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At least 1,549 records · Page 86

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NASA NTRS2023

Space Networking Implementation for Lunar Operations

The High-Rate Delay Tolerant Networking (HDTN) project at NASA has developed a performance optimized and open-source Delay Tolerant Networking (DTN) implementation. The primary goal is to create a scalable networking solution to increase the scientific data return rate of space missions. To reach this goal, HDTN must span multiple edge cases in space networking by including tools and configurations to accommodate a wide range of space systems. Typically, HDTN evaluations are conducted on a laboratory emulation test bed, made up of hardware accelerated x86 based systems capable of data rates over 10 Gbps. HDTN must have an effective implementation process on a wide range of systems to increase the sustainability of the design. One important implementation option is with low-level embedded systems which could be used on small robotic missions. This paper details the implementation process, benchmark testing, and performance results of HDTN in multiple configurations on Raspberry Pi 4 devices. By implementing HDTN on a Raspberry Pi 4, a process for building HDTN onto ARM processors was developed and utilized to conduct benchmark tests in multiple network configurations, achieving a data rate performance exceeding 600 Mbps. Based on these results, HDTN proved to run on small ARM based systems with slight modifications to the build procedure. These results were then extended to evaluating an implementation of the HDTN software parsed across several Raspberry Pi 4 nodes. To test this capability, HDTN was configured in a simplified cut-through setup and distributed among multiple Raspberry Pi 4 processors. This distributed architecture was benchmark tested in a similar fashion to the testing of a singular HDTN implementation. The results from the benchmark testing are used to examine how these implementation options and capabilities can expand the use cases for DTN, and particularly with small robotic missions.

Space Networking↗

NASA NTRS2024

High-Rate Delay Tolerant Networking (HDTN) User Guide Version 1.3.0

Delay Tolerant Networking (DTN) has been identified as a key technology to enable and facilitate the development and growth of future space networks. Classically, space communications networks are collections of disparate links that are manually managed either point-to-point or use space relays. The accelerating accessibility of space enables a new scaling of space nodes, yet both the manual management of configurations and scheduling and the lack of structure connecting links precisely prohibit scaling. This challenge gives rise to newer and larger classes of communications needs that are met by DTN, which must overcome the disconnection, disruption, latency, and mobility featured in space communications systems. DTN joins the underlying links as an overlay, and can be made to communicate over any protocol stack. The core actions of DTN are store, carry, and forward, where data are stored instead of dropped if there is no immediately available outduct. It does this by taking the DTN unit of data, bundles, and providing necessary layers to adapt these bundles to the underlying transport protocols of choice; these are called convergence layers. DTN's Bundle Protocol (BP) can then be used on top of terrestrial protocol stacks, such as TCP/IP, as well as protocols for space, such as LTP/AOS, all in the same network. For emphasis it is noted that bundles can be of essentially any size, and hence this convergence to lower layers of choice is necessary. Existing DTN implementations have operated in constrained environments with limited resources, resulting in low data speeds. However, as various technologies have advanced, data transfer rates and efficiency have advanced, which has pushed the need for a DTN implementation for ground systems and for spacecraft that is performance-oriented in order to not impose an unnecessary bottleneck. High-rate Delay Tolerant Networking (HDTN) takes advantage of modern hardware platforms to substantially reduce latency and improve throughput compared to today’s DTN operations. The HDTN implementation maintains interoperability with existing deployments of DTN that conform to IETF RFCs 4838, 5050, and 9171. At the same time, HDTN defines a new data format better suited to higher-rate operation. It defines and adopts a massively parallel pipelined and message-oriented architecture, allowing the system to scale gracefully as its resources increase. HDTN’s architecture also supports hooks to replace various processing pipeline elements with specialized hardware accelerators. This offers improved Size, Weight, and Power (SWaP) characteristics while reducing development complexity and cost.

Delay Tolerant Networking↗

DOE OSTI2024

The dynamics and stratigraphy of distributary channel networks (Final Technical Report)

Sedimentary reservoirs are frequently composed of the deposits of distributary channel networks. These networks are associated with coarse sediments (and high permeability) within the channel network, and relatively fine sediments (and low permeability) outside the channel network. The shape of the network is complex, and a product of the environmental boundary conditions under which it grows (Figure 1). These systems control heterogeneity at small and large scales within sedimentary basins, and understanding this heterogeneity and the links between scales represents the important scientific frontier addressed by this project.

