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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

The Mission Operations System for Wide-field Infrared Survey Explorer (WISE)

The goal of the Wide-field Infrared Survey Explorer (WISE) mission is to perform a highly sensitive all-sky survey in 4 wavebands from 3 to 25(mu)m. Launched on a Delta II rocket into a 500km Sun-synchronous orbit in June 2009, during its 7 months of operations, WISE will acquire about 50GBytes of raw science data every day, which will be down-linked via the TDRSS relay satellite system and processed into an astronomical catalogue and image atlas. The WISE mission operations system is being implemented in collaboration between UCLA, JPL and IPAC (Caltech). In this paper we describe the challenges to manage a high data rate, cryogenic, low earth-orbit mission; maintaining safe on-orbit operations, fast anomaly recoveries (mandated by the desire to provide complete sky coverage in a limited lifetime), production and dissemination of high quality science products, given the constraints imposed by funding profiles for small space missions.

Science Data Processing↗
NASA NTRSDate not supplied

From the Ground Up: Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) Comparison of Vertical Profiles with 2022 Student Airborne Research Program (SARP) In Situ Data

The Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument, installed on the International Space Station (ISS) in 2017, has completed over five years of data collection and the accompanying production of science data products. SAGE III/ISS is a solar and lunar occultation instrument that scans the light from the Sun and Moon through the limb of the Earth’s atmosphere to produce vertical profiles of aerosol, ozone, water vapor, and other trace gasses. This year provided the unique opportunity to compare in situ data collected during the NASA Student Airborne Research Program (SARP) summer airborne campaign with data collected by SAGE III/ISS on coincident overpasses.​ ​ SARP, a summer internship program typically for rising undergraduate seniors, provides students with an opportunity to gain hands-on experience making earth system measurements using NASA’s airborne science platforms. The NASA DC-8 flying laboratory collected in-situ measurements of ozone and other trace gasses during the summer 2022 SARP campaign. While the general focus with SARP is on the airborne research component, this year, a suite of 10 ozonesonde launches spatially and temporally co-located with the research flights collected atmospheric state and in-situ ozone measurements from inland southern California. This presentation shows the results of comparing SAGE III/ISS vertical profiles with those of in situ data collected during the summer 2022 SARP campaign.​

In Situ↗
DOE OSTI2021

COVID-19 Knowledge Graph -- Dataset for SMCDC 2021 Challenge 2

This repository contains the data for the 2021 Smoky Mountains Computational Sciences Data Challenge (SMCDC21) Challenge 2 -- Finding Novel Links in COVID-19 Knowledge Graph. The total size of all files in this repository is 285MB. Challenge website: https://smc-datachallenge.ornl.gov/2021-challenge-2/ More information about the challenge and the data: https://github.com/ORNL/smcdc-2021-covid-kg

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Improving Scientific Data Analysis Through Multi-touch Enabled Interactive Data Visualization with Applications to Neutron Science

Scientific data analysis is an inherently exploratory process that requires flexible human interaction with data visualization and anal- ysis tools. In this paper, we describe challenges in neutron scattering data analysis and present our approach for allowing more effective human interaction. In addition to multi-touch enabled data visualiza- tion techniques, we describe color scale enhancements, layouts, and visual analytics approaches for 3D volume slicing. Since challenges in neutron science are echoed in most scientific fields, we generalize the project objective and present a case for bridging the gap between the latest advances in interactive data visualization and practical scientific scenarios where new critical knowledge is buried in large volumes of complex data.

Steed, Chad↗
NASA NTRS2007

RF and Optical Communications: A Comparison of High Data Rate Returns From Deep Space in the 2020 Timeframe

As NASA proceeds with plans for increased science data return and higher data transfer capacity for science missions, both RF and optical communications are viable candidates for significantly higher-rate communications from deep space to Earth. With the inherent advantages, smaller apertures and larger bandwidths, of optical communications, it is reasonable to expect that at some point in time and combination of increasing distance and data rate, the rapidly emerging optical capabilities would become more advantageous than the more mature and evolving RF techniques. This paper presents a comparison of the burden to a spacecraft by both RF and optical communications systems for data rates of 10, 100, and 1000 Mbps and large distances. Advanced technology for RF and optical communication systems have been considered for projecting capabilities in the 2020 timeframe. For the comparisons drawn, the optical and RF ground terminals were selected to be similar in cost. The RF system selected is composed of forty-five 12-meter antennas, whereas the selected optical system is equivalent to a 10-meter optical telescope. Potential differences in availability are disregarded since the focus of this study is on spacecraft mass and power burden for high-rate mission data, under the assumption that essential communications will be provided by low-rate, high availability RF. For both the RF and optical systems, the required EIRP, for a given data rate and a given distance, was achieved by a design that realized the lowest possible communications subsystem mass (power + aperture) consistent with achieving the lowest technology risk. A key conclusion of this paper is that optical communications has great potential for high data rates and distances of 2.67 AU and beyond, but requires R&D and flight demonstrations to prove out technologies.

