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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

GPS lessons learned

Global geodesy has improved dramatically over the past decade starting with the GPS for IERS and Geodynamics demonstration campaign in 1991 (GIG 91). At the time it took over a week of CPU time to process a network solution based on 21 global receivers and orbit overlaps were in the 40 cm range. Today it is possible to process a network solution based on 80 global receivers in less then one day of CPU time and orbit overlaps are in the 4 cm range. Special methods are under development for efficient processing of increasingly large regional networks which may contain hundreds or thousands of GPS receivers. Along the way there have been many lessons learned about GPS satellites, receivers, monuments, antennas, radomes, analysis, reference frames, error sources, and interpretation. A wide range of scientific disciplines have been impacted including studies of plate motion, post-glacial rebound, seasonal loading, deformation in plate boundary zones, coseismic displacements due to major earthquakes, postseiemic relaxation, and interseismic strain accumulation related to assessment of seismic hazards. Lessons learned will be presented in the context of new dense networks such as the Plate Boundary Observation (PBO).

GPS satellites↗
DOE OSTI2025

Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

Abstract The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering.

Feng, Yuan↗
DOE OSTI2022

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high $R/X$ ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.

42 ENGINEERING↗
NASA NTRS2024

Future Lunar Surface Network Study: Final Project Report-Unlimited Data Rights

Nokia of America Corporation (Nokia) powered by the research and innovation of Nokia Bell Labs, is honored to have been asked by NASA to conduct a Systems Engineering and Integration (SE&I) study to evaluate an Initial Operating Capability (IOC) for a 3rd Generation Partnership Project (3GPP)-based future lunar surface communication network for the Artemis Program. In particular, the SE&I study has focused on developing an architecture and a 3GPP-based network solution that meets the requirements of the Artemis V mission and at the same time can be evolved and expanded to meet the needs and requirements of future missions in the Moon to Mars program. Nokia strongly believes that 3GPP-based communications and networking solutions present the most effective and future-proof technological path for advanced lunar surface communications (and beyond) for the next decades. 3GPP technologies (whether 4G, 5G or 6G in the future) have revolutionized voice, video, and data transmissions on Earth in consumer, enterprise, and industrial applications, and continue to enable significant gains in productivity, efficiency, and safety. The same benefits can be harnessed for space missions and the future lunar economy including, but not limited to the Artemis program. 3GPP technologies will revolutionize lunar surface communications by increasing data-rates, reducing latency, and providing critical voice, video and data communication capabilities across large surface areas while meeting the stringent reliability requirements of human-rated space flight missions.

Nokia Bell Labs↗
DOE OSTI2025

Hierarchical Network Partitioning for Solution of Potential-Driven, Steady-State Nonlinear Network Flow Equations

The solution of potential-driven steady-state flow in large networks is a task which manifests in various engineering applications, such as transport of natural gas or water through pipeline networks. The resultant system of nonlinear equations depends on the network topology, and in general, there is no numerical algorithm that offers guaranteed convergence to the solution (assuming a solution exists). Some methods offer guarantees in cases where the network topology satisfies certain assumptions, but these methods fail for larger networks. On the other hand, the Newton-Raphson algorithm offers a convergence guarantee if the starting point lies close to the (unknown) solution. It would be advantageous to compute the solution of the large nonlinear system through the solution of smaller nonlinear sub-systems wherein the solution algorithms (Newton-Raphson or otherwise) are more likely to succeed. Here, this letter proposes and describes such a procedure, a hierarchical network partitioning algorithm that enables the solution of large nonlinear systems corresponding to potential-driven steady-state network flow equations.

42 ENGINEERING↗
NASA NTRS1996

The Mars Microprobe Mission: A Unique Solution for Network Science

The second deep-space advanced technology validation mission in NASA's New Millenium Program will demonstrate planetary microprobe technologies. Two microporobes, each consisting of a very low-mass aeroshell and penetrator system, are planned to be launched in January 1999 and arrive at at Mars in December 1999... This paper will summarize the engineering techniques that will be implemented to provide safe landing and operation of the Mars microprobe in its unique environment. This paper will also summarize some key mission and system design trades, as well as discuss some of the technologies being developed.

microprobes↗
DOE OSTI2024

From ionic clusters dynamics to network constraints in ionic polymer solutions

Physical networks formed by ionizable polymers with ionic clusters as crosslinks are controlled by coupled dynamics that transcend from ionic clusters through chain motion to macroscopic response. Here, the coupled dynamics, across length scales, from the ionic clusters to the networks in toluene swollen polystyrene sulfonate networks, were directly correlated, as the electrostatic environment of the physical crosslinks was altered. The multiscale insight is attained by coupling neutron spin echo measurements with molecular dynamics simulations, carried out to times typical of relaxation of polymers in solutions. The experimental dynamic structure factor is in outstanding agreement with the one calculated from computer simulations, as the networks are perturbed by elevating the temperature and changing the electrostatic environment. In toluene, the long-lived clusters remain stable over hundreds of ns across a broad temperature range, while the polymer network remains dynamic. In conclusion, though the size of the clusters changes as the dielectric constant of the solvent is modified through the addition of ethanol, they remain stable but morph, enhancing the polymer chain dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions

