SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The activities of the Deep Space Network in support of Radio Astronomy Operations during April and May 1981 are reported. Work in progres in support of an experiment selected for use of the DSN by the Radio Astronomy Experiment Selection Panel, Twin Quasi-Stellar Object VLBI, is reported.
The continuing efforts of the Deep Space Network in support of Pioneer Mission Operations during the cruise phases of the Pioneers 6 through 11 spacecraft and Pioneer Venus orbital operations are discussed.
The support provided by the Telecommunications and Data Acquisition organization of the Jet Propulsion Laboratory (JPL) to the Pioneer Venus missions is described. The missions were the responsibility of the Ames Research Center (ARC). The Pioneer 13 mission and its spacecraft design presented one of the greatest challenges to the Deep Space Network (DSN) in the implementation and operation of new capabilities. The four probes that were to enter the atmosphere of Venus were turned on shortly before arrival at Venus, and the DSN had to acquire each of these probes in order to recover the telemetry being transmitted. Furthermore, a science experiment involving these probes descending through the atmosphere required a completed new data type to be generated at the ground stations. This new data type is known as the differential very long baseline interferometry. Discussions between ARC and JPL of the implementation requirements involved trade-offs in spacecraft design and led to a very successful return of science data. Specific implementation and operational techniques are discussed, not only for the prime mission, but also for the extended support to the Pioneer 12 spacecraft (in orbit around Venus) with its science instruments including that for radar observations of the planet.
The reduction is described of the operation and fault diagnostics of a Deep Space Network heat exchanger to a rule base by the application of propositional calculus to a set of logic statements. The value of this approach lies in the ease of converting the logic and subsequently implementing it on a computer as an expert system. The rule base was written in Process Intelligent Control software.
This document is the viewgraphs that accompanies a paper that presents the preliminary results of an architecture study that provides continuous telemetry coverage for NASA missions for immediate post-separation phase. After launch when the spacecraft separated from the upper stage, the spacecraft typically executes a number of mission-critical operations prior to the deployment of solar panels and the activation of the primary communication subsystem. JPL, GSFC, and APL have similar design principle statements that require continuous coverage of mission-critical telemetry during the immediate post-separation phase. To conform to these design principles, an architecture that consists of a separate spacecraft transmitter and a robust communication network capable of tracking the spacecraft signals is needed. The main results of this study are as follows: 1) At low altitude (< 10000 km) when most post-separation critical operations are executed, Earth-based network (e.g. Deep Space Network (DSN)) can only provide limited coverage, whereas space-based network (e.g. Space Network (SN)) can provide continuous coverage. 2) Commercial-off-the-shelf SN compatible transmitters are available for small satellite applications. In this paper we present the detailed coverage analysis of Earth-based and Space-based networks. We identify the key functional and performance requirements of the architecture, and describe the proposed selection criteria of the spacecraft transmitter. We conclude the paper with a proposed forward plan.
This paper presents the preliminary results of an architecture study that provides continuous telemetry coverage for NASA missions for immediate post-separation phase. This study is a collaboration effort between Jet Propulsion Laboratory (JPL), Goddard Space Flight Center (GSFC), and Applied Physics Laboratory (APL). After launch when the spacecraft separated from the upper stage, the spacecraft typically executes a number of mission-critical operations prior to the deployment of solar panels and the activation of the primary communication subsystem. JPL, GSFC, and APL have similar design principle statements that require continuous coverage of mission-critical telemetry during the immediate post-separation phase. To conform to these design principles, an architecture that consists of a separate spacecraft transmitter and a robust communication network capable of tracking the spacecraft signals is needed.This paper presents the preliminary results of an architecture study that provides continuous telemetry coverage for NASA missions for immediate post-separation phase. This study is a collaboration effort between Jet Propulsion Laboratory (JPL), Goddard Space Flight Center (GSFC), and Applied Physics Laboratory (APL). After launch when the spacecraft separated from the upper stage, the spacecraft typically executes a number of mission-critical operations prior to the deployment of solar panels and the activation of the primary communication subsystem. JPL, GSFC, and APL have similar design principle statements that require continuous coverage of mission-critical telemetry during the immediate post-separation phase. To conform to these design principles, an architecture that consists of a separate spacecraft transmitter and a robust communication network capable of tracking the spacecraft signals is needed. The main results of this study are as follows: 1) At low altitude (< 10000 km) when most post-separation critical operations are executed, Earth-based network (e.g. Deep Space Network (DSN)) can only provide limited coverage, whereas space-based network (e.g. Space Network (SN)) can provide continuous coverage. 2) Commercial-off-the-shelf SN compatible transmitters are available for small satellite applications. In this paper we present the detailed coverage analysis of Earth-based and Space-based networks. We identify the key functional and performance requirements of the architecture, and describe the proposed selection criteria of the spacecraft transmitter. We conclude the paper with a proposed forward plan.
