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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Supramolecular meso -Trick: Ambidextrous Mirror Symmetry Breaking in a Liquid Crystalline Network with Tetragonal Symmetry

Bicontinuous and multicontinuous network phases are among nature's most complex structures in soft matter systems. Here, a chiral bicontinuous tetragonal phase is reported as a new stable liquid crystalline intermediate phase at the transition between two cubic phases, the achiral double gyroid and the chiral triple network cubic phase with an I 23 space group, both formed by dynamic networks of helices. The mirror symmetry of the double gyroid, representing a meso -structure of two enantiomorphic networks, is broken at the transition to this tetragonal phase by retaining uniform helicity only along one network while losing it along the other one. This leads to a conglomerate of enantiomorphic tetragonal space groups, P 4 1 2 1 2 and P 4 3 2 1 2. Phase structures and chirality were analyzed by small-angle X-ray scattering (SAXS), grazing-incidence small-angle X-ray scattering (GISAXS), resonant soft X-ray scattering (RSoXS) at the carbon K-edge, and model-dependent SAXS/RSoXS simulation. In conclusion, our findings not only lead to a new bicontinuous network-type three-dimensional mesophase but also reveal a mechanism of mirror symmetry breaking in soft matter by partial meso -structure racemization at the transition from enantiophilic to enantiophobic interhelical self-assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Distributary Channel Networks as Moving Boundaries: Causes and Morphodynamic Effects

In this study, we propose an exploratory model to describe the morphodynamics of distributary channel network growth on river deltas. The interface between deep channels and the shallow, unchannelized delta front deposits is modeled as a moving boundary. Steady flow over the unchannelized delta front is friction dominated and modeled by Laplace's equation. Shear stress along the network boundary produces nonlinear erosion rates at the interface, causing the boundary to move and network elements (channels and branches) to form. The model was run for boundary conditions resembling the Wax Lake Delta in coastal Louisiana, 20 parameterizations of sediment transport, and 3 parameterizations of discharge. In each case, the model produced a complex channel network with channel number, width, bifurcation angle, and channel shape depending on the sediment transport formula. For reasonable sediment transport parameters and gradually increasing water discharge, the model produced network characteristics and progradation rates similar to theWax Lake Delta. Lastly, this suggests that the model contains the processes responsible for network growth, despite its abstract formulation.

58 GEOSCIENCES↗
DOE OSTI2020

Spatiotemporal Variability in Transport and Reactive Processes Across a First- to Fifth-Order Fluvial Network

Fluvial networks integrate and transport constituents from terrestrial and aquatic ecosystems. To date, most research on water quality dynamics has focused on process understanding at individual streams and, as a result, there is a lack of studies analyzing how small-scale, physical and biogeochemical drivers scale across fluvial networks. We performed tracer tests in five stream orders of the Jemez River continuum in New Mexico, USA, to quantify reach-scale hyporheic exchange during two different seasonal periods to address: how do hyporheic zone contributions to riverine processing change with space and time? And does the spatiotemporal variability of hyporheic exchange scale across fluvial networks? Combining conservative (i.e., bromide) and reactive (i.e., resazurin) tracer analyses with solute transport modeling, we found a dominance of reaction-limited transport conditions in space (for all stream orders) and time (two contrasting flow regimes). Moreover, we found a trend toward more reaction-limited conditions with increasing discharge across all stream orders. While our studied fluvial network did not consistently follow the expectations of decreasing hyporheic exchange contributions with increasing stream order, as it has been previously suggested from modeling studies, we found that processing rate coefficients and Damköhler numbers consistently decreased along the continuum with increasing discharge. Our findings suggest that knowledge transferability of hyporheic zone processing along fluvial networks may be possible if the scaling patterns that we found hold true in other catchments. Thus, more research on scaling patterns along fluvial networks is highly needed.

Gootman, Kaylyn S.↗
DOE OSTI2022

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗
DOE OSTI2022

Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network’s various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. Furthermore, these elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Three-State Majority-vote Model on Scale-Free Networks and the Unitary Relation for Critical Exponents

Abstract We investigate the three-state majority-vote model for opinion dynamics on scale-free and regular networks. In this model, an individual selects an opinion equal to the opinion of the majority of its neighbors with probability 1 − q , and different to it with probability q . The parameter q is called the noise parameter of the model. We build a network of interactions where z neighbors are selected by each added site in the system, a preferential attachment network with degree distribution k − λ , where λ = 3 for a large number of nodes N . In this work, z is called the growth parameter. Using finite-size scaling analysis, we obtain that the critical exponents $$\beta /\bar{\nu }$$ β / ν ¯ and $$\gamma /\bar{\nu }$$ γ / ν ¯ associated with the magnetization and the susceptibility, respectively. Using Monte Carlo simulations, we calculate the critical noise parameter q c as a function of z for the scale-free networks and obtain the phase diagram of the model. We find that the critical exponents add up to unity when using a special volumetric scaling, regardless of the dimension of the network of interactions. We verify this result by obtaining the critical noise and the critical exponents for the two and three-state majority-vote model on cubic lattice networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Difference of operation mechanisms in SWNTs network FETs studied via scanning gate microscopy