58 GEOSCIENCES↗

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↗

DOE OSTI2026

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating a closed-form predictive distribution over outputs informed by non-identically distributed training data. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example out-of-training-sample. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy—referred to as information sharing—that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

17 WIND ENERGY↗

DOE OSTI2025

SPIKANs: separable physics-informed Kolmogorov–Arnold networks

Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use multilayer perceptrons (MLPs) as their underlying architecture, recent advancements have explored alternative neural network structures. One such innovation is the Kolmogorov–Arnold Network (KAN), which has demonstrated benefits over traditional MLPs, including faster neural scaling and better interpretability. The application of KANs to physics-informed learning has led to the development of Physics-Informed KANs (PIKANs), enabling the use of KANs to solve PDEs. However, despite their advantages, KANs often suffer from slower training speeds, particularly in higher-dimensional problems where the number of collocation points grows exponentially with the dimensionality of the system. To address this challenge, we introduce Separable Physics-Informed Kolmogorov–Arnold Networks (SPIKANs). This novel architecture applies the principle of separation of variables to PIKANs, decomposing the problem such that each dimension is handled by an individual KAN. This approach drastically reduces the computational complexity of training without sacrificing accuracy, facilitating their application to higher-dimensional PDEs. Through a series of benchmark problems, we demonstrate the effectiveness of SPIKANs, showcasing their superior scalability and performance compared to PIKANs and highlighting their potential for solving complex, high-dimensional PDEs in scientific computing.

Kolmogorov-Arnold networks↗

DOE OSTI2023

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Deep Learning without Global Optimization by Random Fourier Neural Networks

Here we introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov chain Monte Carlo sampling procedure to iteratively train network layers, avoiding global and gradient-based optimization while maintaining error control. It consistently attains the theoretical approximation rate for residual networks with complex exponential activation functions, determined by network complexity. Additionally, it enables efficient learning of multiscale and high-frequency features, producing interpretable parameter distributions. Despite using sinusoidal basis functions, we do not observe Gibbs phenomena in approximating discontinuous target functions.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Network Emulation Testbed Capabilities for Prototyping Space DTN Software and Protocols

This paper delves into network emulation tools essential for evaluating and designing Delay Tolerant Networking (DTN) protocols in space and satellite networking. It surveys and assesses the capability of current testbeds to create realistic test environments crucial for developing and evaluating DTN protocols. Specifically, this study provides a comprehensive overview of key DTN protocol stacks and related network emulation platforms and a detailed exploration of NASA’s research facilities. Finally, the paper underscores the fundamental emulation capabilities and the importance of a standardized framework for scenario creation, highlighting its vital role in evaluating and advancing future emulation platforms for space DTN.

Network Emulation and Simulation↗

DOE OSTI2024

Uncovering the Network Modifier for Highly Disordered Amorphous Li‐Garnet Glass‐Ceramics

Abstract Highly disordered amorphous Li 7 La 3 Zr 2 O 12 (aLLZO) is a promising class of electrolyte separators and protective layers for hybrid or all‐solid‐state batteries due to its grain‐boundary‐free nature and wide electrochemical stability window. Unlike low‐entropy ionic glasses such as Li x PO y N z (LiPON), these medium‐entropy non‐Zachariasen aLLZO phases offer a higher number of stable structure arrangements over a wide range of tunable synthesis temperatures, providing the potential to tune the LBU‐Li + transport relation. It is revealed that lanthanum is the active “network modifier” for this new class of highly disordered Li + conductors, whereas zirconium and lithium serve as “network formers”. Specifically, within the solubility limit of La in aLLZO, increasing the La concentration can result in longer bond distances between the first nearest neighbors of Zr─O and La─O within the same local building unit (LBU) and the second nearest neighbors of Zr─La across two adjacent network‐former and network‐modifier LBUs, suggesting a more disordered medium‐ and long‐range order structure in LLZO. These findings open new avenues for future designs of amorphous Li + electrolytes and the selection of network‐modifier dopants. Moreover, the wide yet relatively low synthesis temperatures of these glass‐ceramics make them attractive candidates for low‐cost and more sustainable hybrid‐ or all‐solid‐state batteries for energy storage.

