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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

GEMS X-ray Polarimeter Performance Simulations

The Gravity and Extreme Magnetism Small explorer (GEMS) is an X-ray polarization telescope selected as a NASA small explorer satellite mission. The X-ray Polarimeter on GEMS uses a Time Projection Chamber gas proportional counter to measure the polarization of astrophysical X-rays in the 2-10 keV band by sensing the direction of the track of the primary photoelectron excited by the incident X-ray. We have simulated the expected sensitivity of the polarimeter to polarized X-rays. We use the simulation package Penelope to model the physics of the interaction of the initial photoelectron with the detector gas and to determine the distribution of charge deposited in the detector volume. We then model the charge diffusion in the detector,and produce simulated track images. Within the track reconstruction algorithm we apply cuts on the track shape and focus on the initial photoelectron direction in order to maximize the overall sensitivity of the instrument, using this technique we have predicted instrument modulation factors nu(sub 100) for 100% polarized X-rays ranging from 10% to over 60% across the 2-10 keV X-ray band. We also discuss the simulation program used to develop and model some of the algorithms used for triggering, and energy measurement of events in the polarimeter.

Baumgartner, Wayne H.↗
DOE OSTI2018

Drift turbulence, particle transport, and anomalous dissipation at the reconnecting magnetopause

Here, using fully kinetic 3D simulations, the reconnection dynamics of asymmetric current sheets are examined at the Earth's magnetopause. The plasma parameters are selected to model MMS magnetopause diffusion region crossings with guide fields of 0.1, 0.4, and 1 of the reconnecting magnetosheath field. In each case, strong drift-wave fluctuations are observed in the lower-hybrid frequency range at the steep density gradient across the magnetospheric separatrix. These fluctuations give rise to cross-field electron particle transport. In addition, this turbulent mixing leads to significantly enhanced electron parallel heating in comparison to 2D simulations. We study three different methods of quantifying the anomalous dissipation produced by the drift fluctuations, based on spatial averaging, temporal averaging, and temporal averaging followed by integrating along magnetic field lines. A comparison of different methods reveals complications in identifying and measuring the anomalous dissipation. Nevertheless, the anomalous dissipation from short wavelength drift fluctuations appears weak for each case, and the reconnection rates observed in 3D are nearly the same as in 2D models. Finally, the 3D simulations feature a number of interesting new features that are consistent with recent MMS observations, including cold beams of magnetosheath electrons that penetrate into the hotter magnetospheric inflow, the related observation of decreasing temperature in regions of increasing total density, and an effective turbulent diffusion coefficient that agrees with predictions from quasi-linear theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Comparison of Directionally Solidified Samples Solidified Terrestrially and Aboard the International Space Station

This article reports research that has been carried out under the aegis of NASA as part of a collaboration between ESA and NASA for solidification experiments on the International Space Station (ISS). The focus has been on the effect of convection on the microstructural evolution and macrosegregation in hypoeutectic Al-Si alloys during directional solidification (DS). Terrestrial DS-experiments have been carried out at Cleveland State University (CSU) and under microgravity on the International Space Station (ISS). The thermal processing-history of the experiments is well defined for both the terrestrially processed samples and the ISS-processed samples. As of this writing, two dendritic metrics was measured: primary dendrite arm spacings and primary dendrite trunk diameters. We have observed that these dendrite-metrics of two samples grown in the microgravity environment show good agreements with models based on diffusion controlled growth and diffusion controlled ripening, respectively. The gravity-driven convection (i.e., thermosolutal convection) in terrestrially grown samples has the effect of decreasing the primary dendrite arm spacings and causes macrosegregation. Dendrite trunk diameters also show differences between the earth- and space-grown samples. In order to process DS-samples aboard the ISS, the dendritic seed crystals were partially remelted in a stationary thermal gradient before the DS was carried out. Microstructural changes and macrosegregation effects during this period are described and have modeled.

