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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Visualization of the Eastern Renewable Generation Integration Study: Preprint

The Eastern Renewable Generation Integration Study (ERGIS), explores the operational impacts of the wide spread adoption of wind and solar photovoltaics (PV) resources in the U.S. Eastern Interconnection and Quebec Interconnection (collectively, EI). In order to understand some of the economic and reliability challenges of managing hundreds of gigawatts of wind and PV generation, we developed state of the art tools, data, and models for simulating power system operations using hourly unit commitment and 5-minute economic dispatch over an entire year. Using NREL's high-performance computing capabilities and new methodologies to model operations, we found that the EI, as simulated with evolutionary change in 2026, could balance the variability and uncertainty of wind and PV at a 5-minute level under a variety of conditions. A large-scale display and a combination of multiple coordinated views and small multiples were used to visually analyze the four large highly multivariate scenarios with high spatial and temporal resolutions. state of the art tools, data, and models for simulating power system operations using hourly unit commitment and 5-minute economic dispatch over an entire year. Using NRELs high-performance computing capabilities and new methodologies to model operations, we found that the EI, as simulated with evolutionary change in 2026, could balance the variability and uncertainty of wind and PV at a 5-minute level under a variety of conditions. A large-scale display and a combination of multiple coordinated views and small multiples were used to visually analyze the four large highly multivariate scenarios with high spatial and temporal resolutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Spatial distribution of marker gene activity in the mouse lung during alveolarization

Here, this data is a curated collection of visual images of gene expression patterns from the pre- and post-natal mouse lung, accompanied by associated mRNA probe sequences and RNA-Seq expression profiles. Mammalian lungs undergo significant growth and cellular differentiation before and after the transition to breathing air. Documenting normal lung development is an important step in understanding abnormal lung development, as well as the challenges faced during a preterm birth. Images in this dataset indicate the spatial distribution of mRNA transcripts for over 500 different genes that are active during lung development, as initially determined via RNA-Seq. Images were systematically acquired using high-throughput in situ hybridization with non-radioactive digoxigenin-labeled mRNA probes across mouse lungs from developmental time points E16.5, E18.5, P7, and P28.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1981

Monitoring of reforested areas using LANDSAT data

Imagery obtained with channels 5 and 7 was visually interpreted in an effort to determine the spatial, spectral, and temporal characteristics of a 105,000 hectare area of Fazenda Mutum which was reforested with various species of pine and eucalyptus. It was possible to map a reforested area as small as 6 hectare in its initial implantation using contrast with the surrounding targets. Five classes were mapped: nondeforested areas, partially deforested areas, deforested areas, partially reforested areas, and fully reforested areas. In 1979, 12,000 hectare were deforested, 4,330.83 hectare were partially reforested, and 42,744.71 hectare were reforested.

Dejesusparada, N.↗
NASA NTRS2008

Visual Data Analysis for Satellites

The Visual Data Analysis Package is a collection of programs and scripts that facilitate visual analysis of data available from NASA and NOAA satellites, as well as dropsonde, buoy, and conventional in-situ observations. The package features utilities for data extraction, data quality control, statistical analysis, and data visualization. The Hierarchical Data Format (HDF) satellite data extraction routines from NASA's Jet Propulsion Laboratory were customized for specific spatial coverage and file input/output. Statistical analysis includes the calculation of the relative error, the absolute error, and the root mean square error. Other capabilities include curve fitting through the data points to fill in missing data points between satellite passes or where clouds obscure satellite data. For data visualization, the software provides customizable Generic Mapping Tool (GMT) scripts to generate difference maps, scatter plots, line plots, vector plots, histograms, timeseries, and color fill images.

