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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How does your gyroid grow? A mesoatomic perspective on supramolecular, soft matter network crystals

We propose and describe a framework to understand the structure of supramolecular network crystals formed in soft matter in terms of mesoatomic building blocks, collective groupings of amphiphilic molecules that play a role analogous to atomic or molecular subunits of hard matter crystals. While the concept of mesoatoms is intuitive and widely invoked in crystalline arrangements of spherelike or cylinderlike (micellelike) domains, analogous notions of natural and physically meaningful building blocks of triply periodic network (TPN) crystals, like the double-gyroid or double-diamond structures are obscured by the complex, bicontinuous domain shapes and intercatenated topologies of the double networks. Focusing on the specific example of diblock copolymer melts, we propose generic rules for decomposing TPN crystals into a unique set of mesoatomic building blocks. Based on physically motivated principles, the combination of symmetries and topologies of these structures point to mesoatomic elements associated with the nodal connections, leading to mesoatomic volumes that are nonconvex and bound by smoothly curved faces, unlike the more familiar Voronoi polyhedral shapes associated with spherelike and cylinderlike mesoatoms. We analyze the shapes of these mesoatoms, their internal structure, and importantly, their local packing with neighbor mesoatomic units. Importantly, we hypothesize that mesoatoms are kinetically favored intermediate structures whose local shapes and packing template network crystal assembly on long time scales. We propose and study a minimal energetic model of mesoatom assembly for three different cubic double-network crystals, based on local shape packing, which predicts a detailed picture for kinetics of intercatenation and surface growth. Based on these analyses, we discuss several possible extensions and elaborations of the mesoatomic description of supramolecular soft matter network crystals, most notably the implications of mesoatomic malleability, a feature that distinguishes soft matter from hard matter crystals. We describe experimental observations of malleable mesoatomic units in the precursor sponge phase as well as in ordered cubic networks and suggest possibilities for observing mesoatoms in primordial, precrystalline states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1990

Neurocomputing

The present work is intended to give technologists, research scientists, and mathematicians a graduate-level overview of the field of neurocomputing. After exploring the relationship of this field to general neuroscience, attention is given to neural network building blocks, the self-adaptation equations of learning laws, the data-transformation structures of associative networks, and the multilayer data-transformation structures of mapping networks. Also treated are the neurocomputing frontiers of spatiotemporal, stochastic, and hierarchical networks, 'neurosoftware', the creation of neural network-based computers, and neurocomputing applications in sensor processing, control, and data analysis.

Hecht-Nielsen, Robert↗
NASA NTRS2008

Cup Cylindrical Waveguide Antenna

The cup cylindrical waveguide antenna (CCWA) is a short backfire microwave antenna capable of simultaneously supporting the transmission or reception of two distinct signals having opposite circular polarizations. Short backfire antennas are widely used in mobile/satellite communications, tracking, telemetry, and wireless local area networks because of their compactness and excellent radiation characteristics. A typical prior short backfire antenna contains a half-wavelength dipole excitation element for linear polarization or crossed half-wavelength dipole elements for circular polarization. In order to achieve simultaneous dual circular polarization, it would be necessary to integrate, into the antenna feed structure, a network of hybrid components, which would introduce significant losses. The CCWA embodies an alternate approach that entails relatively low losses and affords the additional advantage of compactness. The CCWA includes a circular cylindrical cup, a circular disk subreflector, and a circular waveguide that serves as the excitation element. The components that make it possible to obtain simultaneous dual circular polarization are integrated into the circular waveguide. These components are a sixpost polarizer and an orthomode transducer (OMT) with two orthogonal coaxial ports. The overall length of the OMT and polarizer (for the nominal middle design frequency of 2.25 GHz) is about 11 in. (approximately equal to 28 cm), whereas the length of a commercially available OMT and polarizer for the same frequency is about 32 in. (approximately equal to 81 cm).

