Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “deep networks”

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.

At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

Recent developments integrating micromechanics and neural networks offer promising paths for rapid predictions of the response of heterogeneous materials with similar accuracy as direct numerical simulations. The deep material network is one such approaches, featuring a multi-layer network and micromechanics building blocks trained on anisotropic linear elastic properties. Once trained, the network acts as a reduced-order model, which can extrapolate the material’s behavior to more general constitutive laws, including nonlinear behaviors, without the need to be retrained. However, current training methods initialize network parameters randomly, incurring inevitable training and calibration errors. Here, we introduce a way to visualize the network parameters as an analogous unit cell and use this visualization to “quilt” patches of shallower networks to initialize deeper networks for a recursive training strategy. The result is an improvement in the accuracy and calibration performance of the network and an intuitive visual representation of the network for better explainability.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1971

Radio tracking with the Deep Space Network

Range and Doppler measurements for the Mariner 6 and 7 spacecraft are described. The Doppler tracking system and the two systems of spacecraft ranging apparatus being used in the Deep Space Network are illustrated. Charged particle calibration is considered. Equipment characteristics are presented for the ranging apparatus.

Martin, W. L.↗
NASA NTRS1991

Stochastic availability analysis of operational data systems in the Deep Space Network

Existing availability models of standby redundant systems consider only an operator's performance and its interaction with the hardware performance. In the case of operational data systems in the Deep Space Network (DSN), in addition to an operator system interface, a controller reconfigures the system and links a standby unit into the network data path upon failure of the operating unit. A stochastic (Markovian) process technique is used to model and analyze the availability performance and occurrence of degradation due to partial failures are quantitatively incorporated into the model. Exact expressions of the steady state availability and proportion degraded performance measures are derived for the systems under study. The interaction among the hardware, operator, and controller performance parameters and that interaction's effect on data availability are evaluated and illustrated for an operational data processing system.

Issa, T. N.↗
NASA NTRS2010

Evolutionary Scheduler for the Deep Space Network

A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

Guillaume, Alexandre↗
NASA NTRS2002

Deep Space Network equipment performance, reliability, and operations management information system

The Deep Space Mission System (DSMS) Operations Program Office and the DeepSpace Network (DSN) facilities utilize the Discrepancy Reporting Management System (DRMS) to collect, process, communicate and manage data discrepancies, equipment resets, physical equipment status, and to maintain an internal Station Log. A collaborative effort development between JPL and the Canberra Deep Space Communication Complex delivered a system to support DSN Operations.

discrepancy reporting DSN operations web-based too↗
NASA NTRS1981

The operational performance of hydrogen masers in the deep space network: The performance of laboratory reference frequency standards in an operational environment

Hydrogen masers used as aids in meeting the routine frequency and time operational requirements within the 64 m antenna Deep Space Network. Both the operational syntonation (frequency synchronization) and the the clock (epoch) synchronization requirements were established through the use of specifically calibrated H-P E215061A flying clock. The sync/synt to UTC was maintained using LORAN and TV in simultaneous reception mode. The sync/synt within the 64 m net was maintained through the use of very long base interferometry. Results indicate that the hydrogen masers perform well within the required specifications.

Ward, S. C.↗
NASA NTRS1993

An OSI architecture for the deep space network

The flexibility and robustness of a monitor and control system are a direct result of the underlying inter-processor communications architecture. A new architecture for monitor & Control at the Deep Space Network Communications Complexes has been developed based on the Open System Interconnection (OSI) standards. The suitability of OSI standards for DSN M&C has been proven in the laboratory. The laboratory success has resulted in choosing an OSI-based architecture for DSS-13 M&C. DSS-13 is the DSN experimental station and is not part of the 'operational' DSN; it's role is to provide an environment to test new communications concepts can be tested and conduct unique science experiments. Therefore, DSS-13 must be robust enough to support operational activities, while also being flexible enough to enable experimentation. This paper describes the M&C architecture developed for DSS-13 and the results from system and operational testing.