Angart, S.↗
NASA NTRS1998

Numerical Modeling of Physical Vapor Transport in Contactless Crystal Growth Geometry

Growth from the vapor under conditions of limited contact with the walls of the growth ampoule is beneficial for the quality of the growing crystal due to reduced stress and contamination which may be caused by interactions with the growth container. The technique may be of a particular interest for studies on crystal growth under microgravity conditions: elimination of some factors affecting the crystal quality may make interpretation of space-conducted processes more conclusive and meaningful. For that reason, and as a part of our continuing studies on 'contactless' growth technique, we have developed a computational model of crystal growth process in such system. The theoretical model was built, and simulations were performed using the commercial computational fluid dynamics code, (CFD) ACE. The code uses an implicit finite volume formulation with a gray discrete ordinate method radiation model which accounts for the diffuse absorption and reflection of radiation throughout the furnace. The three-dimensional model computes the heat transfer through the crystal, quartz, and gas both inside and outside the ampoule, and mass transport from the source to the crystal and the sink. The heat transport mechanisms by conduction, natural convection, and radiation, and mass transport by diffusion and convection are modeled simultaneously and include the heat of the phase transition at the solid-vapor interfaces. As the thermal boundary condition, temperature profile along the walls of the furnace is used. For different thermal profiles and furnace and ampoule dimensions, the crystal growth rate and development of the crystal-vapor and source-vapor interfaces (change of the interface shape and location with time) are obtained. Super/under-saturation in the ampoule is determined and critical factors determining the 'contactless' growth conditions are identified and discussed. The relative importance of the ampoule dimensions and geometry, the furnace dimensions and its temperature, and the properties of the grown material are analyzed. The results of the simulations are compared with related experimental results on growth of CdTe, CdZnTe, ZnTe, PbTe, and PbSnTe crystals by this technique.

Palosz, W.↗
NASA NTRS1981

Spectral albedos of midlatitude snowpacks

Spectral albedos of impure-nonhomogeneous snowpacks, typical of midlatitudes, from 400 to 2200 nm were modeled through a numerical solution of the radiative transfer equation in the two-stream approximation. Discrete depth-dependent values of density, grain size and impurity concentration were used to characterize the snowpacks. The model is for diffuse incident radiation, and the numerical method is based on doubling and invariant imbedding. The effect of soot impurities on snowpack albedos is illustrated when a snowpack is several centimeters deep and soot reduces the albedos at visible wavelengths, however, when a snowpack is only a few centimeters deep, soot may increase the albedos at visible wavelengths. By adjusting soot content and snow grain size, good quantitative agreement with some observations at the Cascade Mountains (Washington) and at Point Barrow (Alaska) are obtained; however, the model grain sizes are found to be fifty to four hundred percent larger than the measured values. For satellite snowcover observations, a model for effective albedo of partially snow-covered areas was developed and compared with some NOAA-2 observations of the southeastern United States.

Choudhury, B.↗
DOE OSTI2022

Estimates of Quantum Tunneling Effects for Hydrogen Diffusion in PuO 2

We detail the estimation of activation energies and quantum nuclear vibrational tunneling effects for hydrogen diffusion in PuO 2 based on Density Functional Theory calculations and a quantum double well approximation. We find that results are relatively insensitive to choice of exchange correlation functional. In addition, the representation of spin in the system and use of an extended Hubbard U correction has only a small effect on hydrogen point defect formation energies when the PuO 2 lattice is held fixed at the experimental density. We then compute approximate activation energies for transitions between hydrogen interstitial sites seeded by a semi-empirical quantum model and determine the quantum tunneling enhancement relative to classical kinetic rates. Our model indicates that diffusion rates in H/PuO 2 systems could be enhanced by more than one order of magnitude at ambient conditions and that these effects persist at high temperature. The method we propose here can be used as a fast screening tool for assessing possible quantum nuclear vibrational effects in any number of condensed phase materials and surfaces, where hydrogen hopping tends to follow well defined minimum energy pathways.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions

Here we present a fully analytical, heuristic model – the “Analytical Transport Network Model” – for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a microstructural network's topology and the morphology of its channels to an effective material transport coefficient (a general term meant to encompass, e.g., conductivity or diffusion coefficient). The model's transport coefficient predictions agree well with those from electrochemical fin (ECF) theory and finite element analysis (FEA), but are computed 0.5–1.5 and 5–6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the transport coefficient, whereby the distributions that characterize the structure are readily available for further analysis. Furthermore, ATN's explicit development provides insight into the nature of the tortuosity factor and offers the potential to apply theory from network science and to consider the optimization of a network's effective resistance in a mathematically rigorous manner. The ATN model's speed and relative ease-of-use offer the potential to aid in accelerating the design (with respect to transport), and thus reducing the cost, of energy materials.