Lau, Yee↗
DOE OSTI2017

Overlapping MALDI-Mass Spectrometry Imaging for In-Parallel MS and MS/MS Data Acquisition without Sacrificing Spatial Resolution

Metabolomics experiments require chemical identifications, often through MS/MS analysis. In mass spectrometry imaging (MSI), this necessitates running several serial tissue sections or using a multiplex data acquisition method. We have previously developed a multiplex MSI method to obtain MS and MS/MS data in a single experiment to acquire more chemical information in less data acquisition time. In this method, each raster step is composed of several spiral steps and each spiral step is used for a separate scan event (e.g., MS or MS/MS). One main limitation of this method is the loss of spatial resolution as the number of spiral steps increases, limiting its applicability for high-spatial resolution MSI. In this work, we demonstrate multiplex MS imaging is possible without sacrificing spatial resolution by the use of overlapping spiral steps, instead of spatially separated spiral steps as used in the previous work. Significant amounts of matrix and analytes are still left after multiple spectral acquisitions, especially with nanoparticle matrices, so that high quality MS and MS/MS data can be obtained on virtually the same tissue spot. This method was then applied to visualize metabolites and acquire their MS/MS spectra in maize leaf cross-sections at 10 μm spatial resolution. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

SU-C-201-07: Validation of a GATE Gamma Camera Model for the Siemens Symbia

Purpose: To develop a simulation model of a clinical gamma camera/SPECT system and to validate the model using experimental and published measurements from the clinical system. Methods: Geant4 Application for Tomographic Emission (GATE) was used to create a model of the Siemens Symbia gamma camera. A modular model was implemented that allows specifying combinations of crystal thickness (3/8”, 5/8”) and collimator (LEHR, MELP, HE). Shielding, energy resolution, intrinsic resolution, crystal thickness, and collimator properties were set based on manufacturer specifications. Validation of the model was performed by simulating NEMA 2007 gamma camera tests including spatial resolution and sensitivity for Tc99; these were compared with experimental and published data for the scanner. The simulated energy spectra of a Tc99 line source in acrylic blocks was visually compared with the corresponding experimental acquisition. For a 4 cm diameter sphere filled with Tc99, the attenuation maps were generated from simulation data, and the photopeak and scatter window were extracted from GATE output using ROOT to create DICOM files to use in the clinical reconstruction. Results: Simulated spatial resolutions for LEHR 3/8” crystal at 0, 10 cm, 10 cm (with scatter), and 30 cm were 4, 6.7, 7.9, and 14.5 mm FWHM; these were 9% less than published data. For 5/8” crystal the spatial resolutions were 4.5, 7.0, 8.5, and 14.7 mm FWHM; these were 4% to 10% less than published data. Simulated sensitivity was within 3.5% of published data for both LEHR 3/8” and 5/8”. The simulated energy spectra matched the photopeak and scatter window well, but did overestimate the counts below 90 keV. The simulated attenuation map and projection data were successfully reconstructed with the clinical software, and the passed visual inspection. Conclusions: Validation of a specific clinical scanner allows future studies of quantification accuracy for both planar and SPECT imaging. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA138986. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Stanford Thermal Earth Model for the Conterminous United States

Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below. A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively..

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Visualizing UPSP Data with Python

The Unsteady Pressure-Sensitive Paint (uPSP) projects uses Pressure-Sensitive paint applied over aerospace models during wind tunnel testing to collect pressure data with high spatial and temporal resolution in order to inform unsteady aerodynamics studies. For each of the 800+ experimental runs, four cameras generate up to 50 GB of video data, which must then be processed, analyzed, and visualized on the NASA Advanced Supercomputing system (NAS) to assess the result. One of the final data analysis products is the dynamic modal decomposition (DMD) results, which decomposes the pressure reading signals by their frequency component. The goal of this project is to visualize the DMD results over a 3D rendering of the model, using efficient and parallelized python routines. The software uses the pytecplot library, a high-level API that connects python scripting to a Tecplot 360 engine. Tecplot is an industry standard high-performance visualization tool that can handle large datasets and workflow. Various animation, rendering, and image-combination techniques were investigated to generate the final videos using OpenCV on the NAS. The final result is a software tool that takes in data products from the uPSP processing chain and generates high resolution visualization videos in parallel for every data file, allowing researchers to view their results efficiently and at an unprecedentedly detailed level.

