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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Airport Simulations Using Distributed Computational Resources

The Virtual National Airspace Simulation (VNAS) will improve the safety of Air Transportation. In 2001, using simulation and information management software running over a distributed network of super-computers, researchers at NASA Ames, Glenn, and Langley Research Centers developed a working prototype of a virtual airspace. This VNAS prototype modeled daily operations of the Atlanta airport by integrating measured operational data and simulation data on up to 2,000 flights a day. The concepts and architecture developed by NASA for this prototype are integral to the National Airspace Simulation to support the development of strategies improving aviation safety, identifying precursors to component failure.

McDermott, William J.↗
NASA NTRS1997

Future Vision for NASA Ground Systems

The NASA Space Operations System (NSOS) is defined as the people (organization), processes, services, tools, amd physical elements that do space operations for NASA Missions.

network control center low earth orbit network dee↗
NASA NTRS2003

Status of the international GPS service in terms of co-location and local ties

The IGS network today consists of 364 stations managed by about, 100 different agencies worldwide. The RINEX observations are contributed to the IGS Data Centers, which permanently archive the data and make it freely available to all users. The primary customer of the data set is the IGS Analysis Centers, which acquire the data for generation of precise GPS products such as ephemerides, clocks, earth orientation parameters, and station position and velocities.

reference frame↗
NASA NTRS2012

Flow Analysis Tool White Paper

Faster networks are continually being built to accommodate larger data transfers. While it is intuitive to think that implementing faster networks will result in higher throughput rates, this is often not the case. There are many elements involved in data transfer, many of which are beyond the scope of the network itself. Although networks may get bigger and support faster technologies, the presence of other legacy components, such as older application software or kernel parameters, can often cause bottlenecks. Engineers must be able to identify when data flows are reaching a bottleneck that is not imposed by the network and then troubleshoot it using the tools available to them. The current best practice is to collect as much information as possible on the network traffic flows so that analysis is quick and easy. Unfortunately, no single method of collecting this information can sufficiently capture the whole endto- end picture. This becomes even more of a hurdle when large, multi-user systems are involved. In order to capture all the necessary information, multiple data sources are required. This paper presents a method for developing a flow analysis tool to effectively collect network flow data from multiple sources and provide that information to engineers in a clear, concise way for analysis. The purpose of this method is to collect enough information to quickly (and automatically) identify poorly performing flows along with the cause of the problem. The method involves the development of a set of database tables that can be populated with flow data from multiple sources, along with an easyto- use, web-based front-end interface to help network engineers access, organize, analyze, and manage all the information.

Boscia, Nichole K.↗
NASA NTRS2019

UAS Service Supplier Framework for Authentication and Authorization

The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) Service Supplier (USS) Framework for Authentication and Authorization (UFAA) is the basis for secure and confident data exchanges between the Flight Information Management System (FIMS) and the USS Network and within the USS Network itself. UFAA is built upon the OAuth 2.0 approach to federated authorization, with details supplied by various Internet Engineering Task Force (IETF) Request for Comment documents (RFCs). Whenever possible, a specific standard is used to support a design decision within UFAA, and when an applicable standard is not found/used, the technical decision is explained as much as possible. This document describes UFAA in detail. It is intended to serve as a reference for other key UTM documents and should be useful to many UTM stakeholders. This is an informative document. The requirements stated or implied in this document will need to be fully vetted and reviewed by appropriate stakeholders before becoming operational. It is highly encouraged that in any design decisions for future applicable standards that are counter to or missing from the decisions presented herein be justified in that future standard.

UTM↗
DOE OSTI2021

Priority-enabled Load Balancing for Dispersed Computing

Opportunistic managed access to local in-network compute resources can improve the performance of distributed applications and reduce the dependence on shared network resources. Instead of backhauling application data to a centralized cloud data center for processing, networked services may be adaptively and continuously dispersed into shared compute resources that are closer to the source of need. While this approach has several benefits, support for mission-aware access to computation is often an afterthought, and is implemented as a brittle extension over traditional load-balancer solutions.In this work, we investigate the design of two priority-aware resource allocation strategies and two load-balancing dispatching strategies as first class citizens in an open-source dispersed computing middleware. We present a control theoretic analysis of these load-balancing primitives to identify weaknesses and strengths in our design, and recommend future directions. In parallel, we prototype two priority-aware allocation algorithms to validate our priority predictions. In initial experiments our prototype shows substantial gains in processing prioritized load. Finally, we make our source-code and experimental configurations open source.

