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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 NTRS1983

Spatial Fluctuations in the Diffuse Cosmic X-Ray Background

The bright, essentially isotropic, X-ray sky flux above 2 keV yields information on the universe at large distances. However, a definitive understanding of the origin of the flux is lacking. Some fraction of the total flux is contributed by active galactic nuclei and clusters of galaxies, but less than one percent of the total is contributed by the or approximately 3 keV band resolved sources, which is the band where the sky flux is directly observed. Parametric models of AGN (quasar) luminosity function evolution are examined. Most constraints are by the total sky flux. The acceptability of particular models hinges on assumptions currently not directly testable. The comparison with the Einstein Observatory 1 to keV low flux source counts is hampered by spectral uncertainties. A tentative measurement of a large scale dipole anisotropy is consistent with the velocity and direction derived from the dipole in the microwave background. The impact of the X-ray anisotropy limits for other scales on studies of large-scale structure in the universe is sketched. Models of the origins of the X-ray sky flux are reviewed, and future observational programs outlined.

Shafer, R. A.↗
NASA NTRS1996

[A NASA / University Joint Venture in Space Science]

MILAGRO is a water-Cherenkov detector for observing cosmic gamma rays over a broad energy range of 100 GeV to 100 TeV. MILAGRO will be the first detector that has sensitivity overlapping both air-Cherenkov and air-shower detectors. With this detector scientists in the collaboration will study previously observed celestial sources at their known emission energies, extend these observations into a new energy regime, and search for new sources at unexplored energies. The diffuse gamma-radiation component in our galaxy, which originates from interactions of cosmic rays with interstellar gas and photons, provides important information about the density, distribution, and spectrum of the cosmic rays that pervade the interstellar medium. Events in the Compton Gamma Ray Observatory (GRO) are being observed up to about 30 GeV, differing by slightly more than order of magnitude from the low energy threshold of MILAGRO. By looking in coincidence at sources, correlated observations will greatly extend the astrophysics potential of MILAGRO and NASA's GRO. A survey of cosmic-ray observatories is being prepared for scientists and others to provide a resource and reference which describes high energy cosmic-ray research activities around the world. This summary presents information about each research group, such as names of principal investigators, number of persons in the collaboration, energy range, sensitivity, angular resolution, and surface area of detector. Similarly, a survey of gamma-ray telescopes is being prepared to provide a resource and reference which describes gamma-ray telescopes for investigating galactic diffuse gamma-ray flux currently observed in the GeV energy range, but is expected to extend into the TeV range. Two undergraduate students are compiling information about gamma-ray telescopes and high energy cosmic-ray observatories for these surveys. Funding for this project was provided by the Arkansas Space Grant Consortium. Also enclosed Appendix A, B, C, D and E.

Wold, Donald C.↗
DOE OSTI2022

Passive and active peer effects in the spatial diffusion of residential solar panels: A case study of the Las Vegas Valley

This research analyzes the role of peer influences on the adoption of residential rooftop solar photovoltaic panels (PV) within the context of the Diffusion of Innovation Theory. PV literature indicates that adopters are influenced by word of mouth (WOM) information exchange with peers, i.e., active peer effects, while other studies suggest that living near households with visible rooftop PV installations influences adoption, i.e., passive peer effects. We bridge the gap in this literature by conducting a mixed methods analysis. We develop and administer a survey to Las Vegas Valley (LVV) residents to identify current and potential PV adopters' perceptions of peer-effects and consumer intention variables. We conduct a spatial analysis of Google's Project Sunroof data to identify LVV neighborhoods in the later stages of the PV diffusion process, i.e., those with the highest PV adoption rates. Key results show that current PV adopters living in early diffusion areas report significantly higher active and passive peer effects compared to adopters in later diffusion areas. Potential adopters in later diffusion areas report higher passive peer effects than those in early diffusion areas. Overall, because LVV has a low PV adoption rate (<3%), short term strategies aimed at increasing PV adoption should emphasize WOM active peer effects. Here, we caution against long-term green marketing strategies focusing solely on peer-effects as the PV market matures.

14 SOLAR ENERGY↗
DOE OSTI2025

Microstructurally informed synchrotron x-ray analysis revealing helium defect transitions in ultrafine grained tungsten

