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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Precise Linker Length and Dynamic Bond Exchange Control Penetrant Diffusion in Dense Vitrimers

Polymer networks with dynamic covalent bonds have been investigated for their self-healing ability, recyclability, and potential as more sustainable materials. Recent results have indicated that in some cases, bond exchange can enhance the transport of penetrants in dense networks, pointing to their potential for separations of membranes. Here, imine dynamic bonds in ethylene oxide (EO) networks with precise linker lengths were synthesized to investigate the transport of N,N′-bis(2,5-di-tert-butylphenyl)-3,4,9,10-perylenedicarboximide (BTBP), a large, anisotropic dye molecule. Networks with mesh sizes smaller than, comparable to, and greater than the size of the penetrant axes were investigated to probe the effects of bond exchange and network confinement on transport. Mesh sizes, which ranged from 0.5 to 1.62 nm, were determined from shear rheology, glass transitions by calorimetry, and probe diffusion coefficients by fluorescence recovery after photobleaching. Permanent networks with identical EO chain lengths were prepared as control samples, and up to a 3 orders of magnitude increase in diffusion coefficient is observed in the dynamic systems for short linkers containing 13 backbone atoms. The longest linkers with 71 backbone atoms show no difference between the permanent and dynamic networks. Linkers shorter than 11 backbone atoms, corresponding to a mesh size smaller than the penetrant small axis, diffusion is no longer observable on the experimental time scale, indicating a sharp cutoff attributed to the precise linkers and narrow mesh size distribution. The dynamic imine exchange time scales were compared to the diffusive hopping times of penetrants and indicate that exchange can occur during a diffusive displacement. Furthermore, these findings provide insights into the factors affecting penetrant transport in dense polymers and inspire the development of next-generation selective polymer membranes.

Diffusion↗
NASA NTRS1998

The P-Mesh: A Commodity-based Scalable Network Architecture for Clusters

We designed a new network architecture, the P-Mesh which combines the scalability and fault resilience of a torus with the performance of a switch. We compare the scalability, performance, and cost of the hub, switch, torus, tree, and P-Mesh architectures. The latter three are capable of scaling to thousands of nodes, however, the torus has severe performance limitations with that many processors. The tree and P-Mesh have similar latency, bandwidth, and bisection bandwidth, but the P-Mesh outperforms the switch architecture (a lower bound for tree performance) on 16-node NAB Parallel Benchmark tests by up to 23%, and costs 40% less. Further, the P-Mesh has better fault resilience characteristics. The P-Mesh architecture trades increased management overhead for lower cost, and is a good bridging technology while the price of tree uplinks is expensive.

Nitzberg, Bill↗
DOE OSTI2026

Systematic Evaluation of Atmospheric Forcing, Surface Datasets, and Mesh Effects on Kilometer-Scale Land Surface and River Modeling

Earth system models are advancing toward kilometer-scale resolution to capture local climate impacts and extremes. High-resolution land and river modeling depends on multiple factors, including mesh, surface datasets, and atmospheric forcing, but their relative effects at kilometer scales remain unquantified. We evaluated five Energy Exascale Earth System Model land and river configurations over the Mid-Atlantic region using two mesh (1/8° structured versus variable-resolution unstructured mesh), two surface datasets (default versus newly developed), and three atmospheric forcings (NLDAS2, MSWX, GSWP). Evaluation against satellite, reanalysis, and in situ benchmarks across water, energy, and carbon cycles quantifies how these factors affect model performance. Forcing selection produces the largest bias reductions (12-99% across variables), followed by surface datasets (7-75%) and mesh (up to 21%). Forcing effects vary by variable, with MSWX reducing biases for snow water equivalent, evapotranspiration, albedo, temperature, and gross primary productivity, GSWP for snow cover and runoff, and NLDAS for soil moisture and streamflow. The use of newly developed surface datasets improves gross primary productivity (58% bias reduction) and evapotranspiration but increase soil moisture and albedo biases due to current modeling limitations. Variable-resolution unstructured mesh improves the simulation of small-basin streamflow through better capturing drainage networks, though mesh minimally affects other land variables. These findings provide important guidance for high-resolution modeling development and actionable science.