Paulos, Aaron↗
DOE OSTI2022

Secure Control Regions for Distributed Stochastic Systems with Application to Distributed Energy Resource Dispatch

With the increasing connectedness and interdependence of systems that are stochastic in nature, the issue of how to manage and coordinate them for safe operation has evidently become more important. In many networked system architectures, the system-wide output must be delicately managed, often within a prescribed set of bounds. In this paper, a novel control framework is proposed where the bounds on the outputs are translated into independent bounds on the controllable inputs of each subsystem. The main benefit of this framework is that respecting the individual control bounds suffices to guarantee that the system-wide outputs will remain within safe boundaries. Because the systems are assumed to be stochastic, the bounds on the output are introduced as probabilistic chance constraints. The benefits of this framework are demonstrated by applying it to the control of distributed energy resources in a distribution network where the main goal is to keep the voltage magnitudes within their prescribed bounds. The control bounds are evaluated using real data on an IEEE test system.

chance constrained optimization↗
DOE OSTI2025

Integrated Routing and Traffic Signal Control for CAVs via Reinforcement Learning Approach

Incorporating Connected and Automated Vehicles (CAVs) into urban traffic networks presents opportunities and challenges for traffic management systems. This paper aims to develop an integrated routing and traffic signal control system designed explicitly for CAVs, utilizing a Reinforcement Learning (RL) approach. The objective is to enhance traffic flow and improve overall transportation efficiency in the controlled areas. We propose an innovative framework that employs the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Q-network (DQN), to dynamically adjust the number of vehicles in the routes and the duration of traffic signals. Our simulation results demonstrate that a DQN agent successfully optimizes the number of vehicles in the routes and traffic signal timings of traffic signal controllers, eventually reducing total travel time. The study illustrates the potential usage of RL-based systems in managing routing and traffic signals for CAVs, offering a promising opportunity for future urban traffic management strategies.

Park, Jiho [New York University]↗
NASA NTRS1995

Description of a MIL-STD-1553B Data Bus Ada Driver for the LeRC EPS Testbed

This document describes the software designed to provide communication between control computers in the NASA Lewis Research Center Electrical Power System Testbed using MIL-STD-1553B. The software drivers are coded in the Ada programming language and were developed on a MSDOS-based computer workstation. The Electrical Power System (EPS) Testbed is a reduced-scale prototype space station electrical power system. The power system manages and distributes electrical power from the sources (batteries or photovoltaic arrays) to the end-user loads. The electrical system primary operates at 120 volts DC, and the secondary system operates at 28 volts DC. The devices which direct the flow of electrical power are controlled by a network of six control computers. Data and control messages are passed between the computers using the MIL-STD-1553B network. One of the computers, the Power Management Controller (PMC), controls the primary power distribution and another, the Load Management Controller (LMC), controls the secondary power distribution. Each of these computers communicates with two other computers which act as subsidiary controllers. These subsidiary controllers are, in turn, connected to the devices which directly control the flow of electrical power.

Mackin, Michael A.↗
NASA NTRSDate not supplied

A Technical Overview of the Mission Engagement Onboarding Process Managed by the Mission Engagement Working Group (MEWG)

NASA's intricate network, encompassing the Near Space Network (NSN) and Deep Space Network (DSN), plays a pivotal role in supporting an array of space missions. These range from those in Low Earth Orbit (LEO) and Geosynchronous Orbit (GEO), to the more distant Cislunar and Deep Space endeavors. To manage the vast communications needs, we utilize multiple ground stations globally, coupled with the Tracking and Data Relay Satellite System (TDRSS). The Mission Engagement Working Group (MEWG), within the Commercialization, Innovation, and Synergies (CIS) division, stands as the primary gateway for all Space Communications and Network (SCaN) communication and navigation requests. This includes not only NASA's internal missions but also extends to other governmental agencies and commercial sector endeavors. How does the MEWG Process Work? - Initial Contact : Clients initiate their interaction with the NSN by submitting their service requirements through a dedicated online portal. - Preliminary Assessment by MEWG: Upon submission, MEWG embarks on a primary screening of the request. This involves evaluating the client's identity and the foundational concept of their mission. - Detailed Analysis by the NSN Team: Parallelly, the NSN team conducts a comprehensive review of the service request. This often necessitates additional clarification from the requester, ensuring that the final assessment is both thorough and accurate. - Coordination & Streamlining: MEWG's overarching objective is to effectively log, classify, orchestrate, and guarantee that pertinent actions are delegated based on initial client interactions. Acting as the central hub for these primary contacts, MEWG ensures that each request is sufficiently detailed for an in-depth evaluation. - Feedback & Remediation: If a request is deemed unsuitable or lacking, MEWG doesn't merely reject it. Instead, the team discerns the reasons for the inadequacy and suggests potential rectification strategies. This approach ensures that feedback delivered to clients is precise, prompt, constructive, and actionable. Conclusion: This plenary presentation will detail the efforts of the MEWG resulting in a greatly streamlined and refined onboarding process for space communication Direct-To-Earth (DTE) and Space Relay (SR) support requests. By centralizing the preliminary interactions and assessments, we've reduced the complexity for clients, ensuring they engage with a singular, efficient, and responsive point of contact. This initiative, we believe, fortifies NASA's commitment to fostering effective and synergistic collaborations with its partners.

