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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Process control and recovery in the Link Monitor and Control Operator Assistant

This paper describes our approach to providing process control and recovery functions in the Link Monitor and Control Operator Assistant (LMCOA). The focus of the LMCOA is to provide semi-automated monitor and control to support station operations in the Deep Space Network. The LMCOA will be demonstrated with precalibration operations for Very Long Baseline Interferometry on a 70-meter antenna. Precalibration, the task of setting up the equipment to support a communications link with a spacecraft, is a manual, time consuming and error-prone process. One problem with the current system is that it does not provide explicit feedback about the effects of control actions. The LMCOA uses a Temporal Dependency Network (TDN) to represent an end-to-end sequence of operational procedures and a Situation Manager (SM) module to provide process control, diagnosis, and recovery functions. The TDN is a directed network representing precedence, parallelism, precondition, and postcondition constraints. The SM maintains an internal model of the expected and actual states of the subsystems in order to determine if each control action executed successfully and to provide feedback to the user. The LMCOA is implemented on a NeXT workstation using Objective C, Interface Builder and the C Language Integrated Production System.

Lee, Lorrine↗
NASA NTRS2008

Increasing the Cost-efficiency of the DSN

JPL has operated the Deep Space Network (DSN) on behalf of NASA since the 1960's. Over the last two decades, the DSN budget has generally declined in real-year dollars while the aging assets required more attention, and the missions became more complex. As a result, the budget has been increasingly consumed by Operations and Maintenance (O and M), significantly reducing the funding wedge available for technology investment and for enhancing the DSN capability and capacity. Responding to this budget squeeze, the DSN launched an effort to improve the cost-efficiency of the O and M. In this paper we: Analyze the components of O&M. We note for example that, for the DSN, less than 20% of the staff engage in the traditional human-in-front-a-console role, so any effort to increase the cost efficiency must go beyond reducing the number of "Real-time operators." Explain the underlying organizational and cultural structures. Any cost-efficiency activities changes either accept, or carefully modify these structures. For example, the DSN O&M is based on the concept that there are three nearly identical antenna complexes separated by approximately 1200 in latitude and that each antenna complex is operated by a different contractor (driven by international agreements). Explore planned changes in the customer interface, e.g. web-based automated scheduling, and the processes required for a transition. Changes have to be evaluated in the larger end-to-end context, e.g. do the changes provide a net cost-efficiency for the DSN and the missions, or do they merely shift cost from the DSN to the missions. Consider possible significant changes in real-time pass management, e.g. full-remoting of operations, and lights-dim operations, while maintaining (or improving) the performance metrics of the DSN. Investigate how procedural and administrative changes could increase cost-efficiency, in conjunction with changes in the customer interfaces and real-time pass management. Examples would be handling of inter-governmental agreements, improved sharing of resources with other agencies, and better use of commercial (rather than government) resources

Deep Space Network (DSN)↗
NASA NTRS2010

Use of Business Intelligence Tools in the DSN

JPL has operated the Deep Space Network (DSN) on behalf of NASA since the 1960's. Over the last two decades, the DSN budget has generally declined in real-year dollars while the aging assets required more attention, and the missions became more complex. As a result, the DSN budget has been increasingly consumed by Operations and Maintenance (O&M), significantly reducing the funding wedge available for technology investment and for enhancing the DSN capability and capacity. Responding to this budget squeeze, the DSN launched an effort to improve the cost-efficiency of the O&M. In this paper we: elaborate on the methodology adopted to understand "where the time and money are used"-surprisingly, most of the data required for metrics development was readily available in existing databases-we have used commercial Business Intelligence (BI) tools to mine the databases and automatically extract the metrics (including trends) and distribute them weekly to interested parties; describe the DSN-specific effort to convert the intuitive understanding of "where the time is spent" into meaningful and actionable metrics that quantify use of resources, highlight candidate areas of improvement, and establish trends; and discuss the use of the BI-derived metrics-one of the most fascinating processes was the dramatic improvement in some areas of operations when the metrics were shared with the operators-the visibility of the metrics, and a self-induced competition, caused almost immediate improvement in some areas. While the near-term use of the metrics is to quantify the processes and track the improvement, these techniques will be just as useful in monitoring the process, e.g. as an input to a lean-six-sigma process.

Metrics↗
NASA NTRS2006

Challenging Implementation and Operations Traditions

The Deep Space Network (DSN) that provides for the communications link between the deep space missions and the science users currently consists of a small set of very large monolithic tracking antennas. This ground-based network includes a total of 12 antennas located in three roughly equidistant longitudes around the earth and utilizes a decentralized approach to it operations. Recently, however, studies have suggested that the number, complexity, and data throughput of the future set of space probes will be increasing dramatically. This demands more performance from the DSN than is currently available. In identifying the architecture for the future DSN required to support this mission set, one concept that proves promising is one that consists of a great many number of much smaller antennas configured in an array. This concept has been supported by the developments in antenna manufacturing technology and the consistent decrease in the cost of electronics required to receive, amplify, and combine signals from deep space probes. Furthermore, it is clear that past developments in the DSN have not benefited from the applications of economies of scale.