Land and River modeling↗
DOE OSTI2025

Resilient Entanglement Distribution in a Multihop Quantum Network

The evolution of quantum networking requires architectures capable of dynamically reconfigurable entanglement distribution to meet diverse user needs and ensure tolerance against transmission disruptions. We introduce multihop quantum networks to improve network reach and resilience by enabling quantum communications across intermediate nodes, thus broadening network connectivity and increasing scalability. We present multihop two-qubit polarization-entanglement distribution within a quantum network at the Oak Ridge National Laboratory campus. Our system uses wavelength-selective switches for adaptive bandwidth management on a software-defined quantum network that integrates a quantum data plane with classical data and control planes, creating a flexible, reconfigurable mesh. Our network distributes entanglement across six nodes within three subnetworks, each located in a separate building, optimizing quantum state fidelity and transmission rate through adaptive resource management. Additionally, we demonstrate the network's resilience by implementing a link recovery approach that monitors and reroutes quantum resources to maintain service continuity despite link failures—paving the way for scalable and reliable quantum networking infrastructures.

Alshowkan, Muneer [Oak Ridge National Laboratory (↗
NASA NTRS1967

Thermal garment

An anthropomorphic thermal garment made entirely of fluid-carrying tubing, joined in such a way that the tubes form a network or mesh fabric, is described.

Hopper, J. H.↗
NASA NTRS1989

Three approaches to reliability analysis

It is noted that current reliability analysis tools differ not only in their solution techniques, but also in their approach to model abstraction. The analyst must be satisfied with the constraints that are intrinsic to any combination of solution technique and model abstraction. To get a better idea of the nature of these constraints, three reliability analysis tools (HARP, ASSIST/SURE, and CAME) were used to model portions of the Integrated Airframe/Propulsion Control System architecture. When presented with the example problem, all three tools failed to produce correct results. In all cases, either the tool or the model had to be modified. It is suggested that most of the difficulty is rooted in the large model size and long computational times which are characteristic of Markov model solutions.

Mesh networks↗
DOE OSTI2025

PruningAMR

PruningAMR is an algorithm that reads in data stored as a neural network and outputs an adaptive mesh with values of the neural network stored at its vertices. Examples of input data include implicit neural representations (INRs) and physics-informed neural networks (PINNs). The output mesh is a grid-based adaptive mesh with larger elements for regions in which the neural network has coarse-scale variation and smaller elements for regions with fine scale variation. The goal of the software is to discretize the neural network to a mesh that faithfully captures the details encoded in the data without resorting a fine scale mesh.

Zvonek, JenniferE [Lawrence Livermore National Lab↗
DOE OSTI2024

Zapiary: Creating Visibility in IOT Networks

Zigbee and Z-Wave are the main networking protocols used by low-power Internet of Things (IOT) devices. These protocols use low frequencies. Mesh architecture, and unique address formats that make them not compatible with traditional network traffic tools like IX-Discovery Tools. Zapiary is a software that takes CSV files with Zigbee and Z-Wave traffic and generates Structured Threat Information eXpression (STIX) JSON bundles illustrating the communication within IOT networks. The bundles can then be viewed within Structured Threat Intelligence Graph (STIG) or used with AI/ML models to provide deeper visibility into nodes that make up the network and the ability to trend the mesh network over time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

A performance analysis of DS-CDMA and SCPC VSAT networks

Spread-spectrum and single-channel-per-carrier (SCPC) transmission techniques work well in very small aperture terminal (VSAT) networks for multiple-access purposes while allowing the earth station antennas to remain small. Direct-sequence code-division multiple-access (DS-CDMA) is the simplest spread-spectrum technique to use in a VSAT network since a frequency synthesizer is not required for each terminal. An examination is made of the DS-CDMA and SCPC Ku-band VSAT satellite systems for low-density (64-kb/s or less) communications. A method for improving the standardf link analysis of DS-CDMA satellite-switched networks by including certain losses is developed. The performance of 50-channel full mesh and star network architectures is analyzed. The selection of operating conditions producing optimum performance is demonstrated.