Heuser, W. Randy↗
NASA NTRS2017

SmallSat Navigation via the Deep Space Network: Lunar Transport

Spacecraft component miniaturization, the standardized CubeSat form factor, and a corresponding increase in launch ride-share availability have led to a renewed interest in smaller, more agile spacecraft missions. Naturally, interest in SmallSat capabilities extends outside of low Earth orbit for a variety of science and technology demonstration applications. As with larger missions, near-term deep-space SmallSats will more than likely rely on telecommunications and tracking via NASA's Deep Space Network or similar facilities. Given the predicted growth in the number of deep space missions, e ective use of telecommunication resources will be more critical than ever, even with Multiple Spacecraft Per Aperture capability. In particular, SmallSat missions will likely face stricter limitations on two-way contact with the tracking stations, thus making other options like one-way data types more attractive. While all missions must eventually develop their own detailed navigation plans, a common set of references is needed to support SmallSat missions, especially in the early development phases. Our investigation provides this initial survey of expected navigation performance for standard radiometric data types, from traditional two-way Doppler and ranging capabilities to one-way equivalents, including delta-di erential one-way range. In this investigation, we examine transport within the Earth-Moon region, the rst step outward from Earth into deep space exploration.

Wood, Lincoln↗
NASA NTRS1986

Deep space network enhancement for the Galileo mission to Jupiter

The Galileo mission to Jupiter has unique scientific objectives never attempted before by a planetary mission. These objectives have presented technical challenges to the NASA Deep Space Network. New technologies and system concepts have been developed to meet these challenges. Major implementations are underway to equip the ground stations in the Network. Significant improvement in performance is expected. The ground-based navigation is expected to achieve an angular precision of 50 nanoradians using very-long-baseline interferometry (VLBI). The frequency stability of the ground instrument will be 5 x 10 to the -15th for the detection of gravitational waves. The precision of the Faraday rotation angle measurement of the spacecraft signal will be better than 2 degrees.

Peng, T. K.↗
DOE OSTI2020

Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks

Abstract This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
NASA NTRS1993

Cost and reliability study for a large array of small reflector antennas for JPL/NASA Deep Space Network (DSN)

A study performed to estimate the cost of an array of small aperture reflector antennas to be used instead of or in conjunction with the current large reflector antennas of the JPL/NASA Deep Space Network (DSN) is described. The arrays are used for servicing present and future NASA missions that involve smaller spacecraft with limited power and smaller, lower-gain antennas. The advantages of the array configuration in terms of cost and reliability are discussed. A probabilistic determination of the reliability and availability of the array as a function of the number of array elements and the availability of individual array elements is made. The impact of additional marginal elements on the operational availability of the array is studied. Parametric cost and reliability plots are presented and directions for further investigation are outlined.

Jamnejad, Vahraz↗
NASA NTRS1993

Cost and Reliability Study for a Large Array of Small Reflector Antennas for JPL/NASA Deep Space Network (DSN)

A study is performed to estimate the cost of an array of small aperture reflector antennas to be used instead or in conjunction with the current large reflector antennas of the JPL/NASA Deep Space Network (DSN), for servicing present and future NASA missions which involve smaller spacecraft with limited power and smaller, lower gain antennas. The advantages of the array configuration in terms of cost and reliability are studied. A probabilistic determination of the reliability and availability of the array as a function of the number of array elements and the availability of individual array elements is made. The impact of additional marginal elements on the operational availability of the array is studied. Parametric cost and reliability plots are presented and directions for further investigation are outlined.

Resch, G.↗
NASA NTRS1972

The deep space network, volume 12

Progress in the development of the DSN is reported along with TDA research and technology, network engineering, hardware, and software implementation. Included are descriptions of the DSN function and facilities, Helios mission support, Mariner Venus/Mercury 1973 mission support, Viking mission support, tracking and ground-based navigation, communications, network control and data processing, and deep space stations.

Source record↗
NASA NTRS1974

The deep space network, volume 19

The progress is reported in the DSN for Nov. and Dec. 1973. Research is described for the following areas: functions and facilities, mission support for flight projects, tracking and ground-based navigation, spacecraft/ground communication, network control and operations technology, and deep space stations.

Source record↗