Acosta, Roberto J.↗
NASA NTRS1990

The role of data management in discipline-independent data visualization

The common data format (CDF) is described in terms of its support applications for the database management of visualization systems. The CDF is a self-describing data abstraction technique for the storage and manipulation of multidimensional data that are based on block structures. The discipline-independent approach is designed to manage, manipulate, archive, display, and analyze data, and can be applied to heterogeneous equipment communicating different data structures over networks. An improved CDF version incorporates a hyperplane access allowing random aggregate access to subdimensional blocks within a multidimensional variable. The visualization pipeline is also discussed, which controls the flow of data and permits the visualization of different classes of data representation techniques. The system is found to accommodate a large variety of scientific data structures and large disk-based data sets.

Treinish, Lloyd A.↗
DOE OSTI2025

Investigating the effect of screen-printed structured graphite electrodes with low tortuosity for high-capacity and fast-charging lithium-ion batteries

A flexible screen-printed graphite electrode was fabricated as an anode for developing fast-charging lithium-ion batteries with low tortuosity. A homogenous anode ink was prepared by mixing graphite as the active material, carbon black (C45) as the conductive additive, and polyvinylidene fluoride (PVDF) as the binder in N-Methyl-2-pyrrolidone (NMP) solvent. The ink was deposited on a flexible copper foil via a stainless-steel screen consisting of an array of pores, that act as secondary pore networks (SPNs), using the screen-printing process. Lithium-ion battery half-cells were assembled using the printed graphite anode, lithium metal foil as the counter electrode, and 1.2 M lithium hexafluorophosphate (LiPF 6 ) in ethyl carbonate: ethyl methyl carbonate (EC: EMC = 3:7) as the electrolyte. The effect of SPNs on the cell performance was investigated by performing formation, rate and cycling tests on the assembled cells, at different C-rates. It was observed that the cells consisting of SPNs with a pore size of 100 μm and edge-to-edge distance of 100 μm between the pores exhibited significantly higher specific capacities of 168 and 129 mAh/g when compared to reference cells without SPNs, which had capacities of 120 and 85 mAh/g, at high C-rates of 4 C and 6 C, respectively. The cells with SPNs also demonstrated excellent cycling performance with ~ 95% capacity retention after 100 cycles at 2 C.

Fast charging lithium-ion battery↗
DOE OSTI2022

Power System Waveform Classification Using Time-Frequency and CNN

Many modern reclosers and circuit breakers have microprocessor relays that record waveforms of system events. In some cases, utilities may record a half-a-dozen event captures for every event. This is thousands of events per year. The industry needs faster, more automated, more conclusive, and easy-to-use systems that can process massive amounts of event recordings without extensive input/support from power system engineers. To address the need for a commercially viable solution that can classify waveform data, energies were directed to develop a universal neural network (NN) structure (deep learning algorithm) that works for a wide variety of system event types. The structure that showed the most promise was one that included the use of spectrograms. The technique has shown positive results in audio engineering, particularly with respect to speech recognition. A waveform signature could be treated as a spoken word like audio waveforms for specific things such as “YES” or “UP”. No two people produce the exact same waveform when speaking each of these words, but audio processing algorithms based on spectrograms and convolutional neural networks (CNN) can still distinguish the word regardless of the speaker. No two circuits produce the exact same waveform for a given event, but the NN can be trained to classify the event type regardless of the circuit or location on the circuit. A Power System Neural Network (PSNN) has been developed to use a CNN to classify events within waveform data for power systems. The waveform is converted to an array of values by way of spectrograms and interpreted as an image. This image is passed into the CNN. The test results on independent simulated test and validation datasets show greater than 99% accuracy. While the results thus far are based on simulated data, the performance of the PSNN is very promising and should work for a wide variety of power system conditions of interest. Ultimately, much of the custom code and tools used today and much of the manual effort expended today may be automated using this PSNN.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Systems and methods for quality of service (QoS) based management of bottlenecks and flows in networks

Techniques based on the Theory of Bottleneck Ordering can reveal the bottleneck structure of a network, and the Theory of Flow ordering can take advantage of the revealed bottleneck structure to manage and configure network flows so as to improve the overall network performance. These two techniques provide insights into the inherent topological properties of a network at least in three areas: (1) identification of the regions of influence of each bottleneck; (2) the order in which bottlenecks (and flows traversing them) may converge to their steady state transmission rates in distributed congestion control algorithms; and (3) the design of optimized traffic engineering policies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Network analysis uncovers the communication structure of SARS-CoV-2 spike protein identifying sites for immunogen design

