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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Data Recipes: Toward Creating How-To Knowledge Base for Earth Science Data

Both the diversity and volume of Earth science data from satellites and numerical models are growing dramatically, due to an increasing population of measured physical parameters, and also an increasing variety of spatial and temporal resolutions for many data products. To further complicate matters, Earth science data delivered to data archive centers are commonly found in different formats and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and or formats. The data recipe project at Goddard Earth Science Data and Information Services Center (GES DISC) was initiated in late 2012 for enhancing user support. A data recipe is a How-To online explanatory document, with step-by-step instructions and examples of accessing and working with real data (http:disc.sci.gsfc.nasa.govrecipes). The current suite of recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant, even though the data recipe topics are still limited. An Earth Science Data System Working Group (ESDSWG) for data recipes was established in the spring of 2014, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group is working on an inventory and analysis of existing data recipes and tutorials, and will provide guidelines and recommendation for writing and grouping data recipes, and for cross linking recipes to data products. This presentation gives an overview of the data recipe activites at GES DISC and ESDSWG. We are seeking requirements and input from a broader data user community to establish a strong knowledge base for Earth science data research and application implementations.

data recipe↗
DOE OSTI2023

System design and prototyping for the CMS Level-1 Trigger at the High-Luminosity LHC

For the High-Luminosity Large Hadron Collider, the trigger and data acquisition system of the CMS experiment will be entirely replaced. Novel design choices have been explored, including ATCA prototyping platforms with SoC controllers and newly available interconnect technologies with serial optical links with data rates up to 28 Gb/s. Trigger analyses will be performed through sophisticated algorithms, including widespread use of Machine Learning, in large FPGAs, such as the Xilinx Ultrascale family. The system will process over 50 Tb/s of detector data with an event rate of 750 kHz. Here, we describe system design and prototyping and review trigger algorithm exemplars.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1970

The role of remotely sensed and relayed data in the Delaware River Basin

A discussion is presented of the planned integration of the existing Delaware River Basin water quality monitoring and data processing systems with a data relay experiment proposed for the Earth Resources Technology Satellite (ERTS)-A, which will be launched in 1972. The experiment is designed to use ERTS-A as a data relay link for a maximum of 20 hydrologic stations in the basin, including streamgaging, reservoir level, ground water level,and water quality monitoring stations. This experiment has the potential for reducing the timelag between data collection and dissemination to less than 12 hours. At present there is a significant timelag between the time when the data are recorded at a monitoring site and the water resources agencies receive the data. The timelag exists because most of these instruments operate in remote locations without telementry, and the data records are removed manually, generally at a weekly frequency. For most water quality monitoring, the data do not reach water resources agencies for a period of 2 weeks to 2 months.

Paulson, R. W.↗
NASA NTRS2000

Observations on Rotating Cavitation and Cavitation Surge from the Development of the Fastrac Engine Turbopump

Observations regarding rotating cavitation and cavitation surge experienced during the development of the Fastrac engine turbopump are discussed. Detailed observations acquired from the analysis of both water flow and liquid oxygen test data are offered in this paper. Scaling and general comparison of rotating cavitation between water flow and liquid oxygen testing are discussed. Complex data features linking the localized rotating cavitation mechanism of the inducer to system surge components are described in detail. Finally a description of a lumped-parameter hydraulic system model developed to better understand observed data is given.

Zoladz, Thomas F.↗
NASA NTRS2006

Controlled Vocabularies Boost International Participation and Normalization of Searches

The Global Change Master Directory's (GCMD) science staff set out to document Earth science data and provide a mechanism for it's discovery in fulfillment of a commitment to NASA's Earth Science progam and to the Committee on Earth Observation Satellites' (CEOS) International Directory Network (IDN.) At the time, whether to offer a controlled vocabulary search or a free-text search was resolved with a decision to support both. The feedback from the user community indicated that being asked to independently determine the appropriate 'English" words through a free-text search would be very difficult. The preference was to be 'prompted' for relevant keywords through the use of a hierarchy of well-designed science keywords. The controlled keywords serve to 'normalize' the search through knowledgeable input by metadata providers. Earth science keyword taxonomies were developed, rules for additions, deletions, and modifications were created. Secondary sets of controlled vocabularies for related descriptors such as projects, data centers, instruments, platforms, related data set link types, and locations, along with free-text searches assist users in further refining their search results. Through this robust 'search and refine' capability in the GCMD users are directed to the data and services they seek. The next step in guiding users more directly to the resources they desire is to build a 'reasoning' capability for search through the use of ontologies. Incorporating twelve sets of Earth science keyword taxonomies has boosted the GCMD S ability to help users define and more directly retrieve data of choice.

Olsen, Lola M.↗
NASA NTRS2001

Overview of Rotating Cavitation and Cavitation Surge in the Fastrac Engine LOX Turbopump

Observations regarding rotating cavitation and cavitation surge experienced during the development of the Fastrac 60 Klbf engine turbopump are discussed. Detailed observations from the analysis of both water flow and liquid oxygen test data are offered. Scaling and general comparison of rotating cavitation between water flow and liquid oxygen testing are discussed. Complex data features linking the localized rotating cavitation mechanism of the inducer to system surge components are described in detail. Finally a description of a simple lumped-parameter hydraulic system model developed to better understand observed data is given.

