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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 NTRS1994

Field Demonstration of Photonic Antenna Remoting at X-Band in the NASA Deep Space Network

We report on the successful demonstration of antenna remoting at X-band using a low phase noise and high dynamic range fiber optic link in an operating antenna receiving system, while tracking Magellan spacecraft. The insertion of the fiber link into the existing system adds no observable degradation in noise temperature to the system. We compare the experimental results with our theoretical predictions of degradations in noise temperature and dynamic range of the system caused by the insertion of the fiber link.

antenna remoting at X-band high dynamic range fibe↗
DOE OSTI2020

Power System Event Classification and Localization Using a Convolutional Neural Network

Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, here we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

58 GEOSCIENCES↗
NASA NTRS1972

Telecommunications Systems Design Techniques Handbook

The Deep Space Network (DSN) increasingly supports deep space missions sponsored and managed by organizations without long experience in DSN design and operation. The document is intended as a textbook for those DSN users inexperienced in the design and specification of a DSN-compatible spacecraft telecommunications system. For experienced DSN users, the document provides a reference source of telecommunication information which summarizes knowledge previously available only in a multitude of sources. Extensive references are quoted for those who wish to explore specific areas more deeply.

Edelson, R. E.↗
NASA NTRS1985

Determining Availability Characteristics of DSN Data Systems Using Discrepancy Report Data

A reasonably economical way was developed to determine availability characteristics of Deep Space Network (DSN) data systems, subsystems, and assemblies using the DSN discrepancy report (DR) data base and DSN operating schedule and history data bases. Operating mean time between failures (OMTBF), operating mean time to restore service (OMTTRS), and operating functional availability (OFA) can be computed by year, by system, by subsystem, by assembly, and by station. The effort required to produce the desired reports is described, specific data on the telemetry, command, and tracking systems are presented, and major contributors to system outages are identified. Future improvements in preparing and analyzing DR data are also outlined to enhance their use in correcting conditions that lead to outages.

Ruskin, A. M.↗
NASA NTRS1990

The Telecommunications and Data Acquisition Report

Archival reports are given on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA), including space communications, radio navigation, radio science, ground-based radio and radar astronomy, and the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) in planning, supporting research and technology, implementation, and operations. Also included is TDA-funded activity at JPL on data and information systems and reimbursable DSN work performed for other space agencies through NASA. In the search for extraterrestrial intelligence (SETI), implementation and operations for searching the microwave spectrum are reported. Use of the Goldstone Solar System Radar for scientific exploration of the planets, their rings and satellites, asteroids, and comets are discussed.

Posner, E. C.↗
NASA NTRS1991

Evolution of the large Deep Space Network antennas

The evolution of the largest antenna of the US NASA Deep Space Network (DSN) is described. The design, performance analysis, and measurement techniques, beginning with its initial 64-m operation at S-band (2295 MHz) in 1966 and continuing through the present ka-band (32-GHz) operation at 70 m, is described. Although their diameters and mountings differ, these parabolic antennas all employ a Cassegrainian feed system, and each antenna dish surface is constructed of precision-shaped perforated-aluminum panels that are secured to an open steel framework

Imbriale, William A.↗
NASA NTRS2017

Delay/Disruption Tolerant Networking for the International Space Station (ISS)

Disruption Tolerant Networking (DTN) is an emerging data networking technology designed to abstract the hardware communication layer from the spacecraft/payload computing resources. DTN is specifically designed to operate in environments where link delays and disruptions are common (e.g., space-based networks). The National Aeronautics and Space Administration (NASA) has demonstrated DTN on several missions, such as the Deep Impact Networking (DINET) experiment, the Earth Observing Mission 1 (EO-1) and the Lunar Laser Communication Demonstration (LLCD). To further the maturation of DTN, NASA is implementing DTN protocols on the International Space Station (ISS). This paper explains the architecture of the ISS DTN network, the operational support for the system, the results from integrated ground testing, and the future work for DTN expansion.

Schlesinger, Adam↗
NASA NTRS2003

Power, data latency, and radio frequnecy interference issues in Mars Realy Network scheduling

At different time periods in the future, missions to Mars will overlap. Previous studies indicate that during such periods, existing deep space communication infrastructure cannot handle all Mars communication needs. A plausible solution is to take into account the end-to-end communication performances of network along with operational constraints, and optimize the resource usage by scheduling communication at highest possible data throughputs. As a result, shorter communication time is required and more missions can be accommodated. This principle is demonstrated in this paper for a Mars relay communication network; a network consisting of multiple surface units and orbiters on Mars and the Deep Space Stations.

power latency RFI Mars Relay Network scheduling↗
NASA NTRS1996

Sequence-of-events-driven automation of the deep space network

In February 1995, sequence-of-events (SOE)-driven automation technology was demonstrated for a Voyager telemetry downlink track at DSS 13. This demonstration entailed automated generation of an operations procedure (in the form of a temporal dependency network) from project SOE information using artificial intelligence planning technology and automated execution of the temporal dependency network using the link monitor and control operator assistant system. This article describes the overall approach to SOE-driven automation that was demonstrated, identifies gaps in SOE definitions and project profiles that hamper automation, and provides detailed measurements of the knowledge engineering effort required for automation.