Field effect transistors (FETs) whose channel is composed of a network of single wall carbon nanotubes (SWNTs) have been studied to investigate the mechanism of the device operation via scanning gate microscopy (SGM) at room temperature. We observed different SGM response in networks of SWNTs either synthesized by CoMoCAT process or semiconducting enriched by density gradient ultracentrifuge process. In the former case, SGM response was observed at specific inter-tube junctions suggesting a Schottky junction formed with semiconducting and metallic SWNTs in the network. In contrast, multiple concentric rings in the SGM response are observed within the tubes in a network of the latter SWNTs suggesting a possibility of quantum mechanical transport at room-temperature. Different type of SGM responses are confirmed in the two kinds of SWNTs networks, nevertheless such active positions would likely have an important role in the FET operation mechanism in each network.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems

We investigate a generalised version of the recently proposed ordinal partition time series to network transformation algorithm. First, we introduce a fixed time lag for the elements of each partition that is selected using techniques from traditional time delay embedding. The resulting partitions define regions in the embedding phase space that are mapped to nodes in the network space. Edges are allocated between nodes based on temporal succession thus creating a Markov chain representation of the time series. We then apply this new transformation algorithm to time series generated by the Rössler system and find that periodic dynamics translate to ring structures whereas chaotic time series translate to band or tube-like structures—thereby indicating that our algorithm generates networks whose structure is sensitive to system dynamics. Furthermore, we demonstrate that simple network measures including the mean out degree and variance of out degrees can track changes in the dynamical behaviour in a manner comparable to the largest Lyapunov exponent. We also apply the same analysis to experimental time series generated by a diode resonator circuit and show that the network size, mean shortest path length, and network diameter are highly sensitive to the interior crisis captured in this particular data set.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Self-assembled Ag nanoparticle network passivated by a nano-sized ZnO layer for transparent and flexible film heaters

We investigated a self-assembled Ag nanoparticle network electrode passivated by a nano-sized ZnO layer for use in high-performance transparent and flexible film heaters (TFFHs). The low temperature atomic layer deposition of a nano-sized ZnO layer effectively filled the uncovered area of Ag network and improved the current spreading in the self-assembled Ag network without a change in the sheet resistance and optical transmittance as well as mechanical flexibility. The time-temperature profiles and heat distribution analysis demonstrate that the performance of the TFTH with the ZnO/Ag network is superior to that of a TFFH with Ag nanowire electrodes. In addition, the TFTHs with ZnO/Ag network exhibited better stability than the TFFH with a bare Ag network due to the effective current spreading through the nano-sized ZnO layer.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stability of Kuramoto networks subject to large and small fluctuations from heterogeneous and spatially correlated noise

Oscillatory networks subjected to noise are broadly used to model physical and technological systems. Due to their nonlinear coupling, such networks typically have multiple stable and unstable states that a network might visit due to noise. In this article, we focus on the assessment of fluctuations resulting from heterogeneous and spatially correlated noise inputs on Kuramoto model networks. We evaluate the typical, small fluctuations near synchronized states and connect the network variance to the overlap between stable modes of synchronization and the input noise covariance. Going beyond small to large fluctuations, we introduce the indicator mode approximation that projects the dynamics onto a single amplitude dimension. Such an approximation allows for estimating rates of fluctuations to saddle instabilities, resulting in phase slips between connected oscillators. Statistics for both regimes are quantified in terms of effective noise amplitudes that are compared and contrasted for several noise models. In conclusion, bridging the gap between small and large fluctuations, we show that a larger network variance does not necessarily lead to higher rates of large fluctuations.

42 ENGINEERING↗
DOE OSTI2017

Punishment diminishes the benefits of network reciprocity in social dilemma experiments

Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism—costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

SOLAR CYCLE VARIATION OF THE INTER-NETWORK MAGNETIC FIELD

The solar inter-network magnetic field is the weakest component of solar magnetism, but it contributes most of the solar surface magnetic flux. The study of its origin has been constrained by the inadequate tempospatial resolution and sensitivity of polarization observations. With dramatic advances in spatial resolution and detecting sensitivity, the solar spectropolarimetry provided by the Solar Optical Telescope on board Hinode in an interval from the solar minimum to maximum of cycle 24 opens an unprecedented opportunity to study the cyclic behavior of the solar inter-network magnetic field. More than 1000 Hinode magnetograms observed from 2007 January to 2014 August are selected in the study. It has been found that there is a very slight correlation between sunspot number and magnetic field at the inter-network flux spectrum. From solar minimum to maximum of cycle 24, the flux density of the solar inter-network field is invariant, at 10 ± 1 G. The observations suggest that the inter-network magnetic field does not arise from flux diffusion or flux recycling of solar active regions, thereby indicating the existence of a local small-scale dynamo. Combining the full-disk magnetograms observed by the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager in the same period, we find that the area ratio of the inter-network region to the full disk of the Sun apparently decreases from solar minimum to maximum but always exceeds 60%, even in the phase of solar maximum.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

DOES THE VARIATION OF THE SOLAR INTRA-NETWORK HORIZONTAL FIELD FOLLOW THE SUNSPOT CYCLE?

