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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

An Analysis of Near Fields of 34m Antennas of JPL/NASA Deep Space Network

This paper addresses the issue of calculating near fields of the 34m Beam Waveguide (BWG) antennas of the NASA/JPL Deep Space Network (DSN). Calculating the near fields of DSN antennas are of interest in receive mode where the transmitting signals from nearby flying objects such as helicopters and airplanes could interfere with the operation of sensitive RF receiving system of DSN antennas, and in the transmit mode where fields from high-powered DSN antennas interfere with receivers on nearby flying objects, as well as safety considerations for the operators and visitors to the grounds surrounding the antenna sites. A complete and detailed analysis has been performed using PO/PTD techniques, including surface errors and support struts effects. Some results are presented, including comparisons with preliminary field tests.

near fields↗
NASA NTRS2011

Automated scheduling for NASA’s Deep Space Network

This paper describes the DSN Scheduling Engine (DSE) component of a new scheduling system being deployed for NASA’s Deep Space Network. The DSE provides core automation functionality for scheduling the network, including the interpretation of scheduling requirements expressed by users, their elaboration into tracking passes, and resolving conflicts and constraint violations. The DSE incorporates both systematic search and repair-based algorithms, used for different phases and purposes in the overall system. It has been integrated in with a web application which provides its functionality to all DSN users through a standard web browser. The system is in the process of deployment to operational status.

Tran, Daniel↗
DOE OSTI2022

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery

Numerical simulation has become indispensable in advancing cost-effective process optimization and control of flow batteries. We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in a neural network to assure model generalization for various battery operation conditions. However, limited by the simplifications of the 0D model, the PCDNN cannot capture sharp voltage changes in the extreme SOC regions. To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing with experimental data, we demonstrate that the ePCDNN approach can accurately capture the voltage response throughout the charge–discharge cycle, including the tail region of the voltage discharge curve. The loss function for training the ePCDNN is designed to be flexible by adjusting the weights of the physics-constrained DNN and the enhanced DNN. In conclusion, this allows the ePCDNN framework to be transferable to battery systems with variable physical model fidelity.

25 ENERGY STORAGE↗
NASA NTRS1982

The Deep Space Network: Noise temperature concepts, measurements, and performance

The use of higher operational frequencies is being investigated for improved performance of the Deep Space Network. Noise temperature and noise figure concepts are used to describe the noise performance of these receiving systems. The ultimate sensitivity of a linear receiving system is limited by the thermal noise of the source and the quantum noise of the receiver amplifier. The atmosphere, antenna and receiver amplifier of an Earth station receiving system are analyzed separately and as a system. Performance evaluation and error analysis techniques are investigated. System noise temperature and antenna gain parameters are combined to give an overall system figure of merit G/T. Radiometers are used to perform radio ""star'' antenna and system sensitivity calibrations. These are analyzed and the performance of several types compared to an idealized total power radiometer. The theory of radiative transfer is applicable to the analysis of transmission medium loss. A power series solution in terms of the transmission medium loss is given for the solution of the noise temperature contribution.

Stelzried, C. T.↗
NASA NTRS1981

A high performance, continuously variable data rate, digitally implemented BPSK modem for deep space network

This paper describes a high performance, digital, BPSK modem designed to improve the telemetry data handling capability of NASA's Deep Space Network. The data rate is continuously variable from 0.5 Mbps to 30 Mbps. It uses newly designed, high speed digital alogrithms for receive filtering, carrier tracking, bit timing and AGC. The carrier and bit time tracking loops have been designed to provide fast acquisition and low tracking phase jitter at E sub b/N sub 0 as low as -4 dB and bit transition density as low as 10%. The performance of the modem is within 0.65 dB below 10 Mbps, 1.0 dB from 10 Mbps to 20 Mbps, and 1.5 dB above 20 Mbps of theoretical coherent BPSK at a BER of 0.0004.

