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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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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Remote-sensing and radiosonde datasets collected in the San Luis Valley during the LAPSE-RATE campaign

Abstract. In July 2018, the International Society for Atmospheric Research using Remotely piloted Aircraft (ISARRA) hosted a flight week to showcase the role remotely piloted aircraft systems (RPASs) can have in filling the atmospheric data gap. This campaign was called Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). In support of this campaign, ground-based remote and in situ systems were also deployed for the campaign. The University of Oklahoma deployed the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), the University of Colorado deployed two Doppler wind lidars, and the National Severe Storms Laboratory deployed a mobile mesonet with the ability to launch radiosondes. This paper focuses on the data products from these instruments that result in profiles of the atmospheric state. The data are publicly available in the Zenodo LAPSE-RATE community portal (https://zenodo.org/communities/lapse-rate/, 19 January 2021). The profile data discussed are available at https://doi.org/10.5281/zenodo.3780623 (Bell and Klein, 2020), https://doi.org/10.5281/zenodo.3780593 (Bell et al., 2020b), https://doi.org/10.5281/zenodo.3727224 (Bell et al., 2020a), https://doi.org/10.5281/zenodo.3738175 (Waugh, 2020b), https://doi.org/10.5281/zenodo.3720444 (Waugh, 2020a), and https://doi.org/10.5281/zenodo.3698228 (Lundquist et al., 2020).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1975

Command and data handling of science signals on Spacelab

The Orbiter Avionics and the Spacelab Command and Data Management System (CDMS) combine to provide a relatively complete command, control, and data handling service to the instrument complement during a Shuttle Sortie Mission. The Spacelab CDMS services the instruments and the Orbiter in turn services the Spacelab. The CDMS computer system includes three computers, two I/O units, a mass memory, and a variable number of remote acquisition units. Attention is given to the CDMS high rate multiplexer, CDMS tape recorders, closed circuit television for the visual monitoring of payload bay and cabin area activities, methods of science data acquisition, questions of transmission and recording, CDMS experiment computer usage, and experiment electronics.

Mccain, H. G.↗

DOE OSTI2022

The Need for a System Science Approach to Global Magnetospheric Models

This perspective advocates for the need of a combined system science approach to global magnetospheric models and to spacecraft magnetospheric data to answer the question “Do simulations behave in the same manner as the magnetosphere does?” (instead of the standard validation question “How well do simulations reproduce spacecraft data?”). This approach will 1) validate global magnetospheric models statistically, without the need for a direct comparison against spacecraft data, 2) expose the deficiencies of the models, and 3) provide physics support to the system analysis performed on the magnetospheric system.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1988

Research and technology 1988

This report presents the on-going research activities at the NASA Marshall Space Flight Center for the year 1988. The subjects presented are space transportation systems, shuttle cargo vehicle, materials processing in space, environmental data base management, microgravity science, astronomy, astrophysics, solar physics, magnetospheric physics, aeronomy, atomic physics, rocket propulsion, materials and processes, telerobotics, and space systems.

Source record↗

DOE OSTI2018

NREL Data Center Cuts Water Use in Half With Hybrid Thermosyphon System

In 2016, NREL installed a thermosyphon hybrid cooling system to reduce water use in the already extremely energy efficient data center at the Energy Systems Integration Facility. In this video, NREL Computational Science Center Director Steve Hammond describes how the system has successfully cut onsite water use in half without negative impacts to the data center’s energy efficient operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2017

Web Coverage Service Challenges for NASA's Earth Science Data

In an effort to ensure that data in NASA's Earth Observing System Data and Information System (EOSDIS) is available to a wide variety of users through the tools of their choice, NASA continues to focus on exposing data and services using standards based protocols. Specifically, this work has focused recently on the Web Coverage Service (WCS). Experience has been gained in data delivery via GetCoverage requests, starting out with WCS v1.1.1. The pros and cons of both the version itself and different implementation approaches will be shared during this session. Additionally, due to limitations with WCS v1.1.1 ability to work with NASA's Earth science data, this session will also discuss the benefit of migrating to WCS 2.0.1 with EO-x to enrich this capability to meet a wide range of anticipated user's needs This will enable subsetting and various types of data transformations to be performed on a variety of EOS data sets.

