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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

The Deep Space Network

The various systems and subsystems are discussed for the Deep Space Network (DSN). A description of the DSN is presented along with mission support, program planning, facility engineering, implementation and operations.

Source record↗
NASA NTRS1977

The Deep Space Network, volume 39

The functions, facilities, and capabilities of the Deep Space Network and its support of the Pioneer, Helios, and Viking missions are described. Progress in tracking and data acquisition research and technology, network engineering and modifications, as well as hardware and software implementation and operations are reported.

Source record↗
NASA NTRS1977

Automation of the deep space network

Approaches to automating the radiofrequency system used in the deep space network (DSN) are discussed. The goal is to operate DSN, which communicates with U.S. space probes that travel beyond the moon, in an unattended mode in order to reduce operating costs. The philosophy of a hierachical automation plan, which emphasizes distributed control and the utilization of DSN hardware development engineers to develop automation software, is discussed, and software development as well as common software and common microcontroller hardware are considered. It is planned that a station controller will serve as the operator interface and control and coordinate the operation of three system controllers (RF system, digital system, and antenna system). These systems are described.

Crow, R. B.↗
NASA NTRS1982

A federated information management system for the Deep Space Network

General requirements for an information management system for the deep space network (DSN) are examined. A concise review of available database management system technology is presented. It is recommended that a federation of logically decentralized databases be implemented for the Network Information Management System of the DSN. Overall characteristics of the federation are specified, as well as reasons for adopting this approach.

Dobinson, E.↗
NASA NTRS1986

Application of the Deep Space Network (DSN) to the testing of general relativity

The NASA Deep Space Network, a precision telecommunications and radio navigation facility, is described in detail. The first spacecraft relativity test with Mariner 6 and Mariner 7 at solar conjunction is discussed as well as more accurate tests using the Mariner 9 anchored to Mars. Consideration is also given to solar system tests of relativistic celestial mechanics and future prospects. It is noted that the NASA Mars Observer orbital mission is under development and is expected to reach Mars in 1991.

Anderson, J. D.↗
NASA NTRS2021

NASA’s Deep Space Network: Automation in the Follow-the-Sun Era

The Deep Space Network (DSN) comprises three sites, located in California, Spain, and Australia; each site operates one 70m and multiple 34m antennas that provide communications and navigation services to NASA and international partners. As part of a multiyear upgrade in automation of the network, the DSN has undertaken a series of operations efficiency improvements which has fundamentally changed the operations paradigm of the network. At the start of this effort, each site operated only their local antennas and equipment 24 hours/day, 7 days/week, and each spacecraft activity (link) had a single dedicated Link Control Operator. In its final stage, realized in 2020, each one of the three sites operates the entire network during their local day shift, handing off control to the next site as their day ends, and tracks can be run in a fully automated manner with no operator intervention. This paradigm change has realized significant operations cost savings, but has posed some major challenges along the way. This paper describes the system changes that enabled the operations efficiency improvements, some the challenges, and plans for the future.

Levesque, Michael↗
NASA NTRSDate not supplied

Deep Neural Network Based Convergence Classification for Computational Fluid Dynamics

A supervised deep learning approach is coupled with heuristic convergence criteria to construct a classification model for detecting the completion (convergence) of computational fluid dynamics (CFD) simulations. Heuristic convergence criteria alone are not always sufficient and more complex decisions are often left to a human analyst. The proposed approach leverages heuristic convergence criteria as well as two deep neural network (DNN) models, one binary and one multi-class, to improve the efficiency and consistency of convergence classification across a wide range of flight regimes. The DNN models presented are each trained on a subset of ascent aerodynamic CFD simulations for NASA’s Space Launch System and were produced using NASA’s unstructured Navier-Stokes solver FUN3D. Individual solutions are analyzed intermittently and are classified as sufficiently converged, further iterations required, or switch from steady Reynolds Averaged Navier-Stokes (RANS) to unsteady RANS CFD based on the iterative histories of four aerodynamic coefficients. The implemented classification model is shown to produce solutions that closely correlate to solutions produced by a human analyst. This work lays groundwork for expanding the capabilities of DNNs for automating and improving more of the CFD process.

