths. The X-ray emission can be modeled by either a single thermal component with a temperature of approx 1.5 keV, or with two thermal components with temperatures of 1.5 and 0.2 keY. Previous studies suggest that the hot component may originate from reverse-shocked SN ejecta. However, our new analysis shows no definitive evidence for enhanced abundances of Si, S, Ar, Mg, and Fe, as expected from supernova (SN) ejecta, or for the IR spectral signatures characteristic of confirmed SN condensed dust, thus favoring a circumstellar or interstellar origin for the X-ray and IR emission. The X-ray and IR emission in the shell are spatially correlated, suggesting that the dust particles are collisionally heated by the X-ray emitting gas. The IR spectrum of the shell is dominated by continuum emission from dust with little, or no line emission. Modeling the IR spectrum shows that the dust is heated to a temperature of approx 140 K by a relatively dense, hot plasma, that also gives rise to the hot X-ray emission component. The density inferred from the IR emission is significantly higher than the density inferred from the X-ray models, suggesting a low filling factor for this X-ray emitting gas. The total mass of the warm dust component is at least 1.3 x 10(exp -2) Solar Mass, assuming no significant dust destruction has occurred in the shell. The IR data also reveal the presence of an additional plasma component with a cooler temperature, consistent with the 0.2 keV gas component. Our IR analysis therefore provides an independent verification of the cooler component of the X-ray emission. The complementary analyses of the X-ray and IR emission provide quantitative estimates of density and filling factors of the clumpy medium swept up by the SNR.

Temim, Tea↗
NASA NTRS2009

Infrared and X-Ray Spectroscopy of the KES 75 Supernova Remnant Shell: Characterizing the Dust and Gas Properties

We present deep Chandra observations and Spitzer Space Telescope infrared (IR) spectroscopy of the shell in the composite supernova remnant (SNR) Kes 75 (G29.7-0.3). The remnant is composed of a central pulsar wind nebula and a bright partial shell in the south that is visible at radio, IR, and X-ray wavelengths. The X-ray emission can be modeled by either a single thermal component with a temperature of ~ 1.5 keV, or with two thermal components with temperatures of 1.5 and 0.2 keV. Previous studies suggest that the hot component may originate from reverse-shocked SN ejecta. However, our new analysis shows no definitive evidence for enhanced abundances of Si, S, Ar, Mg, and Fe, as expected from supernova (SN) ejecta, or for the IR spectral signatures characteristic of confirmed SN condensed dust, thus favoring a circumstellar or interstellar origin for the X-ray and IR emission. The X-ray and ill emission in the shell are spatially correlated, suggesting that the dust particles are collisionally heated by the X-ray emitting gas. The IR spectrum of the shell is dominated by continuum emission from dust with little, or no line emission. Modeling the IR spectrum shows that the dust is heated to a temperature of ~ 140 K by a relatively dense, hot plasma, that also gives rise to the hot X-ray emission component. The density inferred from the IR emission is significantly higher than the density inferred from the X-ray models, suggesting a low filling factor for this X-ray emitting gas. The total mass of the warm dust component is at least 1.3 x 10(exp -2) solar mass, assuming no significant dust destruction has occurred in the shell. The IR data also reveal the presence of an additional plasma component with a cooler temperature, consistent with the 0.2 keV gas component. Our IR analysis therefore provides an independent verification of the cooler component of the X-ray emission. The complementary analyses of the X-ray and IR emission provide quantitative estimates of density and filling factors of the clumpy medium swept up by the SNR.

Temim, Tea↗
NASA NTRS2011

Infrared and X-Ray Spectroscopy of the Kes 75 Supernova Remnant Shell: Characterizing the Dust and Gas Properties