Web Coverage Services↗

NASA NTRS1985

Activities of the Pilot Land Data System project

The University of Maryland's Remote Sensing Systems Laboratory submitted to NASA/Goddard an interim progress report on the work being conducted within its Pilot Land Data System IPLDS project. The Remote Sensing Systems Laboratory addressed the following tasks: (1) identify data types and data sources needed to describe the selected test sites in collaboration with Goddard's Hydrological Sciences Branch; (2) define the procedures necessary to access/acquire this data; (3) conduct meetings with the PLDS Systems Engineering Group to identify functional specification priorities for PLDS development; (4) assemble documentation on historical remotely sensed imagery and transfer of such information to the PLDS Data Management Group; (5) collect data identified by Goodard's Hydrological Sciences Branch for data set inventory in PLD; (6) develop a Workstation-PLDS system interface over high speed lines, (7) develop and test through a Phase 1 demonstration of a micro workstation to access PLDS; and (8) establish interdepartmental agreement of development of computer link for electronic access of water resources data from USGS.

Sircar, J. K.↗

NASA NTRS2001

Captain, sensors report ...: Correction of Remote Sensing Data

Remote sensing data acquired by airborne and satellite Systems have many impressive virtues for ecology and other sciences. They are synoptic. They are extremely cheap per data point. They cover large areas. They are not subjective. They detect features that are of clear and obvious interest for many uses. But, they also yield totally irreproducible measurements. Sensors that operate in the visible, near infrared and thermal portions of the spectrum are sensitive to a host of phenomena that are usually of little interest to the average user. Fully recognizing the power and sophistication of the algorithms currently available to users of remote sensing, the authors as long time developers (20+ years) of such algorithms, demonstrate that a host of other signals are convolved with the typical data set, and that many of these signals are very commonly not even recognized, and that these signals are often larger than the signal from the putative target. These derive from both geometric and radiometric sources as well as fundamental flaws in sensor engineering. Until such extraneous factors can be controlled or eliminated the utility of the data is greatly restricted. Finally, the authors lay out a design elements for a rational sensor and methods of deconvolving the complexity of the real world data using such a sensor.

Rickman, Doug↗

DOE OSTI2021

2020 High Energy Physics Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the United States and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2020,ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for this event included identification of key stakeholders: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion. ESnet and ASCR use requirements reviews to discuss and analyze current and planned science use cases and anticipated data output of a particular program, user facility, or project to inform ESnet’s strategic planning, including network operations, capacity upgrades, and other service investments. A requirements review comprehensively surveys major science stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

FY16 ISCP Nuclear Counting Facility Hardware Expansion Summary

Hardware expansion and detector calibrations were the focus of FY 16 ISCP efforts in the Nuclear Counting Facility. Work focused on four main objectives: 1) Installation, calibration, and validation of 4 additional HPGe gamma spectrometry systems; including two Low Energy Photon Spectrometers (LEPS). 2) Re-Calibration and validation of 3 previously installed gamma-ray detectors, 3) Integration of the new systems into the NCF IT infrastructure, and 4) QA/QC and maintenance of current detector systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

