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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deep Learning-Based Dynamic Modeling of Three-Phase Voltage Source Inverters

Inverter-based resource (IBR) models are necessary to analyze modern power system stability and create effective control strategies. Modeling IBRs in converter-rich power systems is crucial, yet challenging due to the lack of commercial information on converter topologies and control parameters. This paper proposes novel convolutional neural network (CNN)–based data-driven techniques for modeling IBRs, addressing adaptability and proprietary concerns without requiring internal system physics knowledge. The proposed method is tested using real grid-tied commercial IBR transient data and demonstrates effectiveness and accuracy. Furthermore, the developed modeling approach is integrated and implemented in the open-source power distribution simulation and analysis tool, GridLAB-D, to illustrate the potentiality of dynamic analysis of large-scale power systems with high IBRs.

deep learning, artificial intelligence↗
DOE OSTI2024

Agent-Based Model of Combined Community- and Jail-Based Take-Home Naloxone Distribution

Importance Opioid-related overdose accounts for almost 80 000 deaths annually across the US. People who use drugs leaving jails are at particularly high risk for opioid-related overdose and may benefit from take-home naloxone (THN) distribution. Objective To estimate the population impact of THN distribution at jail release to reverse opioid-related overdose among people with opioid use disorders. Design, Setting, and Participants This study developed the agent-based Justice-Community Circulation Model (JCCM) to model a synthetic population of individuals with and without a history of opioid use. Epidemiological data from 2014 to 2020 for Cook County, Illinois, were used to identify parameters pertinent to the synthetic population. Twenty-seven experimental scenarios were examined to capture diverse strategies of THN distribution and use. Sensitivity analysis was performed to identify critical mediating and moderating variables associated with population impact and a proxy metric for cost-effectiveness (ie, the direct costs of THN kits distributed per death averted). Data were analyzed between February 2022 and March 2024. Intervention Modeled interventions included 3 THN distribution channels: community facilities and practitioners; jail, at release; and social network or peers of persons released from jail. Main Outcomes and Measures The primary outcome was the percentage of opioid-related overdose deaths averted with THN in the modeled population relative to a baseline scenario with no intervention. Results Take-home naloxone distribution at jail release had the highest median (IQR) percentage of averted deaths at 11.70% (6.57%-15.75%). The probability of bystander presence at an opioid overdose showed the greatest proportional contribution (27.15%) to the variance in deaths averted in persons released from jail. The estimated costs of distributed THN kits were less than $\$$15 000 per averted death in all 27 scenarios. Conclusions and Relevance This study found that THN distribution at jail release is an economical and feasible approach to substantially reducing opioid-related overdose mortality. Training and preparation of proficient and willing bystanders are central factors in reaching the full potential of this intervention.

Tatara, Eric [Argonne National Laboratory (ANL), A↗
DOE OSTI2022

Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity

The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how these internal variables represent a history of deformation, the lack of direct measurement of these internal variables for calibration and validation, and the weak physical underpinning of those phenomenological laws have long been criticized as barriers to creating realistic models. In this work, geometric machine learning on graph data (e.g. finite element solutions) is used as a means to establish a connection between nonlinear dimensional reduction techniques and plasticity models. Geometric learning-based encoding on graphs allows the embedding of rich time-history data onto a low-dimensional Euclidean space such that the evolution of plastic deformation can be predicted in the embedded feature space. Finally, a corresponding decoder can then convert these low-dimensional internal variables back into a weighted graph such that the dominating topological features of plastic deformation can be observed and analyzed.

42 ENGINEERING↗
DOE OSTI2021

Search for W' bosons decaying to a top and a bottom quark at s=13TeV in the hadronic final state

A search is performed for W' bosons decaying to a top and a bottom quark in the all-hadronic final state, in proton-proton collisions at a center-of-mass energy of 13 TeV. The analyzed data were collected by the CMS experiment between 2016 and 2018 and correspond to an integrated luminosity of 137 fb -1 . Deep neural network algorithms are used to identify the jet initiated by the bottom quark and the jet containing the decay products of the top quark when the W' boson from the top quark decays hadronically. No excess above the estimated standard model background is observed. Upper limits on the production cross sections of W' bosons decaying to a top and a bottom quark are set. Both left- and right-handed W' bosons with masses below 3.4 TeV are excluded at 95% confidence level, and the most stringent limits to date on W bosons decaying to a top and a bottom quark in the all-hadronic final state are obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

14 SOLAR ENERGY↗
DOE OSTI2024

Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile

Abstract The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.

