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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Measurement of isosteric heat of gas adsorption and Brunauer–Emmett–Teller (BET) surface area using a quartz crystal microbalance

The study of gas adsorption on a solid surface evaluates the affinity between sorbate gas and sorbent substrate and factors that contribute to this. This report presents a test platform for adsorption experiments of various gases on various solid surfaces. Controlled environmental conditions enable investigations in materials surface science and increase the consistency among adsorption data. The system utilizes a quartz crystal microbalance to perform gravimetric analysis of deposition and adsorption, enabling investigation of the interaction of gaseous molecules with solid surfaces. In this study, a quartz crystal microbalance as gas adsorption detector was integrated with an environmental chamber to create a versatile tool for gas adsorption experiments on thin films. Experimental operation of this apparatus was demonstrated via acquisition of the adsorption isotherms of cyclohexane vapor on a gold surface at 55 and 70 °C. The result indicated International Union of Pure and Applied Chemistry Type II adsorption. Consequentially, application of the Brunauer–Emmett–Teller model to the isotherm data subject to predefined criteria for linear region selection yielded a surface area of the sorbent of 0.53 cm2 at 55 °C. From the monolayer region of the isotherms, the isosteric heat of adsorption of the cyclohexane vapor on gold was calculated to be 37 kJ mol-1.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

The Fabric for Frontier Experiments Project at Fermilab

The FabrIc for Frontier Experiments (FIFE) project is a new, far-reaching initiative within the Fermilab Scientific Computing Division to drive the future of computing services for experiments at FNAL and elsewhere. It is a collaborative effort between computing professionals and experiment scientists to produce an end-to-end, fully integrated set of services for computing on the grid and clouds, managing data, accessing databases, and collaborating within experiments. FIFE includes 1) easy to use job submission services for processing physics tasks on the Open Science Grid and elsewhere, 2) an extensive data management system for managing local and remote caches, cataloging, querying, moving, and tracking the use of data, 3) custom and generic database applications for calibrations, beam information, and other purposes, 4) collaboration tools including an electronic log book, speakers bureau database, and experiment membership database. All of these aspects will be discussed in detail. FIFE sets the direction of computing at Fermilab experiments now and in the future, and therefore is a major driver in the design of computing services worldwide.

Kirby, Michael↗
DOE OSTI2019

An Overview of Technologies for Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility Consortium

WholeTraveler is a project of the Department of Energy's Smart Mobility consortium that explores how an individual's attitudes and lifestyle characteristics are embodied in transportation behaviors throughout one's life trajectory. As a study at the nexus of decision science and urban systems analysis, fine-grained location data (FGLD), or records of an individual's location collected at a resolution sufficient to determine both the unique destinations a traveler visits each day and the modes used to travel between them, underlie high quality results. Moreover, the methods for collecting FGLD and the means through which they are associated with a traveler's personal attributes have significant implications for the burden on research participants, the cost and complexity of the study, as well as the confidence with which hypotheses can be validated. This paper provides a broad overview of existing technologies for FGLD collection and is intended to provide a literature review for researcher exploring similar domain areas. Technologies are categorized as either network or onboard approaches. An onboard system, such as smartphone or GPS device, sends signals to external networks to capture its own location. Conversely, network systems, such as cell towers, are comprised of sensing nodes with known locations that record when individually identifiable assets come within their range of detection. Our analysis indicates that the most sensible approach to FGLD collection, for the purposes of WholeTraveler, is to leverage onboard devices, and in particular the existing Moves or Google Maps applications for Android and iOS smartphones. Dedicated GPS devices have been widely implemented by the transportation research community and pose an alternatively acceptable form of FGLD collection. Independently developing a custom smartphone application is not recommended because of the notable challenges in establishing minimal viable functionality, as well as in ensuring compatibility across an ever-growing combination of hardware devices and software versions. No matter the technical means of data collection, the secure handling of FGLD is of critical importance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