Williams, W. Dan↗
NASA NTRS2007

Gravity Probe B Timing System and Roll Phase Determination

An oven-controlled crystal oscillator at 16.368 MHz provides clock signals to all GP-B components and synchronizes the data collection, transmission and processing. The sampled science data signals are stamped with the vehicle time, a counter of the 10Hz data strobe divided down from the clock. The GPS receiver supplies an external reference for time transfer between the vehicle time and coordinated universal time. Ground and space flight tests show the time transfer error is within 1 microsecond. The time latency between the effective sample time of science signals and the stamped vehicle time is verified to 1 ms in the ground tests. The GP-B satellite is controlled to roll with a period of 77.5 sec about an axis along the direction to the guide star to average out the disturbance torques fixed to the body of the satellite and reduce the gyroscope readout noise. The roll phase is determined on the ground to high accuracy with the telemetry data from two star trackers and used in the data analysis to separate the drifts of gyroscope spin axes in the orbital plane and perpendicular to the orbital plane. The flight data shows that the roll phase is controlled to within 40 arcsec with a measurement uncertainty of 7 arcsec.

Jie, Li↗
DOE OSTI2020

Flat-histogram extrapolation as a useful tool in the age of big data

In this work, we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurate estimates of physical properties across a broad range of conditions have been achieved using this approach, greatly reducing the number of simulations needed to explore a given system's behaviour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2019

Planetary Boundary Layer Height from AIRS and MERRA-2 Products at NASA GES DISC, and Insights from Data Intercomparison

The Atmospheric Infrared Sounder (AIRS) is the hyperspectral infrared sounder onboard NASA's Aqua satellite, launched in 2002. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in collaboration with NASA Sounder Team at JPL, provides processing, archiving, and distribution services for NASA sounders: the Aqua AIRS mission and the subsequent Suomi-National Polar-orbiting Partnership Cross-track Infrared Sounder (CrIS) mission. The Planetary Boundary Layer (PBL) Height is a new variable added in the AIRS Version 6 support product. It is derived based on gradients of the retrieved atmospheric thermodynamic profile, and gives the pressure at the top of PBL over the ocean. The GES DISC also provides services for the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product generated by the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system. The monthly PBL Height variable has been available in the Giovanni system, which is a Web-based application developed by the GES DISC providing a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. In this work, we will present the monthly PBL Height data from AIRS and MERRA-2 and the services to support data intercomparison, such as access, plotting, subsetting, re-gridding, and generation of a multi-year monthly mean. We will also show intercomparison results, and evaluate whether (over the ocean) AIRS can observe PBL features similar to the reanalysis product at monthly and longer-term scales.

AIRS↗
DOE OSTI2018

Integrating Distributed-Memory Machine Learning into Large-Scale HPC Simulations

Lawrence Livermore National Laboratory (LLNL) is a national lab based in Livermore, California. It was established in 1952 to meet increasing national security needs. They specialize in earth and atmospheric science, bioscience and bioengineering, lasers and optical science and technology, advanced materials and manufacturing, high-energy-density science, nuclear, chemical, and isotopic science and technology, and HPC, simulation and data science ((cor, 2018)). LLNL is also home to a number of large Linux clusters and supercomputers which are used to support research in their areas of specialization (mac, 2018). One purpose of the LLNL supercomputers is to run highly detailed and accurate physics simulations. These simulations allow scientists to get data on experiments that would be too expensive, difficult, or dangerous to perform in reality. With the help of simulations scientists at LLNL are able to study processes in more depth than they would be able to otherwise.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2019

TESS Data Release Notes: Sectors 1-9, Multi-Sector Search, DR15

This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from the Sector 1-9 transiting planet search with observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics.

Burke, Christopher J.↗
NASA NTRS2020

TESS Data Release Notes: Sectors 14 – 19, Multi-Sector Search, DR28

This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from the multi-sector search of Sectors 14-19 observed with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics.

Burke, Christopher J.↗
NASA NTRS2003

Data Access Tools And Services At The Goddard Distributed Active Archive Center (GDAAC)

As one of the largest providers of Earth Science data from the Earth Observing System, GDAAC provides the latest data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Solar Radiation and Climate Experiment (SORCE) data products via GDAAC's data pool (50TB of disk cache). In order to make this huge volume of data more accessible to the public and science communities, the GDAAC offers multiple data access tools and services: Open Source Project for Network Data Access Protocol (OPeNDAP), Grid Analysis and Display System (GrADS/DODS) (GDS), Live Access Server (LAS), OpenGlS Web Map Server (WMS) and Near Archive Data Mining (NADM). The objective is to assist users in retrieving electronically a smaller, usable portion of data for further analysis. The OPeNDAP server, formerly known as the Distributed Oceanographic Data System (DODS), allows the user to retrieve data without worrying about the data format. OPeNDAP is capable of server-side subsetting of HDF, HDF-EOS, netCDF, JGOFS, ASCII, DSP, FITS and binary data formats. The GrADS/DODS server is capable of serving the same data formats as OPeNDAP. GDS has an additional feature of server-side analysis. Users can analyze the data on the server there by decreasing the computational load on their client's system. The LAS is a flexible server that allows user to graphically visualize data on the fly, to request different file formats and to compare variables from distributed locations. Users of LAS have options to use other available graphics viewers such as IDL, Matlab or GrADS. WMS is based on the OPeNDAP for serving geospatial information. WMS supports OpenGlS protocol to provide data in GIs-friendly formats for analysis and visualization. NADM is another access to the GDAAC's data pool. NADM gives users the capability to use a browser to upload their C, FORTRAN or IDL algorithms, test the algorithms, and mine data in the data pool. With NADM, the GDAAC provides an environment physically close to the data source. NADM will benefit users with mining or offer data reduction algorithms by reducing large volumes of data before transmission over the network to the user.

