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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Portable Rabies Virus Sequencing in Canine Rabies Endemic Countries Using the Oxford Nanopore MinION

As countries with endemic canine rabies progress towards elimination by 2030, it will become necessary to employ techniques to help plan, monitor, and confirm canine rabies elimination. Sequencing can provide critical information to inform control and vaccination strategies by identifying genetically distinct virus variants that may have different host reservoir species or geographic distributions. However, many rabies testing laboratories lack the resources or expertise for sequencing, especially in remote or rural areas where human rabies deaths are highest. We developed a low-cost, high throughput rabies virus sequencing method using the Oxford Nanopore MinION portable sequencer. A total of 259 sequences were generated from diverse rabies virus isolates in public health laboratories lacking rabies virus sequencing capacity in Guatemala, India, Kenya, and Vietnam. Phylogenetic analysis provided valuable insight into rabies virus diversity and distribution in these countries and identified a new rabies virus lineage in Kenya, the first published canine rabies virus sequence from Guatemala, evidence of rabies spread across an international border in Vietnam, and importation of a rabid dog into a state working to become rabies-free in India. Taken together, our evaluation highlights the MinION’s potential for low-cost, high volume sequencing of pathogens in locations with limited resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Kinematic Perspective on the Formation Process of the Stellar Groups in the Rosette Nebula

Stellar kinematics is a powerful tool for understanding the formation process of stellar associations. Here, we present a kinematic study of the young stellar population in the Rosette nebula using recent Gaia data and high-resolution spectra. We first isolate member candidates using the published mid-infrared photometric data and the list of X-ray sources. A total of 403 stars with similar parallaxes and proper motions are finally selected as members. The spatial distribution of the members shows that this star-forming region is highly substructured. The young open cluster NGC 2244 in the center of the nebula has a pattern of radial expansion and rotation. We discuss its implication on the cluster formation, e.g., monolithic cold collapse or hierarchical assembly. On the other hand, we also investigate three groups located around the border of the H ii bubble. The western group seems to be spatially correlated with the adjacent gas structure, but their kinematics is not associated with that of the gas. The southern group does not show any systematic motion relative to NGC 2244. These two groups might be spontaneously formed in filaments of a turbulent cloud. The eastern group is spatially and kinematically associated with the gas pillar receding away from NGC 2244. This group might be formed by feedback from massive stars in NGC 2244. Our results suggest that the stellar population in the Rosette Nebula may form through three different processes: the expansion of stellar clusters, hierarchical star formation in turbulent clouds, and feedback-driven star formation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

The Impact of Void-finding Algorithms on Galaxy Classification

We explore how the definition of a void influences the conclusions drawn about the impact of the void environment on galactic properties using two void-finding algorithms in the Void Analysis Software Toolkit: Voronoi Voids (V 2 ), a Python implementation of ZOnes Bordering On Voidness (ZOBOV); and VoidFinder, an algorithm that grows and merges spherical void regions. Using the Sloan Digital Sky Survey Data Release 7, we find that galaxies found in VoidFinder voids tend to be bluer and fainter and to have higher (specific) star formation rates than galaxies in denser regions. Conversely, galaxies found in V 2 voids show less significant differences when compared to galaxies in denser regions, less consistent with the large-scale environmental effects on galaxy properties expected from both simulations and previous observations. These results align with previous simulation results that show V 2 -identified voids “leak” into the dense walls between voids because their boundaries extend up to the density maxima in the walls. As a result, when using ZOBOV-based void-finders, galaxies likely to be part of wall regions are instead classified as void galaxies, a misclassification that can be critical to our understanding of galaxy evolution.

cosmic web↗
DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Historical and projected future runoff over the Mekong River basin

The Mekong River (MR) crosses the borders and connects six countries, including China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It provides critical water resources and supports natural and agricultural ecosystems, socioeconomic development, and the livelihoods of the people living in this region. Understanding changes in the runoff of this important international river under projected climate change is critical for water resource management and climate change adaptation planning. However, research on long-term runoff dynamics for the MR and the underlying drivers of runoff variability remains scarce. Here, we analyse historical runoff variations from 1971 to 2020 based on runoff gauge data collected from eight hydrological stations along the MR. With these runoff data, we then evaluate the runoff simulation performance of five global hydrological models (GHMs) forced by four global climate models (GCMs) under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Furthermore, based on the best simulation combination, we quantify the impact of future climate change on river runoff changes in the MR. The result shows that the annual runoff in the MR has not changed significantly in the past 5 decades, while the establishment of dams and reservoirs in the basin visibly affected the annual runoff distribution. The ensemble-averaged result of the Water Global Assessment and Prognosis version 2 (WaterGAP2; i.e. GHM) forced by four GCMs has the best runoff simulation performance. Under Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), the runoff of the MR is projected to increase significantly (p<0.05); e.g. 3.81 ± 3.47 m 3 s -1 a -1 (9 ± 8 % increase in 100 years) at the upper reach under RCP2.6 and 16.36 ± 12.44 m 3 s -1 a -1 (13 ±10 % increase in 100 years) at the lower reach under RCP6.0. In particular, under the RCP6.0 scenario, the increase in annual runoff is most pronounced in the middle and lower reaches, due to increased precipitation and snowmelt. Under the RCP8.5 scenario, the runoff distribution in different seasons varies obviously, increasing the risk of flooding in the wet season and drought in the dry season.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

US East Coast synthetic aperture radar wind atlas for offshore wind energy

We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s -1 . Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s -1 , while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s -1 . The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s -1 for SAR and less than 0.2m s -1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

17 WIND ENERGY↗
DOE OSTI2023

A comparison of eight optimization methods applied to a wind farm layout optimization problem

Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.