Emma Dolores McMillian↗
NASA NTRS1981

Shaded computer graphic techniques for visualizing and interpreting analytic fluid flow models

Mathematical models which predict the behavior of fluid flow in different experiments are simulated using digital computers. The simulations predict values of parameters of the fluid flow (pressure, temperature and velocity vector) at many points in the fluid. Visualization of the spatial variation in the value of these parameters is important to comprehend and check the data generated, to identify the regions of interest in the flow, and for effectively communicating information about the flow to others. The state of the art imaging techniques developed in the field of three dimensional shaded computer graphics is applied to visualization of fluid flow. Use of an imaging technique known as 'SCAN' for visualizing fluid flow, is studied and the results are presented.

Parke, F. I.↗
NASA NTRS2002

Discovering Communicable Models from Earth Science Data

This chapter describes how we used regression rules to improve upon results previously published in the Earth science literature. In such a scientific application of machine learning, it is crucially important for the learned models to be understandable and communicable. We recount how we selected a learning algorithm to maximize communicability, and then describe two visualization techniques that we developed to aid in understanding the model by exploiting the spatial nature of the data. We also report how evaluating the learned models across time let us discover an error in the data.

Schwabacher, Mark↗
DOE OSTI2018

Global Oil & Gas Infrastructure Features Database EDX Spatial Web Map

This submission offers a link to a web mapping application hosted instance of the Global Oil & Gas Features Database (GOGI), via EDX Spatial. This offers users with the ability to visualize, interact, and create maps with data of their choice, as well as download specific attributes or fields of view from the database. This data can also be downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database. Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.

gas↗
DOE OSTI2019

EGS Collab Earth Modeling: Integrated 3D Model of the Testbed

The EGS Collab project is conducting a series of stimulation and interwell flow tests in an intermediate scale (~10-20 m) testbed located on the 4850 level in the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The testbed consists of eight ~200 ft (~60 m) HQ-diameter (9.6 cm) boreholes that are drilled into the Poorman Formation from the western rib of the West Access Drift. Of the eight boreholes, one borehole is used as an injection/stimulation well, another sub-parallel borehole located about 10 m away from the injection well is used as a production well, and rest of the other boreholes are used as monitoring wells. For 3D visualization of the testbed as well as to provide spatially accurate and consistent parameter data for geomechanical and geophysical process modeling tasks, we are using Leapfrog Software (Seequent Limited). For analysis of fractures and creation of conditioned stochastic discrete fracture network (CS-DFN) model of the testbed, we are using FracMan (Golder Associates Inc.). As of mid-May 2019, the EGS Collab Leapfrog database contains the general layout of the drifts/shafts at the 4850 and 4100 levels; generalized geologic framework model of a domain that spans both the 4850 and 4100 levels; the CS-DFN of the testbed; and layouts of the EGS Collab testbed boreholes, kISMET testbed boreholes, and other existing boreholes at the 4850 level. This information has been obtained from the SURF Vulcan database, detailed laser scan and point surveys of borehole collar locations, directional surveys of borehole trajectories, and mapping of features in the drifts, boreholes, and cores. Similarly, other types of data that have been incorporated into the Leapfrog database include static features of the testbed such as natural discontinuities (e.g., fractures, weeps), location of monitoring instruments, and stimulation notches. Baseline geophysical characterization results (e.g., seismic, ERT, temperature, etc.) are also imported into the Leapfrog database. The location and size of packers, various point-source signals detected during stimulation and flow tests (e.g., temperature anomalies, micro-earthquake events, etc.) are also compiled in the database. All feature locations are reported using the local Homestake Mine coordinate system. In this paper, we present some of the geostructural characteristics as well as the status of the testbed as represented by Leapfrog/FracMan database/visualizations.

