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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 109 records · Page 6

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NASA NTRS1989

Distributed Finite Element Analysis Using a Transputer Network

The principal objective of this research effort was to demonstrate the extraordinarily cost effective acceleration of finite element structural analysis problems using a transputer-based parallel processing network. This objective was accomplished in the form of a commercially viable parallel processing workstation. The workstation is a desktop size, low-maintenance computing unit capable of supercomputer performance yet costs two orders of magnitude less. To achieve the principal research objective, a transputer based structural analysis workstation termed XPFEM was implemented with linear static structural analysis capabilities resembling commercially available NASTRAN. Finite element model files, generated using the on-line preprocessing module or external preprocessing packages, are downloaded to a network of 32 transputers for accelerated solution. The system currently executes at about one third Cray X-MP24 speed but additional acceleration appears likely. For the NASA selected demonstration problem of a Space Shuttle main engine turbine blade model with about 1500 nodes and 4500 independent degrees of freedom, the Cray X-MP24 required 23.9 seconds to obtain a solution while the transputer network, operated from an IBM PC-AT compatible host computer, required 71.7 seconds. Consequently, the $80,000 transputer network demonstrated a cost-performance ratio about 60 times better than the $15,000,000 Cray X-MP24 system.

Watson, James↗

NASA NTRS2011

TPSAS-NF1676L-12321-DND

The operation of some networks, such as air transportation networks, can be complicated by congestion through a small subset of nodes. The congestion may be influenced by the connectivity of the network, or by the presence of constraints restricting the flow through particular nodes. This work investigates the effects of both connectivity and node flow constraints on the operation of a network. We develop the Minimax Node Load Problem (MNLP), a multicommodity flow model which minimizes the worst-case flow through any node in a given input network. The optimal solution to this problem provides us with the minimax node load, which we propose as a measure of network congestion. Keeping the number of nodes fixed, we first increase connectivity in a series of networks, and observe that topologies with more distributed connections result in a reduction in the minimax node load. However, when connectivity is increased further, the reductions diminish and are accompanied by solutions with undesirable qualities such as longer commodity paths. We then perform a second set of experiments over the same network, constraining flow through different subsets of nodes at different magnitudes of flow restriction, finding that (1) more constrained nodes lead to the largest increases in minimax node load and (2) constraints on the most connected nodes have the greatest effect on both congestion and commodity path length.

Douglas W Lee↗

NASA NTRS2011

TPSAS-NF1676L-12264-DND

Douglas Lee↗

DOE OSTI2025

Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1993

Synthesis of Optimal Nonlinear Feedback Laws for Dynamic Systems Using Neural Networks

Open-loop solutions of dynamical optimization problems can be numerically computed usingexisting software packages. The computed time histories of the state and control variables, formultiple sets of end conditions can then be used to train a neural network to 'recognize' the optimal,nonlinear feedback relation between the states and controls of the system. The 'learned' network canthen be used to output an approximate optimal control given a full set (or a partial set) of measuredsystem states. With simple neural networks, we have successfully demonstrated the efficacy of theproposed approach using a minimum-time orbit injection problem. The usefulness and limitations ofthis novel approach on real-life optimal guidance and control problems, with many state and control variables as well as path inequality constraints, remain to be seen.

Orbit injection problem↗

NASA NTRS1990

Identification of the connections in biologically inspired neural networks

We developed an identification method to find the strength of the connections between neurons from their behavior in small biologically-inspired artificial neural networks. That is, given the network external inputs and the temporal firing pattern of the neurons, we can calculate a solution for the strengths of the connections between neurons and the initial neuron activations if a solution exists. The method determines directly if there is a solution to a particular neural network problem. No training of the network is required. It should be noted that this is a first pass at the solution of a difficult problem. The neuron and network models chosen are related to biology but do not contain all of its complexities, some of which we hope to add to the model in future work. A variety of new results have been obtained. First, the method has been tailored to produce connection weight matrix solutions for networks with important features of biological neural (bioneural) networks. Second, a computationally efficient method of finding a robust central solution has been developed. This later method also enables us to find the most consistent solution in the presence of noisy data. Prospects of applying our method to identify bioneural network connections are exciting because such connections are almost impossible to measure in the laboratory. Knowledge of such connections would facilitate an understanding of bioneural networks and would allow the construction of the electronic counterparts of bioneural networks on very large scale integrated (VLSI) circuits.