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered myriad efforts to understand the structure and dynamics of this complex pathogen. The spike glycoprotein of SARS-CoV-2 is a significant target for immunogens as it is the means by which the virus enters human cells, while simultaneously sporting mutations responsible for immune escape. These functional and escape processes are regulated by complex molecular-level interactions. Our study presents quantitative insights on domain and residue contributions to allosteric communication, immune evasion, and local- and global-level control of functions through the derivation of a weighted graph representation from all-atom MD simulations. Focusing on the ancestral form and the D614G-variant, we provide evidence of the utility of our approach by guiding the selection of a mutation that alters the spike’s stability. Taken together, the network approach serves as a valuable tool to evaluate communication “hot-spots” in proteins to guide design of stable immunogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Digital Twin User Guide for Chelan County Public Utility District

This user manual offers a comprehensive guide for developing a Digital twin (DT) of a Kaplan turbine at Chelan County Public Utility District (Chelan PUD) using neural networks. As variable renewable generation expands, hydropower units must operate with optimal efficiency and stability. For Kaplan machines, this flexibility is achieved through coordinated control of guide vane (wicket gates) opening and runner blade pitch, which amplifies the plant’s inherent nonlinear behavior and challenges traditional physics-only modeling. The efficiency of the Kaplan turbine varies with different combinations of the guide vans (wicket gate) opening and the blade angle. Each guide van opening and blade angle has a corresponding highest efficiency point, forming a cam relationship that represents the optimal combination.The discharge of a hydraulic turbine is controlled by the opening angle of the guide vans. Therefore, for each value of head, there is a certain guide van opening and blade angle that corresponds to the highest efficiency. For a given head, different combinations of the guide van opening and blade angle have different efficiencies. Therefore, coordinate cam curves are used to describe the relationship between the wicket gate opening and blade angle with different water head. To address these challenges, the manual details a data-driven modeling and learning workflow centered on structured neural networks. The approach is designed to forecast critical operational variables—discharge flow, net head, penstock (or scroll-case) pressure, and generator electrical outputs—by leveraging real-time inputs such as the generator power control setpoint, exciter field current and field voltage, together with hydromechanical commands (e.g., gate position and, when available, runner blade-pitch angle). The neural models are trained and validated on operational data from a Kaplan unit operated by Chelan PUD, demonstrating that the structured NN architecture can learn the coupled gate–blade–electrical dynamics. The result is a robust DT that improves situational awareness and supports data-informed decision-making for Chelan PUD’s Kaplan turbine operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Comparative genomic analysis of thermophilic fungi reveals convergent evolutionary adaptations and gene losses

Thermophily is a trait scattered across the fungal tree of life, with its highest prevalence within three fungal families (Chaetomiaceae, Thermoascaceae, and Trichocomaceae), as well as some members of the phylum Mucoromycota. We examined 37 thermophilic and thermotolerant species and 42 mesophilic species for this study and identified thermophily as the ancestral state of all three prominent families of thermophilic fungi. Thermophilic fungal genomes were found to encode various thermostable enzymes, including carbohydrate-active enzymes such as endoxylanases, which are useful for many industrial applications. At the same time, the overall gene counts, especially in gene families responsible for microbial defense such as secondary metabolism, are reduced in thermophiles compared to mesophiles. We also found a reduction in the core genome size of thermophiles in both the Chaetomiaceae family and the Eurotiomycetes class. The Gene Ontology terms lost in thermophilic fungi include primary metabolism, transporters, UV response, and O-methyltransferases. Comparative genomics analysis also revealed higher GC content in the third base of codons (GC3) and a lower effective number of codons in fungal thermophiles than in both thermotolerant and mesophilic fungi. Furthermore, using the Support Vector Machine classifier, we identified several Pfam domains capable of discriminating between genomes of thermophiles and mesophiles with 94% accuracy. Using AlphaFold2 to predict protein structures of endoxylanases (GH10), we built a similarity network based on the structures. We found that the number of disulfide bonds appears important for protein structure, and the network clusters based on protein structures correlate with the optimal activity temperature. Thus, comparative genomics offers new insights into the biology, adaptation, and evolutionary history of thermophilic fungi while providing a parts list for bioengineering applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