Hayes, David P.↗
NASA NTRS1993

Fault-tolerant onboard digital information switching and routing for communications satellites

The NASA Lewis Research Center is developing an information-switching processor for future meshed very-small-aperture terminal (VSAT) communications satellites. The information-switching processor will switch and route baseband user data onboard the VSAT satellite to connect thousands of Earth terminals. Fault tolerance is a critical issue in developing information-switching processor circuitry that will provide and maintain reliable communications services. In parallel with the conceptual development of the meshed VSAT satellite network architecture, NASA designed and built a simple test bed for developing and demonstrating baseband switch architectures and fault-tolerance techniques. The meshed VSAT architecture and the switching demonstration test bed are described, and the initial switching architecture and the fault-tolerance techniques that were developed and tested are discussed.

Shalkhauser, Mary JO↗
NASA NTRS1984

Test experience on an ultrareliable computer communication network

The dispersed sensor processing mesh (DSPM) is an experimental, ultrareliable, fault-tolerant computer communications network that exhibits an organic-like ability to regenerate itself after suffering damage. The regeneration is accomplished by two routines - grow and repair. This paper discusses the DSPM concept for achieving fault tolerance and provides a brief description of the mechanization of both the experiment and the six-node experimental network. The main topic of this paper is the system performance of the growth algorithm contained in the grow routine. The characteristics imbued to DSPM by the growth algorithm are also discussed. Data from an experimental DSPM network and software simulation of larger DSPM-type networks are used to examine the inherent limitation on growth time by the growth algorithm and the relationship of growth time to network size and topology.

Abbott, L. W.↗
NASA NTRS1984

Test experience on an ultrareliable computer communication network

The dispersed sensor processing mesh (DSPM) is an experimental, ultra-reliable, fault-tolerant computer communications network that exhibits an organic-like ability to regenerate itself after suffering damage. The regeneration is accomplished by two routines - grow and repair. This paper discusses the DSPM concept for achieving fault tolerance and provides a brief description of the mechanization of both the experiment and the six-node experimental network. The main topic of this paper is the system performance of the growth algorithm contained in the grow routine. The characteristics imbued to DSPM by the growth algorithm are also discussed. Data from an experimental DSPM network and software simulation of larger DSPM-type networks are used to examine the inherent limitation on growth time by the growth algorithm and the relationship of growth time to network size and topology.

Abbott, L. W.↗
NASA NTRS1993

Isomorphic routing on a toroidal mesh

We study a routing problem that arises on SIMD parallel architectures whose communication network forms a toroidal mesh. We assume there exists a set of k message descriptors (xi, yi), where (xi, yi) indicates that the ith message's recipient is offset from its sender by xi hops in one mesh dimension, and yi hops in the other. Every processor has k messages to send, and all processors use the same set of message routing descriptors. The SIMD constraint implies that at any routing step, every processor is actively routing messages with the same descriptors as any other processor. We call this isomorphic routing. Our objective is to find the isomorphic routing schedule with least makespan. We consider a number of variations on the problem, yielding complexity results from O(k) to NP-complete. Most of our results follow after we transform the problem into a scheduling problem, where it is related to other well-known scheduling problems.

Mao, Weizhen↗
DOE OSTI2025

Axion Mass Prediction from Adaptive Mesh Refinement Cosmological Lattice Simulations

The quantum chromodynamics (QCD) axion arises as the pseudo-Goldstone mode of a spontaneously broken Abelian Peccei-Quinn (PQ) symmetry. If the scale of PQ symmetry breaking occurs below the inflationary reheat temperature and the domain wall number is unity, then there is a unique axion mass that gives the observed dark matter (DM) abundance. Computing this mass has been the subject of intensive numerical simulations for decades since the mass prediction informs laboratory experiments. Axion strings develop below the PQ symmetry-breaking temperature, and as the string network evolves, it emits axions that go on to become the DM. A key ingredient in the axion mass prediction is the spectral index of axion radiation emitted by the axion strings. We compute this index in this Letter using the most precise and accurate large-scale simulations to date of the axion-string network leveraging adaptive mesh refinement to achieve the precision that would, otherwise, require a static lattice with 262,144 3 lattice sites. We find a scale-invariant axion radiation spectrum to within 1% precision and find no evidence that the spectral index of radiation evolves with time. Accounting for axion production from strings prior to the QCD phase transition leads us to predict that the axion mass should be approximately 𝑚 𝑎 ∈ (45, 65) μ ⁢eV. However, we provide preliminary evidence that axions are produced in greater quantities from the string-domain-wall network collapse during the QCD phase transition, potentially increasing the mass prediction to as much as 300 μ ⁢eV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1998

