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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Supercritical fluids behave as complex networks

Abstract Supercritical fluids play a key role in environmental, geological, and celestial processes, and are of great importance to many scientific and engineering applications. They exhibit strong variations in thermodynamic response functions, which has been hypothesized to stem from the microstructural behavior. However, a direct connection between thermodynamic conditions and the microstructural behavior, as described by molecular clusters, remains an outstanding issue. By utilizing a first-principles-based criterion and self-similarity analysis, we identify energetically localized molecular clusters whose size distribution and connectivity exhibit self-similarity in the extended supercritical phase space. We show that the structural response of these clusters follows a complex network behavior whose dynamics arises from the energetics of isotropic molecular interactions. Furthermore, we demonstrate that a hidden variable network model can accurately describe the structural and dynamical response of supercritical fluids. These results highlight the need for constitutive models and provide a basis to relate the fluid microstructure to thermodynamic response functions.

42 ENGINEERING↗
NASA NTRSDate not supplied

Developing and Testing a Physics Guided Machine Learning NeuralNetwork to Predict Tonal Noise Emitted by a Propeller

Artificial neural networks offer a highly nonlinear and adaptive model for predicting complex interactions between input-output parameters. However, these networks require large datasets which often exceed practical considerations in modeling experimental results. To alleviate the dataset size requirement, a method known as physics guided machine learning has been applied to construct several neural networks for predicting propeller tonal noise in the time domain over a broad range of flight conditions. Three space-filling designs, namely, Latin-Hypercube, Sphere-Packing, and Grid-Space, were used to distribute points throughout the input parameter space encompassing nondimensional flight conditions and observer geometry. Each neural network’s performance was validated by conditions outside of the training set and compared to the Propeller Analysis System tool from the NASA Aircraft Noise Prediction Program. Compared to the Grid-Space input design, the Latin-Hypercube and the Sphere-Packing designs provided a better representation of the domain for training. Regarding the network archetype, a fully connected perceptron was found to outperform the partially connected perceptron in their ability to predict tonal noise for small datasets. The black-box nature of these neural networks was also explored to understand how the networks constructed the waveform and understand why some network designs produce better models.

Propeller noise↗
DOE OSTI2021

Learning intermolecular forces at liquid–vapor interfaces

By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. Here, we find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Advances in materials and current collecting networks for AMTEC electrodes

Electrode materials for the Alkali Metal Thermal to Electric Converter (AMTEC) play a significant role in the efficiency of the device. RhW and PtW alloys have been studied to determine the best performing material. While RhW electrodes typically have power densities somewhat lower than PtW electrodes, PtW performance is strongly influenced by the Pt/W ratio. The best performing Pt/W ratio is about 3.4. RhW electrodes sinter more slowly than PtW and are predicted to have operating lifetimes up to 40 years; PtW electrodes are predicted to have lifetimes up to 7 years. Interaction with the current collection network can significantly decrease lifetime by inducing metal migration and segregation and by accelerating the sintering rate.

Ryan, M. A.↗
DOE OSTI2023

Structure of a HIV-1 IN-Allosteric inhibitor complex at 2.93 Å resolution: Routes to inhibitor optimization

HIV integrase (IN) inserts viral DNA into the host genome and is the target of the strand transfer inhibitors (STIs), a class of small molecules currently in clinical use. Another potent class of antivirals is the allosteric inhibitors of integrase, or ALLINIs. ALLINIs promote IN aggregation by stabilizing an interaction between the catalytic core domain (CCD) and carboxy-terminal domain (CTD) that undermines viral particle formation in late replication. Ongoing challenges with inhibitor potency, toxicity, and viral resistance motivate research to understand their mechanism. Here, we report a 2.93 Å X-ray crystal structure of the minimal ternary complex between CCD , CTD , and the ALLINI BI-224436. This structure reveals an asymmetric ternary complex with a prominent network of π-mediated interactions that suggest specific avenues for future ALLINI development and optimization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1995

SPACEWAY: Providing affordable and versatile communication solutions

By the end of this decade, Hughes' SPACEWAY network will provide the first interactive 'bandwidth on demand' communication services for a variety of applications. High quality digital voice, interactive video, global access to multimedia databases, and transborder workgroup computing will make SPACEWAY an essential component of the computer-based workplace of the 21st century. With relatively few satellites to construct, insure, and launch -- plus extensive use of cost-effective, tightly focused spot beams on the world's most populated areas -- the high capacity SPACEWAY system can pass its significant cost savings onto its customers. The SPACEWAY network is different from other proposed global networks in that its geostationary orbit location makes it a truly market driven system: each satellite will make available extensive telecom services to hundreds of millions of people within the continuous view of that satellite, providing immediate capacity within a specific region of the world.