QuTree: A tree tensor network package

Here we present QuTree, a C++ library for tree tensor network approaches. QuTree provides class structures for tensors, tensor trees, and related linear algebra functions that facilitate the fast development of tree tensor network approaches such as the multilayer multiconfigurational time-dependent Hartree approach or the density matrix renormalization group approach and its various extensions. We investigate the efficiency of relevant tensor and tensor network operations and show that the overhead for managing the network structure is negligible, even in cases with a million leaves and small tensors. QuTree focuses on providing simple, high-level routines while retaining easy access to the backend to facilitate novel developments. We demonstrate the capabilities of the package by computing the eigenstates of coupled harmonic oscillator Hamiltonians and performing random circuit simulations on a virtual quantum computer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Improved lithospheric attenuation structure of the Arabian Peninsula through the use of national network data

We characterize the attenuation structure of the Arabian Peninsula through the measurement of regional phase amplitudes. High-resolution is achieved by combining stations from global networks with national network data through the cooperative effort of several countries in the region, including Saudi Arabia, Oman, Iraq, and Kuwait. The result is an improved attenuation model of the crust and upper mantle for a broad frequency band that extends from 0.5 to 10 Hz. The observed attenuation is in accordance with various elements of earth structure, including plate boundary type, style of tectonism, thermo-tectonic age, and temperature. Emerging features from the model include details in the structure along the Red Sea, and improved imaging of the southern Arabian Peninsula extending north from the Gulf of Aden. Finally, the resulting attenuation model can be employed for better magnitude estimates, in isolating tectonic and structural features, and in characterizing strong ground motion in the Arabian Peninsula.

58 GEOSCIENCES↗
NASA NTRS2019

Tilt-Insensitive Optical Seismometer for the Lunar Geophysical Network

Understanding the structure and composition of the lunar interior has been designated a high priority for lunar science in two National Academies studies and the LEAG Roadmap. To that end, the Planetary Decadal Survey recommends the Lunar Geophysical Network concept as a prioritized mission under New Frontiers 5.

Weber, R.↗
DOE OSTI2023

Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets

Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called network Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD relationships facilitate the modeling of traffic congestion at a regional level and have led to the development of various regional traffic control strategies. However, real street networks are not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) promote higher mobility than others (e.g., local roads). Here, this paper examines how the presence of hierarchical roadway structures may potentially cause non-unimodal patterns in a network's MFD. These are observed using three types of tools: analytical models of simple network structures, simulations of various idealized roadway networks, and empirical data. The impacts of street hierarchy depend on how vehicles use different roadway types to move within the network; i.e., their routing strategy. The findings suggest that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles, which is closest to what would be observed in realistic situations. Such patterns are contrary to what is traditionally assumed in most MFD-based modeling frameworks. However, the unimodal and concave MFD should be expected under system optimal routing conditions that maximize network productivity for a given traffic state.

42 ENGINEERING↗
DOE OSTI2022

Consolidation of composite cathodes with NCM and sulfide solid-state electrolytes by hot pressing for all-solid-state Li metal batteries

