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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

THREE-DIMENSIONAL STELLAR KINEMATICS AT THE GALACTIC CENTER: MEASURING THE NUCLEAR STAR CLUSTER SPATIAL DENSITY PROFILE, BLACK HOLE MASS, AND DISTANCE

We present three-dimensional (3D) kinematic observations of stars within the central 0.5 pc of the Milky Way (MW) nuclear star cluster (NSC) using adaptive optics imaging and spectroscopy from the Keck telescopes. Recent observations have shown that the cluster has a shallower surface density profile than expected for a dynamically relaxed cusp, leading to important implications for its formation and evolution. However, the true 3D profile of the cluster is unknown due to the difficulty in de-projecting the stellar number counts. Here, we use spherical Jeans modeling of individual proper motions and radial velocities to constrain, for the first time, the de-projected spatial density profile, cluster velocity anisotropy, black hole mass (M {sub BH}), and distance to the Galactic center (R {sub 0}) simultaneously. We find that the inner stellar density profile of the late-type stars, ρ(r)∝r {sup –γ}, have a power law slope γ=0.05{sub −0.60}{sup +0.29}, much more shallow than the frequently assumed Bahcall-Wolf slope of γ = 7/4. The measured slope will significantly affect dynamical predictions involving the cluster, such as the dynamical friction time scale. The cluster core must be larger than 0.5 pc, which disfavors some scenarios for its origin. Our measurement of M{sub BH}=5.76{sub −1.26}{sup +1.76}×10{sup 6} M {sub ☉} and R{sub 0}=8.92{sub −0.55}{sup +0.58} kpc is consistent with that derived from stellar orbits within 1'' of Sgr A*. When combined with the orbit of S0-2, the uncertainty on R {sub 0} is reduced by 30% (8.46{sub −0.38}{sup +0.42} kpc). We suggest that the MW NSC can be used in the future in combination with stellar orbits to significantly improve constraints on R {sub 0}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Nested solitons in two-field fuzzy dark matter

ABSTRACT Dark matter as scalar particles consisting of multiple species is well motivated in string theory where axion fields are ubiquitous. A two-field fuzzy dark matter (FDM) model features two species of ultralight axion particles with different masses, m1 ≠ m2, which is extended from the standard one-field model with $m_a \sim 10^{-22} \, {\rm eV}$. Here we perform numerical simulations to explore the properties of two-field FDM haloes. We find that the central soliton has a nested structure when m2 ≫ m1, which is distinguishable from the generic flat-core soliton in one-field haloes. However, the formation of this nested soliton is subject to many factors, including the density fraction and mass ratio of the two fields. Finally, we study non-linear structure formation in two-field cosmological simulations with self-consistent initial conditions and find that the small-scale structure in two-field cosmology is also distinct from the one-field model in terms of DM halo counts and soliton formation time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Cohesive Relations for Surface Atoms in the Iron-Technetium Binary System

Iron-technetium alloys are of relevance to the development of waste forms for disposition of radioactive technetium-99 obtained from spent nuclear fuel. Corrosion of candidate waste forms is a function of the local cohesive energy () of surface atoms. A theoretical model for calculating is developed. Density functional theory was used to construct a modified embedded atom (MEAM) potential for iron-technetium. Materials properties determined for the iron-technetium system were in good agreement with the literature. To explore the relationship between local structure and corrosion, MEAM simulations were performed on representative iron-technetium alloys and intermetallics. Technetium-rich phases have lower , suggesting that these phases will be more noble than iron-rich ones. Quantitative estimates of based on numbers of nearest neighbors alone can lead to errors up to 0.5 eV. Consequently, atomistic corrosion simulations for alloy systems should utilize physics-based models that consider not only neighbor counts, but also local compositions and atomic arrangements.

