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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Research Institute for Advanced Computer Science: Annual Report October 1998 through September 1999

The Research Institute for Advanced Computer Science (RIACS) carries out basic research and technology development in computer science, in support of the National Aeronautics and Space Administration's missions. RIACS is located at the NASA Ames Research Center (ARC). It currently operates under a multiple year grant/cooperative agreement that began on October 1, 1997 and is up for renewal in the year 2002. ARC has been designated NASA's Center of Excellence in Information Technology. In this capacity, ARC is charged with the responsibility to build an Information Technology Research Program that is preeminent within NASA. RIACS serves as a bridge between NASA ARC and the academic community, and RIACS scientists and visitors work in close collaboration with NASA scientists. RIACS has the additional goal of broadening the base of researchers in these areas of importance to the nation's space and aeronautics enterprises. RIACS research focuses on the three cornerstones of information technology research necessary to meet the future challenges of NASA missions: (1) Automated Reasoning for Autonomous Systems. Techniques are being developed enabling spacecraft that will be self-guiding and self-correcting to the extent that they will require little or no human intervention. Such craft will be equipped to independently solve problems as they arise, and fulfill their missions with minimum direction from Earth. (2) Human-Centered Computing. Many NASA missions require synergy between humans and computers, with sophisticated computational aids amplifying human cognitive and perceptual abilities; (3) High Performance Computing and Networking Advances in the performance of computing and networking continue to have major impact on a variety of NASA endeavors, ranging from modeling and simulation to data analysis of large datasets to collaborative engineering, planning and execution. In addition, RIACS collaborates with NASA scientists to apply information technology research to a variety of NASA application domains. RIACS also engages in other activities, such as workshops, seminars, and visiting scientist programs, designed to encourage and facilitate collaboration between the university and NASA information technology research communities.

Leiner, Barry M.↗
NASA NTRS2000

Research Institute for Advanced Computer Science

The Research Institute for Advanced Computer Science (RIACS) carries out basic research and technology development in computer science, in support of the National Aeronautics and Space Administration's missions. RIACS is located at the NASA Ames Research Center. It currently operates under a multiple year grant/cooperative agreement that began on October 1, 1997 and is up for renewal in the year 2002. Ames has been designated NASA's Center of Excellence in Information Technology. In this capacity, Ames is charged with the responsibility to build an Information Technology Research Program that is preeminent within NASA. RIACS serves as a bridge between NASA Ames and the academic community, and RIACS scientists and visitors work in close collaboration with NASA scientists. RIACS has the additional goal of broadening the base of researchers in these areas of importance to the nation's space and aeronautics enterprises. RIACS research focuses on the three cornerstones of information technology research necessary to meet the future challenges of NASA missions: (1) Automated Reasoning for Autonomous Systems. Techniques are being developed enabling spacecraft that will be self-guiding and self-correcting to the extent that they will require little or no human intervention. Such craft will be equipped to independently solve problems as they arise, and fulfill their missions with minimum direction from Earth; (2) Human-Centered Computing. Many NASA missions require synergy between humans and computers, with sophisticated computational aids amplifying human cognitive and perceptual abilities; (3) High Performance Computing and Networking. Advances in the performance of computing and networking continue to have major impact on a variety of NASA endeavors, ranging from modeling and simulation to data analysis of large datasets to collaborative engineering, planning and execution. In addition, RIACS collaborates with NASA scientists to apply information technology research to a variety of NASA application domains. RIACS also engages in other activities, such as workshops, seminars, and visiting scientist programs, designed to encourage and facilitate collaboration between the university and NASA information technology research communities.

Gross, Anthony R.↗
NASA NTRS2001

[Activities of Research Institute for Advanced Computer Science]

The Research Institute for Advanced Computer Science (RIACS) carries out basic research and technology development in computer science, in support of the National Aeronautics and Space Administrations missions. RIACS is located at the NASA Ames Research Center, Moffett Field, California. RIACS research focuses on the three cornerstones of IT research necessary to meet the future challenges of NASA missions: 1. Automated Reasoning for Autonomous Systems Techniques are being developed enabling spacecraft that will be self-guiding and self-correcting to the extent that they will require little or no human intervention. Such craft will be equipped to independently solve problems as they arise, and fulfill their missions with minimum direction from Earth. 2. Human-Centered Computing Many NASA missions require synergy between humans and computers, with sophisticated computational aids amplifying human cognitive and perceptual abilities. 3. High Performance Computing and Networking Advances in the performance of computing and networking continue to have major impact on a variety of NASA endeavors, ranging from modeling and simulation to analysis of large scientific datasets to collaborative engineering, planning and execution. In addition, RIACS collaborates with NASA scientists to apply IT research to a variety of NASA application domains. RIACS also engages in other activities, such as workshops, seminars, visiting scientist programs and student summer programs, designed to encourage and facilitate collaboration between the university and NASA IT research communities.

