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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
NASA NTRS1982

The soft X-ray diffuse background

Maps of the diffuse X-ray background intensity covering essentially the entire sky with approx. 7 deg spatial resolution are presented for seven energy bands. The data were obtained on a series of ten sounding rocket flights conducted over a seven-year period. The different nature of the spatial distributions in different bands implies at least three distinct origins for the diffuse X-rays, none of which is well-understood. At energies or approx. 2000 eV, an isotropic and presumably extraglalactic 500 and 1000 eV, an origin which is at least partially galactic seems called for. At energies 284 eV, the observed intensity is anticorrelated with neutral hydrogen column density, but we find it unlikely that this anticorrelation is simply due to absorption of an extragalactic or halo source.

Mccammon, D.↗
NASA NTRS1983

The soft X-ray diffuse background

Maps of the diffuse X-ray background intensity covering essentially the entire sky with approximately 7 deg spatial resolution are presented for seven energy bands. The data were obtained on a series of ten sounding rocket flights conducted over a seven-year period. The different nature of the spatial distributions in different bands implies at least three distinct origins for the diffuse X-rays, none of which is well-understood. At energies of approximately 2000 eV, an isotropic and presumably extragalactic 500 and 1000 eV, an origin which is at least partially galactic seems called for. At energies 284 eV, the observed intensity is anticorrelated with neutral hydrogen column density, but we find it unlikely that this anticorrelation is simply due to absorption of an extragalactic or halo source. Previously announced in STAR as N83-20892

Mccammon, D.↗
DOE OSTI2025

From disorganized data to emergent dynamic models: Questionnaires to partial differential equations

Starting with sets of disorganized observations of spatially varying and temporally evolving systems, obtained at different (also disorganized) sets of parameters, we demonstrate the data-driven derivation of parameter dependent, evolutionary partial differential equation (PDE) models capable of generating the data. This tensor type of data is reminiscent of shuffled (multidimensional) puzzle tiles. The independent variables for the evolution equations (their “space” and “time”) as well as their effective parameters are all emergent , i.e. determined in a data-driven way from our disorganized observations of behavior in them. We use a diffusion map based questionnaire approach to build a smooth parametrization of our emergent space/time/parameter space for the data. This approach iteratively processes the data by successively observing them on the “space,” the “time” and the “parameter” axes of a tensor. Once the data become organized, we use machine learning (here, neural networks) to approximate the operators governing the evolution equations in this emergent space. Our illustrative examples are based (i) on a simple advection–diffusion model; (ii) on a previously developed vertex-plus-signaling model of Drosophila embryonic development; and (iii) on two complex dynamic network models (one neuronal and one coupled oscillator model) for which no obvious smooth embedding geometry is known a priori. This allows us to discuss features of the process like symmetry breaking, translational invariance, and autonomousness of the emergent PDE model, as well as its interpretability.

generative models↗
DOE OSTI2024

Revealing the hidden structure of disordered materials by parameterizing their local structural manifold

Abstract Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
DOE OSTI2023

Learning effective SDEs from Brownian dynamic simulations of colloidal particles

We construct a reduced, data-driven, parameter dependent effective stochastic differential equation (eSDE) for electric-field mediated colloidal crystallization using data obtained from Brownian dynamics simulations. We use diffusion maps (a manifold learning algorithm) to identify a set of useful latent observables. In this latent space we identify an eSDE using a deep learning architecture inspired by numerical stochastic integrators and compare it with the traditional Kramers–Moyal expansion estimation. We show that the obtained variables and the learned dynamics accurately encode the physics of the Brownian dynamic simulations. We further illustrate that our reduced model captures the dynamics of corresponding experimental data. Further, our dimension reduction/reduced model identification approach can be easily ported to a broad class of particle systems dynamics experiments/models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability

Abstract We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established analytically.