We present deep Chandra observations and Spitzer Space Telescope infrared (IR) spectroscopy of the shell in the composite supernova remnant (SNR) Kes 75 (G29.7-0.3). The remnant is composed of a central pulsar wind nebula and a bright partial shell in the south that is visible at radio, IR, and X-ray wavelengths. The X-ray emission can be modeled by either a single thermal component with a temperature of approximately 1.5 keY, or with two thermal components with temperatures of 1.5 and 0.2 keY. Previous studies suggest that the hot component may originate from reverse-shocked supernova (SN) ejecta. However, our new analysis shows no definitive evidence for enhanced abundances of Si, S, Ar, Mg, and Fe, as expected from SN ejecta, or for the IR spectral signatures characteristic of confirmed SN condensed dust, thus favoring a circumstellar or interstellar origin for the X-ray and IR emission. The X-ray and IR emission in the shell are spatially correlated, suggesting that the dust particles are collisionally heated by the X-ray emitting gas. The IR spectrum of the shell is dominated by continuum emission from dust with little, or no line emission. Modeling the IR spectrum shows that the dust is heated to a temperature of approximately 140 K by a relatively dense, hot plasma that also gives rise to the hot X-my emission component. The density inferred from the IR emission is significantly higher than the density inferred from the X-ray models, suggesting a low filling factor for this X-my emitting gas. The total mass of the warm dust component is at least 1.3 x 10(exp -2) x solar mass, assuming no significant dust destruction has occurred in the shell. The IR data also reveal the presence of an additional plasma component with a cooler temperature, consistent with the 0.2 keV gas component. Our IR analysis therefore provides an independent verification of the cooler component of the X-ray emission. The complementary analyses of the X-ray and IR emission provide quantitative estimates of density and filling factors of the clumpy medium swept up by the SNR.

Temim, Tea↗
NASA NTRS1976

On the flux and the energy spectrum of interstellar ions in the solar system

The flux density of ions created by ionization of interstellar neutral particles in the solar system and picked up by the solar wind is calculated as a function of the neutral particles. For atomic hydrogen the flux density is estimated to exceed 10,000/sq cm/sec over the distance range from a few to nearly 100 AU. The velocity space distribution of the interstellar ions is calculated under the assumption of no significant energy diffusion but with inclusion of adiabatic effects as well as a possible strong pitch angle diffusion. The energy spectrum is highly nonthermal and much broader than that of the solar wind ions; interstellar protons are easily distinguishable from solar wind protons by their location in velocity space. If charge exchange is an important contributor to the ionization of hydrogen, the observed local intensity of interstellar protons should exhibit time variations correlated with the density changes of the solar wind stream structure.

Vasyliunas, V. M.↗
NASA NTRS2012

Multiple Asteroid Systems: Dimensions and Thermal Properties from Spitzer Space Telescope and Ground-based Observations

We collected mid-IR spectra from 5.2 to 38 microns using the Spitzer Space Telescope Infrared Spectrograph of 28 asteroids representative of all established types of binary groups. Photometric light curves were also obtained for 14 of them during the Spitzer observations to provide the context of the observations and reliable estimates of their absolute magnitudes. The extracted mid-IR spectra were analyzed using a modified standard thermal model (STM) and a thermophysical model (TPM) that takes into account the shape and geometry of the large primary at the time of the Spitzer observation. We derived a reliable estimate of the size, albedo, and beaming factor for each of these asteroids, representing three main taxonomic groups: C, S, and X. For large (volume-equivalent system diameter Deq > 130 km) binary asteroids, the TPM analysis indicates a low thermal inertia (Lambda < or = approx.100 J/1/2 s/K/sq m2) and their emissivity spectra display strong mineral features, implying that they are covered with a thick layer of thermally insulating regolith. The smaller (surface-equivalent system diameter Deff < 17 km) asteroids also show some emission lines of minerals, but they are significantly weaker, consistent with regoliths with coarser grains, than those of the large binary asteroids. The average bulk densities of these multiple asteroids vary from 0.7-1.7 g/cu cm (P-, C-type) to approx. 2 g/cu cm (S-type). The highest density is estimated for the M-type (22) Kalliope (3.2 +/- 0.9 g/cu cm). The spectral energy distributions (SEDs) and emissivity spectra, made available as a supplement document, could help to constrain the surface compositions of these asteroids.

