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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Major technological innovations introduced in the large antennas of the Deep Space Network

The NASA Deep Space Network (DSN) is the largest and most sensitive scientific, telecommunications and radio navigation network in the world. Its principal responsibilities are to provide communications, tracking, and science services to most of the world's spacecraft that travel beyond low Earth orbit. The network consists of three Deep Space Communications Complexes. Each of the three complexes consists of multiple large antennas equipped with ultra sensitive receiving systems. A centralized Signal Processing Center (SPC) remotely controls the antennas, generates and transmits spacecraft commands, and receives and processes the spacecraft telemetry.

large antennas beam waveguide dual reflector shapi↗
NASA NTRS2021

Utilizing Schedule Constraints to Improve Automated Scheduling in NASA's Deep Space Network

NASA’s Deep Space Network (DSN) is a mission critical facility that supports many different space missions,from LEO to deep space exploration. In recent years, as the network has suffered from increasing oversubscription, more restrictions have been added to reduce the amount of manual scheduling labor necessary to come to consensus. In this paper, we describe a new auto-mated scheduling tool which takes advantage of these restrictions to bring the scheduling problem closer to feasibility. In addition, we describe an algorithm to reduce problem inputs given a partially-solved problem,thereby bringing the problem even closer to feasibility

Johnston, Mark↗
NASA NTRS2021

User Preference Optimization for Oversubscribed Scheduling of NASA’s Deep Space Network

NASA’s Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. Growth in mission demand, both in number of spacecraft and in data return, has led to increased loading levels on the network, and actual demand frequently exceeds network capacity. The DSN scheduling process involves peer-to-peer collaborative negotiation, which consumes significant time and resources in order to reach a baseline version of the schedule, and then to manage and agree to changes. Process delays are exacerbated by the high level of oversubscription experienced by the DSN: it is not unusual for the scheduling process to start with 20-40% more requested time can be accommodated on the available antennas. The other NASA networks make use of a static mission priority list to address a similar problem: missions are ranked in priority order, then the schedule is populated by priority from highest to lowest. Such a mechanism would not work for DSN due to the heterogeneity of the mission set, and to the time-varying mission requirements with mission phase. This paper describes an alternative approach for the DSN that addresses key problems inherent in the current process — oversubsubscription and how to “fairly” reduce it to a manageable level. The main characteristics of the new approach are the use of loading-based limits based on balancing requested time, along with priorities and user preferences as the basis for optimization criteria that can be used by new algorithms.

Johnston, Mark D↗
NASA NTRS1983

The Deep Space Network: A Radio Communications Instrument for Deep Space Exploration

The primary purpose of the Deep Space Network (DSN) is to serve as a communications instrument for deep space exploration, providing communications between the spacecraft and the ground facilities. The uplink communications channel provides instructions or commands to the spacecraft. The downlink communications channel provides command verification and spacecraft engineering and science instrument payload data.

Renzetti, N. A.↗
NASA NTRS2003

Master clock and time distribution system for the NASA Deep Space Network

NASA's Deep Space Network (DSN) consists of more than 20 antennas located at three globally spaced ground communications facilities. Local generation and distribution of precise time and frequency reference signals comprise an essential and central component of each complex. Within each complex synchronized timing references are required by approximately 100 users located at distances up to 30 kilometers from the central control center and station Master Clock. In this paper a highly modular, hot-swappable, and expandable system design for generation, delivery, and synchronization of highly precise and stable timing signals over fiber optic cables is described.

Tjoelker, R. L.↗
NASA NTRS2021

A Demand Access Paradigm for NASA’s Deep Space Network

NASA’s Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. As part of an investigation into improved efficiency and responsiveness, we have been exploring and prototyping the infusion of a ”demand access” model into the DSN scheduling process. Today, DSN is fully pre-scheduled in advance, and many users rely on a stable schedule to plan their own spacecraft activities, weeks in advance of execution. However, a new class of missions is emerging that may not be scheduled as far in advance, and may be event-driven in coming across science targets at unpredictable times. These users could take advantage of an on-demand mechanism to download data. Simulations have shown that such a mechanism could improve latency (time from data collection to download) by 2x, as well as more efficiently utilize the available DSN antennas. In this paper, we describe a prototype of a demand access process and how it addresses the challenges of co-existing with a staticallyscheduled body of missions, while providing the benefits of lower latency science data return.

