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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Magnetohydrodynamic Jump Conditions for Oblique Relativistic Shocks with Gyrotropic Pressure

Shock jump conditions, i.e., the specification of the downstream parameters of the gas in terms of the upstream parameters, are obtained for steady-state, plane shocks with oblique magnetic fields and arbitrary flow speeds. This is done by combining the continuity of particle number flux and the electromagnetic boundary conditions at the shock with the magnetohydrodynamic conservation laws derived from the stress-energy tensor. For ultrarelativistic and nonrelativistic shocks, the jump conditions may be solved analytically. For mildly relativistic shocks, analytic solutions are obtained for isotropic pressure using an approximation for the adiabatic index that is valid in high sonic Mach number cases. Examples assuming isotropic pressure illustrate how the shock compression ratio depends on the shock speed and obliquity. In the more general case of gyrotropic pressure, the jump conditions cannot be solved analytically with- out additional assumptions, and the effects of gyrotropic pressure are investigated by parameterizing the distribution of pressure parallel and perpendicular to the magnetic field. Our numerical solutions reveal that relatively small departures from isotropy (e.g., approximately 20%) produce significant changes in the shock compression ratio, r , at all shock Lorentz factors, including ultrarelativistic ones, where an analytic solution with gyrotropic pressure is obtained. In particular, either dynamically important fields or significant pressure anisotropies can incur marked departures from the canonical gas dynamic value of r = 3 for a shocked ultrarelativistic flow and this may impact models of particle acceleration in gamma-ray bursts and other environments where relativistic shocks are inferred. The jump conditions presented apply directly to test-particle acceleration, and will facilitate future self-consistent numerical modeling of particle acceleration at oblique, relativistic shocks; such models include the modification of the fluid velocity profile due to the contribution of energetic particles to the momentum and energy fluxes.

Double, Glen P.↗
DOE OSTI2025

Neural network-based model of galaxy power spectrum: fast full-shape galaxy power spectrum analysis

ABSTRACT We present a neural network-based emulator for the galaxy redshift-space power spectrum that enables several orders of magnitude acceleration in the galaxy clustering parameter inference, while preserving 3$\sigma$ accuracy better than 0.5 per cent up to $k_{\mathrm{max}}$ = 0.25 $\, h\text{Mpc}^{-1}$ within Lambda-cold dark matter ($\Lambda$CDM) and around 0.5 per cent $w_0$–$w_a$CDM. Our surrogate model only emulates the galaxy bias-invariant terms of one-loop perturbation theory predictions, these terms are then combined analytically with galaxy bias terms, counter-terms, and stochastic terms in order to obtain the non-linear redshift-space galaxy power spectrum. This allows us to avoid any galaxy bias prescription in the training of the emulator, which makes it more flexible. Moreover, we include the redshift $z \in [0,1.4]$ in the training which further avoids the need for re-training the emulator. We showcase the performance of the emulator in recovering the cosmological parameters of $\Lambda$CDM by analysing the suite of 25 AbacusSummit simulations that mimic the Dark Energy Spectroscopic Instrument luminous red galaxies at $z=0.5$ and 0.8, together as the emission line galaxies at $z=0.8$. We obtain similar performance in all cases, demonstrating the reliability of the emulator for any galaxy sample at any redshift in $0 \lt z \lt 1.4$. We will make our emulator public at github repository.

Trusov, Svyatoslav (ORCID:0000000224146720)↗
DOE OSTI2022

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic experiments, and with accelerators like GPUs and TPUs making inference using large models rapid, genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold v.2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 40,526 protein sequences, corresponding to four prokaryotic proteomes and one plant proteome, using under 4,400 total Summit node hours, equivalent to using the majority of the supercomputer for a little over one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.

Gao, Mu↗
NASA NTRS1973

S-band transponder experiment

The purpose of this experiment was to measure the variations in the lunar gravitational field near the trajectory of orbiting space vehicles (the command and service module (CSM) and the small particles and fields subsatellites ejected from the Apollo 15 and 16 spacecraft). New information has been obtained from all Apollo orbiting spacecraft; however, this report shall be limited to the results from the Apollo 17 CSM and the Apollo 16 subsatellite. The data acquired are precise speed measurements of the orbiting spacecraft from which accelerations or gravity profiles may be inferred. Feature resolution is controlled by the spacecraft altitude and is almost a direct relationship (i.e., data taken from a 50-km altitude will resolve approximately a 50-km feature). Therefore, revolutions 3 to 12, when the CSM was in the low-altitude orbits, provided the clearest information.