Zoladz, Thomas↗
NASA NTRS2010

Updates of Land Surface and Air Quality Products in NASA MAIRS and NEESPI Data Portals

Following successful support of the Northern Eurasia Earth Sciences Partner Initiative (NEESPI) project with NASA satellite remote sensing data, from Spring 2009 the NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has been working on collecting more satellite and model data to support the Monsoon Asia Integrated Regional Study (MAIRS) project. The established data management and service infrastructure developed for NEESPI has been used and improved for MAIRS support.Data search, subsetting, and download functions are available through a single system. A customized Giovanni system has been created for MAIRS.The Web-based on line data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) allows scientists to explore, quickly analyze, and download data easily without learning the original data structure and format. Giovanni MAIRS includes satellite observations from multiple sensors and model output from the NASA Global Land Data Assimilation System (GLDAS), and from the NASA atmospheric reanalysis project, MERRA. Currently, we are working on processing and integrating higher resolution land data in to Giovanni, such as vegetation index, land surface temperature, and active fire at 5km or 1km from the standard MODIS products. For data that are not archived at the GESDISC,a product metadata portal is under development to serve as a gateway for providing product level information and data access links, which include both satellite, model products and ground-based measurements information collected from MAIRS scientists.Due to the large overlap of geographic coverage and many similar scientific interests of NEESPI and MAIRS, these data and tools will serve both projects.

Shen, Suhung↗
NASA NTRS1971

The Role of Remotely Sensed and Relayed Data in the Delaware River Basin

The planned integration of the existing water quality monitoring and data processing systems in the Delaware River Basin with a data relay experiment proposed for the ERTS-1 is discussed. The experiment is designed to use ERTS-1 as a data relay link for a maximum of 20 hydrologic stations in the basin, including stream gaging, reservoir level, ground water level, and water quality monitoring stations. This experiment has the potential for reducing the time lag between data collection and dissemination to less than 12 hours. The experiment will also provide impetus to develop an operational system of real time data processing and dissemination to handle the large quantity of data that will be obtained from the stations in the basin. The results of this experiment will demonstrate the relative merits of satellite relay of data versus conventional means of data telemetry and will provide a basis for the development of operational satellite relay of hydrologic data.

Paulson, R. W.↗
DOE OSTI2021

A model-independent data assimilation (MIDA) module and its applications in ecology

Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

58 GEOSCIENCES↗
NASA NTRS2021

A model-independent data assimilation (MIDA) module and its applications in ecology

Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

Earth system dynamics↗
NASA NTRS2006

Inspection and Verification of Domain Models with PlanWorks and Aver

When developing a domain model, it seems natural to bring the traditional informal tools of inspection and verification, debuggers and automated test suites, to bear upon the problems that will inevitably arise. Debuggers that allow inspection of registers and memory and stepwise execution have been a staple of software development of all sorts from the very beginning. Automated testing has repeatedly proven its considerable worth, to the extent that an entire design philosophy (Test Driven Development) has been developed around the writing of tests. Unfortunately, while not entirely without their uses, the limitations of these tools and the nature of the complexity of models and the underlying planning systems make the diagnosis of certain classes of problems and the verification of their solutions difficult or impossible. Debuggers provide a good local view of executing code, allowing a fine-grained look at algorithms and data. This view is, however, usually only at the level of the current scope in the implementation language, and the data-inspection capabilities of most debuggers usually consist of on-line print statements. More modem graphical debuggers offer a sort of tree view of data structures, but even this is too low-level and is often inappropriate for the kinds of structures created by planning systems. For instance, god or constraint networks are at best awkward when visualized as trees. Any any non-structural link between data structures, as through a lookup table, isn't captured at all. Further, while debuggers have powerful breakpointing facilities that are suitable for finding specific algorithmic errors, they have little use in the diagnosis of modeling errors.

Bedrax-Weiss, Tania↗
NASA NTRS1975

Wideband 1.064 micrometer detector evaluation

The performance of several candidate detectors for use as communications detectors in a 400 Mbps 1.064 micrometers laser communication system was evaluated. The results of communication system Bit Error Rate (BER) testing for the best detector of each type are summarized. Complete testing data of each type detector is presented. The 400 Mbps 1.064 micrometers communication system receiver test bed is described. The best communication system results for each detector type are included. Performance comparisons are made at 0.000001 BER, the specification level chosen for satellite laser communication links. The data is presented in two groups. The first indicates the best performance levels that can be expected on normal space laser communication system operation. The second cites the best performance levels which can be achieved by focusing the signal to diffraction limited spots on the photosensitive area.