36 MATERIALS SCIENCE↗

DOE OSTI2017

A Highly Stretchable and Fatigue-Free Transparent Electrode Based on an In-Plane Buckled Au Nanotrough Network

Flexible transparent electrodes are an indispensable component of next-generation soft optoelectronics such as wearable electronics and electronic artificial skins (E-skins). Among the existing candidate materials, metal nanotrough networks exhibit optimal overall optoelectronic performance with impressive bendability. However, their further practical applications are hindered by their limited mechanical stretchability, which is highly desired in biointegrated systems. Here it is demonstrated that superior mechanical stretchability with tensile strains up to 300% can be achieved in Au nanotrough networks by introducing an in-plane sinusoidal wavy structure. For the first time it is shown that with a precisely tuned nanotrough geometry along with an optimized network configuration, the buckled Au nanotrough network can be repeatedly stretched to strains up to 120% for 100 000 cycles, exhibiting excellent fatigue performance. Such highly stretchable and fatigue-free (at a high tensile strain up to 100%) Au nanotrough networks present a competent sheet resistance (R sh ≈ 10 Ω sq -1 ) with high optical transparency (T = 91%), which are demonstrated to be highly compatible with human skins as conformal flexible transparent electrodes.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Intrinsic and Extrinsically Limited Thermoelectric Transport within Semiconducting Single-Walled Carbon Nanotube Networks

Doped networks of semiconducting single-walled carbon nanotubes (s-SWCNTs) have recently demonstrated high thermoelectric (TE) power factors and figures of merit. Efforts to further improve the TE performance of s-SWCNT networks hinge upon deeper understanding of the mechanisms underlying charge transport. This study explores the dependence of conductivity, thermopower, and resulting TE power factor on carrier density and temperature in s-SWCNT networks. Careful control of charge-carrier density illustrates a distinct transition between transport that is limited by energetic barriers between nanotube bundles to an 'intrinsic' regime where these barriers are small enough to reveal the intrinsic transport mechanism of the nanotubes. Transport is activated in the s-SWCNT networks, although a critical survey of the literature demonstrates that the activation energies in s-SWCNT networks are appreciably smaller than typical semiconducting polymers. At high conductivity, transport behavior is consistent with deformation potential scattering. The analysis demonstrates that mitigation of the 'extrinsic' limitations to transport (e.g., inter-nanotube junctions), and the concomitant reduction of conductivity activation energies, can lead to at least a doubling of the TE power factor. Further comparison to prototypical semiconducting polymers demonstrates that this strategy likely represents a general design principle for improving the TE performance of organic materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Carbodiimide‐Driven Toughening of Interpenetrated Polymer Networks

Abstract Recent work has demonstrated that temporary crosslinks in polymer networks generated by chemical “fuels” afford materials with large, transient changes in their mechanical properties. This can be accomplished in carboxylic‐acid‐functionalized polymer hydrogels using carbodiimides, which generate anhydride crosslinks with lifetimes on the order of minutes to hours. Here, the impact of the polymer network architecture on the mechanical properties of transiently crosslinked materials was explored. Single networks (SNs) were compared to interpenetrated networks (IPNs). Notably, semi‐IPN precursors that give IPNs on treatment with carbodiimide give much higher fracture energies (i.e., resistance to fracture) and superior resistance to compressive strain compared to other network architectures. A precursor semi‐IPN material featuring acrylic acid in only the free polymer chains yields, on treatment with carbodiimide, an IPN with a fracture energy of 2400 J/m 2 , a fourfold increase compared to an analogous semi‐IPN precursor that yields a SN. This resistance to fracture enables the formation of macroscopic complex cut patterns, even at high strain, underscoring the pivotal role of polymer architecture in mechanical performance.