GPS Monitoring of Surface Change During and Following the Fortuitous Occurrence of the M(sub w) = 7.3 Landers Earthquake in our Network

Accomplishments: (1) Continues GPS monitoring of surface change during and following the fortuitous occurrence of the M(sub w) = 7.3 Landers earthquake in our network, in order to characterize earthquake dynamics and accelerated activity of related faults as far as 100's of kilometers along strike. (2) Integrates the geodetic constraints into consistent kinematic descriptions of the deformation field that can in turn be used to characterize the processes that drive geodynamics, including seismic cycle dynamics. In 1991, we installed and occupied a high precision GPS geodetic network to measure transform-related deformation that is partitioned from the Pacific - North America plate boundary northeastward through the Mojave Desert, via the Eastern California shear zone to the Walker Lane. The onset of the M(sub w) = 7.3 June 28, 1992, Landers, California, earthquake sequence within this network poses unique opportunities for continued monitoring of regional surface deformation related to the culmination of a major seismic cycle, characterization of the dynamic behavior of continental lithosphere during the seismic sequence, and post-seismic transient deformation. During the last year, we have reprocessed all three previous epochs for which JPL fiducial free point positioning products available and are queued for the remaining needed products, completed two field campaigns monitoring approx. 20 sites (October 1995 and September 1996), begun modeling by development of a finite element mesh based on network station locations, and developed manuscripts dealing with both the Landers-related transient deformation at the latitude of Lone Pine and the velocity field of the whole experiment. We are currently deploying a 1997 observation campaign (June 1997). We use GPS geodetic studies to characterize deformation in the Mojave Desert region and related structural domains to the north, and geophysical modeling of lithospheric behavior. The modeling is constrained by our existing and continued GPS measurements, which will provide much needed data on far-field strain accumulation across the region and on the deformational response of continental lithosphere during and following a large earthquake, forming the basis for kinematic and dynamic modeling of secular and seismic-cycle deformation. GPS geodesy affords both regional coverage and high precision that uniquely bear on these problems.

Miller, M. Meghan↗
NASA NTRS1993

A module concept for a cable-mesh deployable antenna

This paper describes the design, manufacture, and deployment tests of a modular mesh deployable antenna. Reaction forces and moments created by a mesh and cable network are estimated using CASA. Deployment analysis is carried out using DADS. Three types of deployable antenna modules are developed and fabricated. Their design approach and deployment characteristics are also presented. Ground deployment tests are performed to verify design criteria.

Meguro, Akira↗
DOE OSTI2024

Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

This is the conference paper accompanying an oral presentation “Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects” at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. Carbon capture and storage (CCS) technology is critical for mitigating climate change but requires effective subsurface reservoir management to ensure safe containment of injected CO2. Accurate predictions of reservoir pressure and saturation are essential for assessing long-term CCS performance. Traditional numerical simulations, while effective, are computationally intensive, time-consuming, and constrained by data discretization. Previous work has shown the effectiveness of MeshGraphNets (MGN), a graph-based machine learning framework, as an innovative alternative for predicting reservoir behavior. MGN leverages graph neural networks (GNNs) and mesh representations to model complex geological formations, offering superior adaptability across different discretizations and reservoir configurations. Classic MGN implementations utilize an autoregressive technique to predict future behavior based on current predictions, but this technique is hampered by error accumulation over time. To enhance the model accuracy in time-series predictions, this study implemented a multi-step rollout strategy that integrates autoregressive predictions during training to stabilize prediction of saturation over time. Using the Illinois Basin – Decatur Project (IBDP) dataset, comprising 100 simulations of CO2 injection, pressure, and saturation changes, the framework demonstrated its ability to learn spatial dependencies and temporal dynamics. With inputs including permeabilities, porosities, and injection rates, MGN accurately predicted CO2 plume evolution over time, even with limited training data. Moreover, the addition of a multi-step rollout procedure during training improved the ability of MGN to predict stably over time by ~15%. This research positions MGN, enhanced with multi-step rollout capabilities, as a robust and efficient tool for CCS applications. It advances the field by enabling precise, computationally efficient predictions of reservoir behavior, providing a foundation for the broader adoption of machine learning frameworks in CCS and other geoscience domains.

Holcomb, Paul↗