Sjogren, W. L.↗
NASA NTRS2002

A Study of the Non-Thermal X-Ray Emission of Shell-Type Supernova Remnants

The term of the second year of the award is the period from March 15, 2001 to March 14, 2002. As was the specified goal of the second year, we analyzed the spatial and spectral X-ray data for several young supernova remnants. I published a paper about an analysis of the ROSAT, ASCA, and RXTE data for the supernova remnant SN 1006. A copy of this paper is enclosed. As described in the paper, we believe that we accurately modeled the nonthermal X-ray emission from the remnant. The results of this analysis are used to infer properties about the cosmic rays accelerated in the remnant and to argue that the strength of the magnetic field in the remnant is considerably larger than the value of about 10 micro G reported elsewhere. The results were presented at the August 2001 International Cosmic Ray Conference in Hamburg, German),. I began analyzing new Chandra X-ray data for SN 1006. This analysis will yield the first measure of the strength of the magnetic field in the remnant for the first time. Preliminary results support our previous conclusion that the magnetic field strength in the remnant is much larger than 10 micro G. The field strength seems to be about the strength expected based on an equipartition calculation. The result supports recent models that describe the how the shock structure is influenced by the efficient acceleration of cosmic rays. This work will be presented at the April 2002 High Energy Astrophysics Division meeting in Albuquerque and published this summer. A copy of the abstract for the talk is enclosed. I began studying new Chandra X-ray data for the supernova remnant Cas A. The results of this work show that the forward shock is a region where cosmic-ray electrons are accelerated, which is consistent with theoretical expectations. The work was presented at the September 2001 Two Years of Science with Chandra symposium in Washington, DC. A copy of the poster paper is enclosed. Dr. Thomas Pannuti, whose research work is supported by the award, analyzed ROSAT, ASCA, and RXTE data for the supernova remnant G347.3-0.5. The results show for the first time that thermal X-ray emission is produced in the remnant. As expected, the thermal emission is consistent with a model in which the remnant is expanding into a very low density environment. The results also provide an accurate description of the nonthermal emission from the remnant. Dr. Pannuti presented this work at several conferences. A copy of the paper for the proceedings of the August 2001 Neutron Stars in Supernova Remnants symposium in enclosed. The work will be submitted to the Astrophysical Journal in the next few months.

Allen, Glenn E.↗
NASA NTRS2004

Aeolian Sediment Transport Pathways and Aerodynamics at Troughs on Mars

Interaction between wind regimes and topography can give rise to complex suites of aeolian landforms. This paper considers aeolian sediment associated wit11 troughs on Mars and identifies a wider range of deposit types than has previously been documented. These include wind streaks, falling dunes, "lateral" dunes, barchan dunes, linear dunes, transverse ridges, sand ramps, climbing dunes, sand streamers, and sand patches. The sediment incorporated into these deposits is supplied by wind streaks and ambient Planitia sources as well as originating within the trough itself, notably from the trough walls and floor. There is also transmission of sediment between d~~neTsh. e flow dynamics which account for the distribution of aeolian sediment have been modeled using two-dimensional computational fluid dynamics. The model predicts flow separation on the upwind side of the trough followed by reattachment and acceleration at the downwind margin. The inferred patterns of sediment transport compare well with the distribution of aeolian forms. Model data indicate an increase of wind velocity by approx. 30 % at the downwind trough margin. This suggests that the threshold wind speed necessary for sand mobilization on Mars will be more freq~~entmlye t in these inclined locations.

Bourke, Mary C.↗
NASA NTRS2005

Electron Density Images of the Middle and High Latitude Magnetosphere in Response to the Solar Wind

Electron density images and plasma dynamics in the middle and high latitude dayside magnetosphere are studied with the IMAGE/RPI remote measurements of the electron densities along magnetic field lines, measured before and during a magnetic storm when the solar wind and interplanetary magnetic field (IMF) impinging on the magnetopause varied considerably. Several regions of different density distribution characteristics, including plasmasphere, plasma trough, sub-auroral/auroral density depletion, density enhancements in the aurora/cusp, and polar cap, are identified in 'two dimensional images', i.e., along the satellite orbit and field lines. The plasma dynamics, such as the plasma refilling in the outer plasmasphere and the plasma acceleration in the aurora/cusp region are inferred from density gradients along the field lines. It is shown that the densities and locations of the plasma regions vary in accordance with the solar wind, particularly with the IMF variations for the case examined. The changes in these regions reflect the way the magnetosphere reconfigures in response to changes in the solar wind.