ANTENNA ARRAYS↗
NASA NTRS2004

Operation's concept for Deep Space Array-based Network (DSAN) for NASA

The Deep Space Array-based Network (DSAN) is part of >1000 times increases in the downlink/telemetry capability of the Deep Space Network. The key function of the DSAN is provision of cost-effective, robust, Telemetry, Tracking and Command (TT&C) services to the space missions of NASA and its international parameters. This paper presents the architecture of DSAN and its operations philosophy. It also briefly describes customer's view of operations, operations management, logistics, anomaly analysis and reporting.

Ops concept↗
NASA NTRS1970

DSN operations

Describing operations of Deep Space Network command system

Source record↗
NASA NTRS1981

Use of Faraday-rotation data from beacon satellites to determine ionospheric corrections for interplanetary spacecraft navigation

Faraday-rotation data from the linearly polarized 137-MHz beacons of the ATS-1, SIRIO, and Kiku-2 geosynchronous satellites are used to determine the ionospheric corrections to the range and Doppler data for interplanetary spacecraft navigation. The JPL operates the Deep Space Network of tracking stations for NASA; these stations monitor Faraday rotation with dual orthogonal, linearly polarized antennas, Teledyne polarization tracking receivers, analog-to-digital converter/scanners, and other support equipment. Computer software examines the Faraday data, resolves the pi ambiguities, constructs a continuous Faraday-rotation profile and converts the profile to columnar zenith total electron content at the ionospheric reference point; a second program computes the line-of-sight ionospheric correction for each pass of the spacecraft over each tracking complex. Line-of-sight ionospheric electron content using mapped Faraday-rotation data is compared with that using dispersive Doppler data from the Voyager spacecraft; a difference of about 0.4 meters, or 5 x 10 to the 16th electrons/sq m is obtained. The technique of determining the electron content of interplanetary plasma by subtraction of the ionospheric contribution is demonstrated on the plasma torus surrounding the orbit of Io.

Royden, H. N.↗
NASA NTRS1983

Interference susceptibility of a typical deep space network receiving system

The Deep Space Network (DSN), operated by the Jet Propulsion Laboratory for NASA, is used primarily for communication with interplanetary spacecraft. The high sensitivity required to achieve communication with distant spacecraft makes the DSN very susceptible to radio frequency interference (RFI). In this article a comprehensive description of the interference susceptibility of a typical DSN receiving system is given. Specifically, the effects of interference on the carrier tracking loop, the telemetry receiving subsystem, and the saturation effect on the maser, are presented.

Sue, M. K.↗
NASA NTRS2017

The Challenges of Human-Autonomy Teaming

Machine intelligence is improving rapidly based on advances in big data analytics, deep learning algorithms, networked operations, and continuing exponential growth in computing power (Moores Law). This growth in the power and applicability of increasingly intelligent systems will change the roles humans, shifting them to tasks where adaptive problem solving, reasoning and decision-making is required. This talk will address the challenges involved in engineering autonomous systems that function effectively with humans in aeronautics domains.

artificial intelligence↗
NASA NTRS2017

The Challenges of Human-Autonomy Teaming

Machine intelligence is improving rapidly based on advances in big data analytics, deep learning algorithms, networked operations, and continuing exponential growth in computing power (Moores Law). This growth in the power and applicability of increasingly intelligent systems will change the roles humans, shifting them to tasks where adaptive problem solving, reasoning and decision-making is required. This talk will address the challenges involved in engineering autonomous systems that function effectively with humans in aeronautics domains.

Human-Autonomy teaming↗
NASA NTRS2005

Operation's Concept for Array-Based Deep Space Network

The Array-based Deep Space Network (DSNArray) will be a part of more than 10(exp 3) times increase in the downlink/telemetry capability of the Deep space Network (DSN). The key function of the DSN-Array is to provide cost-effective, robust Telemetry, Tracking and Command (TT&C) services to the space missions of NASA and its international partners. It provides an expanded approach to the use of an array-based system. Instead of using the array as an element in the existing DSN, relying to a large extent on the DSN infrastructure, we explore a broader departure from the current DSN, using fewer elements of the existing DSN, and establishing a more modern Concept of Operations. This paper gives architecture of DSN-Array and its operation's philosophy. It also describes customer's view of operations, operations management and logistics - including maintenance philosophy, anomaly analysis and reporting.

operations concept↗
NASA NTRS1977

Tracking and data system support for the Viking 1975 mission to Mars. Volume 3: Planetary operations

The support provided by the Deep Space Network to the 1975 Viking Mission from the first landing on Mars July 1976 to the end of the Prime Mission on November 15, 1976 is described and evaluated. Tracking and data acquisition support required the continuous operation of a worldwide network of tracking stations with 64-meter and 26-meter diameter antennas, together with a global communications system for the transfer of commands, telemetry, and radio metric data between the stations and the Network Operations Control Center in Pasadena, California. Performance of the deep-space communications links between Earth and Mars, and innovative new management techniques for operations and data handling are included.

Mudgway, D. 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 NTRS1979

Automatic filament warm-up controller

As part of the unattended operations objective of the Deep Space Network deep space stations, this filament controller serves as a step between manual operation of the station and complete computer control. Formerly, the operator was required to devote five to fifteen minutes of his time just to properly warm up the filaments on the klystrons of the high power transmitters. The filament controller reduces the operator's duty to a one-step command and is future-compatible with various forms of computer control.

Mccluskey, J.↗