Evangelou, Nikolaos↗
DOE OSTI2024

Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

Deriving closed-form analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM—the Desai-Zwanzig model—in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and we show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ordinary differential equation (ODE) in these coordinates. Additionally, we identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE—enabled through an odd symmetry transformation—to construct the bifurcation diagram exhibiting the phase transition.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

New Measurements of the Cosmic Infrared Background Fluctuations in Deep SpitzerllRAC Survey Data and their Cosmological Implications

We extend the previous measurements of CIB fluctuations to angular scales of less than or equal to 1 degree new data obtained in the course of the 2,000+ hour Spitzer Extended Deep Survey. Two fields with completed observations of approximately equal to 12 hr/pixel are analyzed for source-subtracted CIB fluctuations at 3.6 and 4.5 micrometers. The fields, EGS and UDS, cover a total area of approximately 0.25 deg and lie at high Galactic and Ecliptic latitudes, thus minimizing cirrus and zodiacal light contributions to the fluctuations. The observations have been conducted at 3 distinct epochs separated by about 6 months. As in our previous studies, the fields were assembled using the self-calibration method which is uniquely suitable for probing faint diffuse backgrounds. The assembled fields were cleaned off the bright sources down to the low shot noise levels corresponding to AB mag approximately equal to 25, Fourier-transformed and their power spectra evaluated. The noise was estimated from the time-differenced data and subtracted from the signal isolating the fluctuations remaining above the noise levels. The power spectra of the source-subtracted fields remain identical (within the observational uncertainties) for the three epochs of observations indicating that zodiacal light contributes negligibly to the fluctuations. By comparing to the measurements for the same regions at 8 micrometers we demonstrate that Galactic cirrus cannot account for the levels of the fluctuations either. The signal appears isotropically distributed on the sky as required by its origin in the CIB fluctuations. This measurement thus extends our earlier results to the important range of sub-degree scales. We find that the CIB fluctuations continue to diverge to more than 10 times those of known galaxy populations on angular scales out to less than or equal to 1 degree. The low shot noise levels remaining in the diffuse maps indicate that the large scale fluctuations arise from spatial clustering of faint sources well within the confusion noise. The spatial spectrum of these fluctuations is in reasonable agreement with simple fitting assuming that they originate in early populations spatially distributed according to the standard cosmological model (ACDM) at epochs coinciding with the first stars era. The alternative to this identification would require a new population never observed before, nor expected on theoretical grounds, but if true this would represent an important discovery in its own right.

Kashlinsky, A.↗
DOE OSTI2023

Python codes for the paper - "Trade-offs in the latent representation of microstructure evolution"

SAND2024-00946O Python codes used in "Trade-offs in the latent representation of microstructure evolution," a manuscript accepted for publication in Acta Materialia, are part of a repository. The code was developed to perform analysis of microstructure evolution. The repository consists of two main directories: models, which train and test models such as autoencoders and diffusion maps, and analysis, which analyzes microstructures. Source code is used to perform dimensionality reduction of microstructure data for analysis of its evolution in time. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Dingreville, Remi↗
NASA NTRS1984

HEAO-1 Diffuse Soft X-ray Sky Maps

Maps of the diffuse X-ray background intensity are presented, spanning four energy intervals. The lowest energy interval (0.18 to 0.56 keV) is dominated by local emission, while the next two intervals (0.56 to 1.0 keV and 1.0 to 1.4 keV) reveal more distant and more sharply defined structures. Enlarged maps of several of these structures are presented, including the North Polar Spur, the Galactic Center region and the Eridanus Loop.