17 WIND ENERGY↗
DOE OSTI2023

HarDWR - Water Management Area (WMA) Shapefiles

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Borders of all Water Management Areas (WMAs) across the 11 Western-most states of the continental United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. Arizona is unique among this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis, due to technical reasons we decided to focus on one set of boundaries, those for surface water. Due to this, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as the second file azGroundWMAs as a companion to the first file for completeness and general reference, although the boundaries were no longer referenced in the analysis (Grogan et al., in review) past a certain point. The columns for both shapefiles are: basinNum: the state provided unique numerical ID basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID

Economics↗
DOE OSTI2020

HarDWR - Water Management Area (WMA) Shapefiles

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - No changes v1.0 - Initial public release Description Borders of all Water Management Areas (WMAs) across the 11 western-most states of the coterminous United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. WMAs refer to the set of boundaries a particular state uses to manage its water resources. Each set of boundaries was collected from the states individually, and then merged into one spatial layer. The merging process included renaming some columns to enable merging with all other source layers, as well as removing columns deemed not required for followup analysis. The retained columns for each boundary are: basinNum - the state provided unique numerical ID; basinName - the state provided English name of the area, where applicable; state - the state name; and uniID - a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID. Arizona is unique within this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis (Grogan et al., in review) we decided to focus on one set of boundaries, those for surface water. This is due to the recommendation of our hydrologists that the surface water boundary set is a more realist representation of how water moves across the landscape, as a few of the groundwater boundaries are based on political and/or economic considerations. Therefore, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as a separate file, azGroundWMAs, as a companion to the first file for completeness and general reference. WMA spatial boundary data sources by state: Arizona: Arizona Surface Water Watersheds; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/surface-watershed/explore?location=34.158174%2C-111.970823%2C7.50 Arizona: Arizona Ground Water Basins; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/groundwater-basin-2/explore?location=34.158174%2C-111.970823%2C7.50 California: California CalWater 2.2.1; Collected February, 2020; https://www.mlml.calstate.edu/mpsl-mlml/data-center/data-entry-tools/data-tools/gis-shapefile-layers/ Colorado: Colorado Water District Boundaries; Collected February, 2020; https://www.colorado.gov/pacific/cdss/gis-data-category Idaho: Idaho Department of Water Resources (IDWR) Administrative Basins; Collected November, 2015; https://data-idwr.opendata.arcgis.com/datasets/fb0df7d688a04074bad92ca8ef74cc26_4/explore?location=45.018686%2C-113.862284%2C6.93 Montana: Collected June, 2019; Directly contacted Montana Department of Natural Resources and Conservation (DNRC) Office of Information Technology (OIT) Nevada: Nevada State Engineer Admin Basin Boundaries; Collected April, 2020 https://ndwr.maps.arcgis.com/apps/mapviewer/index.html?layers=1364d0c3a0284fa1bcd90f952b2b9f1c New Mexico: New Mexico Office of the State Engineer (OSE) Declared Groundwater Basins; Collected April, 2020 https://geospatialdata-ose.opendata.arcgis.com/datasets/ose-declared-groundwater-basins/explore?location=34.179783%2C-105.996542%2C7.51 Oregon: Oregon Water Resources Department (OWRD) Administrative Basins; Collected February, 2020; https://www.oregon.gov/OWRD/access_Data/Pages/Data.aspx Utah: Utah Adjudication Books; Collected April, 2020; https://opendata.gis.utah.gov/datasets/utahDNR::utah-adjudication-books/explore?location=39.497165%2C-111.587782%2C-1.00 Washington: Washington Water Resource Inventory Areas (WRIA); Collected June, 2017; https://ecology.wa.gov/Research-Data/Data-resources/Geographic-Information-Systems-GIS/Data Wyoming: Wyoming State Engineer's Office Board of Control Water Districts; Collected June, 2019; Directly contacted Wyoming State Engineer's Office

Economics↗
DOE OSTI2020

Charting the native architecture of Chlamydomonas thylakoid membranes with single-molecule precision

Thylakoid membranes scaffold an assortment of large protein complexes that work together to harness the energy of light. It has been a longstanding challenge to visualize how the intricate thylakoid network organizes these protein complexes to finely tune the photosynthetic reactions. Previously, we used in situ cryo-electron tomography to reveal the native architecture of thylakoid membranes (Engel et al., 2015). Here, we leverage technical advances to resolve the individual protein complexes within these membranes. Combined with a new method to visualize membrane surface topology, we map the molecular landscapes of thylakoid membranes inside green algae cells. Our tomograms provide insights into the molecular forces that drive thylakoid stacking and reveal that photosystems I and II are strictly segregated at the borders between appressed and non-appressed membrane domains. This new approach to charting thylakoid topology lays the foundation for dissecting photosynthetic regulation at the level of single protein complexes within the cell.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1971