The formation of insoluble gaseous defects in materials due to nuclear transmutation or ion implantation involves the diffusion of impurity atoms to form atomic defect clusters that coalesce into bubbles or cavities and ultimately degrade the material properties. Transmission electron microscopy (TEM) is limited in its ability to resolve sub-nanometer gas clusters whereas X-ray diffraction (XRD) provides information pertaining to local atomic changes. Here, in this study, helium (He) implanted ultrafine grained tungsten is explored through a multimodal defect characterization campaign combining TEM-informed Small Angle X-ray Scattering (SAXS) analysis, XRD lattice parameter measurements, and nanoscale He cluster quantification from a region of reciprocal space accessible via Wide Angle X-ray Scattering (WAXS). Moderate elevated temperature implantations are shown to produce high concentrations of sub-nanoscale He clusters and small, homogeneously distributed cavities, which collectively are linked to lattice expansion and further substantiated through complementary atomistic simulations. Increased implantation temperatures encourage the diffusion of these defects to the grain boundaries (GBs), leading to lattice relaxation and the growth of large GB cavities manifesting as bimodal size distributions in the SAXS analysis. Overall, our results demonstrate the utility of multimodal synchrotron X-ray analysis in bridging the gap between microscale He cavity quantification and atomic-scale defect analysis.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Cosmic Ray Propagation Models

Astrophysics of cosmic rays and gamma rays depends very much on the quality of the data, which become increasingly accurate each year and therefore more constraining. While direct measurements of cosmic rays are possible in only one location on the outskirts of the Milky Way, the Galactic diffuse gamma-ray emission provides insights into the spectra of cosmic rays in distant locations, therefore complementing the local cosmic-ray studies. This connection, however, requires extensive modeling and is yet to be explored in detail. The GUST mission, which is scheduled for launch in 2007 and is capable of measuring gamma-rays in the range 20 MeV - 300 GeV, will change the status quo dramatically. Galactic diffuse gamma-ray emission gathered by GUST will require adequate theoretical models. The efforts will be rewarded by the wealth of information on cosmic ray spectra and fluxes in remote locations. In its turn, a detailed cosmic ray propagation model will provide a reliable basis for other studies such as search for dark matter signals in cosmic rays and diffuse gamma rays, spectrum and origin of the extragalactic gamma-ray emission, theories of nucleosynthesis and evolution of elements etc. In this talk, I will discuss what we can learn studying the cosmic ray propagation and diffuse gamma-ray emission.

Moskalenko, I. V.↗
NASA NTRS2004

Propagation of Cosmic Rays

Astrophysics of cosmic rays and gamma rays depends very much on the quality of the data, which become increasingly accurate each year and therefore more constraining. While direct measurements of cosmic rays are possible in only one location on the outskirts of the Milky Way, the Galactic diffuse gamma-ray emission provides insights into the spectra of cosmic rays in distant locations, therefore complementing the local cosmic-ray studies. This connection, however, requires extensive modeling and is yet to be explored in detail. The GLAST mission, which is scheduled for launch in 2007 and is capable of measuring gamma-rays in the range 20 MeV - 300 GeV, will change the status quo dramatically. The detailed spectra and skymaps of the Galactic diffuse gamma-ray emission gathered by GLAST will require adequate theoretical models. The efforts will be rewarded by the wealth of information on cosmic ray spectra and fluxes in remote locations. In its turn, a detailed cosmic ray propagation model will provide a reliable basis for other studies such as search for dark matter signals in cosmic rays and diffuse gamma rays, spectrum and origin of the extragalactic gamma-ray'emission, theories of nucleosynthesis and evolution of elements etc. In this talk, I will discuss what we can learn studying the cosmic ray propagation and diffuse gamma-ray emission.

Moskalenko, I. V.↗
DOE OSTI2025

Generative AI models for learning flow maps of stochastic dynamical systems in bounded domains

Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. Here, the performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.

Bounded domains↗
DOE OSTI2023

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

The goal of influence maximization is to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. The problem has a number of important applications such as viral marketing, information spread, and epidemic control. Although computing optimal seed set is NP-Hard, due to the submodular nature of the problem efficient approximation algorithms exist. However, even state-of-the-art parallel implementations are limited by a sampling step that incurs large memory footprints. This in turn limits the problem size reach and approximation quality. In this work, we study the memory footprint of the sampling process collecting reverse reachability information in the IMM algorithm over large real-world social networks. We present an adaptive and memory-efficient optimization approach for a state-of-the-art multi-threaded parallel influence maximization algorithm. Our approach,HuffMax, uses a portion of the reverse reachable (RR) sets collected by the algorithm to learn the characteristics of the graph. Then, it compresses the intermediate reverse reachability information with Huffman coding, and queries directly on the compressed data to preserve the memory savings obtained through compression. We also propose an efficient sampling strategy based on the distribution of RR sets, which can further reduce the computation time for typical social networks with long-tail distributions. Considering a NUMA architecture, we scale up our solution on 128-core CPUs and reduce the memory footprint by up to 45.7% with negligible time overhead (or even faster) and without perceivable loss of accuracy.