Gross, Anthony R.↗
NASA NTRS1992

A model to compare performance of space and ground network support of low-Earth orbiters

This article compares the downlink performance in a gross average sense between space and ground network support of low-Earth orbiters. The purpose is to assess what the demand for DSN support of future small, low-cost missions might be, if data storage for spacecraft becomes reliable enough and small enough to support the storage requirements needed to enable support only a fraction of the time. It is shown that the link advantage of the DSN over space reception in an average sense is enormous for low-Earth orbiters. The much shorter distances needed to communicate with the ground network more than make up for the speedup in data rate needed to compensate for the short contact times with the DSN that low-Earth orbiters have. The result is that more and more requests for DSN-only support of low-Earth orbiters can be expected.

Posner, E. C.↗
NASA NTRS1989

Learning in stochastic neural networks for constraint satisfaction problems

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

Johnston, Mark D.↗
DOE OSTI2021

Oscillatory Spreading and Inertia in Power Grids

The increase in variable renewable generators (VRGs) in power systems has altered the dynamics from a historical experience. VRGs introduce new sources of power oscillations, and the stabilizing response provided by synchronous generators (SGs, e.g., natural gas, coal, etc.), which help avoid some power fluctuations, will lessen as VRGs replace SGs. These changes have led to the need for new methods and metrics to quickly assess the likely oscillatory behavior for a particular network without performing computationally expensive simulations. This work studies the impact of a critical dynamical parameter - the inertia value - on the rest of a power system's oscillatory response to representative VRG perturbations. We use a known localization metric in a novel way to quantify the number of nodes responding to a perturbation and the magnitude of those responses. This metric allows us to relate the spread and severity of a system's power oscillations with inertia. We find that as inertia increases, the system response to node perturbations transitions from localized (only a few close nodes respond) to delocalized (many nodes across the network respond). We introduce a heuristic computed from the network Laplacian to relate this oscillatory transition to the network structure. We show that our heuristic accurately describes the spread of oscillations for a realistic power-system test case. Using a heuristic to determine the likely oscillatory behavior of a system given a set of parameters has wide applicability in power systems, and it could decrease the computational workload of planning and operation.

dynamical systems↗
NASA NTRS1982

The Deep Space Network: Noise temperature concepts, measurements, and performance

The use of higher operational frequencies is being investigated for improved performance of the Deep Space Network. Noise temperature and noise figure concepts are used to describe the noise performance of these receiving systems. The ultimate sensitivity of a linear receiving system is limited by the thermal noise of the source and the quantum noise of the receiver amplifier. The atmosphere, antenna and receiver amplifier of an Earth station receiving system are analyzed separately and as a system. Performance evaluation and error analysis techniques are investigated. System noise temperature and antenna gain parameters are combined to give an overall system figure of merit G/T. Radiometers are used to perform radio ""star'' antenna and system sensitivity calibrations. These are analyzed and the performance of several types compared to an idealized total power radiometer. The theory of radiative transfer is applicable to the analysis of transmission medium loss. A power series solution in terms of the transmission medium loss is given for the solution of the noise temperature contribution.

Stelzried, C. T.↗
NASA NTRS2012

SCaN Network Ground Station Receiver Performance for Future Service Support

Objectives: Examine the impact of providing the newly standardized CCSDS Low Density Parity Check (LDPC) codes to the SCaN return data service on the SCaN SN and DSN ground stations receivers: SN Current Receiver: Integrated Receiver (IR). DSN Current Receiver: Downlink Telemetry and Tracking (DTT) Receiver. Early Commercial-Off-The-Shelf (COTS) prototype of the SN User Service Subsystem Component Replacement (USS CR) Narrow Band Receiver. Motivate discussion of general issues of ground station hardware design to enable simple and cheap modifications for support of future services.

Deep Space Network (DSN)↗
NASA NTRS2017

Mesh Network Architecture for Enabling Inter-Spacecraft Communication

To enable communication between spacecraft operating in a formation or small constellation, a mesh network architecture was developed and tested using a time division multiple access (TDMA) communication scheme. The network is designed to allow for the exchange of telemetry and other data between spacecraft to enable collaboration between small spacecraft. The system uses a peer-to-peer topology with no central router, so that it does not have a single point of failure. The mesh network is dynamically configurable to allow for addition and subtraction of new spacecraft into the communication network. Flight testing was performed using an unmanned aerial system (UAS) formation acting as a spacecraft analogue and providing a stressing environment to prove mesh network performance. The mesh network was primarily devised to provide low latency, high frequency communication but is flexible and can also be configured to provide higher bandwidth for applications desiring high data throughput. The network includes a relay functionality that extends the maximum range between spacecraft in the network by relaying data from node to node. The mesh network control is implemented completely in software making it hardware agnostic, thereby allowing it to function with a wide variety of existing radios and computing platforms..

Becker, Christopher↗
DOE OSTI2023

In-network compute assistance

A method and apparatus for performing operations by network interface cards in a network of computers. A network interface card is configured to receive a message and to interpret the message to identify a primitive operation to be performed. The primitive operation is one of a plurality of primitive operations that are performed to perform an operation. The primitive operation is performed by the network interface card and a trigger signal is generated in response to performing the primitive operation. The trigger signal is interpreted to identify a triggered message. The triggered message identifies a next one of the plurality of primitive operations to be performed. The triggered message is transmitted back to the network interface card or to another network interface card in the network for processing.