GROWdb US River Systems - Samples

GROW Overview We developed the Genome Resolved Open Watersheds database (GROWdb), which aims to increase genomic sampling and understanding of global river microbiomes. An emphasis of GROWdb is to create a publicly available and ever-expanding microbial genome database that is focused on rivers while being interoperable with databases from other ecosystems. GROWdb is based on a network-of-networks approach to move beyond a small collection of well-studied rivers, towards a spatially distributed, global network of systematic observations. GROWdb represents the first microbial, river-focused resource parsed at various scales from genes to MAGs to community level including expression and potential based measurements that will be of interest to microbiologists, ecologists, geochemists, hydrologists, and modelers. Dataset Acknowledgement GROWdb contains data from various research campaigns, please acknowledge the following data generators, as appropriate: WHONDRS derived genomes or samples - include this statement in your acknowledgements: “This study used data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) under the River Corridor Science Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL) that was generated at the U.S. Department of Energy (DOE) Joint Genome Institute User Facility. PNNL is operated by Battelle Memorial Institute for the U.S. DOE under Contract No. DE-AC05-76RL01830. The SFA is supported by the U.S. DOE, Office of Biological and Environmental Research (BER), Environmental System Science (ESS) Program.” Total Samples loaded onto this Narrative: 178 Note: Not all GROW samples may be loaded into KBase Data Availability The data underlying GROWdb are accessible across various platforms to ensure all levels of data structure are widely available. First, all reads and MAGs are publicly hosted on National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. Second, all data related data presented here including MAG annotations, extended data tables, phylogenetic tree files, antibiotic resistance gene database files, and MAG abundance tables are available in Zenodo (link). Beyond the flat database files listed above, our aim for GROWdb was to maximize data use by making the data available in searchable and interactive platforms including the National Microbiome Data Collaborative (NMDC) data portal, the Department of Energy’s Systems Biology Knowledgebase (KBase), and a GROW specific user interface released here, GROWdb Explorer. Each platform provides different ways to interact with GROWdb: NMDC GROWdb formed a pilot project for the NMDC. Specifically, individual GROWdb datasets (metagenomes, metatranscriptomes, etc) are easily accessible and searchable through the NMDC data portal, where they are systematically connected to each other and to a rich suite of sample information and standard analysis results, following Findable, Accessible, Interoperable, and Reusable (FAIR) data practices. KBase GROWdb is publicly available within KBase, including samples (this Narrative), MAGs, and corresponding genome scale metabolic models. Access within KBase allows for immediate access and reuse of data, including comparison to private data using KBase’s 500+ analysis tools. Other linked narratives in KBase: GROW Metagenome Assembled Genomes (MAGs) GROW Metabolic Models GROWdb Explorer GROWdb data is also explorable through a graphical user interface built through the Colorado State University Geospatial Centroid (https://geocentroid.shinyapps.io/GROWdatabase/), allowing users to search and graph microbial and spatial data simultaneously. In summary, this microbial genome resource represents the first publicly available genome collection from rivers and offers data that can be leveraged across microbiome studies. GROWdb is an expanding repository to incorporate and unify global river multi-omic data for the future.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2008

Europa Explorer Operational Scenarios Development

In 2007, NASA conducted four advanced mission concept studies for outer planets targets: Europa, Ganymede, Titan and Enceladus. The studies were conducted in close cooperation with the planetary science community. Of the four, the Europa Explorer Concept Study focused on refining mission options, science trades and implementation details for a potential flagship mission to Europa in the 2015 timeframe. A science definition team (SDT) was appointed by NASA to guide the study. A JPL-led engineering team worked closely with the science team to address 3 major focus areas: 1) credible cost estimates, 2) rationale and logical discussion of radiation risk and mitigation approaches, and 3) better definition and exploration of science operational scenario trade space. This paper will address the methods and results of the collaborative process used to develop Europa Explorer operations scenarios. Working in concert with the SDT, and in parallel with the SDT's development of a science value matrix, key mission capabilities and constraints were challenged by the science and engineering members of the team. Science goals were advanced and options were considered for observation scenarios. Data collection and return strategies were tested via simulation, and mission performance was estimated and balanced with flight and ground system resources and science priorities. The key to this successful collaboration was a concurrent development environment in which all stakeholders could rapidly assess the feasibility of strategies for their success in the full system context. Issues of science and instrument compatibility, system constraints, and mission opportunities were treated analytically and objectively leading to complementary strategies for observation and data return. Current plans are that this approach, as part of the system engineering process, will continue as the Europa Explorer Concept Study moves toward becoming a development project.