25 ENERGY STORAGE↗
DOE OSTI2015

Study of the diffuse gamma-ray emission from the galactic plane with ARGO-YBJ

The events recorded by ARGO-YBJ in more than five years of data collection have been analyzed to determine the diffuse gamma-ray emission in the Galactic plane at Galactic longitudes 25° < l < 100° and Galactic latitudes |b|<5{sup ∘}. The energy range covered by this analysis, from ∼350 GeV to ∼2 TeV, allows the connection of the region explored by Fermi with the multi-TeV measurements carried out by Milagro. Our analysis has been focused on two selected regions of the Galactic plane, i.e., 40° < l < 100° and 65° < l < 85° (the Cygnus region), where Milagro observed an excess with respect to the predictions of current models. Great care has been taken in order to mask the most intense gamma-ray sources, including the TeV counterpart of the Cygnus cocoon recently identified by ARGO-YBJ, and to remove residual contributions. The ARGO-YBJ results do not show any excess at sub-TeV energies corresponding to the excess found by Milagro, and are consistent with the predictions of the Fermi model for the diffuse Galactic emission. From the measured energy distribution we derive spectral indices and the differential flux at 1 TeV of the diffuse gamma-ray emission in the sky regions investigated.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Scattering by whistler-mode waves during a quiet period perturbed by substorm activity

We study the dynamics of radiation belt electrons during a 10-day quiet period perturbed by substorm activity and preceding a high-speed stream (HSS), aiming at a global description of the radiation belts in L-shell, L in [2, 6], and energy [0.1, 10] MeV. We combine Van Allen Probes observations and Fokker-Planck numerical simulations of pitch-angle diffusion. The Fokker-Planck model uses event-driven pitch angle diffusion coefficients from whistler-mode waves, built from the wave properties and the ambient plasma density measurements from the Van Allen Probes. We first find this event has some similar characteristics to regular quiet times previously studied; a widely extended plasmasphere within which we observe strong and varying whistler-mode waves. These ambient conditions lead to strong pitch-angle scattering, which contributes to the creation of a wide slot region as well as a significant decay of the outer radiation belts, which are observed and qualitatively well simulated. In addition, we find the substorm activity causes short duration (within ± 4h) decay of the plasma density and a lowering amplitude of the whistler-mode waves within the plasmasphere, both causing opposite effects in terms of pitch angle diffusion. This leads to a diminution of pitch-angle diffusion at the time of the main substorm activity. Conversely, whistler-mode waves become enhanced in the time periods between the substorm injections. All effects cumulated, we find an enhancement of pitch angle diffusion by whistler-mode waves above L~4.7 during the 10-day period. This directly relates to the combination of quietness and substorm activity which allows pitch angle diffusing of up to 1 MeV electrons in the outer belt. Relativistic electrons of 1–2 MeV remain trapped in the outer belt, from L~4.7 to L~5.2, forming, in both the observations and the simulations, a distinct pocket of remnant electrons.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1982

An iterative radiative transfer code for ocean-atmosphere systems

The details of an iterative radiative transfer code for computing the intensity and degree of polarization of diffuse radiation in models of the ocean-atmosphere system are described. The present code neglects the upwelling radiation from below the ocean surface and as such can be applied to the part of the spectrum where the absorption by water is strong. To establish the reliability of the numerical scheme and the computer code, the results are compared with those of Fraser and Walker (1968), Dave (1972), and Mullamaa (1964); they are found to be in excellent agreement. The computations also show that both the intensity and the degree of polarization of the upwelling diffuse radiation at the top of the atmosphere vary significantly when the rough ocean at the base of the atmosphere is replaced by a Lambertian surface that reflects the same energy as the rough ocean.