Here we propose a new type of neural networks, Kronecker neural networks (KNNs), that form a general framework for neural networks with adaptive activation functions. KNNs employ the Kronecker product, which provides an efficient way of constructing a very wide network while keeping the number of parameters low. Our theoretical analysis reveals that under suitable conditions, KNNs induce a faster decay of the loss than that by the feed-forward networks. This is also empirically verified through a set of computational examples. Furthermore, under certain technical assumptions, we establish global convergence of gradient descent for KNNs. As a specific case, we propose the Rowdy activation function that is designed to get rid of any saturation region by injecting sinusoidal fluctuations, which include trainable parameters. The proposed Rowdy activation function can be employed in any neural network architecture like feed-forward neural networks, Recurrent neural networks, Convolutional neural networks etc. The effectiveness of KNNs with Rowdy activation is demonstrated through various computational experiments including function approximation using feed-forward neural networks, solution inference of partial differential equations using the physics-informed neural networks, and standard deep learning benchmark problems using convolutional and fully-connected neural networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Programmable synaptic devices for electronic neural nets

The architecture, design, and operational characteristics of custom VLSI and thin film synaptic devices are described. The devices include CMOS-based synaptic chips containing 1024 reprogrammable synapses with a 6-bit dynamic range, and nonvolatile, write-once, binary synaptic arrays based on memory switching in hydrogenated amorphous silicon films. Their suitability for embodiment of fully parallel and analog neural hardware is discussed. Specifically, a neural network solution to an assignment problem of combinatorial global optimization, implemented in fully parallel hardware using the synaptic chips, is described. The network's ability to provide optimal and near optimal solutions over a time scale of few neuron time constants has been demonstrated and suggests a speedup improvement of several orders of magnitude over conventional search methods.

Moopenn, A.↗
NASA NTRS1983

Communications network design and costing model technical manual

This computer model provides the capability for analyzing long-haul trunking networks comprising a set of user-defined cities, traffic conditions, and tariff rates. Networks may consist of all terrestrial connectivity, all satellite connectivity, or a combination of terrestrial and satellite connectivity. Network solutions provide the least-cost routes between all cities, the least-cost network routing configuration, and terrestrial and satellite service cost totals. The CNDC model allows analyses involving three specific FCC-approved tariffs, which are uniquely structured and representative of most existing service connectivity and pricing philosophies. User-defined tariffs that can be variations of these three tariffs are accepted as input to the model and allow considerable flexibility in network problem specification. The resulting model extends the domain of network analysis from traditional fixed link cost (distance-sensitive) problems to more complex problems involving combinations of distance and traffic-sensitive tariffs.

Logan, K. P.↗
DOE OSTI2018

A Comparison of Neuromorphic Classification Tasks

A variety of neural network models and machine learning techniques have arisen over the past decade, and their successes with image classification have been stunning. With other classification tasks, selecting and configuring a neural network solution is not straightforward. In this paper, we evaluate and compare a variety of neural network models, trained by a variety of machine learning techniques, on a variety of classification tasks. While Deep Learning typically exhibits the best classification accuracy, we note the promise of Reservoir Computing, and evolutionary optimization on spiking neural networks. In many cases, these technologies perform as well as, or better than Deep Learning, and the resulting networks are much smaller than their Deep Learning counterparts.

Reynolds, John↗
NASA NTRSDate not supplied

NASA Space Communications and Navigation: One Network Evolution

The NASA Space Communications and Navigation (SCaN) Program is responsible for providing the essential connectivity to robotic and human space explorers. The missions relying on SCaN range from suborbital and balloon missions to those traveling beyond the edge of the solar system. The demands for communications and navigation services enabled by SCaN (and its affiliated partners) are projected to increase and outpace the current network capacity. At the same time, the Agency finds itself surrounded by a burgeoning commercial space marketplace, technological advancement, and other government agencies that share common interests in space resiliency, robustness, and performance. As a result, SCaN has begun pivoting toward commercial services and collaborating with partners to close capacity and capability gaps. Given these growing demands of the Agency there is increasing need for multi-network solutions. Future mission concepts will rely on both government and commercial capabilities, both Near Space Network capacity and Deep Space Network capacity. Integrating these diverse support services together from a technical, programmatic and implementation standpoint will be key to meet the growing needs of the future. To accomplish this, a more substantive shift is required, and SCaN is reshaping itself to be a customer-centric, service-oriented, high-performance leader in the space communications community. This paper outlines the SCaN One Team, One Mission, One Network approach, and provides a vision for future mission community experience that includes streamlined mission commitment interfaces and clear processes, dynamic network scheduling and load balancing, and higher efficiency data transport and delivery through the integration of cloud infrastructure and services.