Lucena, Girly↗
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 NTRS2009

Ka-Band High-Rate Telemetry System Upgrade for the NASA Deep Space Network

The NASA Deep Space Network (DSN) has a new requirement to support high-data-rate Category A (Cat A) missions (within 2 million kilometers of Earth) with simultaneous S-band uplink, S-band downlink and Ka-band downlink. The S-band links are required for traditional TT&C (Telemetry, Tracking, and Command) support to the spacecraft, while the Ka-band link is intended for high-data-rate science returns. The new Ka-band system combines the use of proven DSN cryogenic designs, for low system temperature, and high data rate capability using commercial telemetry receivers. The initial Cat A support is required for the James Webb Space Telescope (JWST) in 2013 and possibly other missions. The upgrade has been implemented into 3 different 34-meter Beam Waveguide (BWG) antennas in the DSN, one at each of the complexes in Canberra (Australia), Goldstone (California) and Madrid (Spain). System test data is presented to show that the requirements were met and the DSN is ready for Cat A Ka-band operational support.

space communications↗
NASA NTRS2010

Coherent Frequency Reference System for the NASA Deep Space Network

The NASA Deep Space Network (DSN) requires state-of-the-art frequency references that are derived and distributed from very stable atomic frequency standards. A new Frequency Reference System (FRS) and Frequency Reference Distribution System (FRD) have been developed, which together replace the previous Coherent Reference Generator System (CRG). The FRS and FRD each provide new capabilities that significantly improve operability and reliability. The FRS allows for selection and switching between frequency standards, a flywheel capability (to avoid interruptions when switching frequency standards), and a frequency synthesis system (to generate standardized 5-, 10-, and 100-MHz reference signals). The FRS is powered by redundant, specially filtered, and sustainable power systems and includes a monitor and control capability for station operations to interact and control the frequency-standard selection process. The FRD receives the standardized 5-, 10-, and 100-MHz reference signals and distributes signals to distribution amplifiers in a fan out fashion to dozens of DSN users that require the highly stable reference signals. The FRD is also powered by redundant, specially filtered, and sustainable power systems. The new DSN Frequency Distribution System, which consists of the FRS and FRD systems described here, is central to all operational activities of the NASA DSN. The frequency generation and distribution system provides ultra-stable, coherent, and very low phase-noise references at 5, l0, and 100 MHz to between 60 and 100 separate users at each Deep Space Communications Complex.

Tucker, Blake C.↗
NASA NTRS1980

Deep space network energy program

If the Deep Space Network is to exist in a cost effective and reliable manner in the next decade, the problems presented by international energy cost increases and energy availability must be addressed. The Deep Space Network Energy Program was established to implement solutions compatible with the ongoing development of the total network.

Friesema, S. E.↗
NASA NTRS1971

The Deep Space Network, volume 4

Deep Space Network progress report, including mission support, development and implementation, and operations and facilities

Source record↗
NASA NTRS1972

The deep space network, Volume 11

Deep Space Network progress in flight project support, Tracking and Data Acquisition research and technology, network engineering, hardware and software implementation, and operations are presented. Material is presented in each of the following categories: description of DSN; mission support; radio science; support research and technology; network engineering and implementation; and operations and facilities.

Source record↗
NASA NTRS1975

Single- and dual-carrier microwave noise abatement in the deep space network

The NASA/JPL Deep Space Network (DSN) microwave ground antenna systems are presented which simultaneously uplink very high power S-band signals while receiving very low level S- and X-band downlinks. Tertiary mechanisms associated with elements give rise to self-interference in the forms of broadband noise burst and coherent intermodulation products. A long-term program to reduce or eliminate both forms of interference is described in detail. Two DSN antennas were subjected to extensive interference testing and practical cleanup program; the initial performance, modification details, and final performance achieved at several planned stages are discussed. Test equipment and field procedures found useful in locating interference sources are discussed. Practices deemed necessary for interference-free operations in the DSN are described. Much of the specific information given is expected to be easily generalized for application in a variety of similar installations. Recommendations for future investigations and individual element design are given.

Bathker, D. A.↗
NASA NTRS1977

The deep space network

Presented is Deep Space Network (DSN) progress in flight project support, tracking and data acquisition (TDA) research and technology, network engineering, hardware and software implementation, and operations.

Source record↗
NASA NTRS2008

Technology Infusion of CodeSonar into the Space Network Ground Segment (RII07): Software Assurance Symposium Technical Summary

Presents a source code analysis tool (CodeSonar) for use in the Space Network Ground Segment. The Space Network requires 99.9% proficiency and 97.0% availability of systems. Software has historically accounted for an annual average of 28% of the Space Network loss of availability and proficiency. CSCI A and CSCI B account for 42% of the previous eight months of software data loss. The technology infusion of CodeSonar into the Space Network Ground segment is meant to aid in determining the impact of the technology on the project both in the expenditure of effort and the technical results of the technology. Running a CodeSonar analysis and performing a preliminary review of the results averaged 3.5 minutes per finding (approximately 20 hours total). An additional 40 hours is estimated to analyze the 37 findings deemed too complex for the initial review. Using CodeSonar's tools to suppress known non-problems, delta tool runs will not repeat findings that have been marked as non-problems, further reducing the time needed for review. The 'non-interesting' finding rate of 70% is a large number, but filtering, search, and detailed contextual features of CodeSonar reduce the time per finding. Integration of the tool into the build process may also provide further savings by preventing developers from having to configure and operate the tool separately. These preliminary results show the tool to be easy to use and incorporate into the engineering process. These findings also provide significant potential improvements in proficiency and availability on the part of the software. As time-to-fix data become available a better cost trade can be made on person hours saved versus tool cost. Selective factors may be necessary to determine where best to apply CodeSonar to balance cost and benefits.

Benson, Markland J.↗