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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Identifying Improvements to Airborne and Field Data Stewardship from Data User and Producer Needs (Findings from the March 2022 Airborne and Field Data Workshop)

Workshop Purpose: to gather valuable feedback from data users and data producers in order to improve the acquisition and use of NASA’s airborne and field data. Workshop Goals: - Identify new ways to improve the discovery, access, and reuse of NASA’s airborne and field data - Intentionally include the airborne community in future planning to ensure the community needs are met - Understand how NASA can better help researchers realize the full value of airborne and field data - Enable Open Source Science and determine what this means for airborne and field data communities

Geoffrey Stano↗
NASA NTRSDate not supplied

The Hourly GHI, DHI and DNI: Intercomparison of the CERES-Based Data and the NSRDB Data through Comparison with the Ground-Based BSRN Data

The NASA Clouds and the Earth’s Radiant Energy System (CERES) SYN1deg(Ed4.1) provides hourly global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct horizontal irradiance (DirHI) at 1-degree latitude by 1-degree longitude resolution, and the time span is from March 2000 to near present. While the GHI agrees well with the ground-based Baseline Surface Radiation Network (BSRN) data, the DHI and DirHI are, respectively, positively and negatively biased against the BSRN data, and so is the direct normal irradiance (DNI) derived simply by dividing the DirHI by the cosine of the hourly solar zenith angle (SZA). We found that the biases tend to be well-defined functions of cos(SZA) and the cloud fraction (CLFR). We thus performed a bias-correction on the hourly DHI and DNI in the latitude-cos(SZA)-CLFR phase space, and therefrom, we derived the hourly global tilted irradiance (GTI) at a number of tilt angles using the isotropic diffuse irradiance model, and the results agree well with the GTI derived from the original BSRN near-instantaneous records. Meanwhile, the National Renewable Energy Laboratory (NREL) has produced the National Solar Radiation Database (NSRDB) with finer spatiotemporal resolution using its Physical Solar Model (PSM) and surface-based measured data, model results and satellite-based data as inputs. Now the NSRDB data covers not only the United States and its neighboring regions, but Europe, Africa, Asia and Oceania. The available temporal resolutions are 5, 10, 15, 30 and 60 minutes and the spatial resolutions are 2, 4 and 10 km, depending on the selected region. In this presentation, we compare both the CERES-based and NSRDB GHI, DHI and DNI with their BSRN counterparts and show how finer spatial resolution may give us an advantage in data accuracy and usability.

Taiping Zhang↗
DOE OSTI2015

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

A system and method of integrating diverse sources of data and data streams is presented. The method can include selecting a scenario based on a topic, creating a multi-relational directed graph based on the scenario, identifying and converting resources in accordance with the scenario and updating the multi-directed graph based on the resources, identifying data feeds in accordance with the scenario and updating the multi-directed graph based on the data feeds, identifying analytical routines in accordance with the scenario and updating the multi-directed graph using the analytical routines and identifying data outputs in accordance with the scenario and defining queries to produce the data outputs from the multi-directed graph.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Deep in Data: Empirical Data Based Software Accuracy Testing Using the Building America Field Data Repository

This paper describes progress toward developing a usable, standardized, empirical data-based software accuracy test suite using home energy consumption and building description data. Empirical data collected from around the United States have been translated into a uniform Home Performance Extensible Markup Language format that may enable software developers to create translators to their input schemes for efficient access to the data. This could allow for modeling many homes expediently, and thus implementing software accuracy test cases by applying the translated data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1995

The Planetary Data System Web Catalog Interface--Another Use of the Planetary Data System Data Model

The Planetary Data System Data Model consists of a set of standardized descriptions of entities within the Planetary Science Community. These can be real entities in the space exploration domain such as spacecraft, instruments, and targets; conceptual entities such as data sets, archive volumes, and data dictionaries; or the archive data products such as individual images, spectrum, series, and qubes.

interface science metadata↗
DOE OSTI2013

Deep in Data: Empirical Data Based Software Accuracy Testing Using the Building America Field Data Repository: Preprint

An opportunity is available for using home energy consumption and building description data to develop a standardized accuracy test for residential energy analysis tools. That is, to test the ability of uncalibrated simulations to match real utility bills. Empirical data collected from around the United States have been translated into a uniform Home Performance Extensible Markup Language format that may enable software developers to create translators to their input schemes for efficient access to the data. This may facilitate the possibility of modeling many homes expediently, and thus implementing software accuracy test cases by applying the translated data. This paper describes progress toward, and issues related to, developing a usable, standardized, empirical data-based software accuracy test suite.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Getting Data Out of the Ground: Modern Data Challenges Facing EGS Collab, the DOE Geothermal Data Repository, and the Geothermal Industry

The amount of data being generated by the geothermal industry is increasing exponentially. Advanced tools, higher resolution sensors, and digitally integrated equipment are providing valuable insight into geothermal exploration and operational efficiencies, but the volumes of data they generate and the unique properties of Enhanced Geothermal System (EGS) projects are creating challenges of their own. This paper explores those challenges and the steps the U.S. Department of Energy (DOE) is taking to address them through comprehensive data management strategies, improvements to DOE's Geothermal Data Repository (GDR), and the development of a secure data collaboration tool for DOE's Frontier Observatory for Research in Geothermal Energy (FORGE) and EGS Collab projects.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Big Data and Deep data in scanning and electron microscopies: functionality from multidimensional data sets

The development of electron, and scanning probe microscopies in the second half of the twentieth century have produced spectacular images of internal structure and composition of matter with, at nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition and analysis. The progress in imaging technologies in the beginning of the twenty first century has opened the proverbial floodgates of high-veracity information on structure and functionality. High resolution imaging now allows information on atomic positions with picometer precision, allowing for quantitative measurements of individual bond length and angles. Functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this data into physically and chemically relevant information from imaging data.