Pham, Long↗
NASA NTRS2006

The MY NASA DATA Project

On the one hand, locating the right dataset, then figuring out how to use it, is a daunting task that is familiar to almost any scientist or graduate student in the fields of Earth system science. On the other hand, the ability to explore authentic Earth system science data, through inquiry-based education, is an important goal in US national education standards. Fortunately, in the digital age, tools are emerging that can make such data exploration commonplace at all educational levels. This paper describes the conception and development of one project that aims to bridge this gap: Mentoring and inquiry using NASA Data on Atmospheric and Earth science for Teachers and Amateurs (MY NASA DATA; mynasadata.larc.nasa.gov). With funding from NASA's Science Mission Directorate, this project was launched in early 2004 with the aim of developing microsets and identifying other enablers for making data accessible. A key feature of the project is a Live Access Server, the first educational implementation of this open source software, developed by NOAA, that makes it possible to explore multiple data formats through a single interface. This powerful tool is made more useful to the primary target audiences (K-12 and amateur scientists) through careful selection of the data offered, user-friendly explanations of the tool itself, and age-appropriate explanations of the parameters. However experience already shows that graduate students and even practicing scientists can also make use of this resource. The website also hosts teacher-contributed lesson plans, and seeks to feature reports of research projects that use the data.

Chambers, Lin H.↗
NASA NTRSDate not supplied

Meeting SPD-41a Requirements for BPS

In April 2021 BPS Formed the Science Data Management Work Group. This Poster describes the process and forward steps.

BPS Science Data Management↗
DOE OSTI2025

Comparisons of the v11.1 Orbiting Carbon Observatory‐2 (OCO‐2) X CO2 Measurements With GGG2020 TCCON

The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first Earth observation satellite mission dedicated to studying the sources and sinks of carbon dioxide (CO 2 ) on a global scale. The observations of reflected sunlight are inverted in a retrieval algorithm to produce estimates of the dry air mole-fractions of CO 2 (X CO2 ). The OCO-2 Level 2 data release, version 11.1 (v11.1) retrievals from the Atmospheric Carbon Observations from Space (ACOS) algorithm, includes significant improvements in the X CO2 data product compared to older OCO-2 data versions. This work compares the v11.1 X CO2 from OCO-2 against X CO2 estimates collected from a global ground-based network known as the Total Carbon Column Observing Network (TCCON), OCO-2's primary validation source. The OCO-2 project provides a version of the Level 2 data product, called “lite” files that include calibrated and bias-corrected XCO2 values, accessible together with all OCO-2 data products through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). This work shows that OCO-2 X CO2 observations made between September 2014 and December 2023, after quality filtering and the application of an averaging kernel correction, agree well with coincident TCCON data for all OCO-2 observational modes of land (nadir, glint, target) and ocean (glint). The aggregated, bias-corrected, and quality-filtered absolute average bias values are less than or equal to 0.20 parts per million (ppm) globally for all OCO-2 observation modes, where the biases do not indicate a statistically significant time dependence. The land nadir/glint mode has the lowest bias value of −0.03 ± 0.85 ppm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A characterization of workflow management systems for extreme-scale applications

We present that the automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies, workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today’s computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. Finally, the paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An overview of data tools for representing and managing building information and performance data

Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Defining a Metric Space of Host Logs and Operational Use Cases

Host logs, in particular, Windows Event Logs, are a valuable source of information often collected by security operation centers (SOCs). The semi-structured nature of host logs inhibits automated analytics, and while manual analysis is common, the sheer volume makes manual inspection of all logs impossible. Although many powerful algorithms for analyzing time-series and sequential data exist, utilization of such algorithms for most cyber security applications is either infeasible or requires tailored, research-intensive preparations. In particular, basic mathematic and algorithmic developments for providing a generalized, meaningful similarity metric on system logs is needed to bridge the gap between many existing sequential data mining methods and this currently available but under-utilized data source. In this paper, we provide a rigorous definition of a metric product space on Windows Event Logs, providing an embedding that allows for the application of established machine learning and time-series analysis methods. We then demonstrate the utility and flexibility of this embedding with multiple use-cases on real data: (1) comparing known infected to new host log streams for attack detection and forensics, (2) collapsing similar streams of logs into semantically-meaningful groups (by user, by role), thereby reducing the quantity of data but not the content, (3) clustering logs as well as short sequences of logs to identify and visualize user behaviors and background processes over time. Overall, we provide a metric space framework for general host logs and log sequences that respects semantic similarity and facilitates a wide variety of data science analytics to these logs without data-specific preparations for each.

Verma, Miki↗