Chen, Xinyu↗
DOE OSTI2020

Predictive Integrated Modeling of Heated Tokamak Plasmas (Final Technical Report)

The research carried out by the Lehigh University fusion group during this reporting period: (1) Self-consistent predictive core-pedestal ITER scenario modeling; (2) Microtearing instabilities and electron thermal transport in standard and spherical tokamak discharges; (3) Neural network model of the Multi-Mode anomalous transport module; (4) Anomalous transport in KSTAR, JET, EAST, Alcator C-Mod and DIII-D discharges; (5) Theory for transport in magnetized plasmas; (6) Control-oriented modeling of the q-profile dynamic response in preparation for advanced scenario control in KSTAR and in EAST; (7) Modeling of edge harmonic oscillation in DIII-D QH mode discharges; (8) Elucidating plasma dynamics in Hasegawa-Wakatani turbulence by information geometry; (9) Non-linear Langevin and Fractional Fokker–Planck equations for anomalous diffusion by Levy stable processes; and (10) Investigation of the plasma shaping effects on the H-mode pedestal structure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Safety Testing of AGR-5/6/7 Compacts 2-2-2 and 2-2-4

Compacts 2-2-2 and 2-2-4 from the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program’s final irradiation campaign (AGR-5/6/7) were each separately subjected to a thermal soak at 1600°C for 300 h under flowing helium to simulated conditions experienced during a postulated high-temperature gas-cooled reactor (HTGR) depressurization conduction cooldown event. The safety testing of the fuel compacts’ performance under accident conditions was conducted in the Oak Ridge National Laboratory (ORNL) Core Conduction Cooldown Test Facility (CCCTF), which utilizes a stand-alone hot cell at the Irradiated Fuels Examination Laboratory (IFEL). The CCCTF heats fuel to temperatures up to 1900°C in a non-oxidizing environment while continuously monitoring the sweep gas for radioactive 85 Kr to detect release levels associated with hermetic failure of the tristructural-isotropic (TRISO) coating surrounding each fuel kernel. In addition, certain metallic radionuclides that escape the fuel compact are collected on deposition cups that are periodically exchanged with a new cup to obtain information on the overall retention behavior of the TRISO coating layers. Because cesium can diffuse through intact pyrocarbon layers, abnormal degradation of the silicon carbide (SiC) layer in the absence of holistic TRISO coating failure is indicated by release of 134 Cs at levels equivalent to an individual particle inventory in the absence of significant 85 Kr release (Hunn et al. 2014). Additional description of the CCCTF system is provided in Appendix A.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Fermi-LAT View on Three Ultra-high-energy 1LHAASO Sources in the 52° < l < 55° Region

Using more than 17 yr of Fermi-LAT data, we performed a detailed investigation of the complex 52° < l < 55° region, which encompasses the three ultra-high-energy sources 1LHAASO J1928+1746u, 1LHAASO J1928+1813u, and 1LHAASO J1929+1846u. This region hosts multiple supernova remnants (SNRs), pulsars, GeV and TeV sources. Our analysis resolves the GeV emission into three pointlike sources (J1925+1729P, J1930+1851P, and J1932+1916P) and two extended sources (J1929+1732E and J1930+1826E), and improves significantly on the description based on the 4FGL-DR4 catalog. Source J1932+1916P is identified as the known gamma-ray pulsar PSR J1932+1916, while J1925+1729P may be a new gamma-ray pulsar candidate distinct from the known gamma-ray pulsar PSR J1925+1720. This warrants future investigation and a search for pulsations. Source J1930+1851P coincides with the TeV source PWN/SNR G54.1+0.3, and its GeV−TeV spectrum is consistent with both leptonic and hadronic interpretations, although a leptonic origin in relation to the known pulsar wind nebula (PWN) is more likely. The GeV−TeV spectrum of J1929+1732E is consistent with a hybrid lepto-hadronic scenario in which the TeV emission traces the PWN powered by the pulsar PSR J1928+1746, while the GeV emission may result from interactions between particles escaped from the parent SNR and illuminating the gas environment. Similarly, J1930+1826E is likely connected to PWN/SNR G54.1+0.3 under a hadronic scenario involving escaped particles in their early propagation stage. Owing to spectral and/or morphological mismatches, the connection of these five GeV sources to the three LHAASO sources is not clear. This warrants deeper observations with HAWC and LHAASO, and a dedicated study of the modeling of the Galactic diffuse emission. Future CTAO observations with higher angular resolution are expected to deliver crucial information for the study of this region.

Liu, Linjie [Chinese Academy of Sciences (CAS), Ku↗
NASA NTRS1983

Studies of low energy cosmic rays - The anomalous component

Data from the cosmic ray subsystem on the Voyager spacecraft is used to measure the spectra of He, C, N, O, and Ne with about 4-124 MeV/nuc (for O) near 1 AU. By subtracting both a low-energy solar/interplanetary component and the high-energy galactic component the energy spectra of the anomalous cosmic-ray species He, N, O, and Ne have been determined. It is suggested that the shapes of these spectra carry information about the charge state of the particles and the rigidity dependence of the diffusion coefficient. For similar power-law source spectra at the boundary of the modulation region, the location of features in the energy spectra indicates that the anomalous particles are singly ionized.