The Work Coordination Engine (WCE) is a Java application integrated into the Service Management Database (SMDB), which coordinates the dispatching and monitoring of a work order system. WCE de-queues work orders from SMDB and orchestrates the dispatching of work to a registered set of software worker applications distributed over a set of local, or remote, heterogeneous computing systems. WCE monitors the execution of work orders once dispatched, and accepts the results of the work order by storing to the SMDB persistent store. The software leverages the use of a relational database, Java Messaging System (JMS), and Web Services using Simple Object Access Protocol (SOAP) technologies to implement an efficient work-order dispatching mechanism capable of coordinating the work of multiple computer servers on various platforms working concurrently on different, or similar, types of data or algorithmic processing. Existing (legacy) applications can be wrapped with a proxy object so that no changes to the application are needed to make them available for integration into the work order system as "workers." WCE automatically reschedules work orders that fail to be executed by one server to a different server if available. From initiation to completion, the system manages the execution state of work orders and workers via a well-defined set of events, states, and actions. It allows for configurable work-order execution timeouts by work-order type. This innovation eliminates a current processing bottleneck by providing a highly scalable, distributed work-order system used to quickly generate products needed by the Deep Space Network (DSN) to support space flight operations. WCE is driven by asynchronous messages delivered via JMS indicating the availability of new work or workers. It runs completely unattended in support of the lights-out operations concept in the DSN.
Discrepancy Reporting Management System (DRMS) is a computer program designed for use in the stations of NASA's Deep Space Network (DSN) to help establish the operational history of equipment items; acquire data on the quality of service provided to DSN customers; enable measurement of service performance; provide early insight into the need to improve processes, procedures, and interfaces; and enable the tracing of a data outage to a change in software or hardware. DRMS is a Web-based software system designed to include a distributed database and replication feature to achieve location-specific autonomy while maintaining a consistent high quality of data. DRMS incorporates commercial Web and database software. DRMS collects, processes, replicates, communicates, and manages information on spacecraft data discrepancies, equipment resets, and physical equipment status, and maintains an internal station log. All discrepancy reports (DRs), Master discrepancy reports (MDRs), and Reset data are replicated to a master server at NASA's Jet Propulsion Laboratory; Master DR data are replicated to all the DSN sites; and Station Logs are internal to each of the DSN sites and are not replicated. Data are validated according to several logical mathematical criteria. Queries can be performed on any combination of data.
EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders. Between May 2021 and August 2021, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from the National Oceanic and Atmospheric Administration (NOAA)'s N-Wave (the Enterprise network that supports the NOAA mission) and National Centers for Environmental Information (NCEI)'s Fisheries Acoustics Archive for the purpose of a recording a Deep Dive into research drivers. The goal of these meetings was to help characterize the requirements for the research use case, and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support.
Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.
Deep space network - engineering and development progress in tracking station, space flight operations, communications, and advanced antenna systems
This paper discusses the array architecture, the resulting challenges in operations, maintenance and sustaining engineering, and preliminary operations concepts to overcome these challenges.
EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Questions crafted to explore this space include the following: 1) How, and where, will new data be analyzed and used? 2) How will the process of doing science change over the next 5–10 years? and 3) How will changes to the underlying hardware and software technologies influence scientific discovery? Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders
DINET is a technology development experiment intended to increase the technical readiness of JPL s implementation of DTN protocols - "ION". The objective is to advance ION in flight and ground SW to TRL 8, with code of sufficient quality that future flight projects can easily use it at low risk. DINET is to be implemented on the Deep Impact flyby spacecraft. DINET operations will be performed in late 2008 during the Deep Impact spacecraft team "stand down" after EPOCH operations and before the start of development for DIXI operations.
Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.
We have the technical capability to model hybrid deep-space RF and Optical communications networks, but we have little guidance on how the missions and ground architectures will be designed and operated. We exposed several issues that must be resolved before an optimal solution can be reached. With future studies, we may be able to determine solutions to these issues, and perform more precise loading performance analyses to arrive at the most optimal RF and optical ground assets combinations for meeting the projected future demand. The results will then help inform NASA as it plans for infrastructure development to prepare for human/robotic missions in the coming decades.
Operational deep space navigation has been in the past, and is currently, performed using systems whose architecture requires constant human supervision and intervention. A prototype for a system which allows relatively automated processing of radio metric data received in near real-time from NASA's Deep Space Network (DSN) without any redesign of the existing operational data flow has been developed. This system can allow for more rapid response as well as much reduced staffing to support mission navigation operations.
The goal of the JPL reuse activity is to develop a quantitative understanding of the factors which encourage or inhibit software reuse, and of productivity improvements achievable through reuse. The primary activity is the measurement of parameters relevant to reuse in the environment of actual projects. The program has three objectives: (1) to develop a model to allow assessment of competing reuse techniques, (2) to extend reuse from the unit to the sub-system level, and (3) to expand from specific applications to a broader application domain. Application domains, which apply to all interplanetary projects, include Mission Operations, Science Information Systems, Flight Software, and Simulations. The program is targeting all phases and activities of the life cycle and a full range of software products. The approach will be both experimental (observe, hypothesize and evaluate) and constructive (introduce new tools and techniques). The primary target projects are Deep Space Network activities - the Ground Facilities facility upgrade, the Network Operations Control Center upgrade, and the Signal Processing Center. This is the first group of closely related projects being done in Ada at JPL. A reuse base will be developed initially by classifying potentially reusable components from one project; it will be used and expanded with additional projects.