Devin L Bitner↗
NASA NTRSDate not supplied

A Technical Overview of the Mission Engagement Onboarding Process Managed by the Mission Engagement Working Group (MEWG)

NASA's intricate network, encompassing the Near Space Network (NSN) and Deep Space Network (DSN), plays a pivotal role in supporting an array of space missions. These range from those in Low Earth Orbit (LEO) and Geosynchronous Orbit (GEO), to the more distant Cislunar and Deep Space endeavors. To manage the vast communications needs, we utilize multiple ground stations globally, coupled with the Tracking and Data Relay Satellite System (TDRSS). The Mission Engagement Working Group (MEWG), within the Commercialization, Innovation, and Synergies (CIS) division, stands as the primary gateway for all Space Communications and Network (SCaN) communication and navigation requests. This includes not only NASA's internal missions but also extends to other governmental agencies and commercial sector endeavors. How does the MEWG Process Work? - Initial Contact : Clients initiate their interaction with the NSN by submitting their service requirements through a dedicated online portal. - Preliminary Assessment by MEWG: Upon submission, MEWG embarks on a primary screening of the request. This involves evaluating the client's identity and the foundational concept of their mission. - Detailed Analysis by the NSN Team: Parallelly, the NSN team conducts a comprehensive review of the service request. This often necessitates additional clarification from the requester, ensuring that the final assessment is both thorough and accurate. - Coordination & Streamlining: MEWG's overarching objective is to effectively log, classify, orchestrate, and guarantee that pertinent actions are delegated based on initial client interactions. Acting as the central hub for these primary contacts, MEWG ensures that each request is sufficiently detailed for an in-depth evaluation. - Feedback & Remediation: If a request is deemed unsuitable or lacking, MEWG doesn't merely reject it. Instead, the team discerns the reasons for the inadequacy and suggests potential rectification strategies. This approach ensures that feedback delivered to clients is precise, prompt, constructive, and actionable. Conclusion: This plenary presentation will detail the efforts of the MEWG resulting in a greatly streamlined and refined onboarding process for space communication Direct-To-Earth (DTE) and Space Relay (SR) support requests. By centralizing the preliminary interactions and assessments, we've reduced the complexity for clients, ensuring they engage with a singular, efficient, and responsive point of contact. This initiative, we believe, fortifies NASA's commitment to fostering effective and synergistic collaborations with its partners.

Devin Bitner↗
DOE OSTI2021

Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks

In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate networks. Additionally, we show that utilizing a more centralized working relationship network rather than a strict administrative network further increases overall innovation reach. In fact, this more centralized structure in conjunction with management layers is essential to both reaching a plurality of nodes and creating a stable adopted community in the long time horizon. Further, we show that the selection of seed nodes affects the final stability of the adopted community, and while the most influential nodes often produce the highest peak adoption, this is not always the case. In some circumstances, seeding nodes near but not in the highest positions in the graph produces larger peak adoption and more stable long-time adoption.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

A distributed data base management system

Major system design features of a distributed data management system for the NASA Deep Space Network (DSN) designed for continuous two-way deep space communications are described. The reasons for which the distributed data base utilizing third-generation minicomputers is selected as the optimum approach for the DSN are threefold: (1) with a distributed master data base, valid data is available in real-time to support DSN management activities at each location; (2) data base integrity is the responsibility of local management; and (3) the data acquisition/distribution and processing power of a third-generation computer enables the computer to function successfully as a data handler or as an on-line process controller. The concept of the distributed data base is discussed along with the software, data base integrity, and hardware used. The data analysis/update constraint is examined.

Bryan, A. I.↗
NASA NTRS1994

Scheduling: A guide for program managers

The following topics are discussed concerning scheduling: (1) milestone scheduling; (2) network scheduling; (3) program evaluation and review technique; (4) critical path method; (5) developing a network; (6) converting an ugly duckling to a swan; (7) network scheduling problem; (8) (9) network scheduling when resources are limited; (10) multi-program considerations; (11) influence on program performance; (12) line-of-balance technique; (13) time management; (14) recapitulization; and (15) analysis.

Source record↗
DOE OSTI2023

AmeriFlux BASE data pipeline to support network growth and data sharing

Abstract AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2006

Content Integration Networks: Turning Search Upside Down

The slide presentation examines information management and retrieval among NASA centers and with outside sources. It provides an overview and problem statement, vision and high level technologies, strategies, knowledge management benefits, and a summary and challenge.

taxonomy↗