Hill, R., Jr.↗
NASA NTRS1996

Sequence-of-Events-Driven Automation of the Deep Space Network

In February 1995, sequence-of-events (SOE)-driven automation technology was demonstrated for a Voyager telemetry downlink track at DSS 13. This demonstration entailed automated generation of an operations procedure (in the form of a temporal dependency network) from project SOE information using artificial intelligence planning technology and automated execution of the temporal dependency network using the link monitor and control operator assistant system. This article describes the overall approach to SOE-driven automation that was demonstrated, identifies gaps in SOE definitions and project profiles that hamper automation, and provides detailed measurements of the knowledge engineering effort required for automation.

Hill, R., Jr.↗
NASA NTRS2014

Networked Operations of Hybrid Radio Optical Communications Satellites

In order to address the increasing communications needs of modern equipment in space, and to address the increasing number of objects in space, NASA is demonstrating the potential capability of optical communications for both deep space and near-Earth applications. The Integrated Radio Optical Communications (iROC) is a hybrid communications system that capitalizes on the best of both the optical and RF domains while using each technology to compensate for the other's shortcomings. Specifically, the data rates of the optical links can be higher than their RF counterparts, whereas the RF links have greater link availability. The focus of this paper is twofold: to consider the operations of one or more iROC nodes from a networking point of view, and to suggest specific areas of research to further the field. We consider the utility of Disruption Tolerant Networking (DTN) and the Virtual Mission Operation Center (VMOC) model.

Delay Tolerant Networking↗
NASA NTRS1987

Design considerations for the beamwaveguide retrofit of a ground antenna station

A primary requirement of the NASA Deep Space Network (DSN) is to provide for optimal reception of very low signal levels. This requirement necessitates optimizing the antenna gain to the total system operating noise level quotient. Low overall system noise levels of 16 to 20 K are achieved by using cryogenically cooled preamplifiers closely coupled with an appropriately balanced antenna gain/spillover design. Additionally, high-power transmitters (up to 400 kW CW) are required for spacecraft emergency command and planetary radar experiments. The frequency bands allocated for deep space telemetry are narrow bands near 2.1 and 2.3 GHz (Ka-band), 7.1 and 8.4 GHz (X-band), and 32 and 34.5 GHz (Ka-band). In addition, planned operations for the Search for Extraterrestrial Intelligence (SETI) program require continuous low-noise receive coverage over the 1 to 10 GHz band. To summarize, DSN antennas must operate efficiently with low receive noise and high-power uplink over the 1 to 35 GHz band.

Veruttipong, T.↗
DOE OSTI2021

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Here, deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

42 ENGINEERING↗
DOE OSTI2021

Evaluating Automated Face Identity-Masking Methods with Human Perception and a Deep Convolutional Neural Network

Face de-identification (or “masking”) algorithms have been developed in response to the prevalent use of video recordings in public places. Here, we evaluated the success of face identity masking for human perceivers and a deep convolutional neural network (DCNN). Eight de-identification algorithms were applied to videos of drivers’ faces, while they actively operated a motor vehicle. These masks were pre-selected to be applicable to low-quality video and to maintain coarse information about facial actions. Humans studied high-resolution images to learn driver identities and were tested on their recognition of active drivers in low-resolution videos. Faces in the videos were either unmasked or were masked by one of the eight algorithms. When participants were tested immediately after learning (Experiment 1), all masks reduced identification, with six of eight masks reducing identification to extremely poor performance. In a second experiment, two of the most effective masks were tested after a delay of 7 or 28 days. The delay did not further reduce identification of the masked faces. In all masked conditions, participants maintained stringent decision criteria, with low confidence in recognition, further indicating the effectiveness of the masks. Next, the DCNN performed an identity-matching task between high-resolution images and masked videos—a task analogous to that done by humans. The pattern of accuracy for the DCNN mirrored some, but not all, aspects of human performance, highlighting the need to test the effectiveness of identity masking for both humans and machines. The DCNN was also tested on its ability to match identity between masked and unmasked versions of the same video, based only on the face. DCNN performance for the eight masks offers insight into the nature of the information in faces that is coded in these networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