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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Arizona State University Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Between October 2021 and February 2022 staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from Arizona State University (ASU) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

University of Central Florida Campus-Wide Deep Dive

EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders. Between December 2020 and August 2021, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff at University of Central Florida (UCF) for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

A seamless multiscale operator neural network for inferring bubble dynamics

Modelling multiscale systems from nanoscale to macroscale requires the use of atomistic and continuum methods and, correspondingly, different computer codes. Here, we develop a seamless method based on DeepONet, which is a composite deep neural network (a branch and a trunk network) for regressing operators. In particular, we consider bubble growth dynamics, and we model tiny bubbles of initial size from 100 nm to 10 $\mathrm {\mu }\textrm {m}$ , modelled by the Rayleigh–Plesset equation in the continuum regime above 1 $\mathrm {\mu }\textrm {m}$ and the dissipative particle dynamics method for bubbles below 1 $\mathrm {\mu }\textrm {m}$ in the atomistic regime. After an offline training based on data from both regimes, DeepONet can make accurate predictions of bubble growth on-the-fly (within a fraction of a second) across four orders of magnitude difference in spatial scales and two orders of magnitude in temporal scales. The framework of DeepONet is general and can be used for unifying physical models of different scales in diverse multiscale applications.

Mechanics↗
NASA NTRS2005

Multi-Agent Modeling and Simulation Approach for Design and Analysis of MER Mission Operations

A space mission operations system is a complex network of human organizations, information and deep-space network systems and spacecraft hardware. As in other organizations, one of the problems in mission operations is managing the relationship of the mission information systems related to how people actually work (practices). Brahms, a multi-agent modeling and simulation tool, was used to model and simulate NASA's Mars Exploration Rover (MER) mission work practice. The objective was to investigate the value of work practice modeling for mission operations design. From spring 2002 until winter 2003, a Brahms modeler participated in mission systems design sessions and operations testing for the MER mission held at Jet Propulsion Laboratory (JPL). He observed how designers interacted with the Brahms tool. This paper discussed mission system designers' reactions to the simulation output during model validation and the presentation of generated work procedures. This project spurred JPL's interest in the Brahms model, but it was never included as part of the formal mission design process. We discuss why this occurred. Subsequently, we used the MER model to develop a future mission operations concept. Team members were reluctant to use the MER model, even though it appeared to be highly relevant to their effort. We describe some of the tool issues we encountered.

Seah, Chin↗
NASA NTRS1973

Demonstration of intercontinental DSN clock synchronization by VLBI

The prototype system for Deep Space Network clock synchronization by VLBI has been demonstrated to operate successfully over intercontinental baselines in a series of experiments between Deep Space Stations at Madrid, Spain, and Goldstone, California. As predicted by analysis and short baseline demonstration, the system achieves reliable synchronization between 26m and 64m antenna stations with 17 and 37K nominal system temperatures using under one million bits of data from each station. Semi-real-time operation is feasible since this small amount of data can be transmitted to JPL and processed within minutes. The system resolution is 50 to 400ns, depending on the amount of data processed and the source intensity. The accuracy is believed to be comparable to the resolution, although it could be independently confirmed to only about 5 microseconds using LORAN C.

Hurd, W. J.↗
NASA NTRS1979

DSN command system Mark 3-78

The Deep Space Network Command System Mark III-78 implementation and functional operation are described. Software upgrade enables expanded storage capacity in the Deep Space Station Command Processor Assembly. Store-and-forward command data handling is operational for Voyager and Helios. Near-real-time command data handling was retained for Viking and Pioneer.

Thorman, H. C.↗
NASA NTRS1976

A history of the deep space network

The Deep Space Network (DSN) has been managed and operated by the Jet Propulsion Laboratory (JPL) under NASA contract ever since NASA was formed in late 1958. The Tracking and data acquisition tasks of the DSN are markedly different from those of the other NASA network, STDN. STDN, which is an amalgamation of the satellite tracking network (STADAN) and the Manned Space Flight Network (MSFN), is primarily concerned with supporting manned and unmanned earth satellites. In contrast, the DSN deals with spacecraft that are thousands to hundreds of millions of miles away. The radio signals from these distant craft are many orders of magnitude weaker than those from nearby satellites. Distance also makes precise radio location more difficult; and accurate trajectory data are vital to deep space navigation in the vicinities of the other planets of the solar system. In addition to tracking spacecraft and acquiring data from them, the DSN is required to transmit many thousands of commands to control the sophisticated planetary probes and interplanetary monitoring stations. To meet these demanding requirements, the DSN has been compelled to be in the forefront of technology.

