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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Principles of robust innate immune response to viral infections: a multiplex network analysis

The human innate immune response, particularly the type-I interferon (IFN) response, is highly robust and effective first line of defense against virus invasion. IFN molecules are produced and secreted frominfected cells upon virus infection and recognition. They then act as signaling/communicationmolecules to activate an antiviral response in neighboring cells so that those cells become refractory to infection. Previous experimental studies have identified the detailed molecular mechanisms for the IFN signaling and response. However, the principles underlying how host cells use IFN to communicate with each other to collectively and robustly halt an infection is not understood. Here we take a multiplex network modeling approach to provide a theoretical framework to identify key factors that determine the effectiveness of the IFN response against virus infection of a host. In this approach, we consider the virus spread among host cells and the interferon signaling to protect host cells as a competition process on a two-layer multiplex network. We focused on two types of network topology, i.e., the Erdős-Rényi (ER) network and the Geometric Random (GR) network, which represent the scenarios when infection of cells is mostly well mixed (e.g., in the blood) and when infection is spatially segregated (e.g., in tissues), respectively.We show that in general, the IFN response works effectively to stop viral infection when virus infection spreads spatially (a most likely scenario for initial virus infection of a host at the peripheral tissue). Importantly, we show that the effectiveness of the IFN response is robust against large variations in the distance of IFN diffusion as long as IFNs diffuse faster than viruses and they can effectively induce antiviral responses in susceptible host cells. This suggests that the effectiveness of the IFN response is insensitive to the specific arrangement of host cells in peripheral tissues. Thus, our work provides a quantitative explanation of why the IFN response can serve an effective and robust response in different tissue types to a wide range of viral infections of a host.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation

For the cooperative operation of networked microgrids, a distributed energy management considering network operational objectives and constraints is proposed in this work. Considering various ownership and privacy requirements of microgrids, utility directly interfaced distributed energy resources (DERs) and demand response, a distributed optimization is proposed for obtaining optimal network operational objectives with constraints satisfied through iteratively updated price signals. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve the formulated distributed optimization. The proposed distributed energy management provides microgrids, utility-directly interfaced DERs and responsive demands the opportunity of contributing to better network operational objectives while preserving their privacy and autonomy. Results of numerical simulation using a networked microgrids system consisting of several microgrids, utility directly interfaced DERs and responsive demands validate the soundness and accuracy of the proposed distributed energy management. The proposed method is further tested on a practical two-microgrid system located in Adjuntas, Puerto Rico, and the applicability of the proposed strategy is validated through hardware-in-the-loop (HIL) testing.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Networked Microgrid Energy Management Considering Ownership and Control Structures: A Comparison

With the growing deployment of microgrids, networked microgrids have emerged for their additional advantages of economy, reliability and resilience by coordinating the operation of multiple microgrids. As microgrids are operated with different ownership, objectives and functionalities, the formed networked microgrids show characteristics of mixed ownership, inconsistent objectives and various functionalities. To enable the coordinated operation of networked microgrids, three control structures, i.e., centralized, distributed and decentralized, have been constructed in the literature. However, the data sharing enabling these different paradigms and the resulted value propositions are not well defined, leading to poor resource management and resilience, etc. To solve this issue, a complete comparison of networked microgrid energy management under centralized, distributed and decentralized structures are performed. As a novel contribution, the required minimum data exchange of networked microgrid energy management under three control structures are identified, respectively. The value propositions are calculated and compared against each other by the results of case studies.

control structures↗
DOE OSTI2021

Automated Detection of Instability-Inducing Channel Geometry Transitions in Saint-Venant Simulation of Large-Scale River Networks

A new sweep-search algorithm (SSA) is developed and tested to identify the channel geometry transitions responsible for numerical convergence failure in a Saint-Venant equation (SVE) simulation of a large-scale open-channel network. Numerical instabilities are known to occur at “sharp” transitions in discrete geometry, but the identification of problem locations has been a matter of modeler’s art and a roadblock to implementing large-scale SVE simulations. The new method implements techniques from graph theory applied to a steady-state 1D shallow-water equation solver to recursively examine the numerical stability of each flowpath through the channel network. The SSA is validated with a short river reach and tested by the simulation of ten complete river systems of the Texas–Gulf Coast region by using the extreme hydrological conditions recorded during hurricane Harvey. The SSA successfully identified the problematic channel sections in all tested river systems. Subsequent modification of the problem sections allowed stable solution by an unsteady SVE numerical solver. The new SSA approach permits automated and consistent identification of problem channel geometry in large open-channel network data sets, which is necessary to effectively apply the fully dynamic Saint-Venant equations to large-scale river networks or for city-wide stormwater networks.

54 ENVIRONMENTAL SCIENCES↗
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↗
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

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 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↗