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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Predicting dust emission using galactic 21 cm data

Understanding large-angular-scale galactic foregrounds is crucial for future CMB experiments aiming to detect B-mode polarization from primordial gravitational waves. Traditionally, the dust component has been separated using its different frequency dependence. However, using non-CMB observations has potential to increase fidelity and decrease the re-construction noise. In this exploratory paper we investigate the capability of galactic 21 cm observations to predict the dust foreground in intensity. We train a neural network to predict the dust foreground as measured by the Planck Satellite from the full velocity data-cube of galactic 21 cm emission as measured by the HI4PI survey. We demonstrate that information in the velocity structure clearly improves the predictive power over both a simple integrated emission model and a simple linear model. The improvement is significant at arc-minute scales but more modest at degree scales. Finally, this proof of principle on temperature data indicates that it might also be possible to improve foreground polarization templates from the same input data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1990

Orbital debris removal and salvage system

Four Texas A&M University projects are discussed. The first project is a design to eliminate a majority of orbital debris. The Orbital Debris and Salvage System will push the smaller particles into lower orbits where their orbits will decay at a higher rate. This will be done by momentum transfer via laser. The salvageable satellites will be delivered to the Space Station by an Orbital Transfer Vehicle. The rest of the debris will be collected by Salvage I. The second project is the design of a space based satellite system to prevent the depletion of atmospheric ozone. The focus is on ozone depletion in the Antarctic. The plan is to use an orbiting solar array system designed to transmit microwaves at a frequency of 22 GHz over the region in order to dissipate polar stratospheric clouds that form during the months beginning in August and ending in October. The third project, Project Poseidon, involves a conceptual design of a space based hurricane control system consisting of a network of 21 low-orbiting laser platforms arranged in three rings designed to heat the upper atmosphere of a developing tropical depression. Fusion power plants are proposed to provide power for the lasers. The fourth project, Project Donatello, involves a proposed Mars exploration initiative for the year 2050. The project is a conceptual design for a futuristic superfreighter that will transport large numbers of people and supplies to Mars for the construction of a full scale scientific and manufacturing complex.

Source record↗
NASA NTRS2005

Comparison of Fiber Optic Strain Demodulation Implementations

NASA Langley Research Center is developing instrumentation based upon principles of Optical Frequency-Domain Reflectometry (OFDR) for the provision of large-scale, dense distribution of strain sensors using fiber optics embedded with Bragg gratings. Fiber Optic Bragg Grating technology enables the distribution of thousands of sensors immune to moisture and electromagnetic interference with negligible weight penalty. At Langley, this technology provides a key component for research and development relevant to comprehensive aerospace vehicle structural health monitoring. A prototype system is under development that includes hardware and software necessary for the acquisition of data from an optical network and conversion of the data into strain measurements. This report documents the steps taken to verify the software that implements the algorithm for calculating the fiber strain. Brief descriptions of the strain measurement system and the test article are given. The scope of this report is the verification of software implementations as compared to a reference model. The algorithm will be detailed along with comparison results.

Quach, Cuong C.↗
NASA NTRS2021

Sub-city Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.

C. Malings↗
DOE OSTI2014

Coronal bright points associated with minifilament eruptions

Coronal bright points (CBPs) are small-scale, long-lived coronal brightenings that always correspond to photospheric network magnetic features of opposite polarity. In this paper, we subjectively adopt 30 CBPs in a coronal hole to study their eruptive behavior using data from the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory. About one-quarter to one-third of the CBPs in the coronal hole go through one or more minifilament eruption(s) (MFE(s)) throughout their lifetimes. The MFEs occur in temporal association with the brightness maxima of CBPs and possibly result from the convergence and cancellation of underlying magnetic dipoles. Two examples of CBPs with MFEs are analyzed in detail, where minifilaments appear as dark features of a cool channel that divide the CBPs along the neutral lines of the dipoles beneath. The MFEs show the typical rising movements of filaments and mass ejections with brightenings at CBPs, similar to large-scale filament eruptions. Via differential emission measure analysis, it is found that CBPs are heated dramatically by their MFEs and the ejected plasmas in the MFEs have average temperatures close to the pre-eruption BP plasmas and electron densities typically near 10{sup 9} cm{sup –3}. These new observational results indicate that CBPs are more complex in dynamical evolution and magnetic structure than previously thought.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Mechanism of co-seismic water level change following four great earthquakes – insights from co-seismic responses throughout the Chinese mainland