Demuth, H.↗

NASA NTRS1991

The control network of Mercury: April 1991

Features identified on Mariner 10 high resolution images of Mercury, acquired during three flybys between 1974 and 1975, form the basis of Mercury's planetwide control network. Although images from all three flybys are used in the net, the large amount of contiguous coverage from the second flyby, a southern bright-side pass, make these images the strongest contributors to the control net. Mercury is in synchronous rotation with a period of 58.6462 days and its spin axis is approximately normal to the equatorial plane. The 20 degree meridian is defined by the crater Hun Kal, located just south of the equator. The control network computations involve the photogrammetric determination of control point coordinates and an analytical triangulation solution. The current control network computations for Mercury are performed in the J2000 coordinate system according to the International Astronomical Union (IAU) convention. In recent years, updates to the control network have included improved trajectory solutions and modification of the standard radii (2439) at several points based on Earth-based radar altimetry data. The current status of the control network calculations is presented. Improvements were made to existing control points and new control points were added to the net to strengthen the overall network and improve the standard error of measurement.

Davies, Merton E.↗

NASA NTRS1989

Learning in stochastic neural networks for constraint satisfaction problems

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

Johnston, Mark D.↗

DOE OSTI2024

Characterizing the impact of finite matrix block size on conservative particle transport through three-dimensional fracture networks

Mass transfer of solutes between fractures and the surrounding rock matrix exerts a noticeable signature on the tail of travel time distributions. When the width of the matrix is assumed to be infinite and advective transport through the fracture is sufficiently fast, the tails of the travel time distributions exhibit a classically expected slope of ψ(t) ∝ t -3/2 . However, studies have yet to characterize how solute transfer between fractures via diffusion through finite matrix blocks influences the tail’s slope in three-dimensional fractured media. Here, in this study, we assess the impact of finite matrix block size on breakthrough curve shape at different spatio-temporal scales by con ducting particle tracking simulations in three-dimensional discrete fracture networks. We consider a variety of hydrodynamic and geostructural proper ties to determine their relative impact on the resulting travel time distributions. We observe that the impact of matrix diffusion through a finite block on travel time distributions is similar to that of an infinite matrix block when the fracture spacing is sufficiently large, matrix diffusion is relatively weak, or transport is considered at an early control plane distance. We observe that the converse of these conditions, results in deviations from the classical ψ(t) ∝ t -3/2 scaling. These results provide a first step toward developing a metric to assess when finite block size effects are expected to significantly influence transport.

58 GEOSCIENCES↗

NASA NTRS2018

Use of Nack Oriented Reliable Multicast (NORM) Protocol for Transport of Spacecraft Telemetry in Ground Networks

Frequently it is necessary to distribute spacecraft telemetry to multiple destinations on Internet Protocol (IP) networks. There are various methods of delivering this data, but when the data is high rate and there are many destinations IP multicast provides the best solution from a network perspective. However, IP multicast only supports the User Datagram Protocol (UDP) which is connectionless and unreliable. This nature of UDP and by extension the low priority treatment of UDP packets by some network hardware creates a difficult environment for distribution of high rate telemetry to a number of recipients over IP networks. NACK (Negative ACKnowledgment) Oriented Reliable Multicast (NORM) is defined by IETF (Internet Engineering Task Force) RFC (Request for Comments) 5740 in 2009 by B. Adamson et al. NORM provides a mechanism of retransmission and or forward error correction for UDP packets, potentially overcoming the limits of UDP for the transmission of spacecraft telemetry. Marshall Space Flight Center’s (MSFC) Huntsville Operations Support Center (HOSC) manages the delivery of the International Space Station (ISS) payload science and health data. The ISS IP Ground Routed (IIGoR) network provides the delivery of the data from the ground stations to the control centers. HOSC personnel have been experimenting with the use of NORM on the link between the ground stations and the front end processor equipment in Huntsville, AL.

