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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Raman spectrum and polarizability of liquid water from deep neural networks

We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H 2 O and D 2 O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Digital Signal Processing Using Deep Neural Networks

Currently there is great interest in the utility of deep neural networks (DNNs) for the physical layer of radio frequency (RF) communications. In this manuscript we describe a custom DNN specially designed to solve problems in the RF domain. Our model leverages the mechanisms of feature extraction and attention through the combination of an autoencoder convolutional network with a transformer network, to accomplish several important communications network and digital signals processing (DSP) tasks. We also present a new open dataset and physical data augmentation model that enables training of DNNs that can perform automatic modulation classification, infer, and correct transmission channel effects, and directly demodulate baseband RF signals.

42 ENGINEERING↗
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↗
DOE OSTI2024

Phase retrieval for refraction-enhanced x-ray radiography using a deep neural network

X-ray refraction-enhanced radiography (RER) or phase contrast imaging is widely used to study internal discontinuities within materials. The resulting radiograph captures both the decrease in intensity caused by material absorption along the x-ray path, as well as the phase shift, which is highly sensitive to gradients in density. A significant challenge lies in effectively analyzing the radiographs to decouple the intensity and phase information and accurately ascertain the density profile. Conventional algorithms often yield ambiguous and unrealistic results due to difficulties in including physical constraints and other relevant information. We have developed an algorithm that uses a deep neural network to address these issues and applied it to extract the detailed density profile from an experimental RER. To generalize the applicability of our algorithm, we have developed a technique that quantitatively evaluates the complexity of the phase retrieval process based on the characteristics of the sample and the configuration of the experiment. Accordingly, this evaluation aids in the selection of the neural network architecture for each specific case. Beyond RER, the model has potential applications for other diagnostics where phase retrieval analysis is required.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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.↗