58 GEOSCIENCES↗
DOE OSTI2022

Shared Mobility Data Availability and Usage Trends

In this report, we summarize data availability of new shared mobility technologies by mobility type and region and analyze how shared mobility usage varies by time and by demographic factors in the United States. The shared mobility technologies include ridehailing from transportation network companies (TNC), bikeshare, and scooter share. There is a wide range in shared mobility usage per capita across the country, even within urban areas. We observe that there was steady growth of new shared mobility usage from 2015 to early 2020, before COVID-19 reduced overall ridership. An analysis focused on Chicago shows that despite the availability of good public transportation, high usage of new shared mobility modes is centered in high income communities, especially by households who own less vehicles. However, we find that TNC is used for first-mile and last-mile in lower income communities. Analysis on the bikeshare usage also shows that household income is shown to not be a statistically significant factor when accounting for other factors including employment density, population density, percentage of college graduates, and bike-lane proximity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1972

Atmospheric pressure, density, temperature and wind variations between 50 and 200 km

Data on atmospheric pressure, density, temperature and winds between 50 and 200 km were collected from sources including Meteorological Rocket Network data, ROBIN falling sphere data, grenade release and pitot tube data, meteor winds, chemical release winds, satellite data, and others. These data were analyzed by a daily difference method and results on the distribution statistics, magnitude, and spatial structure of the irregular atmospheric variations are presented. Time structures of the irregular variations were determined by the analysis of residuals from harmonic analysis of time series data. The observed height variations of irregular winds and densities are found to be in accord with a theoretical relation between these two quantities. The latitude variations (at 50 - 60 km height) show an increasing trend with latitude. A possible explanation of the unusually large irregular wind magnitudes of the White Sands MRN data is given in terms of mountain wave generation by the Sierra Nevada range about 1000 km west of White Sands. An analytical method is developed which, based on an analogy of the irregular motion field with axisymmetric turbulence, allows measured or model correlation or structure functions to be used to evaluate the effective frequency spectra of scalar and vector quantities of a spacecraft moving at any speed and at any trajectory elevation angle.

Justus, C. G.↗
NASA NTRS1979

X-ray observations of the 5 March 1979 gamma-burst field

An extremely intense burst of hard X-rays and gamma-rays that was recorded by the nine interplanetary spacecraft of the burst sensor network and localized by time-of-flight determinations to a position coincident with the supernova remnant N49 in the Large Magellanic Cloud is analyzed. The region of the sky was observed both before and after the gamma-ray event with the soft X-ray imaging instruments aboard the Einstein Observatory. Coupled with the optical plate material, the soft X-ray data is used to place severe constraints on models for the origin of the transient phenomena.

Helfand, D. J.↗
NASA NTRS1982

A new high-power klystron for the DSN

A very high reliability 100 kW klystron for the Deep Space Network (DSN) high power transmitters in support of spacecrafts to the distant planets was studied. The last phases included electron gun fabrication and beam analyzer evaluation and klystron prototype fabrication, mechanical and electrical design improvements resulted in the delivery of a prototype klystron meeting all requirements. It is concluded that the development of a new high power klystron for the DSN was very successful as demonstrated by the prototype results.

Goldfinger, A.↗
NASA NTRS1993

Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

Salu, Yehuda↗
NASA NTRS1994

Doppler radar signatures of developing thunderstorms and their potential to indicate the onset of cloud-to-ground lightning

The capability of Doppler weather radars to short-term forecast the initiation of thunderstorms and the onset of cloud-to-ground (CG) lightning is examined. Doppler weather radar data from 28 thunderstorms were analyzed from August 1990 in the central Florida environment. These radar echoes were associated with CG lightning strike locations from the National Lightning Detection Network and two lightning detection systems operated by the U.S. Air Force in the vicinity of Kennedy Space Center. From a time history of these radar echoes it was found that a 10-dBZ echo, first detected near the freezing level, may be the first definitive echo of a future thunderstorm. This thunderstorm initiation signature is often accompanied by low-altitude convergence and divergence at the top of the radar echo. The observed lead times between this thunderstorm initiation signature and the first detected CG lightning strike ranged from 5 to 45 min with a median lead time of 15 min. All lightning-producing radar echoes were detected using the thunderstorm initiation signature; however, some echoes exceeded the 10-dBZ threshold and did not produce andy CG lightning. The charecteristics of the WSR-88D and Terminal Doppler Weather Radar systems are evaluated for their capability to detect the thunderstorm initiation signature in central Florida with sufficient temporal and spatial resolution.

Hondl, Kurt D.↗
NASA NTRS1993

Sub-Daily Earth Rotation during the Epoch '92 Campaign

Earth rotation measurements were obtained using Global Positioning System (GPS) data for 11 days during the Epoch '92 campaign in the Summer of 1992. Earth orientation was measured simultaneously with several very long baseline interferornetry (VLBI) networks. These data were processed to yield both GPS and VLBI estimates of UT1 with 3-hour time resolution, which were then compared and analyzed. The high frequency behavior of both data sets is similar, although drifts between the two series of approx.0,1 ms over 2-5 days are evident, Models for tidally induced UT1 variations and estimates of atmospheric angular momentum (AAM) at 6-hour intervals were also compared with the geodetic data, These studies indicate that most of the geodetic signal in the diurnal and semidiurnal frequency bands can be attributed to tidal processes, and that UT1 variations over a few days are mostly atmospheric in origin.