36 MATERIALS SCIENCE↗
NASA NTRS2001

Data Mining of NASA Boeing 737 Flight Data: Frequency Analysis of In-Flight Recorded Data

Data recorded during flights of the NASA Trailblazer Boeing 737 have been analyzed to ascertain the presence of aircraft structural responses from various excitations such as the engine, aerodynamic effects, wind gusts, and control system operations. The NASA Trailblazer Boeing 737 was chosen as a focus of the study because of a large quantity of its flight data records. The goal of this study was to determine if any aircraft structural characteristics could be identified from flight data collected for measuring non-structural phenomena. A number of such data were examined for spatial and frequency correlation as a means of discovering hidden knowledge of the dynamic behavior of the aircraft. Data recorded from on-board dynamic sensors over a range of flight conditions showed consistently appearing frequencies. Those frequencies were attributed to aircraft structural vibrations.

Butterfield, Ansel J.↗
NASA NTRS2012

The Virtual Climate Data Server (vCDS): An iRODS-Based Data Management Software Appliance Supporting Climate Data Services and Virtualization-as-a-Service in the NASA Center for Climate Simulation

Scientific data services are becoming an important part of the NASA Center for Climate Simulation's mission. Our technological response to this expanding role is built around the concept of a Virtual Climate Data Server (vCDS), repetitive provisioning, image-based deployment and distribution, and virtualization-as-a-service. The vCDS is an iRODS-based data server specialized to the needs of a particular data-centric application. We use RPM scripts to build vCDS images in our local computing environment, our local Virtual Machine Environment, NASA s Nebula Cloud Services, and Amazon's Elastic Compute Cloud. Once provisioned into one or more of these virtualized resource classes, vCDSs can use iRODS s federation capabilities to create an integrated ecosystem of managed collections that is scalable and adaptable to changing resource requirements. This approach enables platform- or software-asa- service deployment of vCDS and allows the NCCS to offer virtualization-as-a-service: a capacity to respond in an agile way to new customer requests for data services.

Schnase, John L.↗
DOE OSTI2023

Azure Data Tools (FKA: OEDI (Open Energy Data Initiative) Data Access Tools) [SWR-23-92]

The Open Energy Data Initiative (OEDI) provides a number of tools to enable the use of the open data published through this initiative. The source is largely written in Python, including Jupyter notebooks. The Open Energy Data Initiative (OEDI) is a partnership between the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), Amazon, Microsoft, and Google to provide universal access to big data in the cloud. At the heart of OEDI is a centralized repository of high-value energy research datasets aggregated from DOE Program Offices, National Laboratories and other collaborators.

Jonathan, Weers↗
DOE OSTI2018

Determining Patterns from Radiation Portal Monitor Data: Enabling Data Insight with Visual and Interactive Exploratory Data Analysis

Smuggling of nuclear and radiological material is a complex problem that will need to be analyzed from a variety of angles in order to effectively combat something so rare, yet so hazardous. One crucial tool in the effort to detect and deter smuggling of rad/nuc materials is the radiation portal monitor (RPM)—machines that scan passengers at airports, vehicles and trains at border crossings, and shipping containers at ports throughout the world. While data from RPMs are regularly analyzed at the individual lane level to ensure monitor health and good operation, aggregate analysis of the data has not gone far beyond summary statistics such as number of occupancies and alarm rates. In particular, there are opportunities to carry out interactive exploratory data analysis with the help of tools like the R package Shiny and methods that lend themselves to the visual display of spatiotemporal correlations such as spatial PCA and detrended cross-correlation analysis. By doing so, we discover interesting spatiotemporal patterns in the data that point to specific events or anomalous moments in time that warrant further analysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1984

Solar-geophysical data number 479, July 1984. Part 1: (Prompt reports). Data for June 1984, May 1984 and later data

Solar and geophysical data for May and June 1984 are reported. Topics include: detailed index for 1983/1984; data for June 1984 (solar activity indices, solar flares, solar radio emission, mean solar magnetic field, boulder geomagnetic substorm log); data for May 1984 (solar active regions, sudden ionospheric disturbances, solar radio spectral observations, cosmic ray measurements by neutron monitor, geomagnetic indices, radio propagation indices); and late data (geomagnetic indices March and April 1984 sudden commencements/solar flare effects, cosmic ray measurements by neutron monitor, and solar active regions).

Coffey, H. E.↗