Nousek, J. A.↗
NASA NTRS2016

Solar Wind Charge Exchange Contribution To The ROSAT Sky Survey Maps

DXL (Diffuse X-ray emission from the Local Galaxy) is a sounding rocket mission designed to estimate the contribution of solar wind charge eXchange (SWCX) to the diffuse X-ray background and to help determine the properties of the Local Hot Bubble. The detectors are large area thin-window proportional counters with a spectral response that is similar to that of the PSPC (Position Sensitive Proportional Counters) used in the ROSAT All Sky Survey (RASS). A direct comparison of DXL and RASS data for the same part of the sky viewed from quite different vantage points in the solar system, and the assumption of approximate isotropy for the solar wind, allowed us to quantify the SWCX contribution to all six RASS bands (R1-R7, excluding R3). We find that the SWCX contribution at l = 140 degrees, b = 0 degrees, where the DXL path crosses the Galactic plane, is 33 percent plus or minus 6 percent (statistical) plus or minus 12 percent (systematic) for R1, 44 percent plus or minus 6 percent plus or minus 5 percent for R2, 18 percent plus or minus 12 percent plus or minus 11 percent for R4, 14 percent plus or minus 11 percent plus or minus 9 percent for R5, and negligible for the R6 and R7 bands. Reliable models for the distribution of neutral H and He in the solar system permit estimation of the contribution of interplanetary SWCX emission over the the whole sky and correction of the RASS maps. We find that the average SWCX contribution in the whole sky is 26 percent plus or minus 6 percent plus or minus 13 percent for R1, 30 percent plus or minus 4 percent plus or minus 4 percent for R2, 8 percent plus or minus 5 percent plus or minus 5 percent for R4, 6 percent plus or minus 4 percent plus or minus 4 percent for R5, and negligible for R6 and R7.

sxcx↗
DOE OSTI2025

Diffusion and Deformation Mechanism Maps in Li Metal from Atomistic Simulations

The rate of Li transport in the Li metal anode is important for the operation of Li metal-solid state batteries. Here, transport rates due to diffusion and creep are predicted in Li using atomistic simulations. First, molecular dynamics is used to estimate the rate of Li diffusion along dislocations and in grain boundary triple junctions. By combining this data with that from a prior study of grain boundary diffusion the dominant mechanisms and rates of self-diffusion in Li polycrystals are predicted as a function of grain size, grain shape, dislocation density, and temperature. Second, the dominant creep mechanisms are predicted and used to estimate critical current densities and void annihilation times. Grain boundary sliding and coble creep are the dominant mechanisms for micron-sized grains. Lastly, a continuum model for interfacial contact loss reveals that high dislocation densities of ∼10 12 /cm 2 enable achieving battery performance targets for Li grain sizes of ∼10 μm.

Batteries↗
NASA NTRS2014

Local ISM 3D Distribution and Soft X-ray Background Inferences for Nearby Hot Gas

Three-dimensional (3D) interstellar medium (ISM) maps can be used to locate not only interstellar (IS) clouds, but also IS bubbles between the clouds that are blown by stellar winds and supernovae, and are filled by hot gas. To demonstrate this, and to derive a clearer picture of the local ISM, we compare our recent 3D IS dust distribution maps to the ROSAT diffuse Xray background maps after removal of heliospheric emission. In the Galactic plane, there is a good correspondence between the locations and extents of the mapped nearby cavities and the soft (0.25 keV) background emission distribution, showing that most of these nearby cavities contribute to this soft X-ray emission. Assuming a constant dust to gas ratio and homogeneous 106 K hot gas filling the cavities, we modeled in a simple way the 0.25 keV surface brightness along the Galactic plane as seen from the Sun, taking into account the absorption by the mapped clouds. The data-model comparison favors the existence of hot gas in the solar neighborhood, the so-called Local Bubble (LB). The inferred mean pressure in the local cavities is found to be approx.9,400/cu cm K, in agreement with previous studies, providing a validation test for the method. On the other hand, the model overestimates the emission from the huge cavities located in the third quadrant. Using CaII absorption data, we show that the dust to CaII ratio is very small in those regions, implying the presence of a large quantity of lower temperature (non-X-ray emitting) ionized gas and as a consequence a reduction of the volume filled by hot gas, explaining at least part of the discrepancy. In the meridian plane, the two main brightness enhancements coincide well with the LB's most elongated parts and chimneys connecting the LB to the halo, but no particular nearby cavity is found towards the enhancement in the direction of the bright North Polar Spur (NPS) at high latitude. We searched in the 3D maps for the source regions of the higher energy (0.75 keV) enhancements in the fourth and first quadrants. Tunnels and cavities are found to coincide with the main bright areas, however no tunnel nor cavity is found to match the low-latitude b > or approx. 8deg, brightest part of the NPS. In addition, the comparison between the 3D maps and published spectral data favors a NPS central source region location beyond 230 pc, i.e. at larger distance than usually considered. Those examples illustrate the potential use of more detailed 3D distributions of the nearby ISM for the interpretation of the diffuse soft X-ray background.