In this study, we analyze the co-seismic groundwater level responses to four great earthquakes recorded by China's network of groundwater monitoring wells. The large number of operational wells (164 wells for the 2007 Mw 8.5 Sumatra earthquake, 245 wells for the Mw 7.9 Wenchuan earthquake, 228 wells for the Mw 9.0 Tohoku earthquake and 223 wells for 2012 Mw 8.6 Sumatra earthquake) and co-seismic responses provide an opportunity to test hypotheses on mechanisms for co-seismic water level changes. Overall, the co-seismic water level responses are complex over large spatial scales, and there is great variability both in the sign and amplitude of water level responses in the data set. As shown in previous studies, permeability change, rather than static strain, is a more plausible mechanism to explain most of the co-seismic responses. However, we find through tidal analysis of water level responses to solid Earth tide that only one third of these wells that showed a sustained post-seismic response can be explained by earthquake-induced permeability change in aquifers, and these wells had sustained (>30 days) water level changes. Lastly, wells that did not show sustained changes are more likely affected by permeability changes only immediately adjacent to the wellbore.

58 GEOSCIENCES↗
DOE OSTI2023

HydraGNN 2.0

HydraGNN is an Oak Ridge National Laboratory (ORNL)-branded implementation of distributed multi-tasking graph neural networks that supports several scientific applications within the ORNL portfolio to support the US-DOE mission. New or improved capabilities included in v2.0.0 release are as follows: 1) Enhancement in message passing layers through class inheritance 2) Adding transformation to ensure translation and rotation invariance 3) Supporting various optimizers 4) Atomic descriptors 5) Integration with continuous CI test 6) Distributed printouts and timers 7) Profiling 8) Support of ADIOS2 for scalable data loading 9) Large-scale system support, including Summit (ORNL) and Perlmutter (NERSC)

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2022

General-Purpose Bayesian Tensor Learning With Automatic Rank Determination and Uncertainty Quantification

A major challenge in many machine learning tasks is that the model expressive power depends on model size. Low-rank tensor methods are an efficient tool for handling the curse of dimensionality in many large-scale machine learning models. The major challenges in training a tensor learning model include how to process the high-volume data, how to determine the tensor rank automatically, and how to estimate the uncertainty of the results. While existing tensor learning focuses on a specific task, this paper proposes a generic Bayesian framework that can be employed to solve a broad class of tensor learning problems such as tensor completion, tensor regression, and tensorized neural networks. We develop a low-rank tensor prior for automatic rank determination in nonlinear problems. Our method is implemented with both stochastic gradient Hamiltonian Monte Carlo (SGHMC) and Stein Variational Gradient Descent (SVGD). We compare the automatic rank determination and uncertainty quantification of these two solvers. We demonstrate that our proposed method can determine the tensor rank automatically and can quantify the uncertainty of the obtained results. We validate our framework on tensor completion tasks and tensorized neural network training tasks.

Bayesian inference↗
NASA NTRS1992

High-performance computing for flight vehicles; Proceedings of the Symposium, Washington, Dec. 7-9, 1992

The present conference discusses high-performance computing systems for flight vehicles, large-scale simulations on high-performance flight computers and software, multidisciplinary and design/optimization applications of computers, computational electromagnetics and acoustics, the simulation of aircraft powerplant turbomachinery and reacting flows, and flow calculations on parallel machines. Also discussed are direct flow simulation Monte Carlo methods, structural mechanics sensitivity and fracture calculations on parallel machines, grid-generation and advanced algorithms for CFD, advanced solid-mechanics and structures applications, and advancements in flow visualization technology and neural networks.

Noor, Ahmed K.↗
NASA NTRS2009

Carbon Nanotube Based Chemical Sensors for Space and Terrestrial Applications

A nanosensor technology has been developed using nanostructures, such as single walled carbon nanotubes (SWNTs), on a pair of interdigitated electrodes (IDE) processed with a silicon-based microfabrication and micromachining technique. The IDE fingers were fabricated using photolithography and thin film metallization techniques. Both in-situ growth of nanostructure materials and casting of the nanostructure dispersions were used to make chemical sensing devices. These sensors have been exposed to nitrogen dioxide, acetone, benzene, nitrotoluene, chlorine, and ammonia in the concentration range of ppm to ppb at room temperature. The electronic molecular sensing of carbon nanotubes in our sensor platform can be understood by intra- and inter-tube electron modulation in terms of charge transfer mechanisms. As a result of the charge transfer, the conductance of p-type or hole-richer SWNTs in air will change. Due to the large surface area, low surface energy barrier and high thermal and mechanical stability, nanostructured chemical sensors potentially can offer higher sensitivity, lower power consumption and better robustness than the state-of-the-art systems, which make them more attractive for defense and space applications. Combined with MEMS technology, light weight and compact size sensors can be made in wafer scale with low cost. Additionally, a wireless capability of such a sensor chip can be used for networked mobile and fixed-site detection and warning systems for military bases, facilities and battlefield areas.