Corliss, W. R.↗
DOE OSTI2026

Polarized target nuclear magnetic resonance measurements with deep neural networks

Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.

Metrology↗
NASA NTRS1994

Development of a Prototype Real-Time Automated Filter for Operational Deep Space Navigation

Operational deep space navigation has in the past, and is currently, performed using systems whose architecture was designed to accommodate tape data transfers and computing environments with a tiny fraction of the current capability. Additionally, this architecture requires constant human supervision and intervention. A prototype for a system which allows relatively automated processing of radio metric data received in near real-time from NASA's Deep Space Network (DSN) without any redesign of the existing operational flow has been developed. This system will allow for more rapid response as well as much reduced staffing to support mission navigation operations.

deep↗
DOE OSTI2022

Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy

A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, in this study, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1977

DSN test and training system, Mark III-77

The Mark III-77 system is configured to support DSN testing and training in preparation for the Mariner-Jupiter-Saturn 1977 and Pioneer-Venus 1978 missions, in addition to the on-going in-flight missions. DSN Test and Trailing System capabilities include functions performed in the Deep Space Stations, Ground Communications Facility, and Network Operations Control Center.

Thornman, H. C.↗
NASA NTRS1981

DSN scheduling system

The Deep Space Network (DSN) Scheduling Group provides the operationally oriented administrative support necessary for the effective scheduling of the DSN. The scheduling system plus the levels of schedules are explained.

Dorham, R.↗
NASA NTRS1981

Radio astronomy

The activities of the Deep Space Network in support of radio astronomy operations during the first quarter of 1981 are reported. Results of the use of a low noise maser are presented, as well as updates in DSN support of experiments sanctioned by the Radio Astronomy Experiment Selection Panel.

Shaffer, R. D.↗
NASA NTRS1981

Radio astronomy

The activities of the Deep Space Network in support of Radio Astronomy Operations during April and May 1981 are reported. Work in progres in support of an experiment selected for use of the DSN by the Radio Astronomy Experiment Selection Panel, Twin Quasi-Stellar Object VLBI, is reported.

Wolken, P. R.↗
NASA NTRS1981

Pioneers 6 through 12 mission support

The continuing efforts of the Deep Space Network in support of Pioneer Mission Operations during the cruise phases of the Pioneers 6 through 11 spacecraft and Pioneer Venus orbital operations are discussed.

Nevarez, R. E.↗
NASA NTRS1984

Telecommunications and data acquisition support for the Pioneer Venus Project: Pioneers 12 and 13, prelaunch through March 1984

The support provided by the Telecommunications and Data Acquisition organization of the Jet Propulsion Laboratory (JPL) to the Pioneer Venus missions is described. The missions were the responsibility of the Ames Research Center (ARC). The Pioneer 13 mission and its spacecraft design presented one of the greatest challenges to the Deep Space Network (DSN) in the implementation and operation of new capabilities. The four probes that were to enter the atmosphere of Venus were turned on shortly before arrival at Venus, and the DSN had to acquire each of these probes in order to recover the telemetry being transmitted. Furthermore, a science experiment involving these probes descending through the atmosphere required a completed new data type to be generated at the ground stations. This new data type is known as the differential very long baseline interferometry. Discussions between ARC and JPL of the implementation requirements involved trade-offs in spacecraft design and led to a very successful return of science data. Specific implementation and operational techniques are discussed, not only for the prime mission, but also for the extended support to the Pioneer 12 spacecraft (in orbit around Venus) with its science instruments including that for radar observations of the planet.

Miller, R. B.↗
NASA NTRS1988

Heat exchanger expert system logic

The reduction is described of the operation and fault diagnostics of a Deep Space Network heat exchanger to a rule base by the application of propositional calculus to a set of logic statements. The value of this approach lies in the ease of converting the logic and subsequently implementing it on a computer as an expert system. The rule base was written in Process Intelligent Control software.

Cormier, R.↗