FY 2021 Mid-Year HPC Utilization Report M3UF-21IN0701018

Idaho National Laboratory (INL), supported by the Department of Energy Office of Nuclear Energy (DOE-NE) through the Nuclear Science User Facilities, provides access to supercomputer systems and data storage along with support staff for system management, software installation, cybersecurity and user support to the broader DOE-NE user community. Users include individuals at universities, industry, and government laboratories enabling a wide range of research and development and mission-supporting activities. The availability of high-performance computing (HPC) capabilities is a key foundation of collaboration and innovation in nuclear energy systems research. HPC resources and INL directly support the mission and objectives of DOE-NE. From October 2020 through March 2021, INL HPC capabilities were utilized by a diverse set of computing and applied researchers, for a wide range of research and engineering activities. This report focuses on current INL HPC systems and utilization from October 1, 2020 through March 31, 2021.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Fusion Energy Sciences Network Requirements Review. Final Report, April - October 2021

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 US 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 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES 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.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

ARIES Network Requirements Review

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 US 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. On May 1, 2021, ESnet and the DOE Office of Energy Efficiency and Renewable Energy (EERE), organized an ESnet requirements review of the ARIES (Advanced Research on Integrated Energy Systems) platform. Preparation for this event included identification of key stakeholders to the process: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents in order to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Basic Energy Sciences 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 US 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. Between March and September 2022, ESnet and the Office of Basic Energy Sciences (BES) of the DOE SC organized an ESnet requirements review of BES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BES 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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High Energy Physics Network Requirements Review: One-Year Update

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 its laboratories and facilities in the US 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 (R&E) networking community. In April 2022, ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for the review included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about the group’s 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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Biological and Environmental Research 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 its laboratories and facilities in the US 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 (R&E) networking community. Between August 2022 and April 2023, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized an ESnet requirements review of BER-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BER ESS 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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Opportunities for enhancing MLCommons efforts while leveraging insights from educational MLCommons earthquake benchmarks efforts

MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need for benchmark carpentry for scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection called cloudmesh with its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).

58 GEOSCIENCES↗
NASA NTRS1964

Study of the Mariner Mars 66 spacecraft

Mariner Mars 66 spacecraft mission study - probabilities of successfully transmitting encounter TV science data, releasing capsule for Mars landing, and command systems logic

MISSION PLANNING↗
NASA NTRS1977

Autonomous satellite navigation with the Global Positioning System

This paper discusses the potential of using the Global Positioning System (GPS) to provide autonomous navigation capability to NASA satellites in the 1980 era. Some of the driving forces motivating autonomous navigation are presented. These include such factors as advances in attitude control systems, onboard science annotation, and onboard gridding of imaging data. Simulation results which demonstrate baseline orbit determination accuracies using GPS data on Seasat, Landsat-D, and the Solar Maximum Mission are presented. Emphasis is placed on identifying error sources such as GPS time, GPS ephemeris, user timing biases, and user orbit dynamics, and in a parametric sense on evaluating their contribution to the orbit determination accuracies.

A J Fuchs↗
NASA NTRS1982

Information science team

Concerns are expressed about the data handling aspects of system design and about enabling technology for data handling and data analysis. The status, contributing factors, critical issues, and recommendations for investigations are listed for data handling, rectification and registration, and information extraction. Potential supports to individual P.I., research tasks, systematic data system design, and to system operation. The need for an airborne spectrometer class instrument for fundamental research in high spectral and spatial resolution is indicated. Geographic information system formatting and labelling techniques, very large scale integration, and methods for providing multitype data sets must also be developed.

Billingsley, F.↗
NASA NTRS1982

The Voyager mission to Jupiter and Saturn

This paper presents a description of the Voyager mission, in which two United States spacecraft flew by and made scientific observations of Jupiter and Saturn and their satellites. The design of the mission, including mission objectives, trajectory characteristics, and navigation system are depicted, and the spacecraft engineering systems and subsystems are described. The complement of science instruments, the command system, and the tracking and data acquisition systems, are also summarized. Finally, some of the results of the mission are presented.

Gates, C. R.↗
NASA NTRS1986

Research and technology

Activities of the Goddard Space Flight Center are described in the areas of planets and interplanetary media, comets, astronomy and high-energy physics, solar physics, atmospheres, terrestrial physics, ocean science, sensors and space technology, techniques, user space data systems, space communications and navigation, and system and software engineering. Flight projects and mission definition studies are presented, and institutional technology is described.

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