Paik, W. H.↗
NASA NTRS2011

Planetary Radar Imaging with the Deep-Space Network's 34 Meter Uplink Array

A coherent uplink array consisting of up to three 34-meter antennas of NASA's Deep Space Network has been developed for the primary purpose of increasing EIRP at the spacecraft. Greater EIRP ensures greater reach, higher uplink data rates for command and configuration control, as well as improved search and recovery capabilities during spacecraft emergencies. It has been conjectured that Doppler-delay radar imaging of lunar targets can be extended to planetary imaging, where the long baseline of the uplink array can provide greater resolution than a single antenna, as well as potentially higher EIRP. However, due to the well known R-4 loss in radar links, imaging of distant planets is a very challenging endeavor, requiring accurate phasing of the Uplink Array antennas, cryogenically cooled low-noise receiver amplifiers, and sophisticated processing of the received data to extract the weak echoes characteristic of planetary radar. This article describes experiments currently under way to image the planets Mercury and Venus, highlights improvements in equipment and techniques, and presents planetary images obtained to date with two 34 meter antennas configured as a coherently phased Uplink Array.

imaging↗
NASA NTRS2003

Time Analyzer for Time Synchronization and Monitor of the Deep Space Network

A software package has been developed to measure, monitor, and archive the performance of timing signals distributed in the NASA Deep Space Network. Timing signals are generated from a central master clock and distributed to over 100 users at distances up to 30 kilometers. The time offset due to internal distribution delays and time jitter with respect to the central master clock are critical for successful spacecraft navigation, radio science, and very long baseline interferometry (VLBI) applications. The instrument controller and operator interface software is written in LabView and runs on the Linux operating system. The software controls a commercial multiplexer to switch 120 separate timing signals to measure offset and jitter with a time-interval counter referenced to the master clock. The offset of each channel is displayed in histogram form, and "out of specification" alarms are sent to a central complex monitor and control system. At any time, the measurement cycle of 120 signals can be interrupted for diagnostic tests on an individual channel. The instrument also routinely monitors and archives the long-term stability of all frequency standards or any other 1-pps source compared against the master clock. All data is stored and made available for

Cole, Steven↗
NASA NTRS2011

Planetary Radar Imaging with the Deep-Space Network's 34 Meter Uplink Array

A coherent Uplink Array consisting of two or three 34-meter antennas of NASA's Deep Space Network has been developed for the primary purpose of increasing EIRP at the spacecraft. Greater EIRP ensures greater reach, higher uplink data rates for command and configuration control, as well as improved search and recovery capabilities during spacecraft emergencies. It has been conjectured that Doppler-delay radar imaging of lunar targets can be extended to planetary imaging, where the long baseline of the uplink array can provide greater resolution than a single antenna, as well as potentially higher EIRP. However, due to the well known R4 loss in radar links, imaging of distant planets is a very challenging endeavor, requiring accurate phasing of the Uplink Array antennas, cryogenically cooled low-noise receiver amplifiers, and sophisticated processing of the received data to extract the weak echoes characteristic of planetary radar. This article describes experiments currently under way to image the planets Mercury and Venus, highlights improvements in equipment and techniques, and presents planetary images obtained to date with two 34 meter antennas configured as a coherently phased Uplink Array.

uplink array↗
DOE OSTI2020

Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks

Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

Pros and Cons of Using Arrays of Small Antennas Versus Large Single Dish Antennas for the Deep Space Network

This paper briefly describes pros and cons of using arrays of small antennas instead of large single dish antennas for spacecraft telemetry, command, and tracking (TT and C) - communications and navigation (C and N) - and science support that the Deep Space Network (DSN) normally provides. It considers functionality and performance aspects, mainly for TT and C, though it also considers science. It only briefly comments on the cost aspects that seem to favor arrays of small antennas over large single antennas, at least for receiving (downlinks).

Deep Space Network↗
DOE OSTI2023

A detailed study of interpretability of deep neural network based top taggers

Abstract Recent developments in the methods of explainable artificial intelligence (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input–output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton–proton collisions at the Large Hadron Collider. We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important roles in identifying the top jets. We also investigate how and why feature importance varies across different XAI metrics, how correlations among features impact their explainability, and how latent space representations encode information as well as correlate with physically meaningful quantities. Our studies uncover some major pitfalls of existing XAI methods and illustrate how they can be overcome to obtain consistent and meaningful interpretation of these models. We additionally illustrate the activity of hidden layers as neural activation pattern diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning. These studies not only facilitate a methodological approach to interpreting models but also unveil new insights about what these models learn. Incorporating these observations into augmented model design, we propose the particle flow interaction network model and demonstrate how interpretability-inspired model augmentation can improve top tagging performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Abstract Deep learning has emerged as a revolutionary technology for protein residue‐residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning‐based contact predictions have been achieved since then. However, little effort has been put into interpreting the black‐box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention‐based convolutional neural network for protein contact prediction, which consists of two attention mechanism‐based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free‐modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold‐determining residues in proteins. We expect the attention‐based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep Lynx Unidirectional Network Connector