Ahmad, Z.↗
NASA NTRS1977

Gravitational effects on chemically reacting laminar boundary layer flows over a horizontal flat plate

A theoretical study of the chemically reacting laminar boundary layer flow over a horizontal flat plate with gravitationally induced buoyant force is presented. A diffusion flame sheet model was used to describe the combustion process. The effects of gravity on the purely force convection flow can be characterized by a dimensionless coordinate quantity, which is involved in the generation of the governing equations. A numerical solution of the zero and first order governing equations subject to the appropriate physical boundary conditions was obtained. It is shown that the cross stream buoyancy induced body force acts effectively to produce a streamwise pressure gradient in the fluid adjacent to the plate surface. It is concluded that buoyancy plays an important role in boundary layer diffusion flames.

Lavid, M.↗
DOE OSTI2017

Bioethanol enrichment using zeolite membranes: Molecular modeling, conceptual process design and techno-economic analysis

Lignocellulosic biomass-derived ethanol offers a potential alternative to fossil-derived fuels. However, the energy-intensive nature of its recovery process, i.e., a sequence of two distillation columns – beer and rectification columns - enriching from 5 to 37, and 37–93 wt%, respectively, limits its environmental and economic benefits. Here, in this paper, we assess the potential of using zeolite membranes for bioethanol recovery. Atomistic-level and molecular dynamic simulations are performed to determine adsorption and diffusion properties of the ethanol-water mixture in zeolite membranes. These properties are modeled using the real adsorption solution theory and the Maxwell-Stefan equations to describe permeation through the zeolite membranes. A comparison of steady state permeance and selectivity predicted by the model with the values from experiments suggests that the simulated membranes are more permeable and selective than the real membranes. This is attributed to the presence of structural non-idealities and hydrophilic defects in real membranes, while the adsorption and diffusion properties obtained using molecular simulations reflect the behavior in ideal crystals. Thus, a reduced diffusivity model with an empirical relation for enhanced water adsorption is used to capture similar performance as that obtained by real membranes. This model is further used in developing conceptual process designs to assess the viability of zeolite membranes for bioethanol enrichment in industry. Both hydrophobic and hydrophilic zeolite membranes are considered. Hydrophobic zeolite membranes show potential for energy savings but lack in separation performance. On the other hand, hydrophilic zeolite membranes can achieve the separation target but result in no energy savings. Thus, a configuration that uses a combination of hydrophobic and hydrophilic membranes is proposed. It can achieve the separation target and results in 15% energy savings over distillation. Techno-economic analysis suggests that ~ 10-fold improvements in permeation or equivalent cost reductions are required for economic viability of this scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Reaction rates for reaction-diffusion kinetics on unstructured meshes

The reaction-diffusion master equation is a stochastic model often utilized in the study of biochemical reaction networks in living cells. It is applied when the spatial distribution of molecules is important to the dynamics of the system. A viable approach to resolve the complex geometry of cells accurately is to discretize space with an unstructured mesh. Diffusion is modeled as discrete jumps between nodes on the mesh, and the diffusion jump rates can be obtained through a discretization of the diffusion equation on the mesh. Reactions can occur when molecules occupy the same voxel. Here in this paper, we develop a method for computing accurate reaction rates between molecules occupying the same voxel in an unstructured mesh. For large voxels, these rates are known to be well approximated by the reaction rates derived by Collins and Kimball, but as the mesh is refined, no analytical expression for the rates exists. We reduce the problem of computing accurate reaction rates to a pure preprocessing step, depending only on the mesh and not on the model parameters, and we devise an efficient numerical scheme to estimate them to high accuracy. We show in several numerical examples that as we refine the mesh, the results obtained with the reaction-diffusion master equation approach those of a more fine-grained Smoluchowski particle-tracking model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A mechanistic, multiscale model for predicting Pd penetration in TRISO fuels using BISON