Taylor, Christopher D.↗
DOE OSTI2016

A novel analysis method for paired-sample microbial ecology experiments

Many microbial ecology experiments use sequencing data to measure a community s response to an experimental treatment. In a common experimental design, two units, one control and one experimental, are sampled before and after the treatment is applied to the experimental unit. The four resulting samples contain information about the dynamics of organisms that respond to the treatment, but there are no analytical methods designed to extract exactly this type of information from this configuration of samples. Here we present an analytical method specifically designed to visualize and generate hypotheses about microbial community dynamics in experiments that have paired samples and few or no replicates. The method is based on the Poisson lognormal distribution, long studied in macroecology, which we found accurately models the abundance distribution of taxa counts from 16S rRNA surveys. To demonstrate the method s validity and potential, we analyzed an experiment that measured the effect of crude oil on ocean microbial communities in microcosm. Our method identified known oil degraders as well as two clades, Maricurvus and Rhodobacteraceae, that responded to amendment with oil but do not include known oil degraders. Furthermore, our approach is sensitive to organisms that increased in abundance only in the experimental unit but less sensitive to organisms that increased in both control and experimental units, thus mitigating the role of bottle effects .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

The role of density-dependent and –independent processes in spawning habitat selection by salmon in an Arctic riverscape

Density-dependent (DD) and density-independent (DI) habitat selection is strongly linked to a species’ evolutionary history. Determining the relative importance of each is necessary because declining populations are not always the result of altered DI mechanisms but can often be the result of DD via a reduced carrying capacity. Here, we developed spatially and temporally explicit models throughout the Chena River, Alaska to predict important DI mechanisms that influence Chinook salmon spawning success. We used resource-selection functions to predict suitable spawning habitat based on geomorphic characteristics, a semi-distributed water-and-energy balance hydrologic model to generate stream flow metrics, and modeled stream temperature as a function of climatic variables. Spawner counts were predicted throughout the core and periphery spawning sections of the Chena River from escapement estimates (DD) and DI variables. In addition, we used isodar analysis to identify whether spawners actively defend spawning habitat or follow an ideal free distribution along the riverscape. Aerial counts were best explained by escapement and reference to the core or periphery, while no models with DI variables were supported in the candidate set. Moreover, isodar plots indicated habitat selection was best explained by ideal free distributions, although there was strong evidence for active defense of core spawning habitat. These results are surprising, given salmon commonly defend spawning resources, and are likely due to competition occurring at finer spatial scales than addressed in this study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Computer vision-based rock bolt detection in orthomosaic imagery obtained in the Waste Isolation Pilot Plant underground facility

Assessing structural integrity of large underground tunnel facilities is often a time consuming and human-labor intensive task. Thus, research using various modes of sensing and automated detection of key structural components in mines is posed to aid in safety assessments and establishing overall structural health. We propose an approach utilizing off-the-shelf camera and lidar technology fixed to a custom sensing platform, image stitching techniques, fine-tuned object detection models, and specialized model-inference methods to automatically detect, count, and map roof bolts for assessment of structural safety in man-made underground tunnels. Results show a novel workflow for effective object counting in orthomosaic tunnel ceiling images generated from collections in GPS-denied mining environments. Additionally, we demonstrate effective fine-tuning of EfficientDet object detectors utilizing state-of-the-art image augmentation techniques known as the mosaic and mixup transformations. Our work is demonstrated on sensed data and imagery collected from the Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) where miles of tunnel ceiling must be assessed for structural integrity.

42 ENGINEERING↗
DOE OSTI2022

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

58 GEOSCIENCES↗
NASA NTRS1979

Scale covariance and G-varying cosmology. IV - The log N-log S relation

The traditional radio counts N(S) and radio source models are studied within the framework of the scale-covariant cosmology developed to investigate whether the relative strength of the gravitational and electromagnetic constants is a function of cosmological epoch. It is found that a gravitational constant G varying as the inverse of t, where t is the epoch in atomic units, is consistent with all the data analyzed. For a wide class of models the present cosmology allows a finer discrimination of the deceleration parameter than does standard theory. The results, when combined with those of previous papers, namely, those from radio and optical flux and angular-diameter data analysis, favor an open universe.

Canuto, V. M.↗
NASA NTRS1985

Test particle modeling of wave-induced energetic electron precipitation

A test particle computer model of the precipitation of radiation belt electrons is extended to compute the dynamic energy spectrum of transient electron fluxes induced by short-duration VLF wave packets traveling along the geomagnetic field lines. The model is adapted to estimate the count rate and associated spectrum of precipitated electrons that would be observed by satellite-based particle detectors with given geometric factor and orientation with respect to the magnetic field. A constant-frequency wave pulse and a lightning-induced whistler wave packet are used as examples of the stimulating wave signals. The effects of asymmetry of particle mirror heights in the two hemispheres and the atmospheric backscatter of loss cone particles on the computed precipitated fluxes are discussed.

