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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Experiences in Interagency and International Interfaces for Mission Support

The Flight Dynamics Division (FDD) of the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GFSC) provides extensive support and products for Space Shuttle missions, expendable launch vehicle launches, and routine on-orbit operations for a variety of spacecraft. A major challenge in providing support for these missions is defining and generating the products required for mission support and developing the method by which these products are exchanged between supporting agencies. As interagency and international cooperation has increased in the space community, the FDD customer base has grown and with it the number and variety of external interfaces and product definitions. Currently, the FDD has working interfaces with the NASA Space and Ground Networks, the Johnson Space Center, the White Sands Complex, the Jet propulsion Laboratory (including the Deep Space Network), the United States Air Force, the Centre National d'Etudes Spatiales, the German Spaceflight Operations Center, the European Space Agency, and the National Space Development Agency of Japan. With the increasing spectrum of possible data product definitions and delivery methods, the FDD is using its extensive interagency experience to improve its support of established customers and to provide leadership in adapting/developing new interfaces. This paper describes the evolution of the interfaces between the FDD and its customers, discusses many of the joint activities ith these customers, and summarizes key lessons learned that can be applied to current and future support.

Dell, G. T.↗
NASA NTRS2021

NASA Deep Space Network Commitments for Human Missions to the Moon and Beyond

NASA’s Deep Space Network (DSN) serves as a critical element in the exploration of deep space, typically supporting 30-40 operational missions at any given time, each with unique characteristics and telecommunications requirements. In order to meet the needs of its diverse customer base, DSN relies on the Mission Support Definition and Commitments Office to interface with its customers and to develop, negotiate, and document the appropriate service commitments to meet each mission’s needs. Members of this office provide the needed support throughout each mission’s lifetime. While DSN’s primary focus in recent years has been supporting scientific missions by robotic spacecraft deployed across the solar system, the network is ramping-up to provide support for human spaceflight (HSF) endeavours to the moon and beyond, starting with the Artemis missions which aim to land humans on the lunar surface in the 2020’s. These HSF missions pose unique challenges for the DSN, including technical, operational, and programmatic concerns. Examples of challenges in each of these areas are provided, along with descriptions of how they are being addressed and open issues remaining.

Turcios, Ricky↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗
NASA NTRS1976

The deep space network

Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported.

Source record↗
NASA NTRS1978

The deep space network

Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported.

Source record↗
NASA NTRS1979

The Deep Space Network

Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations are reported.

Source record↗
NASA NTRS1979

The Deep Space Network

Progress on the Deep Space Network (DSN) supporting research and technology, advanced development, engineering and implementation, and DSN operations is presented. The functions and facilities of the DSN are described.

Source record↗
NASA NTRS1980

The Deep Space Network capabilities for radio interferometry

The very long baseline interferometry capability as well as the planned capability of the deep space network (DSN) are described. Major emphasis is placed on the following: VLBI development; operational VLBI for DSN calibration; and VLBI for spacecraft navigation.

Fanselow, J. L.↗
NASA NTRS1991

The Telecommunications and Data Acquisition Report

This quarterly publication provides archival reports on developments in programs managed by the Jet Propulsion Laboratory's (JPL's) Office of Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on the activities of the Deep Space Network (DSN) in planning, in supporting research and technology, in implementation, and in operations. Also included is standards activity at JPL for space data, information systems, and reimbursable DSN work performed for other space agencies through NASA.

Posner, Edward C.↗
NASA NTRS1992

The Telecommunications and Data Acquisition Report 42-109: January-March 1992

This quarterly publication provides archival reports on developments in programs managed by JPL's Office Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, in supporting research and technology, in implementation, and in operations. Also included is standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The preceding work is all performed for NASA's Office of Space Operations (OSO). The TDA Office also performs work funded by two other NASA program offices through and with the cooperation of the OSO. These are the Orbital Debris Radar Program and 21st Century Communication Studies.

Posner, E. C.↗
NASA NTRS1992

The Telecommunications and Data Acquisition Report

Archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are provided. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, in supporting research and technology, in implementation, and in operations. Also included is standards activity at JPL for space data and information. In the search for extraterrestrial intelligence (SETI), the TDA Progress Report reports on implementation and operations for searching the microwave spectrum. Topics covered include tracking and ground-based navigation; communications, spacecraft-ground; station control and system technology; capabilities for new projects; network upgrade and sustaining; network operations and operations support; and TDA program management and analysis.

Posner, E. C.↗
NASA NTRS1993

A new technology for manufacturing scheduling derived from space system operations

A new technology for producing finite capacity schedules has been developed in response to complex requirements for operating space systems such as the Space Shuttle, the Space Station, and the Deep Space Network for telecommunications. This technology has proven its effectiveness in manufacturing environments where popular scheduling techniques associated with Materials Resources Planning (MRPII) and with factory simulation are not adequate for shop-floor work planning and control. The technology has three components. The first is a set of data structures that accommodate an extremely general description of a factory's resources, its manufacturing activities, and the constraints imposed by the environment. The second component is a language and set of software utilities that enable a rapid synthesis of functional capabilities. The third component is an algorithmic architecture called the Five Ruleset Model which accommodates the unique needs of each factory. Using the new technology, systems can model activities that generate, consume, and/or obligate resources. This allows work-in-process (WIP) to be generated and used; it permits constraints to be imposed or intermediate as well as finished goods inventories. It is also possible to match as closely as possible both the current factory state and future conditions such as promise dates. Schedule revisions can be accommodated without impacting the entire production schedule. Applications have been successful in both discrete and process manufacturing environments. The availability of a high-quality finite capacity production planning capability enhances the data management capabilities of MRP II systems. These schedules can be integrated with shop-floor data collection systems and accounting systems. Using the new technology, semi-custom systems can be developed at costs that are comparable to products that do not have equivalent functional capabilities and/or extensibility.

Hornstein, R. S.↗
NASA NTRS1994

X/Ka-Band Dual Frequency Horn Design

The Deep Space Network has a need for a dual frequency horn for simulation dual frequency operation in a beam waveguide antenna. The dual frequency horn will receive X-band (8.4-8.5 GHz), KABLE frequency (33.6-33.8 GHz), and DSN Ka-band downlink (31.8-32.3 GHz) signals simultaneously.

X-Band Ka-Band↗
NASA NTRS2006

Enabling Space Science and Exploration

This viewgraph presentation on enabling space science and exploration covers the following topics: 1) Today s Deep Space Network; 2) Next Generation Deep Space Network; 3) Needed technologies; 4) Mission IT and networking; and 5) Multi-mission operations.

industry day↗
NASA NTRS2012

A Service Portal for the Integrated SCaN Network

The Space Communication and Navigation (SCaN) program office owns the assets and services provided by the Deep Space Network (DSN), Near Earth Network (NEN), and Space Network (SN). At present, these individual networks are operated by different NASA centers--JPL for DSN--and Goddard Space Flight Center (GSFC) for NEN and SN--with separate commitments offices for each center. In the near future, SCaN's program office would like to deploy an integrated service portal which would merge the two commitments offices with the goal of easing the task of user planning for space missions requiring services of two or more of these networks. Following interviews with subject matter experts in this field, use cases were created to include the services and functionality mission users would like to see in this new integrated service portal. These use cases provide a guideline for a mock-up of the design of the user interface for the portal. The benefit of this work will ease the time required and streamline/standardize the process for planning and scheduling SCAN's services for future space missions.

space communications and navigation (SCaN)↗