Webber, W. R.↗
NASA NTRS1992

A computer model for one-dimensional mass and energy transport in and around chemically reacting particles, including complex gas-phase chemistry, multicomponent molecular diffusion, surface evaporation, and heterogeneous reaction

Various chemically reacting flow problems highlighting chemical and physical fundamentals rather than flow geometry are presently investigated by means of a comprehensive mathematical model that incorporates multicomponent molecular diffusion, complex chemistry, and heterogeneous processes, in the interest of obtaining sensitivity-related information. The sensitivity equations were decoupled from those of the model, and then integrated one time-step behind the integration of the model equations, and analytical Jacobian matrices were applied to improve the accuracy of sensitivity coefficients that are calculated together with model solutions.

Cho, S. Y.↗
NASA NTRS1994

Conditional statistics in a turbulent premixed flame derived from direct numerical simulation

The objective of this paper is to briefly introduce conditional moment closure (CMC) methods for premixed systems and to derive the transport equation for the conditional species mass fraction conditioned on the progress variable based on the enthalpy. Our statistical analysis will be based on the 3-D DNS database of Trouve and Poinsot available at the Center for Turbulence Research. The initial conditions and characteristics (turbulence, thermo-diffusive properties) as well as the numerical method utilized in the DNS of Trouve and Poinsot are presented, and some details concerning our statistical analysis are also given. From the analysis of DNS results, the effects of the position in the flame brush, of the Damkoehler and Lewis numbers on the conditional mean scalar dissipation, and conditional mean velocity are presented and discussed. Information concerning unconditional turbulent fluxes are also presented. The anomaly found in previous studies of counter-gradient diffusion for the turbulent flux of the progress variable is investigated.

Mantel, Thierry↗
NASA NTRS2001

Hyperspectral Imaging and Spectroscopy of Fluorescently Coupled Acyl-CoA: Cholesterol Acyltransferase in Insect Cells

A high-performance hyperspectral imaging module with high throughput of light suitable for low-intensity fluorescence microscopic imaging and subsequent analysis, including single-pixel-defined emission spectroscopy, was tested on Sf21 insect cells expressing green fluorescence associated with recombinant green fluorescent protein linked or not with the membrane protein acyl-CoA:cholesterol acyltransferase. The imager utilized the phenomenon of optical activity as a new technique providing information over a spectral range of 220-1400 nm, and was inserted between the microscope and an 8-bit CCD video-rate camera. The resulting fluorescence image did not introduce observable image aberrations. The images provided parallel acquisition of well resolved concurrent spatial and spectral information such that fluorescence associated with green fluorescent protein alone was demonstrated to be diffuse within the Sf21 insect cell, and that green fluorescence associated with the membrane protein was shown to be specifically concentrated within regions of the cell cytoplasm. Emission spectra analyzed from different regions of the fluorescence image showed blue shift specific for the regions of concentration associated with the membrane protein.

Malak, H.↗
DOE OSTI2022

Optical Memory, Switching, and Neuromorphic Functionality in Metal Halide Perovskite Materials and Devices

Metal halide perovskite-based materials have emerged over the past few decades as remarkable solution-processable opto-electronic materials with many intriguing properties and potential applications. Notably, these emerging materials have recently been considered for their promise in low-energy memory and information processing applications. In particular, their large optical cross-sections, high photoconductance contrast, large carrier diffusion lengths, and mixed electronic/ionic transport mechanisms are attractive for enabling memory elements and neuromorphic devices that are written and/or read in the optical domain. Here, we review recent progress towards memory and neuromorphic functionality in metal halide perovskite materials and devices where photons are used as a critical degree of freedom for switching, memory, and neuromorphic functionality.

14 SOLAR ENERGY↗
DOE OSTI2026

Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pairwise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through ‘liquid-like’ product layers into a thermodynamic cellular reaction model. In the Ba–Ti–O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion–thermodynamics interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length scales and timescales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity.

Atomistic models↗
DOE OSTI2020

Physics-Informed Machine Learning for Epidemiological Models

One challenge of using compartmental SEIR models for public health planning is the difficulty in manually tuning parameters to capture behavior reflected in the real-world data. This team conducted initial, exploratory analysis of a novel technique to use physics-informed machine learning tools to rapidly develop data-driven models for physical systems. This machine learning approach may be used to perform data assimilation of compartment models which account for unknown interactions between geospatial domains (i.e. diffusion processes coupling across neighborhoods/counties/states/etc.). Results presented here are early, proof-of-concept ideas that demonstrate initial success in using a physically informed neural network (PINN) model to assimilate data in a compartmental epidemiology model. The results demonstrate initial success and warrant further research and development.

60 APPLIED LIFE SCIENCES↗