Global Positioning System (GPS) long baseline inte↗
NASA NTRS2012

Multi-decadal Change of Atmospheric Aerosols and their Effect on Surface Radiation

We present an investigation on multi-decadal changes of atmospheric aerosols and their effects on surface radiation using a global chemistry transport model GOCART along with the near-term to long-term data records. We focus on a 28-year time period of satellite era from 1980 to 2007 during which a suite of aerosol data from satellite observations, ground-based measurements, and intensive field experiments have become available. Particularly: (1) We compare the model calculated clear sky downward radiation at the surface with surface network data from Baseline Surface Radiation Network (BSRN) and CMA (2) We compare the model and surface data with satellite derived downward radiation products from ISCCP and SRB (3) We analyze the long-term global and regional aerosol trends in major anthropogenic source regions (North America, Europe, Asia) that have been experiencing considerable changes of emissions during the three decades, dust and biomass burning regions that have large interannual variability, downwind regions that are directly affected by the changes in the source area, and remote regions that are considered to representing "background" conditions.

Chin, Mian↗
NASA NTRS2017

Guided Tour of Pythonian Museum

At http:hdfeos.orgzoo, we have a large collection of Python examples of dealing with NASA HDF (Hierarchical Data Format) products. During this hands-on Python tutorial session, we'll present a few common hacks to access and visualize local NASA HDF data. We'll also cover how to access remote data served by OPeNDAP (Open-source Project for a Network Data Access Protocol). As a glue language, we will demonstrate how you can use Python for your data workflow - from searching data to analyzing data with machine learning.

hdf↗
NASA NTRS2019

Rising Above the Cloud - Toward High-Rate Delay-Tolerant Networking in Low-Earth Orbit

The High Data Rate Architecture (HiDRA) project is implementing a High-rate Delay Tolerant Networking (HDTN) capability that can support Low Earth Orbit (LEO) applications and environments. The present state of the effort, future work, and other elements of the work to date are described in this paper. This implementation is intended to support applications that run at 1+ Gbps, per the requirements of modern optical and high-frequency RF links. Uniquely, this implementation is also tuned to support relay and data trunking applications, which might require support for large numbers of small bundles per second. The design for this platform is based entirely on commercial-off-the-shelf (COTS) components, and possesses buffering capabilities in the 5 TB range. This document takes results from previous individual tests and integrates them to demonstrate results in the presence of a coherent use-case: consider a network aboard the ISS which intends to utilize an upcoming optical communications capability. For this use-case, orbital analysis software is used to analyze orbital dynamics, from which a list of access times are generated that might take in to account weather, schedule competition, etc. A variant of Contact Graph Routing (CGR) is applied to these windows to determine an optimal schedule. This schedule is then loaded into the HDTN prototype and, in conjunction with various measurement tools, a complete end-to-end analysis of HDTN's performance is conducted. Various bottlenecks (including storage) are identified: these bottlenecks are expected to help us focus our future work on the elements of the system that are most likely to present issues moving forward. Finally, we discuss possible paths for evolution beyond the present rates supported by the system, including (but not limited to) hardware acceleration.

Hylton, Alan↗
DOE OSTI2023

Deep learning methods for event verification and image re-purposing detection

Systems and methods herein describe accessing an image, generating a resized image, generating an image feature vector by applying an image classification neural network to the resized image, generating analysis of the image by processing the image feature vector using a machine-learning classifier trained to analyze the image feature vector, and based on the analysis, determining an event that is attributed to the image.

Nataraj, Lakshmanan↗
NASA NTRS2006

Structural Health Monitoring and Impact Detection Using Neural Networks for Damage Characterization

Detection of damage due to foreign object impact is an important factor in the development of new aerospace vehicles. Acoustic waves generated on impact can be detected using a set of piezoelectric transducers, and the location of impact can be determined by triangulation based on the differences in the arrival time of the waves at each of the sensors. These sensors generate electrical signals in response to mechanical motion resulting from the impact as well as from natural vibrations. Due to electrical noise and mechanical vibration, accurately determining these time differentials can be challenging, and even small measurement inaccuracies can lead to significant errors in the computed damage location. Wavelet transforms are used to analyze the signals at multiple levels of detail, allowing the signals resulting from the impact to be isolated from ambient electromechanical noise. Data extracted from these transformed signals are input to an artificial neural network to aid in identifying the moment of impact from the transformed signals. By distinguishing which of the signal components are resultant from the impact and which are characteristic of noise and normal aerodynamic loads, the time differentials as well as the location of damage can be accurately assessed. The combination of wavelet transformations and neural network processing results in an efficient and accurate approach for passive in-flight detection of foreign object damage.

Ross, Richard W.↗