Fitzpatrick, E. J.↗
DOE OSTI2021

Study of interconnect errors, network congestion, and applications characteristics for throttle prediction on a large scale HPC system

Today’s High Performance Computing (HPC) systems contain thousand of nodes which work together to provide performance in the order of petaflops. The performance of these systems depends on various components like processors, memory, and interconnect. Among all, interconnect plays a major role as it glues together all the hardware components in an HPC system. A slow interconnect can impact a scientific application running on multiple processes severely as they rely on fast network messages to communicate and synchronize frequently. Unfortunately, the HPC community lacks a study that explores different interconnect errors, congestion events and applications characteristics on a large-scale HPC system. In our previous work, we process and analyze interconnect data of the Titan supercomputer to develop a thorough understanding of interconnects faults, errors, and congestion events. In this work, we first show how congestion events can impact application performance. We then investigate application characteristics interaction with interconnect errors and network congestion to predict applications encountering congestion with more than 90% accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Structural variability in Cu(I) and Ag(I) coordination polymers with a flexible dithione ligand: Synthesis, crystal structure, microbiological and theoretical studies

Two new compounds namely [Cu(SCN)(µ-L)]{sub n} (1) and ([Ag (µ{sub 2}-L)](ClO{sub 4})){sub n} (2) have been synthesized at room temperature by one-pot reactions between the 1,1-(1,4-butanediyl)bis(1,3-dihydro-3-methyl-1H-imidazole- 2-thione) (L) and appropriate copper(I) and silver(I) salts. These polymers have been characterized by single crystal X-ray diffraction, XRPD, TGA, elemental analysis, infrared spectroscopy, antibacterial activity and scanning probe microscopy studies. In the crystal structure of 1, copper atoms have a distorted trigonal planar geometry with a CuS{sub 2}N coordination environment. Each of the ligands in the structure of 1 acting as a bidentate S-bridging ligand to form a 1D chain structure. Additionally, the adjacent 1D chains are interconnected by the intermolecular C-H…S interactions to create a 2D network structure. In contrast to 1, in the cationic 3D structure of 2 each of the silver atoms exhibits an AgS{sub 4} tetrahedral geometry with 4-membered Ag{sub 2}S{sub 2} rings. In the structure of 2, the flexible ligand adopts two different conformations; gauche-anti-gauche and anti-anti-anti. The antibacterial studies of these polymers showed that polymer 2 is more potent antibacterial agent than 1. Scanning probe microscopy (SPM) study of the treated bacteria was carried out to investigate the structural changes cause by the interactions between the polymers and target bacteria. Theoretical study of polymer 1 investigated by the DFT calculations indicates that observed transitions at 266 nm and 302 nm in the UV–vis spectrum could be attributed to the π→π* and MLCT transitions, respectively. - Graphical abstract: Two new Cu(I) and Ag(I) coordination polymers have been have been synthesized by one-pot reactions. Copper complex has a 2D non-covalent structure, but silver compound is a 3D coordination compound. These compounds have effective antibacterial activity. - Highlights: • Cu(I) and Ag(I) based coordination polymers have different network structures. • Ag(I) polymer has more antibacterial activity than Cu(I) polymer. • DFT calculations of Cu(I) polymer has been investigated. • Cu(I) and Ag(I) polymers can destroy the structure of chromosomal and plasmid DNA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Hyperexpandable, self-healing macromolecular crystals with integrated polymer networks