Tu, Jiannan↗
DOE OSTI2023

Disentangling Beam Losses in The Fermilab Main Injector Enclosure Using Real-Time Edge AI

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler Ring. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Deciphering the origin of any of the 260 BLM readings is often difficult. The (Accelerator) Real-time Edge AI for Distributed Systems project, or READS, has developed an AI/ML model, and implemented it on fast FPGA hardware, that disentangles mixed beam losses and attributes probabilities to each BLM as to which machine(s) the loss originated from in real-time. The model inferences are then streamed to the Fermilab accelerator controls network (ACNET) where they are available for operators and experts alike to aid in tuning the machines.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models

Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining computational efficiency. However, MoEs introduce several inference-time challenges, including load imbalance across experts and the additional routing computational overhead. To address these challenges and fully harness the benefits of MoE, a systematic evaluation of hardware acceleration techniques is essential. We present MoE-Inference-Bench, a comprehensive study to evaluate MoE performance across diverse scenarios. We analyze the impact of batch size, sequence length, and critical MoE hyperparameters such as FFN dimensions and number of experts on throughput. We evaluate several optimization techniques on Nvidia H100 GPUs, including pruning, Fused MoE operations, speculative decoding, quantization, and various parallelization strategies. Our evaluation includes MoEs from the Mixtral, DeepSeek, OLMoE and Qwen families. The results reveal performance differences across configurations and provide insights for the efficient deployment of MoEs.

Chitty-Venkata, Krishna Teja↗
DOE OSTI2026

First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference

To enable an accurate determination of oscillation parameters, accelerator-based neutrino experiments require detailed simulations of nuclear interaction physics in the GeV regime. While substantial effort from both theory and experiment is currently being invested to improve the fidelity of these simulations, their present deficiencies typically oblige experimental collaborations to resort to empirical tuning of simulation model parameters. As the precision requirements of the field continue to become more stringent, machine learning techniques may provide a powerful means of handling corresponding growth in the complexity of future neutrino interaction model tuning exercises. To study the suitability of simulation-based inference (SBI) for this physics application, in this paper we revisit a tuned configuration of the GENIE neutrino event generator that was originally developed by the MicroBooNE collaboration. Despite closely reproducing the adopted values of four physics parameters when confronted with the tuned cross-section predictions as input, we find that our trained SBI algorithm prefers modestly different values (within MicroBooNE's assigned uncertainties) and achieves slightly better goodness-of-fit when inference is run on the experimental data set originally used by MicroBooNE. We also find that our trained algorithm can create a fair approximation of an alternative neutrino scattering simulation, NuWro, that shares only a subset of its physics model parameters with GENIE.

Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗
DOE OSTI2020

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗
NASA NTRS2002

Broadband Observations and Modeling of the Shell-Type Supernova Remnant G347.3-0.5

The supernova remnant G347.3-0.5 emits a featureless power law in X-rays, thought to indicate shock acceleration of electrons to high energies. We here produce a broadband spectrum of the bright northwest limb of this source by combining radio observations from the Australia Telescope Compact Array (ATCA), X-ray observations from the Advanced Satellite for Cosmology and Astrophysics (ASCA), and TeV gamma-ray observations from the CANGAROO imaging Cerenkov telescope. We assume that this emission is produced by an electron population generated by diffusive shock acceleration at the remnant forward shock. The nonlinear aspects of the particle acceleration force a connection between the widely different wavelength bands and between the electrons and the unseen ions, presumably accelerated simultaneously with the electrons. This allows us to infer the relativistic proton spectrum and estimate ambient parameters such as the supernova explosion energy, magnetic field, matter density in the emission region, and efficiency of the shock acceleration process. We find convincing evidence that the shock acceleration is efficient, placing greater than 25% of the shock kinetic energy flux into relativistic ions. Despite this high efficiency, the maximum electron and proton energies, while depending somewhat on assumptions for the compression of the magnetic field in the shock, are well below the observed 'knee' at 10(exp 15) eV in the Galactic cosmic-ray spectrum.

Ellison, Donald C.↗
NASA NTRS1992

Patterns of seismic activity preceding large earthquakes

A mechanical model of seismic faults is employed to investigate the seismic activities that occur prior to major events. The block-and-spring model dynamically generates a statistical distribution of smaller slipping events that precede large events, and the results satisfy the Gutenberg-Richter law. The scaling behavior during a loading cycle suggests small but systematic variations in space and time with maximum activity acceleration near the future epicenter. Activity patterns inferred from data on seismicity in California demonstrate a regional aspect; increased activity in certain areas are found to precede major earthquake events. One example is given regarding the Loma Prieta earthquake of 1989 which is located near a fault section associated with increased activity levels.