Marchis, F.↗
NASA NTRSDate not supplied

Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain-Corrected Satellite Sea Surface Salinity Using the GMOA S2s Forecast System

The ENSO phenomenon has a significant global socio-economic impact and has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help improve the initialization of the thermocline, while satellite SST aids in constraining surface heat-fluxes, leading to improved coupled system sub-seasonal to seasonal forecasts. However, few studies have focused on improving the near-surface density and mixing through satellite sea surface salinity (SSS) assimilation. The few ocean models that assimilate satellite SSS, bias correct to normalize towards the near-surface Argo data for expediency. This assumption is likely inadequate in rainy regions, where buoyant water forms a fresh surface lens. In previous work, we showed that adjusting SSS to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 improves the near-surface density and mixed layer depth, leading to deeper thermocline and improved NINO3.4 SST forecasts. We now utilize the Soil Moisture and Ocean Salinity rain-corrected (SMOS_RC) SSS, available in SMOS-CATDS products, to represent Sb more accurately at the first model layer (e.g., 5 m). Rather than a diffusivity model as RIM, SMOS_RC uses a statistical correction dependent on Integrated Multi-satellitE Retrievals for GPM (IMERG) rain rates, established on observed SMOS SSS decreases related to Sb in the presence of rain (Supply et al., 2020). For all experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is, and a separate reanalysis assimilates SMOS_RC. We assess the impact on near-surface and subsurface dynamics by validating against observations and explore how SSS assimilation (SMOS vs SMOS_RC) impacts ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (S2S-v3, Molod et al. 2020). We show that improved estimates of density and near-surface mixing led to more accurate coupled air/sea interaction and better ENSO forecasts. The increased SSS, resulting from the removal of the instantaneous rain effect, modifies the ocean state by enhancing mixing and deepening the thermocline.

Veronica Ruiz Xomchuk↗
DOE OSTI2024

Minimum entropy filtering for a single output non-Gaussian stochastic system using state transformation

This paper presents a novel filter design for the single-output stochastic non-linear systems subjected to non-Gaussian noises and the proposed assumptions. Based on a state transformation, the unmeasurable states of the systems can be estimated where non-linear terms in the systems have been eliminated. It has been shown that the estimation error is linearly dynamical regarding to the presented vector-valued filter gain which can be optimised by minimising the entropy-based performance criterion. In addition, the convergence of the presented algorithm is analysed in mean-square sense and a numerical example is given to verify the effectiveness of the presented filtering algorithm. Meanwhile, the extended Kalman filter, unscented particle filter and minimum entropy filter are given for the comparisons of the filtering performance. Following the presented framework, some extensions of the presented filtering algorithm are discussed to indicate the flexibility of the filter design. The contribution of this paper can be summarised as establishing a novel minimum entropy filtering framework which consists of model transformation, entropy optimisation and convergence analysis.

42 ENGINEERING↗
DOE OSTI2020

Estimation of distributions via multilevel Monte Carlo with stratified sampling

We design and implement a novel algorithm for computing a multilevel Monte Carlo (MLMC) estimator of the joint cumulative distribution function (CDF) of a vector-valued quantity of interest in problems with random input parameters and initial conditions. Our approach combines MLMC with stratified sampling of the input sample space by replacing standard Monte Carlo at each level with stratified Monte Carlo initialized with proportionally allocated samples. We show that the resulting stratified MLMC (sMLMC) algorithm is more efficient than its standard MLMC counterpart due to the additional variance reduction provided by the stratification of the random parameter's domain, especially at the coarsest levels. Additional computational cost savings are obtained by smoothing the indicator function with a Gaussian kernel, which proves to be an efficient and robust alternative to recently developed polynomial-based techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Probabilistic learning and updating of a digital twin for composite material systems

This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.

42 ENGINEERING↗
DOE OSTI2023

Uncovering acoustic signatures of pore formation in laser powder bed fusion

Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$\varvec{k}$$ k -means clustering (NMF $$\varvec{k}$$ k ) to learn the underlying spectral patterns associated with pore formation. NMF $$\varvec{k}$$ k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$\varvec{k}$$ k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Multi-Region SEIR Model Incorporating Inter-County Mobility and Time-Dependent Transmission Dynamics: Application to COVID-19 Disease Outbreak Data in North Carolina.

Classical infectious disease compartmental models typically do not incorporate spatial heterogeneity or mobility. We develop a multi-region susceptible-exposed-infected-recovered (SEIR) model in which disease dynamics are coupled via inter-region mobility and the transmission rate is both region and time dependent. We calibrate the model using rolling averages of daily COVID-19 data in all 100 North Carolina counties. Mobility parameters are prescribed using daily inter-county commuter data. The number of transmission rate parameters is substantially reduced by hypothesizing that the dynamics correlate with county-level population density. Parameter estimation is carried out using several objective functions with error terms at different scales. An additive combination of least squares error at the county-level and the state-level, along with a quadratic transmission rate polynomial, yields the lowest overall error at both spatial scales. The calibrated model is used to simulate regional effects of perturbing disease transmission rates in adjacent counties and to illustrate effects of the state’s mobility infrastructure on disease dynamics and spread for a new disease outbreak.