SLS↗
NASA NTRS1990

The Deep Space Network: The challenges of the next 20 years - The 21st century

The Deep Space Network (DSN) has been the radio navigation and communications link between NASA's lunar and deep space missions for 30 years. In this paper, new mission opportunities over the next 20 years are discussed. The system design drivers and the DSN architectural concepts for those challenges are briefly considered.

Dumas, L. N.↗
NASA NTRS2018

Radio Science System Design and Measurement Results for the NASA Deep Space Network (DSN)

Radio science measurements have been performed using the NASA Deep Space Network (DSN) with many different spacecraft over several decades. Radio science has been used for the study of planetary atmospheres, the solar corona and the search for gravity waves, among other things. The majority of these measurements are made using the X and Ka-band deep space bands. Although the primary mission for the DSN is tracking, telemetry and command (TT&C) for NASA’s many deep-space spacecraft, radio science measurements continue to be an important secondary mission. The science requirements for these measurements have resulted in stringent performance requirements for both the spacecraft and ground system equipment. In particular, the requirements for amplitude stability, phase stability (Allen deviation) and phase noise are very demanding. The system Allen deviation requirement at Ka-band is < 2.4 E-15 over 1000 seconds, while the phase noise requirement is < -50 dBc/Hz for a 1 Hz offset. Various design techniques have been used for the DSN radio frequency (RF) electronics, high power transmitters and antenna structures to meet the stringent requirements for all 3 of these parameters. Some details for the design techniques will be described in the paper. Another important consideration for a radio science system is the verification approach for components, as well as for individual subsystems and then the overall system. Phase-locked oscillators (PLOs) are one of the key component types that determine overall phase noise and Allen deviation system performance. Measurement techniques used for PLOs, as well as for the overall ground system, will be discussed. Measurement results for the 2 new DSN antennas, recently built under the DSN Aperture Enhancement Project (DAEP) will also be shown. In addition, some recent radio science measurements from the Cassini and JUNO missions, using the new antennas, will be presented.

LaBelle, Remi C.↗
NASA NTRS2012

Frequency Domain Beamforming for a Deep Space Network Downlink Array

This paper describes a frequency domain beamformer to array up to 8 antennas of NASA's Deep Space Network currently in development. The objective of this array is to replace and enhance the capability of the DSN 70m antennas with multiple 34m antennas for telemetry, navigation and radio science use. The array will coherently combine the entire 500 MHz of usable bandwidth available to DSN receivers. A frequency domain beamforming architecture was chosen over a time domain based architecture to handle the large signal bandwidth and efficiently perform delay and phase calibration. The antennas of the DSN are spaced far enough apart that random atmospheric and phase variations between antennas need to be calibrated out on an ongoing basis in real-time. The calibration is done using measurements obtained from a correlator. This DSN Downlink Array expands upon a proof of concept breadboard array built previously to develop the technology and will become an operational asset of the Deep Space Network. Design parameters for frequency channelization, array calibration and delay corrections will be presented as well a method to efficiently calibrate the array for both wide and narrow bandwidth telemetry.

Filterbank↗
NASA NTRS1989

Automated monitor and control for deep space network subsystems

The problem of automating monitor and control loops for Deep Space Network (DSN) subsystems is considered and an overview of currently available automation techniques is given. The use of standard numerical models, knowledge-based systems, and neural networks is considered. It is argued that none of these techniques alone possess sufficient generality to deal with the demands imposed by the DSN environment. However, it is shown that schemes that integrate the better aspects of each approach and are referenced to a formal system model show considerable promise, although such an integrated technology is not yet available for implementation. Frequent reference is made to the receiver subsystem since this work was largely motivated by experience in developing an automated monitor and control loop for the advanced receiver.

Smyth, P.↗
NASA NTRS1991

Overview of arraying techniques in the deep space network

Four different arraying schemes that can be used by the Deep Space Network are functionally discussed and compared. These include symbol stream combining (SSC), baseband combining (BC), carrier arraying (CA), and full spectrum combining (FSC). In addition, sibeband aiding (SA) is also included and compared even though it is not an arraying scheme, since it uses a single antenna. Moreover, combinations of these schemes are discussed, such as carrier arraying with sideband aiding and baseband combining (CA/SA/BC) or carrier arraying with symbol stream combining (CA/SSC). Complexity versus performance is traded off and the benefits to the reception of existing spacecraft signals are discussed. Recommendations are made as to the best techniques for particular configurations.

Mileant, A.↗