Xray Emission↗
NASA NTRS1997

Effect of Crystal Defects on Minority Carrier Diffusion Length in 6H SiC Measured Using the Electron Beam Induced Current Method

We report values of minority carrier diffusion length in n-type 6H SiC measured using a planar Electron Beam Induced Current (EBIC) method. Values of hole diffusion length in defect free regions of n-type 6H SiC, with a doping concentration of 1.7El7 1/cu cm, ranged from 1.46 microns to 0.68 microns. We next introduce a novel variation of the planar method used above. This 'planar mapping' technique measured diffusion length along a linescan creating a map of diffusion length versus position. This map is then overlaid onto the EBIC image of the corresponding linescan, allowing direct visualization of the effect of defects on minority carrier diffusion length. Measurements of the above n-type 6H SiC resulted in values of hole diffusion length ranging from 1.2 micron in defect free regions to below 0.1 gm at the center of large defects. In addition, measurements on p-type 6H SiC resulted in electron diffusion lengths ranging from 1.42 micron to 0.8 micron.

Tabib-Azar, Massood↗
NASA NTRS2012

Coordinated STEM/FIB/NanoSIMS Analyses of Presolar Silicates in Comet Dust and Primitive Meteorites

Silicate grains were among the most abundant mineralogical building blocks of our Solar System. These grains were the detritus from earlier generations of stars that have been recycled in the early solar nebula. Rare sub-micrometer survivors of this processing have been identified in meteorites, micrometeorites and interplanetary dust particles (IDPs). These silicate grains are recognized as presolar in origin because of their extremely anomalous isotopic compositions that reflect nucleosynthetic processes in their stellar sources (evolved stars, novae and supernovae). We perform coordinated chemical, mineralogical and isotopic studies of these grains to determine their origins and histories. We examine the complex mineralogy and petrography of presolar silicates using imaging, diffraction and chemical data obtained from thin sections with the JSC JEOL 2500 field-emission STEM equipped with a Noran thin window energy dispersive x-ray (EDX) spectrometer and a Gatan Tridiem GIF. Quantitative element x-ray maps (spectrum images) are acquired by rastering a 4 nm incident probe whose dwell time is minimized to avoid beam damage and element diffusion during mapping. Successive image layers are acquired and combined in order to achieve approx 1% counting statistics for major elements. The IDP samples are prepared by ultramicrotomy of particles embedded in epoxy or elemental sulfur. After EDX mapping, the sections are subjected to C, N, and O isotopic imaging with the JSC NanoSIMS 50L ion microprobe. We prepare sections of some meteorite grains using the JSC FEI Quanta 3D focused ion beam (FIB) instrument. The specimen surface is protected from the FIB milling process by layers of electron beam-deposited C and Pt followed by an ion-deposited Pt layer. We also use the FIB to preferentially remove surrounding grains to reduce the background in subsequent NanoSIMS measurements. For mineralogical studies, we again employ the FIB instrument to deposit a protective cap over the grain of interest and then extract the grain and thin it to electron transparency for TEM analysis.

Keller, Lindsay↗