Lock, Robert E.↗

NASA NTRS1999

Earth Science Data Archive and Access at the NASA/Goddard Space Flight Center Distributed Active Archive Center (DAAC)

The Goddard Distributed Active Archive Center (DAAC), as an integral part of the Earth Observing System Data and Information System (EOSDIS), is the official source of data for several important earth remote sensing missions. These include the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) launched in August 1997, the Tropical Rainfall Measuring Mission (TRMM) launched in November 1997, and the Moderate Resolution Imaging Spectroradiometer (MODIS) scheduled for launch in mid 1999 as part of the EOS AM-1 instrumentation package. The data generated from these missions supports a host of users in the hydrological, land biosphere and oceanographic research and applications communities. The volume and nature of the data present unique challenges to an Earth science data archive and distribution system such as the DAAC. The DAAC system receives, archives and distributes a large number of standard data products on a daily basis, including data files that have been reprocessed with updated calibration data or improved analytical algorithms. A World Wide Web interface is provided allowing interactive data selection and automatic data subscriptions as distribution options. The DAAC also creates customized and value-added data products, which allow additional user flexibility and reduced data volume. Another significant part of our overall mission is to provide ancillary data support services and archive support for worldwide field campaigns designed to validate the results from the various satellite-derived measurements. In addition to direct data services, accompanying documentation, WWW links to related resources, support for EOSDIS data formats, and informed response to inquiries are routinely provided to users. The current GDAAC WWW search and order system is being restructured to provide users with a simplified, hierarchical access to data. Data Browsers have been developed for several data sets to aid users in ordering data. These Browsers allow users to specify spatial, temporal, and other parameter criteria in searching for and previewing data.

Leptoukh, Gregory↗

DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗

NASA NTRS1991

Science network resources: Distributed systems

The Master Directory, which is overview information about whole data sets, is outlined. The data system environment is depicted. The question is explored of what is a prototype international directory including purpose and features. Advantages of on-line directories are listed. Interconnected directory assumptions are given. A description of given of DIF (Directory Interchange Format), which is an exchange file for directory information, along with information content of DIF and directories. The directory population status is given in a percentage viewgraph. The present and future directory interconnections status at GSFC is also listed.

Cline, Neal↗

NASA NTRS1990

High rate science data handling on Space Station Freedom

A study by NASA's User Information System Working Group for Space Station Freedom (SSF) has determined that the proposed onboard Data Management System, as initially configured, will be incapable of handling the data-generation rates typical of numerous scientific sensor payloads; many of these generate data at rates in excess of 10 Mbps, and there are at least four cases of rates in excess of 300 Mbps. The SSF Working Group has accordingly suggested an alternative conceptual architecture based on technology expected to achieve space-qualified status by 1995. The architecture encompasses recorders with rapid data-ingest capabilities and massive storage capabilities, optical delay lines allowing the recording of only the phenomena of interest, and data flow-compressing image processors.

Handley, Thomas H., Jr.↗

NASA NTRS1993

Tensoral: A system for post-processing turbulence simulation data

Many computer simulations in engineering and science -- and especially in computational fluid dynamics (CFD) -- produce huge quantities of numerical data. These data are often so large as to make even relatively simple post-processing of this data unwieldy. The data, once computed and quality-assured, is most likely analyzed by only a few people. As a result, much useful numerical data is under-utilized. Since future state-of-the-art simulations will produce even larger datasets, will use more complex flow geometries, and will be performed on more complex supercomputers, data management issues will become increasingly cumbersome. My goal is to provide software which will automate the present and future task of managing and post-processing large turbulence datasets. My research has focused on the development of these software tools -- specifically, through the development of a very high-level language called 'Tensoral'. The ultimate goal of Tensoral is to convert high-level mathematical expressions (tensor algebra, calculus, and statistics) into efficient low-level programs which numerically calculate these expressions given simulation datasets. This approach to the database and post-processing problem has several advantages. Using Tensoral the numerical and data management details of a simulation are shielded from the concerns of the end user. This shielding is carried out without sacrificing post-processor efficiency and robustness. Another advantage of Tensoral is that its very high-level nature lends itself to portability across a wide variety of computing (and supercomputing) platforms. This is especially important considering the rapidity of changes in supercomputing hardware.

Dresselhaus, Eliot↗

NASA NTRS2003

Intelligent Agents for Science Data Processing

In order to conduct research into global warming, the Earth Observing System Data and Information System (EOSDIS) generates a large and growing volume of data. This viewgraph presentation describes the architecture needed to manage the remote sensing data, and the numerical analysis used to process it.

Golden, Keith↗