Baldwin, Patrick A.↗

NASA NTRS1993

Three-dimensional spherical models of layered and whole mantle convection

We present numerical calculations of three-dimensional spherical shell thermal convection for constant viscosity and stratified viscosity models of whole-layer and two-layer mantle convection. These four examples are intended to provide theoretical guidance for determining the style of convection that is occurring in Earth's mantle. An impermeable interface between the upper and lower convecting shells in the two-layer solutions is placed at a depth of 670 km to coincide with the mantle seismic discontinuity that divides the upper and lower mantle. The interface results in an internal thermal boundary layer that raises the mean temperature in the lower shell by about 1400 K compared to the whole-layer solutions. The patterns of convection in the upper part of the whole-layer solutions are dominated by narrow arcuate sheetlike downflows in a background of weak upflow. In contrast, the upper shells of the two-layer solutions have complicated networks of convective rolls with the upflows and downflows having very similar structure. The structure of convection in the lower shells is similar to that in the lower part of the whole-layer solutions. Based on the horizontal structure of subduction zones on Earth's surface and on tomographic images of temperature variations in Earth's mantle, we conclude that the style of convection in Earth's mantle is more like that of the whole-mantle models.

Glatzmaier, Gary A.↗

NASA NTRS1990

The extended tracking network and indications of baseline precision and accuracy in the North Andes

The CASA Uno Global Positioning System (GPS) experiment (January-February 1988) included an extended tracking network which covered three continents in addition to the network of scientific interest in Central and South America. The repeatability of long baselines (400-1000 km) in South America is improved by up to a factor of two in the horizontal vector baseline components by using tracking stations in the Pacific and Europe to supplement stations in North America. In every case but one, the differences between the mean solutions obtained using different tracking networks was equal to or smaller than day-to-day rms repeatabilities for the same baselines. The mean solutions obtained by using tracking stations in North America and the Pacific agreed at the 2-3 millimeter level with those using tracking stations in North America and Europe. The agreement of the extended tracking network solutions suggests that a broad distribution of tracking stations provides better geometric constraints on the satellite orbits and that solutions are not sensitive to changes in tracking network configuration when an extended network is use. A comparison of the results from the North Andes and a baseline in North America suggests that the use of a geometrically strong extended tracking network is most important when the network of interest is far from North America.

Freymueller, Jeffrey T.↗

NASA NTRS2004

Making Wireless Networks Secure for NASA Mission Critical Applications using Virtual Private Network (VPN) Technology

With so many security issues involved with wireless networks, the technology has not been fully utilized in the area of mission critical applications. These applications would include the areas of telemetry, commanding, voice and video. Wireless networking would allow payload operators the mobility to take computers outside of the control room to their offices and anywhere else in the facility that the wireless network was extended. But the risk is too great of having someone sit just inside of your wireless network coverage and intercept enough of your network traffic to steal proprietary data from a payload experiment or worse yet hack back into your system and do even greater harm by issuing harmful commands. Wired Equivalent Privacy (WEP) is improving but has a ways to go before it can be trusted to protect mission critical data. Today s hackers are becoming more aggressive and innovative, and in order to take advantage of the benefits that wireless networking offer, appropriate security measures need to be in place that will thwart hackers. The Virtual Private Network (VPN) offers a solution to the security problems that have kept wireless networks from being used for mission critical applications. VPN provides a level of encryption that will ensure that data is protected while it is being transmitted over a wireless local area network (IAN). The VPN allows a user to authenticate to the site that the user needs to access. Once this authentication has taken place the network traffic between that site and the user is encapsulated in VPN packets with the Triple Data Encryption Standard (3DES). 3DES is an encryption standard that uses a single secret key to encrypt and decrypt data. The length of the encryption key is 168 bits as opposed to its predecessor DES that has a 56-bit encryption key. Even though 3DES is the common encryption standard for today, the Advance Encryption Standard (AES), which provides even better encryption at a lower cycle cost is growing acceptance. The user computer running the VPN client and the. target site that is running the . VPN firewall exchange this encryption key and therefore are the only ones that are able to decipher the data. The level of encryption offered by the VPN is making it possible for wireless networks to pass the strict security policies that have kept them from being used in the past. Now people will be able to benefit from the many advantages that wireless networking has to offer in the area of mission critical applications.