Shaw, Bruce E.↗
NASA NTRSDate not supplied

The Significance of Helium in the Solar Wind: Insights from 1 AU

Doubly ionized helium is the 2nd most abundant element in the solar wind. The solar wind’s properties are a function of the source region on the Sun from which it emanates. This includesthe abundances of solar wind Helium and rarer, heavier elements. The speed of the solar wind, when observed near Earth, is also related to the type of source region from which the solar wind originated. Early solar wind models are unable to explain how the solar wind achieved the asymptotically fastest, non-transient speeds observed at 1 AU and the solar wind must be accelerated during transit through interplanetary space. Only recently have observations shown that the solar wind from different source regions is accelerated by different mechanisms. However, the solar wind speed is also known to be an insufficient for identifying the type of solar source from which it originated, likely because these different acceleration mechanisms accelerate the solar wind into two different speed regimes that overlap. Furthermore, the mechanism that provides the coronal plasma sufficient energy to accelerate into the solar wind is still undetermined. These mysteries are further complicated by the Sun’s evolution through its 11-year activity cycle and the impact of the solar cycle on in situ observations. Using solar wind observations of the helium abundance from the Wind spacecraft and heavy ion abundances from the Advanced Composition Explorer (ACE) along with observations of the Sunspot Number – a long-studied proxy for the Sun’s activity – we will discuss the imprint of different solar wind source regions on observations collected at 1 AU. We will draw inferences about the role of helium in the acceleration of coronal plasma into the solar wind. Using the Alfvénicity – a metric related to the solar wind acceleration that occurs during transit through interplanetary space – we will derive a solar wind classification scheme for near-Earth observations that is related to the differences in the source regions from which it emanates.

Benjamin Alterman↗
NASA NTRS2005

Constraining Hydrological and Cryospheric Mass Flux in Southeastern Alaska using Space-Based Gravity Measurements

Watersheds draining into the Gulf of Alaska (GoA) experience large seasonal and inter-annual variations of water in the form of rain, snow, and ice, but accurate constraints on these variations have been difficult to obtain. Over larger geographic regions, water variations can be inferred directly from the Gravity Recovery and Climate Experiment (GRACE) data. However, because GoA variations occur over such a small region, the inferred average value of water flux increases as the applied smoothing of the GRACE data decreases. We use this observed scaling together with scaling results obtained from forward models to infer a seasonal amplitude of 115 plus or minus 20 cubic kilometers of water and an average contribution to sea level rise over the two years of data of 0.31 plus or minus 0.09 millimeters per year. These results suggest that accelerated melting that began in the late 1990s, as inferred from altimetry, continues unabated.

Tamisiea, M. E.↗
NASA NTRS1986

Heating of upflowing ionospheric ions on auroral field lines

The upflow of ionospheric ions along auroral field lines is investigated on the basis of nearly simultaneous particle measurements obtained with DE-1 at 9000-15,000 km and DE-2 at 400-800 km during four auroral conjunction events in 1981. The data are presented in extensive graphs and maps and characterized in detail. The distributions of the upflowing ions are shown to be consistent with acceleration of a Maxwellian by a parallel electric field, but from the energy levels involved (hundreds of eV) it is inferred that an ionospheric source is being heated within or above the acceleration region, possibly by an ion two-stream instability.

Reiff, P. H.↗
DOE OSTI2020

Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures

The discovery and design of biologically important RNA molecules is outpacing three-dimensional structural characterization. In this work, we demonstrate that cryo-electron microscopy can routinely resolve maps of RNA-only systems and that these maps enable subnanometer-resolution coordinate estimation when complemented with multidimensional chemical mapping and Rosetta DRRAFTER computational modeling. Furthermore ,this hybrid ‘Ribosolve’ pipeline detects and falsifies homologies and conformational rearrangements in 11 previously unknown 119- to 338-nucleotide protein-free RNA structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length Vibrio cholerae and Fusobacterium nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and the computer-designed ATP-TTR-3 aptamer with and without AMP. Simulation benchmarks, blind challenges, compensatory mutagenesis, cross-RNA homologies and internal controls demonstrate that Ribosolve can accurately resolve the global architectures of RNA molecules but does not resolve atomic details. These tests offer guidelines for making inferences in future RNA structural studies with similarly accelerated throughput.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Insights into the Oxidative Degradation Mechanism of Solid Amine Sorbents for CO 2 Capture from Air: Roles of Atmospheric Water

Abstract Direct air capture (DAC) processes for extraction of CO 2 from ambient air are unique among chemical processes in that they operate outdoors with minimal feed pretreatments. Here, the impact of humidity on the oxidative degradation of a prototypical solid supported amine sorbent, poly(ethylenimine) (PEI) supported on Al 2 O 3 , is explored in detail. By combining CO 2 adsorption measurements, oxidative degradation rates, elemental analyses, solid‐state NMR and in situ IR spectroscopic analysis in conjunction with 18 O labeling of water, a comprehensive picture of sorbent oxidation is achieved under accelerated conditions. We demonstrated that the presence of water vapor can play an important role in accelerating the degradation reactions. From the study we inferred the identity and kinetics of formation of the major oxidative products, and the role(s) of humidity. Our data are consistent with a radical mediated autooxidative degradation mechanism.

Carneiro, Juliana S. A.↗