15 GEOTHERMAL ENERGY↗
NASA NTRS2001

Discovering Communicable Scientific Knowledge from Spatio-Temporal Data

This paper describes how we used regression rules to improve upon a result previously published in the Earth science literature. In such a scientific application of machine learning, it is crucially important for the learned models to be understandable and communicable. We recount how we selected a learning algorithm to maximize communicability, and then describe two visualization techniques that we developed to aid in understanding the model by exploiting the spatial nature of the data. We also report how evaluating the learned models across time let us discover an error in the data.

Schwabacher, Mark↗
NASA NTRS1976

The use of spatial characteristics for the improvement of multispectral classification of remotely sensed data

Two techniques for the classification of multispectral remotely sensed data - the image processing technique of texture features, which is modeled after the human visual system, and the ECHO (Extraction and Classification of Homogeneous Objects) numerical technique - are examined. These two spatial analysis techniques are compared using Landsat imagery of an area of Indiana as an example, and it is found that the numerical approach is superior in classification accuracy and more efficient computationally.

Wiersma, D. J.↗
DOE OSTI2020

Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images

Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Global Precipitation Measurement (GPM) Mission Products and Services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) hosts and distributes GPM data within the NASA Earth Observation System Data Information System (EOSDIS). The GES DISC is also home to the data archive for the GPM predecessor, the Tropical Rainfall Measuring Mission (TRMM). Over the past 17 years, the GES DISC has served the scientific as well as other communities with TRMM data and user-friendly services. During the GPM era, the GES DISC will continue to provide user-friendly data services and customer support to users around the world. GPM products currently and to-be available: -Level-1 GPM Microwave Imager (GMI) and partner radiometer products, DPR products -Level-2 Goddard Profiling Algorithm (GPROF) GMI and partner products, DPR products -Level-3 daily and monthly products, DPR products -Integrated Multi-satellitE Retrievals for GPM (IMERG) products (early, late, and final) A dedicated Web portal (including user guides, etc.) has been developed for GPM data (http://disc.sci.gsfc.nasa.gov/gpm). Data services that are currently and to-be available include Google-like Mirador (http://mirador.gsfc.nasa.gov/) for data search and access; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion into various formats (e.g., netCDF, HDF, KML (for Google Earth), ASCII); exploration, visualization, and statistical online analysis through Giovanni (http://giovanni.gsfc.nasa.gov); generation of value-added products; parameter and spatial subsetting; time aggregation; regridding; data version control and provenance; documentation; science support for proper data usage, FAQ, help desk; monitoring services (e.g. Current Conditions) for applications. The United User Interface (UUI) is the next step in the evolution of the GES DISC web site. It attempts to provide seamless access to data, information and services through a single interface without sending the user to different applications or URLs (e.g., search, access, subset, Giovanni, documents).

precipitation↗
NASA NTRSDate not supplied

Entwine Point Tiles for 3D Visualization and Querying of ICESat-2

Point Cloud data from non-optical sensors present challenges in scientific computing in both volume of data and files, even for cloud services environments. As part of the Multi-Mission Algorithm and Analysis Platform (MAAP), a joint open science platform for global biomass modelling, we’ve developed a cloud optimized workflow for using ATL08 (ICESat-2) data as a point cloud. For MAAP, the ATL08 data product is published as Entwine Point Tiles (EPT), allowing users to visualize and query the full extent of this collection interactively without pre-downloading, or preprocessing. The EPT format is a cloud-optimized point cloud data format which re-organizes points into a cloud friendly spatially indexed data structure. MAAP uses AWS S3 to store these point clouds and serves them over OGC specified APIs, 3DTiles for visualization, and WFS for querying. This workflow allows for interactive 3D visualizations in a web browser, including notebook environments and facilitates on the fly subsetting for interactive data exploration, all of which can be applied to other similar sensors.

Alex Mandel↗