COVID-19 modeling↗
DOE OSTI2022

Thermal diffusivity recovery and defect annealing kinetics of self-ion implanted tungsten probed by insitu transient grating spectroscopy

Tungsten is a promising candidate material for plasma-facing armour components in future fusion reactors. A key concern is irradiation-induced degradation of its normally excellent thermal transport properties. In this comprehensive study, thermal diffusivity degradation in ion-implanted tungsten and its evolution from room temperature (RT) to 1073 K is considered. Five samples were exposed to 20 MeV self-ions at RT to achieve damage levels ranging from 3.2 x 10 -4 to 3.2 displacements per atom (dpa). Transient grating spectroscopy with in-situ heating was then used to study thermal diffusivity evolution as a function of temperature. Using a kinetic theory model, an equivalent point defect density is estimated from the measured thermal diffusivity. The results showed a prominent recovery of thermal diffusivity between 450 K and 650 K, which coincides with the onset of mono-vacancy mobility. After 1073 K annealing samples with initial damage of 3.2 x 10 -3 dpa or less recover close to the pristine value of thermal diffusivity. For doses of 3.2 x 10 -2 dpa or higher, on the other hand, a residual reduction in thermal diffusivity remains even after 1073 K annealing. Transmission electron microscopy reveals that this is associated with extended, irradiation-induced dislocation structures that are retained after annealing. A sensitivity analysis shows that thermal diffusivity provides an efficient tool for the assessing total defect content in tungsten up to 1000 K.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On the measurement of shape: With applications to lunar regolith

With the renewed commitment from NASA and other commercial entities for a presence on the Moon, the importance of understanding the characteristics of lunar regolith and how to utilize it have become the target of increasing scrutiny. Much of what is known about lunar regolith was collected during and immediately after the Apollo program, however, analytical techniques and instrumentation have advanced in leaps and bounds in the subsequent decades. Specifically, dynamic image analysis systems have advanced to the point that millions of particles can have morphological characteristics automatically determined in relatively short time frames. Particle morphological data was collected on several lunar samples and a pair of widely used regolith simulants to ascertain the accuracy of these simulants and to explore statistical analysis methods of these large datasets. It is found that these morphology datasets can vary widely depending on the particle size of the particles, and simple averaging of the data skews the results heavily towards the numerically abundant size ranges, the fines. Different reporting methods are suggested to ameliorate these problems. When applied to the lunar regolith, the particles are noted to be less morphologically complex than initially suspected. Compared to the lunar material, the simulants are found to contain some more morphological variability and angular grains. Such difference is likely due to the wildly different comminution processes that these different powder systems are subjected to.

2D shape↗
DOE OSTI2025

Electrochemical recovery of Gd into liquid Bi in molten LiCl-KCl-GdCl 3

The electrochemical recovery of Gd into liquid Bi electrodes was investigated in molten LiCl-KCl-GdCl 3 electrolyte at 773–973 K using constant currents of 10–150 mA cm −2 . High round-trip coulombic efficiency (>97 %) was demonstrated for deposition and removal of Gd into and from liquid Bi at all temperatures, confirming chemically reversible electrode reactions towards high recovery yield of Gd using liquid Bi. Furthermore, the overpotential associated with deposition of Gd into the liquid Bi electrode gradually increased with current density and decreased with temperature. At the lowest temperature of 773 K, a steep increase in overpotential was evident at high current densities due to slower mass transport and low solubility of Gd in liquid Bi (0.5 at.% Gd). Based on the impedance spectra of Bi electrodes at 773–973 K, the exchange current density was estimated to be 110–220 mA cm −2 , indicating facile charge transfer at the liquid electrode-electrolyte interface. Post-mortem characterization of Bi cathodes after electrolysis at 873 K indicated the formation of a solid GdBi(s) compound layer at the Bi electrode surface. Chemical analysis of Bi cathodes at various deposited Gd compositions resulted in Faradaic efficiencies of ∼75–84 %, promising a high recovery yield of Gd from molten salts using a strongly-interacting liquid Bi electrode.

36 - MATERIALS SCIENCE↗