In this study, hot pressing was evaluated as a method of cell fabrication to increase the energy density of next-generation all-solid-state batteries with NCM active material and sulfide solid-state electrolyte. Hot pressing involves consolidating glassy sulfide electrolyte by the application of pressure at a temperature above the electrolyte’s glass transition temperature. Typically, cell stacks are formed at room temperature and retain 15–30% porosity that limits cell energy density. On the other hand, the porosity of hot-pressed cell stacks is reduced to less than 10%. The electrochemical function of hot-pressed cathode composites was assessed as a function of active material and solid-state electrolyte compositions. Specifically, LiNi 0.85 Co 0.10 Mn 0.05 O 2 and LiNi 0.6 Co 0.2 Mn 0.2 O 2 were studied in combination with either glassy Li 7 P 3 S 11 , glassy Li 3 PS 4 , or β-Li 3 PS 4 solid-state electrolytes. Cathode composites composed of LiNi 0.6 Co 0.2 Mn 0.2 O 2 and Li 3 PS 4 maintained the best function after hot pressing at 200 °C and 370 MPa for 10 min. It was found that LiNi 0.6 Co 0.2 Mn 0.2 O 2 ’s resistance to microcracking and the inherent stability of Li 3 PS 4 ’s fully de-networked local structure are critical to maintain good electrochemical function after hot pressing. Furthermore, the results of this study show that hot pressing reduces porosity in the cathode composite and confirms the feasibility of cathode support of consolidated, reinforced glass separators.

25 ENERGY STORAGE↗
DOE OSTI2024

Developing Benzodithiophene-Free donor polymer for 19.36% efficiency Green-Solvent-Processable organic solar cells

Here, in this work, a newly benzodithiophene-free D-A polymer donor, named PDTP-BDD, was developed for realizing green-solvent processed high-performance OSCs. By bridging two electron-rich unit of dithieno[3,2-b:2',3'-d]pyridin-5(4H)-one (DTP) and electron-deficient benzo[1,2-c:4,5c']dithiophene-4,8-dione (BDD) with thiophene units, PDTP-BDD possesses high absorption in the short wavelength range and a deep HOMO energy level. The rigid building blocks also make PDTP-BDD has strong aggregation and poor solubility in common halogen-free solvents (such as o-xylene) at room temperature, but it is readily dissolved and disaggregated at high temperature (120 °C). After cooling down to a lower temperature (60 °C), PDTP-BDD self-assembled and pre-aggregated slowly in the solution at a long time. By employing a delayed processing strategy in the layer-by-layer processed OSCs (LbL-OSCs), an optimized fibril network of the underling layer was realized, enabling the permeation of acceptor into the donor network. The optimized PDTP-BDD/L8-BO-based LbL-OSCs realized a high PCE of 18.42 %. By adding a small amount of D18 to further optimize the PDTP-BDD fibril network, an impressive PCE of 19.36 % was achieved finally in the resulting ternary LbL-OSCs, which is the highest value for OSCs processed by halogen-free solvents.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The interpenetration polymer network in a cement paste–waterborne epoxy system

The formation of the interpenetrating polymer network (IPN) structure within the cement-polymer system has been revealed by experiments from 2 dimensional to 3-dimensional scales. However, the microstructure design and performance prediction of IPN as a function of specific C-S-H/polymer components or ingredients' parameters e.g. water to cement ratios (w/c), polymer to cement ratios (p/c), monomer components, degree of polymerization (DP), etc. is by far not available. Here we developed a mesoscale model for IPN visualization based on the Flory-Huggins interaction theory which was applied to cement science for the first time. All the ingredients in the micro-structure were considered as soluble beads with rational sizes based on their properties obtained by molecular dynamic methods. The interaction parameters of each bead were then determined based on their element ratios and chemical backbones. The model was validated with the waterborne epoxy-cement material (WECM) in which a novel waterborne epoxy resin (WEP) was prepared and impregnated. The verification results on the 2D-3D scale show that the developed model predicts the WECM's IPN structures by rule and line concerning DP, w/c, and p/c in the mixture. The C-S-H beads were progressively scaled in size which alters the C-S-H texture to have completely different dissolution characteristics. Beads of diameter ~6 Å are more unstable and soluble which enable them to form a continuous phase in water or a composite structure with WEP. In contrast, beads with diameters larger than 10 Å have different properties with stronger nucleation effects. The results also suggest the impregnation content of WEP in cement-based material should be limited to 10% vol. to prevent the polymer IPN from decomposing into discrete clusters. The application of Flory-Huggins theory in cement-based composites demonstrates great potential in performance prediction and microstructure design.

36 MATERIALS SCIENCE↗