Chang, H. C.↗
NASA NTRS2004

Parametric Testing of Chevrons on Single Flow Hot Jets

A parametric family of chevron nozzles have been studied, looking for relationships between chevron geometric parameters, flow characteristics, and far-field noise. Both cold and hot conditions have been run at acoustic Mach number 0.9. Ten models have been tested, varying chevron count, penetration, length, and chevron symmetry. Four comparative studies were defined from these datasets which show: that chevron length is not a major impact on either flow or sound; that chevron penetration increases noise at high frequency and lowers it at low frequency, especially for low chevron counts; that chevron count is a strong player with good low frequency reductions being achieved with high chevron count without strong high frequency penalty; and that chevron asymmetry slightly reduces the impact of the chevron. Finally, it is shown that although the hot jets differ systematically from the cold one, the overall trends with chevron parameters is the same.

Bridges, James↗
NASA NTRS2012

Oxygen-Methane Thruster

An oxygen-methane thruster was conceived with integrated igniter/injector capable of nominal operation on either gaseous or liquid propellants. The thruster was designed to develop 100 lbf (approximately 445 N) thrust at vacuum conditions and use oxygen and methane as propellants. This continued development included refining the design of the thruster to minimize part count and manufacturing difficulties/cost, refining the modeling tools and capabilities that support system design and analysis, demonstrating the performance of the igniter and full thruster assembly with both gaseous and liquid propellants, and acquiring data from this testing in order to verify the design and operational parameters of the thruster. Thruster testing was conducted with gaseous propellants used for the igniter and thruster. The thruster was demonstrated to work with all types of propellant conditions, and provided the desired performance. Both the thruster and igniter were tested, as well as gaseous propellants, and found to provide the desired performance using the various propellant conditions. The engine also served as an injector testbed for MSFC-designed refractory combustion chambers made of rhenium.

Pickens, Tim↗
NASA NTRS2016

Heuristics for Relevancy Ranking of Earth Dataset Search Results

As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.

science data management↗
NASA NTRS2017

Relevancy Ranking of Satellite Dataset Search Results

As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.

science data management↗
DOE OSTI2018

Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection

Due to globalization, geographic boundaries no longer serve as effective shields for the spread of infectious diseases. In order to aid bio-surveillance analysts in disease tracking, recent research has been devoted to developing information retrieval and analysis methods utilizing the vast corpora of publicly available documents on the internet. In this work, we present methods for the automated retrieval and classification of documents related to active public health events. We demonstrate classification performance on an auto-generated corpus, using recurrent neural network, TFIDF, and Naive Bayes log count ratio document representations. By jointly modeling the title and description of a document, we achieve 97% recall and 93.3% accuracy with our best performing bio-surveillance event classification model; logistic regression on the combined output from a pair of bidirectional recurrent neural networks.

language analysis, Biosurveillance, document class↗
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 OSTI2018

Generalized scaling relationships on transition metals: Influence of adsorbate-coadsorbate interactions

Linear scaling relations of adsorbate energies across a range of catalytic surfaces have emerged as a central interpretive paradigm in heterogeneous catalysis. They are, however, typically developed for low adsorbate coverages which are not always representative of realistic heterogeneous catalytic environments. Herein, we present generalized linear scaling relations on transition metals that explicitly consider adsorbate-coadsorbate interactions at variable coverages. The slopes of these scaling relations do not follow the simple bond counting principles that govern scaling on transition metals at lower coverages. In this work, the deviations from bond counting are explained using a pairwise interaction model wherein the interaction parameter determines the slope of the scaling relationship on a given metal at variable coadsorbate coverages, and the slope across different metals at fixed coadsorbate coverage is approximated by adding a coverage-dependent correction to the standard bond counting contribution. The analysis provides a compact explanation for coverage-dependent deviations from bond counting in scaling relationships and suggests a useful strategy for incorporation of coverage effects into catalytic trends studies.

36 MATERIALS SCIENCE↗