Inversion of very large matrices encountered in large scale problems of photogrammetry and photographic astrometry

The simultaneous adjustment of very large nets of overlapping plates covering the celestial sphere becomes computationally feasible by virtue of a twofold process that generates a system of normal equations having a bordered-banded coefficient matrix, and solves such a system in a highly efficient manner. Numerical results suggest that when a well constructed spherical net is subjected to a rigorous, simultaneous adjustment, the exercise of independently established control points is neither required for determinancy nor for production of accurate results.

Brown, D. C.↗
NASA NTRS1971

The relation of QRS amplitude to the frontal QRS axis and the heart-electrode distance.

The relation between QRS amplitude, QRS axis deviation and heart position was studied in 360 subjects, using the 12-lead EKG. The height of the V sub 6 R wave was used as the measure of QRS amplitude. The distance from the left lateral border of the heart was used to indicate heart position. The frontal and horizontal QRS axes were determined by noting the isoelectric limb and precordial leads, measured according to Schmitt's values.

Dougherty, J. D.↗
NASA NTRS1972

Flight evaluations of the effect of advanced control systems and displays on the handling qualities of a general aviation airplane.

Flight tests have shown that, by means of improved displays and advanced control systems, it is possible to transform a typical light airplane into a flying machine that borders on being perfect from a handling-qualities standpoint. A flight-director display and an attitude-command control system used in combination transformed a vehicle with poor handling qualities during ILS approaches in turbulent air into a vehicle with extremely good handling qualities. The attitude-command control system also improved the ride qualities of the airplane. A rate-command control system was less beneficial than an attitude-command control system. Although this paper deals primarily with general aviation aircraft, the results presented pertain to other types of aircraft. Short-takeoff-and-landing (STOL) aircraft would be a natural application of the control systems because, as a result of their low speeds, they encounter many of the handling-qualities problems noted on light aircraft. The improved ride qualities should be of interest to all airline operations, and for STOL aircraft in particular, because of their prolonged exposure to low-altitude turbulence.

Loschke, P. C.↗
NASA NTRS1972

Properties of white light flares. I - Association with H-alpha flares and sudden frequency deviations.

All four large EUV bursts for which there were available concurrent white light observations of at least fair quality, were detected as white light flares. The rise times and maxima of the white light emissions coincided with rise times and maxima of the EUV bursts. The frequency of strong EUV bursts suggests that white light flares may occur at the rate of five or six per year near sunspot maximum. All of the white light flare areas coincided with intense bright areas of the H-alpha flares. These small areas appeared to be sources of high velocity ejecta in H-alpha. The white light flares occurred as several knots or patches of 2 to 15 arc-sec diameter, with bright cores perhaps less than 2 arc-sec diameter (1500 km). They preferred the outer penumbral borders of strong sunspots within 10 arc-sec of a longitudinal neutral line in the magnetic field.

Mcintosh, P. S.↗
NASA NTRS1972

Auroral emissions and particle precipitation in the noon sector.

Comparison of airborne optical measurements during December 1969 with satellite particle measurements made earlier in 1969. The latitudinal distributions of H beta, 4278-A and 6300-A emissions during quiet periods are observed to agree with what would be expected from the latitudinal distribution of particle precipitation during similar conditions. In particular, there is a belt 200 to 500 km wide (at ionospheric heights) in which the magnetosheath plasma penetrates down to low altitudes through the cusps in the dayside magnetosphere and in which both H beta and 6300-A emissions are observed. There are brighter red arcs with green lower borders within this belt. It is suggested that these are produced by narrow regions of precipitation of slightly more energetic electrons embedded in the cusp fluxes. Poleward of this belt the flux of particles is low, and the atmospheric emissions are very weak, except for occasional narrow regions of electron precipitation (without measurable protons) and the corresponding polar-cap aurora (without the H beta emission).

Heikkila, W. J.↗
NASA NTRS1973

Application of automated multispectral analysis to Delaware's coastal vegetation mapping

There are no author-identified significant results in this report. Overlay maps of Delaware's wetlands have been prepared, showing the dominant species or group of species of vegetation present. Five such categories of vegetation were used indicating marshes dominated by: (1) salt marsh cord grass; (2) salt marsh hay and spike grass; (3) reed grass; (4) high tide bush and sea myrtle; and (5) a group of fresh water species found in impounded areas built to attract water fowl. Fifteen such maps cover Delaware's wetlands from the Pennsylvania to the Maryland borders. The mapping technique employed utilizes the General Electric multispectral data processing system. This system is a hybrid analog-digital system designed as an analysis tool to be used by an operator whose own judgment and knowledge of ground truth can be incorporated at any time into the analyzing process. The result is a high speed, cost effective method for producing enhanced photomaps showing a number of spectral classes, each enhanced spectral class being representative of a vegetative species or group of species.

Klemas, V.↗