Grant, Ryan↗
DOE OSTI2025

Tunable Crosslinked Ether Polymer Network Electrolytes for High‐Performance All‐Solid‐State Sodium Batteries

All-solid-state batteries (ASSBs) are critical for achieving high energy density and enhanced safety. Solid polymer electrolytes (SPEs) offer key advantages over other electrolytes, including improved safety, flexibility, and interfacial contact. Among the SPEs, ether-based polymers are widely studied due to their ease of processing and high ionic conductivity (σi) in the amorphous state. In this work, the introduction of poly(ethylene glycol) methyl ether methacrylate (PEGMEMA) into an SPE matrix composed of poly(ethylene glycol) diacrylate (PEGDA), poly(ethylene glycol) (PEG2k), and sodium bis(fluorosulfonyl)imide (NaFSI) salt is investigated to facilitate the formation of amorphous, high σ i SPEs through end-group engineering and polymer ratio optimization. PEGMEMA enhances structural integrity via crosslinking with PEGDA through its methacrylate group, while its methyl end group aids ion conduction. A 2:1:7 ratio of PEGDA:PEGMEMA:PEG2k exhibits a σi of 1.16 x 10 -4 S cm -1 and oxidative stability up to 4.4 V at 60 °C. A solid-state cell incorporating this SPE, a Na 2/3 Ni 1/3 Mn 2/3 O 2 (NM12) cathode, and a sodium-metal anode demonstrates excellent cycling stability, retaining over 80 % of its initial capacity for 150 cycles at 60 °C. The findings highlight the potential of end-group engineering in improving the electrochemical performance of SPEs.

25 ENERGY STORAGE↗
DOE OSTI2025

Highly functional microspheres facilitating Diels–Alder network formation

Introducing particles to dynamic covalent networks is a common approach to improve their performance. However, network formation can be impacted by their size and functionality. The influence can be predicted by common theories for small molecular precursors, but it is unclear whether they are applicable to precursors bearing numerous reactive groups and micrometer-scale dimensions. In this work, an experimental study was undertaken using dynamic covalent networks formed by the Diels–Alder reaction between furan and maleimide groups. The gelation behavior of the Diels–Alder networks was studied using rheometry to track their network formation at 40 °C with varying maleimide-functionalized microsphere loading. The highly functional microspheres can interact with the furan precursor, aiding in the formation of the Diels–Alder networks. A 5 wt% microsphere sample can reduce the gelation time by 23% and facilitate network formation in an unbalanced stoichiometry near the critical composition to form a percolating network.

36 MATERIALS SCIENCE↗
DOE OSTI2020

ASCR@40: Highlights and Impacts of ASCR’s Programs

A report compiled by the ASCAC Subcommittee on the 40-year history of ASCR for the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. The Office of Advanced Scientific Computing Research (ASCR) sits within the Office of Science in the Department of Energy (DOE). Per their web pages, “the mission of the ASCR program is to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the DOE.” This succinct statement encompasses a wide range of responsibilities for computing and networking facilities; for procuring, deploying, and operating high performance computing, networking, and storage resources; for basic research in mathematics and computer science; for developing and sustaining a large body of software; and for partnering with organizations across the Office of Science and beyond. While its mission statement may seem very contemporary, the roots of ASCR are quite deep—long predating the creation of DOE. Applied mathematics and advanced computing were both elements of the Theoretical Division of the Manhattan Project. In the early 1950s, the Manhattan Project scientist and mathematician John von Neumann, then a commissioner for the AEC (Atomic Energy Commission), advocated for the creation of a Mathematics program to support the continued development and applications of digital computing. Los Alamos National Laboratory (LANL) scientist John Pasta created such a program to fund researchers at universities and AEC laboratories. Under several organizational name changes, this program has persisted ever since, and would eventually grow to become ASCR.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Investigation into the Performance Benefits of Exposing Network Backpressure in UPC++ and GASNet-EX

This document is a brief summary of the research, and supporting development efforts, conducted by the project "Investigation into Improving Dynamic Adaptivity to System-Level Asynchrony in UPC++". We tested the hypothesis "The UPC++ and GASNet-EX runtimes can expose information from the network stack that enables applications to dynamically adapt to congestion, improving total throughput". We present experimental results from both a microbenchmark and an application benchmark that support this hypothesis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (a five-lab demonstration) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, photovoltaics, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactors, control centers, and gas turbines, across five U.S. Department of Energy (DOE) national laboratories: National Laboratory of the Rockies (NLR), Idaho National Laboratory, National Energy Technology Laboratory, Lawrence Berkeley National Laboratory, and Sandia National Laboratories. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance DOE network, and are controlled via a centralized energy controller hosted at NLR's Advanced Research on Integrated Energy Systems facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1987

The performance of multicomputer interconnection networks

The interdependency of nodes and multicomputer interconnection networks is examined using simple calculations based on the asymptotic properties of queueing networks. Methods are described for choosing interconnection networks that fit individual classes of applications. It is also shown how analytic models can be extended to benchmark existing interconnection networks.

Reed, Daniel A.↗