Formation of condensed matter typically involves a trade-off between structural order and flexibility. As the extent and directionality of interactions between atomic or molecular components increase, materials generally become more ordered but less compliant and vice versa. Yet, high levels of structural order and flexibility need not necessarily be mutually exclusive: there are many biological (e.g., microtubules, flagella, viruses) and synthetic assemblies (e.g., dynamic molecular crystals and frameworks) that can undergo considerable structural transformations without losing their crystalline order and possess remarkable mechanical properties with utility in diverse applications such as selective sorption, separation, sensing, and mechanoactuation. Ultimately, however, the extent of structural changes by such flexible crystals and their elasticity are constrained by the necessity to maintain a continuous network of bonding interactions between the constituents of the lattice. Consequently, even the most dynamic porous materials tend to be brittle and isolated as microcrystalline powders,14 and flexible organic/inorganic molecular crystals cannot expand without fracturing. Owing to their rigidity, crystalline materials rarely display self-healing behavior. Here we report that macromolecular ferritin crystals with integrated hydrogel polymers can isotropically expand to 180% of their original dimensions and >500% of their original volume while retaining periodic order and faceted Wulff morphologies. Even after the separation of neighboring ferritin molecules by 50 Å upon lattice expansion, specific molecular contacts between them can be reformed upon lattice contraction, resulting in the recovery of atomic-level periodicity and the highest resolution ferritin structure reported. Dynamic bonding interactions between the hydrogel network and the ferritin molecules endow the crystals with the ability to resist fragmentation and self-heal efficiently, while the chemical tailorability of the ferritin molecules enables the creation of chemically and mechanically differentiated domains within single crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Three-dimensional modeling of hyphal fusion, branching, and nutrient transport in filamentous fungi

Fungi exhibit behaviors distinct from other microbes. Filamentous fungi grow by extending complex networks of branched filaments collectively referred to as the mycelium. These networks can expand over large distances and traverse low-nutrient areas by translocating nutrients through the filament network. This spatial characteristic makes filamentous fungi crucial for soil ecosystems, supporting stable microbial communities and promoting plant growth. However, simulating these behaviors is complex. The elongated nature of fungal compartments results in different mechanical interactions compared to the commonly modeled spherical bacteria. These detailed hyphal mechanics require specialized consideration and are often excluded from conventional fungal simulation packages. Additionally, the extensive fungal networks in nature demand computationally intensive simulations, necessitating high-performance algorithms. Therefore, realistic fungi simulations require specialized software. Here, we introduce a fungal modeling expansion to the high-performance biological modelling and interface exchange (bmx) software suite. bmx leverages adaptive mesh refinement in AMReX for chemical diffusion and incorporates a full mechanical model for bacterial cells, accelerated by GPUs. By extending bmx to model filamentous particles, we demonstrate the formation of complex filament networks through interactions like hyphal branching and fusion (anastomosis). We show that the networks produced match real-world fungal structures through various metrics. This work supports computational studies of fungal growth dynamics and can be adapted to investigate the growth of other filamentous structures in biology or materials science. The expanded-BMX package is open-sourced and is available online.

Cell mechanics↗
DOE OSTI2018

Water Dynamics in Polyacrylamide Hydrogels

Polymeric hydrogels have wide applications including electrophoresis, biocompatible materials, water superadsorbents, and contact lenses. The properties of hydrogels involve the poorly characterized molecular dynamics of water and solutes trapped within the three-dimensional cross-linked polymer networks. In this study, we apply ultrafast infrared two-dimensional infrared (2D IR) vibrational echo and polarization-selective pump-probe (PSPP) spectroscopies to investigate the ultrafast molecular dynamics of water and a small molecular anion solute, selenocyanate (SeCN - ), in polyacrylamide hydrogels. For all mass concentrations of polymer studied (5% and above), the hydrogen bonding network reorganization (spectral diffusion) dynamics and reorientation dynamics reported by both water and SeCN - solvated by water are significantly slower than in bulk water. As the polymer mass concentration increases, molecular dynamics in the hydrogels slow further. The magnitudes of the slowing, measured with both water and SeCN - , are similar. However, the entire hydrogen bonding network of water molecules appear to slow down as a single ensemble, without a difference between the core water population and the interface water population at the polymer-water surface. In contrast, the dissolved SeCN - do exhibit two-component dynamics, where the major component is assigned to the anions fully solvated in the confined water nanopools. Lastly, the slower component has a small amplitude which is correlated with the polymer mass concentration, and is assigned to adsorbed anions strongly interacting with the polymer fiber networks.