Nichols, Kelvin F.↗

NASA NTRS2004

Making Wireless Networks Secure for NASA Mission Critical Applications Using Virtual Private Network (VPN) Technology

With so many security issues involved with wireless networks, the technology has not been fully utilized in the area of mission critical applications. These applications would include the areas of telemetry, commanding, voice and video. Wireless networking would allow payload operators the mobility to take computers outside of the control room to their off ices and anywhere else in the facility that the wireless network was extended. But the risk is too great of having someone sit just inside of your wireless network coverage and intercept enough of your network traffic to steal proprietary data from a payload experiment or worse yet hack back into your system and do even greater harm by issuing harmful commands. Wired Equivalent Privacy (WEP) is improving but has a ways to go before it can be trusted to protect mission critical data. Today s hackers are becoming more aggressive and innovative, and in order to take advantage of the benefits that wireless networking offer, appropriate security measures need to be in place that will thwart hackers. The Virtual Private Network (VPN) offers a solution to the security problems that have kept wireless networks from being used for mission critical applications. VPN provides a level of encryption that will ensure that data is protected while it is being transmitted over a wireless local area network (LAN). The VPN allows a user to authenticate to the site that the user needs to access. Once this authentication has taken place the network traffic between that site and the user is encapsulated in VPN packets with the Triple Data Encryption Standard (3DES). 3DES is an encryption standard that uses a single secret key to encrypt and decrypt data. The length of the encryption key is 168 bits as opposed to its predecessor DES that has a 56-bit encryption key. Even though 3DES is the common encryption standard for today, the Advance Encryption Standard (AES), which provides even better encryption at a lower cycle cost is growing acceptance. The user computer running the VPN client and the target site that is running the VPN firewall exchange this encryption key and therefore are the only ones that are able to decipher the data. The level of encryption offered by the VPN is making it possible for wireless networks to pass the strict security policies that have kept them from being used in the past. Now people will be able to benefit from the many advantages that wireless networking has to offer in the area of mission critical applications.

Nichols, Kelvin F.↗

NASA NTRS2011

Markov Chain Monte Carlo Bayesian Learning for Neural Networks

Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.

Goodrich, Michael S.↗

NASA NTRS2009

The Deep Impact Network Experiment Operations Center

Delay/Disruption Tolerant Networking (DTN) promises solutions in solving space communications challenges arising from disconnections as orbiters lose line-of-sight with landers, long propagation delays over interplanetary links, and other phenomena. DTN has been identified as the basis for the future NASA space communications network backbone, and international standardization is progressing through both the Consultative Committee for Space Data Systems (CCSDS) and the Internet Engineering Task Force (IETF). JPL has developed an implementation of the DTN architecture, called the Interplanetary Overlay Network (ION). ION is specifically implemented for space use, including design for use in a real-time operating system environment and high processing efficiency. In order to raise the Technology Readiness Level of ION, the first deep space flight demonstration of DTN is underway, using the Deep Impact (DI) spacecraft. Called the Deep Impact Network (DINET), operations are planned for Fall 2008. An essential component of the DINET project is the Experiment Operations Center (EOC), which will generate and receive the test communications traffic as well as "out-of-DTN band" command and control of the DTN experiment, store DTN flight test information in a database, provide display systems for monitoring DTN operations status and statistics (e.g., bundle throughput), and support query and analyses of the data collected. This paper describes the DINET EOC and its value in the DTN flight experiment and potential for further DTN testing.

flight experiment↗

NASA NTRS2021

Semantic Segmentation of High-Resolution Satellite Imagery using Generative Adversarial Networks with Progressive Growing

With increase in urbanization and Earth Sciences research into urban areas, the need to quickly and accurately segment urban rooftop maps has never been greater. Cur-rent machine learning techniques struggle to produce high accuracy maps in dense urban zones where there is high image noise and foot print overlap. In this paper, we evaluate a training methodology for pixel-wise segmentation for high resolution satellite imagery using progressive growing of generative adversarial networks as a solution. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We evaluate our approach using the SpaceNet version 2 and xView datasets. Our experiments show that for SpaceNet, progressive Generative Adversarial Network (GAN) training achieved a test accuracy of 93% compared to 89% for traditional GAN training and 87% for U-Net architecture, while for xView, we achieved 71% accuracy using progressive GAN training compared to 69% through traditional GAN training and 65% using U-Net.

Semantic↗

NASA NTRS2000

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

NASA Discipline Environmental Health↗