Li, Jing↗
NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗
DOE OSTI2020

Corrigendum and Addendum: Helium flux effects on bubble growth and surface morphology in plasma-facing tungsten from large-scale molecular dynamics simulations (2019 Nucl. Fusion 59 066035)

Two of the simulations discussed in a prior article (Hammond et al 2019 Nucl. Fusion 59 066035) were affected by a simulation glitch. We repeated the affected calculations and discuss them here. Here, the overall conclusions are essentially unchanged, though the details are different. In particular, observations that we referred to as ‘concerted bursting’ were caused primarily by non-physical heating and cooling applied by the thermostat after most atoms’ velocities were deleted (for reasons that are not known for certain). The phenomenon of one bubble bursting and causing another nearby bubble to burst does exist, though its effects are much less spectacular in the absence of non-physical driving forces. The observation of an interconnected network of sub-surface cavities formed by burst bubbles is real, and the observation of holes on the surface 1–2 nm in diameter is also confirmed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1992

Latest developments in the display of large-scale ionospheric and thermospheric data sets

Over the past decade, data base sizes have continually increased and will continue to do so in the future. This problem of size is further compounded because the trend in present-day studies is to use data from many different locations and different instruments and then compare it with data from global scale physical models. The latter produce data bases of comparable if not even larger size. Much of the data can be viewed as 'image' time sequences and is most readily viewed on color display terminals. These data sets reside in national or owner-generated data bases linked together by computer networks. As the size increases, just moving this data around, taking 'quick-looks' at the data, or even storing it locally become severe problems compromising the scientific return from the data. Is the present-day technology with these analysis techniques being used in the best way? What are the prospects for reducing the storage and transmission size of the data sets? Examples of such problems and potential solutions are described in this paper.

Sojka, J. J.↗
DOE OSTI2022

From force chains to nonclassical nonlinear dynamics in cemented granular materials

In this letter, we present evidence for a mechanism responsible for the nonclassical nonlinear dynamics observed in many cemented granular materials that are generally classified as mesoscopic nonlinear elastic materials. We demonstrate numerically that force chains are created within the complex grain-pore network of these materials when subjected to dynamic loading. The interface properties between grains along with the sharp and localized increase of the stress occurring at the grain-grain contacts leads to a reversible decrease of the elastic properties at macroscopic scale and peculiar effects on the propagation of elastic waves when grain boundary properties are appropriately considered. These effects are observed for relatively small amplitudes of the elastic waves, i.e., within tens of microstrain, and relatively large wavelengths, i.e., orders of magnitude larger than the material constituents. The mechanics are investigated numerically using the hybrid finite-discrete-element method and match those observed experimentally using nonlinear resonant ultrasound spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2017

On Parallelizing Single Dynamic Simulation Using HPC Techniques and APIs of Commercial Software

Time-domain simulations are heavily used in today’s planning and operation practices to assess power system transient stability and post-transient voltage/frequency profiles following severe contingencies to comply with industry standards. Because of the increased modeling complexity, it is several times slower than real time for state-of-the-art commercial packages to complete a dynamic simulation for a large-scale model. With the growing stochastic behavior introduced by emerging technologies, power industry has seen a growing need for performing security assessment in real time. This paper presents a parallel implementation framework to speed up a single dynamic simulation by leveraging the existing stability model library in commercial tools through their application programming interfaces (APIs). Several high performance computing (HPC) techniques are explored such as parallelizing the calculation of generator current injection, identifying fast linear solvers for network solution, and parallelizing data outputs when interacting with APIs in the commercial package, TSAT. The proposed method has been tested on a WECC planning base case with detailed synchronous generator models and exhibits outstanding scalable performance with sufficient accuracy.

Diao, Ruisheng↗
DOE OSTI2015

Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation

ABSTRACT Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. IMPORTANCE Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.

Noecker, Cecilia↗
DOE OSTI2019

PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks

Abstract Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model.

54 ENVIRONMENTAL SCIENCES↗