14 SOLAR ENERGY↗
DOE OSTI2015

A comparison of numerical simulations and analytical theory of the dynamics of interacting magnetic vortices

Magnetostatic interactions between vortices in closely spaced planar structures are important for applications including vortex-based magnonic crystals and spin torque oscillator networks. Analytical theories that include magnetostatic interaction effects have been proposed but have not yet been rigorously tested. Here, we compare micromagnetic simulations of the dynamics of magnetic vortices confined in three disks in an equilateral triangle configuration to analytical theories that include coupling. Micromagnetic simulations show that the magnetostatic coupling between the disks leads to splitting of the gyrotropic resonance into three modes and that the frequency splitting increases with decreasing separation. The temporal profiles of the magnetization depend on the vortex polarities and chiralities; however, the frequencies depend only on the polarity combinations and will fall into one of two categories: all polarities equal or one polarity opposite to the others, where the latter leads to a larger frequency splitting. Although the magnitude of the splitting observed in the simulations is larger than what is expected based on purely dipolar interactions, a simple analytical model that assumes dipole-dipole coupling captures the functional form of the frequency splitting and the motion patterns just as well as more complex models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Virtualized Network Control (VNC)

The focus of this project was on the development of a "Network Service Plane" as an abstraction model for the control and provisioning of multi-layer networks. The primary motivation for this work were the requirements of next generation networked applications which will need to access advanced networking as a first class resource at the same level as compute and storage resources. A new class of "Intelligent Network Services" were defined in order to facilitate the integration of advanced network services into application specific workflows. This new class of network services are intended to enable real-time interaction between the application co-scheduling algorithms and the network for the purposes of workflow planning, real-time resource availability identification, scheduling, and provisioning actions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Tuning counterion chemistry to reduce carrier localization in doped semiconducting carbon nanotube networks

Understanding and controlling the impact that electrostatic interactions have on the transport of injected charge carriers is important for the utilization of pi-conjugated semiconductors in opto-electronic applications. Here, we explore the impact of dopant chemical and electronic structure on the doping efficacy and charge carrier transport in semiconducting single-walled carbon nanotube (s-SWCNT) networks using molecular charge-transfer dopants based on functionalized icosahedral dodecaborane (DDB) clusters. Calculations indicate that localization of electron density on the DDB core reduces the coulombic interactions that contribute to hole localization in the s-SWCNTs, thereby improving charge carrier transport. The enhanced delocalization produces an increase in the electrical conductivity and thermopower at lower charge carrier densities, yielding enhanced thermoelectric transport and a thermoelectric power factor that surpasses the previous best in class for enriched s-SWCNT thin-film networks. This strategy can be applied broadly across pi-conjugated semiconductors to tune and enhance performance in a variety of energy harvesting devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Cryo-EM Visualization of Intermolecular π-Electron Interactions within π-Conjugated Peptidic Supramolecular Polymers

The self-assembly of “π-peptides” – molecules with π-electron cores substituted with two or more oligopeptide chains – brings organic electronic function into biologically relevant nanomaterials. π-Peptides assemble into fibrillar nanomaterials as driven by enthalpic peptide-based hydrogen bonding networks and pi-core-based quadrupolar interactions. A large body of spectroscopic, morphological and computational studies informs on the nature of the self-assembly process and the resulting nanostructures, but detailed structural information has remained elusive. Here, inspired by the recent use of cryogenic electron microscopy (cryo-EM) to provide high-resolution structures for synthetic peptide nanomaterials, we present here the use of cryo-EM to offer ca. 3 Å resolution of π-peptide nanomaterial assemblies, visualizing for the first time the nature of the intermolecular π-core electronic interactions responsible for energy transport through these supramolecular materials.

Group theory↗
DOE OSTI2024

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

Abstract In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy‐to‐use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Self-organization of network dynamics into local quantized states

Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of the Swift-Hohenberg continuum model—a minimal-ingredients model of nodal activation and interaction within a complex network—is able to produce a complex suite of localized patterns. Thus, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.

59 BASIC BIOLOGICAL SCIENCES↗