The Deep Lynx UNC is a small utility written in Python that uses only basic Python libraries and the Deep Lynx Python Software Development Kit (SDK). It queries Deep Lynx for tasks that indicate some data to be run in the UNC's environment. Once a task is acquired, the UNC retrieves the associated data and provides it to some other application for processing, waits for output, and sends the output from the other application to Deep Lynx. As data flows only from the UNC to Deep Lynx, this software solves the problem of connecting Deep Lynx with applications and resources that exist in a network where the network can reach Deep Lynx, but Deep Lynx cannot reach that network (unidirectional network).

Browning, JerenM↗
NASA NTRS1992

Generalized probability model for calculation of interference to the Deep Space Network due to circularly Earth-orbiting satellites

The probability of exceeding interference power levels and the duration of interference at the Deep Space Network (DSN) antenna is calculated parametrically when the state vector of an Earth-orbiting satellite over the DSN station view area is not known. A conditional probability distribution function is derived, transformed, and then convolved with the interference signal uncertainties to yield the probability distribution of interference at any given instant during the orbiter's mission period. The analysis is applicable to orbiting satellites having circular orbits with known altitude and inclination angle.

Ruggier, C. J.↗
DOE OSTI2021

Learning viscoelasticity models from indirect data using deep neural networks

In this study, we propose a novel approach to model viscoelasticity materials, where rate-dependent and non-linear constitutive relationships are approximated with deep neural networks. We assume that inputs and outputs of the neural networks are not directly observable, and therefore common training techniques with input–output pairs for the neural networks are inapplicable. To that end, we develop a novel computational approach to both calibrate parametric and learn neural-network-based constitutive relations of viscoelasticity materials from indirect displacement data in the context of multiple-physics systems. We show that limited displacement data holds sufficient information to quantify the viscoelasticity behavior. We formulate the inverse computation – modeling viscoelasticity properties from observed displacement data – as a PDE-constrained optimization problem and minimize the error functional using a gradient-based optimization method. The gradients are computed by a combination of automatic differentiation and implicit function differentiation rules. The effectiveness of our method is demonstrated through numerous benchmark problems in geomechanics and porous media transport.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hydrogen and Battery Storage Technologies for Low-Cost Energy Decarbonization in Distribution Networks

Deep energy decarbonization cannot be achieved without high penetration of renewables. At higher renewable energy penetrations, the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues with existing fossil fuel generation, requiring longer term energy storage to increase the reliability of grid operation. Here, a proton exchange membrane electrolyzer can produce H 2 and serves as a utility controllable load. The produced H 2 can then be stored and converted back into electricity, or mixed with natural gas, or used as transportation fuel, or chemical feedstock. This paper considers the perspective of the distribution system operator that operates the distributed energy resources on a standard IEEE 33-node distribution network considering the technical and physical constraints with the goal of minimizing total investment and operation cost. Different case studies, at very high PV penetrations are considered to show the challenges and path to net-zero emission energy production using H 2 energy. Sensitivity of utility PV costs and electrolyzer capital costs on producing H 2 at $\$1$ /kg are presented showing that the distribution network could produce 100% renewable electricity and H 2 could be produced at a cost of $\$1$ /kg by 2050 with conservative cost estimates and by 2030 with accelerated cost declines.

25 ENERGY STORAGE↗
DOE OSTI2020

Development of interatomic potential for Al–Tb alloys using a deep neural network learning method

An interatomic potential for the Al–Tb alloy around the composition of Al 90 Tb 10 is developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for the Al–Tb alloy. Here we show that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of the Al 90 Tb 10 liquid, such as partial pair correlation functions (PPCFs) and bond angle distributions, in comparison with the results from AIMD simulations. Furthermore, the developed DNN interatomic potential predicts the formation energies of the crystalline phases of the Al–Tb system with an accuracy comparable to ab initio calculations. The structure factors of the Al 90 Tb 10 metallic liquid and glass obtained by MD simulations using the developed DNN interatomic potential are also in good agreement with the experimental X-ray diffraction data. The development of short-range order (SRO) in the Al 90 Tb 10 liquid and the undercooled liquid is also analyzed and three dominant SROs, i.e., Al-centered distorted icosahedron (DISICO) and Tb-centered ‘3661’ and ‘15551’ clusters, respectively, are identified.

36 MATERIALS SCIENCE↗