TRistructural ISOtropic (TRISO) particles use silicon carbide (SiC) as the primary structural member and barrier against metallic fission product (FP) release. palladiums (PDs), produced by fission in the fuel kernel, can diffuse to and chemically interact with the SiC layer, degrading its structural integrity and ability to contain radioactive FPs. Existing temperature-dependent correlations for predicting Pd penetration rely on experimental data with significant scatter due to varying conditions, potentially complicating ongoing fuel qualification and licensing efforts for advanced reactors that would subject TRISO fuels to operating conditions outside of those examined in the experiments. A mechanistic model of Pd production, transport, and reaction is developed in this work to better understand and predict PD attack of SiC in TRISO particles. molecular dynamicss (MDs) simulations are utilized to calculate the Pd diffusivity in SiC grain bulk and grain boundaries. A mesoscale phase-field diffusion model, informed by the MD diffusivities, is used to develop a reduced order model (ROM) for the effect of SiC microstructure on the Pd penetration rate. The engineering scale BISON model calculates Pd production and transport, utilizing the ROM to predict penetration rates consistent with experimental data. This novel mechanistic ROM captures the effect of temperature, microstructure, and irradiation history on Pd penetration. In conclusion, these new capabilities are expected to support ongoing qualification and licensing efforts associated with near-term TRISO-fueled reactor applications.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1995

Simulated interannual variability of the Greenland Sea deep water formation and its connection to surface forcing

A fully prognostic Arctic ice-ocean model is used to study the interannual variability of deepwater formation in the Greenland Sea Gyre based on the simulations for the Arctic ice-ocean system for the period 1955 and 1960 - 1985. The model uses monthly climatology for thermodynamic forcing components (such as air temperature and cloudiness), together with constant annual net precipitation and river runoff. The daily wind forcing is derived from analyzed sea level air pressures from the National Center for Atmospheric Research (NCAR). In summary, the model shows that the occurence of deep convection in the Greenland Sea Gyre is controlled by the extensive Fram Strait ice export and/or local wind conditions in the Greenland Sea. In the latter case the weakening of the local wind curl allows the Polar Front to move eastward. The movement of the Polar Front causes adverse ice conditions, often together with much larger than normal ice export from the Arctic, such as in 1968, which can block convection in the gyre. The density difference between upper and lower layers is investigated as an indication of water mass formation through convection, occurring as strong diffusion in the model. The model-simulated density difference between the average top 100 m and deep levels reveals that the period 1960 - 1985 had only a few distinct years with weak stratification, and, especially, the model predicts no deep convection since the nid-1970s. The common factor for the years of the weakest decrease of the model-predicted heat content of the upper 2000 m which can, to a high degree, be explained by local heat loss.

Haekkinen, Sirpa↗
DOE OSTI2017

Quantitative characterization of soil micro-aggregates: New opportunities from sub-micron resolution synchrotron X-ray microtomography

Soil microaggregates are the fundamental building block, at the micron scale, of the highly hierarchical structure of soils, and can exert a significant control on the local biological metabolism and microbial community partitioning. In this study we propose an analysis protocol for the morphometric characterization of complete soil microaggregates based on sub-micron resolution synchrotron X-ray microtomography. A comprehensive characterization of the aggregate morphology is the first step towards a complete characterization of the soil microaggregates, when trying to correlate morphometric parameters with physical and/or biological properties, or when building models (e.g., effective diffusivity, microbial distribution, etc.). In this study, we demonstrate our characterization approach on two single microaggregate samples from dramatically different soil environments: one from Kansas, primarily composed by inorganic particles, and one from Barrow (Alaska) dominated by plant fragments. A series of state-of-the-art morphometric analysis techniques have been employed providing quantitative results highlighting specific differences of the two samples. The role of the microstructure in a scenario microbial population development has been discussed and it has been found that the Barrow microaggregate seems to be more favorable, from a purely geometrical point of view, as also confirmed by a simple model presented in this work. Finally, the potential of this approach, when coupled with chemical and biological analysis for a fully comprehensive characterization of soil aggregates in the larger picture of enhanced biological activity, is evident.

54 ENVIRONMENTAL SCIENCES↗