Green, S. I.↗
NASA NTRSDate not supplied

An Overview of NASA’s Airborne and Field Data Resource Center

A key recommendation from NASA’s 2022 Airborne and Field Data Workshop called for the development of a virtual Resource Center for all stakeholders across the data lifecycle of airborne and field Earth observations. The agency’s Earth Science Data and Information System (ESDIS) Project and Airborne Data Management Group (ADMG) have worked in concert to establish the newly launched NASA Airborne and Field Data Resource Center (AFDRC) to provide a single entry point for a wide assortment of information on and the effective, responsible stewardship of non-satellite observational data. The AFDRC compiles access to many existing resources, but does so in a newly organized way that integrates availability to increase efficiency and holistic understanding while simplifying users’ experience. Initially launched in fall of 2023, the AFDRC is a NASA Earthdata domain website that clarifies several previously disparate resources and provides newly updated information, including: Learning Resources: Educational resources to broaden understanding of the role airborne and field observations play in advancing understanding of our planet and NASA’s role in collecting and archiving these data. Support for data users to Find and Access Data: Advanced contextual browse/search capabilities that efficiently link researchers to data products suitable for their science objectives - this includes linking to NASA’s Catalog of Archived Suborbital Earth Science Investigations (CASEI). Working with Data: Tools specific to individual types of suborbital Earth Science data and their (inter-)disciplinary communities to provide access as well as guidance for their application. Stewardship Responsibilities: Resources for data producers with to lessen requirement burdens at the time of data transfer, and information for data stewards with consistent, authoritative guidance on best practices and agency- and/or community- specific archival procedures. This presentation will give an overview of the motivation for NASA’s AFDRC, approach for the design and content, iterative community-driven improvements, promote the use of the AFDRC, and solicit additional feedback from airborne and field data user communities.

Sara Lubkin↗
DOE OSTI2022

Utah FORGE Downhole Geophone Seismic Data (August 2022)

This is a link to downhole geophone data collected by Schlumberger. These data were collected in the Utah FORGE deep seismic monitoring wells 58-32 and 56-32. The format is a standard SEGY and the units are bits. To convert to acceleration (m/s2) multiply by 2.333 x 10-7. Use one of the scripts linked below to use wget commands to pull the data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Understanding fission gas bubble distribution, lanthanide transportation, and thermal conductivity degradation in neutron-irradiated α-U using machine learning

U—10Zr based metallic nuclear fuel is the leading candidate for next-generation sodium-cooled fast reactors in the United States. US research reactors have used and tested this fuel type since the 1960s and accumulated considerable experience and knowledge about the fuel performance. Most of the knowledge, however, remains empirical. The lack of mechanistic understanding of fuel performance puts a large burden on proof through experimental verification for the qualification of U—10Zr fuel for commercial use. Further, this paper proposes an image data-driven machine learning approach, coupled with domain knowledge provided by advanced post irradiation examination, to provide unprecedented quantified insights into the morphology, size, density and the connectivity of fission gas bubbles and their effects on the fission product transportation and thermal conductivity. Specifically, we developed a method to automatically detect, extract statistics, and classify ~19,000 fission gas bubbles into different categories, and quantitatively link the data to lanthanide transportation through connected bubbles and degradation of thermal conductivity along the radial temperature gradient in a neutron irradiated U—10Zr annular fuel. Results indicate the approach can be modified to study other irradiation effects, such as secondary phase redistribution and gaseous fuel swelling in other irradiated nuclear fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2003

Keeping Communication Continuous

General Dynamics Decision Systems employees have played a role in supplying telemetry, tracking, and control (TT&C) and other communications systems to NASA and the U.S. Department of Defense for over 40 years. Providing integrated communication systems and subsystems for nearly all manned and unmanned U.S. space flights, the heritage of this Scottsdale, Arizona-based company includes S-band transceivers that enabled millions of Americans to see Neil Armstrong and hear his prophetic words from the Moon in 1969. More recently, Decision Systems has collaborated with NASA s Goddard Space Flight Center to develop transponders, wireless communications devices that pick up and automatically respond to an incoming signal, for NASA s Tracking and Data Relay Satellite System (TDRSS). Four generations of Decision Systems TDRSS transponders have been developed under Goddard s sponsorship. The company s Fourth Generation TDRSS User Transponder (TDRSS IV) allows low-Earth-orbiting spacecraft to communicate continuously with a single ground station at White Sands, New Mexico, through a constellation of geostationary relay satellites positioned at key locations around the Earth. In addition to the communications of forward link control commands and return link telemetry data, the TDRSS IV also supports spacecraft orbit tracking through coherent turn-around of a pseudo-noise ranging code and two-way Doppler tracking.When the NSBF adopted the use of global positioning system receivers for balloon position tracking, Decision Systems concluded that a simpler, noncoherent transceiver could provide the NSBF with the necessary TDRSS communications without the additional cost and complexity of a coherent transponder. The solution was to take the core design of the TDRSS IV Transponder, but remove the extra functionality that supported coherent turn-around. This would simplify the production effort, reduce the testing required, and result in a lower cost product with smaller size, weight, and power consumption. Once NSBF and Decision Systems agreed on a concept for this new product, known as the Multi-Mode Transceiver (MMT), the NSBF approached Goddard for approval and funding.

Source record↗
DOE OSTI2025

Hourly Electricity Demand Profiles for Each County in the Contiguous United States

This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.

14 SOLAR ENERGY↗