Near Space Network↗
DOE OSTI2012

Validation of macromolecular flexibility in solution by small-angle X-ray scattering (SAXS)

The dynamics of macromolecular conformations are critical to the action of cellular networks. Solution X-ray scattering studies, in combination with macromolecular X-ray crystallography (MX) and nuclear magnetic resonance (NMR), strive to determine complete and accurate states of macromolecules, providing novel insights describing allosteric mechanisms, supramolecular complexes, and dynamic molecular machines. This review addresses theoretical and practical concepts, concerns, and considerations for using these techniques in conjunction with computational methods to productively combine solution-scattering data with high-resolution structures. I discuss the principal means of direct identification of macromolecular flexibility from SAXS data followed by critical concerns about the methods used to calculate theoretical SAXS profiles from high-resolution structures. The SAXS profile is a direct interrogation of the thermodynamic ensemble and techniques such as, for example, minimal ensemble search (MES), enhance interpretation of SAXS experiments by describing the SAXS profiles as population-weighted thermodynamic ensembles. I discuss recent developments in computational techniques used for conformational sampling, and how these techniques provide a basis for assessing the level of the flexibility within a sample. Although these approaches sacrifice atomic detail, the knowledge gained from ensemble analysis is often appropriate for developing hypotheses and guiding biochemical experiments. Examples of the use of SAXS and combined approaches with X-ray crystallography, NMR, and computational methods to characterize dynamic assemblies are presented.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2006

An Approach to Integrated Spectrum Efficient Network Enhanced Telemetry (iSENET))

As the integrated Network Enhanced Telemetry (iNET) program moves forward in resolving systems engineering design and architecture definition, critical technology "gaps" and a migration path to realizing the integration of this technology are needed to insure a smooth transition from the current legacy point to point telemetry links to a network oriented telemetry system. Specifically, identified by the DoD aeronautical telemetry community is the need for a migration to a network solution for command, control, and transfer of test data by optimizing the physical, data link, and network layers. In this paper, we present a network-centric telemetry approach based on variants of 802.11 that leverages the open standards as well as the previous Advanced Range Telemetry (ARTM) work on the physical layer waveform. Specifically, we present a burst modem approach based on the recent AOFDM 802.11a work, a TDMA-like MAC layer approach based on 802.11e, and then add additional MAC layer features to allow for the multi-hop aeronautical environment using a variant of the current working standards of 802.11s. The combined benefits of the variants obtained from 802.11a, 802.11e, and 802.11s address the needs for both spectrum efficiency in the aeronautical environment and the iNET program.

TDMA↗
NASA NTRS2013

NASA Center for Climate Simulation (NCCS) Advanced Technology AT5 Virtualized Infiniband Report

The NCCS is part of the Computational and Information Sciences and Technology Office (CISTO) of Goddard Space Flight Center's (GSFC) Sciences and Exploration Directorate. The NCCS's mission is to enable scientists to increase their understanding of the Earth, the solar system, and the universe by supplying state-of-the-art high performance computing (HPC) solutions. To accomplish this mission, the NCCS (https://www.nccs.nasa.gov) provides high performance compute engines, mass storage, and network solutions to meet the specialized needs of the Earth and space science user communities

cloud↗
NASA NTRS1993

A Software Package for Neural Network Applications Development

Original Backprop (Version 1.2) is an MS-DOS package of four stand-alone C-language programs that enable users to develop neural network solutions to a variety of practical problems. Original Backprop generates three-layer, feed-forward (series-coupled) networks which map fixed-length input vectors into fixed length output vectors through an intermediate (hidden) layer of binary threshold units. Version 1.2 can handle up to 200 input vectors at a time, each having up to 128 real-valued components. The first subprogram, TSET, appends a number (up to 16) of classification bits to each input, thus creating a training set of input output pairs. The second subprogram, BACKPROP, creates a trilayer network to do the prescribed mapping and modifies the weights of its connections incrementally until the training set is leaned. The learning algorithm is the 'back-propagating error correction procedures first described by F. Rosenblatt in 1961. The third subprogram, VIEWNET, lets the trained network be examined, tested, and 'pruned' (by the deletion of unnecessary hidden units). The fourth subprogram, DONET, makes a TSR routine by which the finished product of the neural net design-and-training exercise can be consulted under other MS-DOS applications.

Baran, Robert H.↗