Rajawasam, Chamoni W. H.↗

DOE OSTI2024

Carbodiimide‐Driven Toughening of Interpenetrated Polymer Networks

Rajawasam, Chamoni W. H.↗

DOE OSTI2022

Energy‐aware relay positioning in flying networks

Summary The ability to move and hover has made rotary‐wing unmanned aerial vehicles (UAVs) suitable platforms to act as flying communications relays (FCRs), aiming at providing on‐demand, temporary wireless connectivity when there is no network infrastructure available or a need to reinforce the capacity of existing networks. However, since UAVs rely on their on‐board batteries, which can be drained quickly, they typically need to land frequently for recharging or replacing them, limiting their endurance and the flying network availability. The problem is exacerbated when a single FCR UAV is used. The FCR UAV energy is used for two main tasks: Communications and propulsion. The literature has been focused on optimizing both the flying network performance and energy efficiency from the communications point of view, overlooking the energy spent for the UAV propulsion. Yet, the energy spent for communications is typically negligible when compared with the energy spent for the UAV propulsion. In this article, we propose energy‐aware relay positioning (EREP), an algorithm for positioning the FCR taking into account the energy spent for the UAV propulsion. Building upon the conclusion that hovering is not the most energy‐efficient state, EREP defines the trajectory and speed that minimize the energy spent by the FCR UAV on propulsion, without compromising in practice the quality of service offered by the flying network. The EREP algorithm is evaluated using simulations. The obtained results show gains up to 26% in the FCR UAV endurance for negligible throughput and delay degradation.

Rodrigues, Hugo↗

DOE OSTI2024

Imaging a solvent-swollen polymer gel network by open liquid transmission electron microscopy

Visualizing the network of a solvent-swollen polymer gel remains problematic. To address this challenge, open transmission electron microscopy (TEM) was applied to thin gel films permeated by a nonvolatile ionic liquid. The targeted physical gels were prepared by cooling concentrated solutions of poly(ethylene glycol) in 1-ethyl-3-methyl imidazolium ethyl sulfate [EMIM][EtSO 4 ]. During the cooling, gelation occurred by a frustrated crystallization of the dissolved polymer, leading to a percolated, solvent-permeated semicrystalline network in which nanoscale polymer crystals acted as crosslinks. Crystalline features ranging from ~5 to ~200 nm were observed, with the visible network strands dominantly consisting of long curvilinear crystallites of ~15–20 nm diameter. Nascent spherulites irregularly decorated the network, creating a complex structural hierarchy that complicated analyses. Lacking diffraction contrast, TEM did not visualize the many disordered, fully solvated PEG chains present in the voids between crystals. Finally, recognizing that a network's three dimensionality is ambiguous when assessed through two-dimensional microscopy projections, a small gel region was studied by TEM tomography, revealing a nearly isotropic three-dimensional arrangement of the curvilinear crystallites, which displayed remarkably uniform cylindrical cross sections.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Tailoring the mechanical properties of nanoparticle networks that encompass biomimetic catch bonds

Biological “catch” bonds display the distinctive attribute that the bond lifetime can increase under an applied force. Insertion of biomimetic catch bonds into hybrid materials could lead to composites that exhibit remarkable mechanical properties. We model the tensile behavior of polymer-grafted nanoparticle (PGN) networks interconnected by a mixture of catch bonds and conventional “slip” bonds, whose lifetimes decrease with force. We formulate a kinetic master equation that provides the complete probabilistic description of a system of two PGNs. The master equation is used to analyze the parameter space that determines the rupture behavior of the catch bonds in the two-particle system. We then utilize two exemplary sets of catch bond parameters in three-dimensional computer simulations of larger PGN networks under strain-controlled tensile deformation. We demonstrate that the strain at break and toughness of the networks can be altered by “tuning” the attributes of the catch bonds and varying the fraction of catch bonds in the network. We show that networks encompassing the catch bonds could exhibit a several-fold increase in the strain-at-break and toughness relative to those interconnected solely by the slip bonds. Our studies can provide valuable guidelines for tailoring the mechanical properties of novel, bio-inspired nanocomposites.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Lessons Learned on the Interface Between Quantum and Conventional Networking

The future Quantum Internet is expected to be based on a hybrid architecture with core quantum transport capabilities complemented by conventional networking. Practical and foundational considerations indicate the need for conventional control and data planes that (i) utilize extensive existing telecommunications fiber infrastructure, and (ii) provide parallel conventional data channels needed for quantum networking protocols. We propose a quantum-conventional network (QCN) harness to implement a new architecture to meet these requirements. The QCN control plane carries the control and management traffic, whereas its data plane handles the conventional and quantum data communications. We established a local area QCN connecting three quantum laboratories over dedicated fiber and conventional network connections. We describe considerations and tradeoffs for layering QCN functionalities, informed by our recent quantum entanglement distribution experiments conducted over this network.

Alshowkan, Muneer↗