The ubiquitousness of the solar inter-network horizontal magnetic field has been revealed by space-borne observations with high spatial resolution and polarization sensitivity. However, no consensus has been achieved on the origin of the horizontal field among solar physicists. For a better understanding, in this study, we analyze the cyclic variation of the inter-network horizontal field by using the spectro-polarimeter observations provided by the Solar Optical Telescope on board Hinode, covering the interval from 2008 April to 2015 February. The method of wavelength integration is adopted to achieve a high signal-to-noise ratio. It is found that from 2008 to 2015 the inter-network horizontal field does not vary when solar activity increases, and the average flux density of the inter-network horizontal field is 87 ± 1 G, In addition, the imbalance between horizontal and vertical fields also keeps invariant within the scope of deviation, i.e., 8.7 ± 0.5, from the solar minimum to maximum of solar cycle 24. This result confirms that the inter-network horizontal field is independent of the sunspot cycle. The revelation favors the idea that a local dynamo is creating and maintaining the solar inter-network horizontal field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Characterizing gravitational wave detector networks: from A # to cosmic explorer

Gravitational-wave observations by the laser interferometer gravitational-wave observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied the science capabilities of a network of LIGO detectors when they reach their best possible sensitivity, called A # , given the infrastructure in which they exist and a new generation of observatories that are factor of 10 to 100 times more sensitive (depending on the frequency), in particular a pair of L-shaped cosmic explorer (CE) observatories (one 40 km and one 20 km arm length) in the US and the triangular Einstein telescope with 10 km arms in Europe. We use a set of science metrics derived from the top priorities of several funding agencies to characterize the science capabilities of different networks. The presence of one or two A # observatories in a network containing two or one next generation observatories, respectively, will provide good localization capabilities for facilitating multimessenger astronomy (MMA) and precision measurement of the Hubble parameter. Two CE observatories are indispensable for achieving precise localization of binary neutron star events, facilitating detection of electromagnetic counterparts and transforming MMA. Their combined operation is even more important in the detection and localization of high-redshift sources, such as binary neutron stars, beyond the star-formation peak, and primordial black hole mergers, which may occur roughly 100 million years after the Big Bang. The addition of the Einstein Telescope to a network of two CE observatories is critical for accomplishing all the identified science metrics including the nuclear equation of state, cosmological parameters, the growth of black holes through cosmic history, but also make new discoveries such as the presence of dark matter within or around neutron stars and black holes, continuous gravitational waves from rotating neutron stars, transient signals from supernovae, and the production of stellar-mass black holes in the early Universe. For most metrics the triple network of next generation terrestrial observatories are a factor 100 better than what can be accomplished by a network of three A # observatories.

Einstein telescope↗
DOE OSTI2013

NON-POTENTIAL FIELDS IN THE QUIET SUN NETWORK: EXTREME-ULTRAVIOLET AND MAGNETIC FOOTPOINT OBSERVATIONS

The quiet Sun (QS) magnetic network is known to contain dynamics which are indicative of non-potential fields. Non-potential magnetic fields forming ''S-shaped'' loop arcades can lead to the breakdown of static activity and have only been observed in high temperature X-ray coronal structures—some of which show eruptive behavior. Thus, analysis of this type of atmospheric structuring has been restricted to large-scale coronal fields. Here we provide the first identification of non-potential loop arcades exclusive to the QS supergranulation network. High-resolution Atmospheric Imaging Assembly data from the Solar Dynamics Observatory have allowed for the first observations of fine-scale ''S-shaped'' loop arcades spanning the network. We have investigated the magnetic footpoint flux evolution of these arcades from Heliospheric and Magnetic Imager data and find evidence of evolving footpoint flux imbalances accompanying the formation of these non-potential fields. The existence of such non-potentiality confirms that magnetic field dynamics leading to the build up of helicity exist at small scales. QS non-potentiality also suggests a self-similar formation process between the QS network and high temperature corona and the existence of self-organized criticality (SOC) in the form of loop-pair reconnection and helicity dissipation. We argue that this type of behavior could lead to eruptive forms of SOC as seen in active region (AR) and X-ray sigmoids if sufficient free magnetic energy is available. QS magnetic network dynamics may be considered as a coronal proxy at supergranular scales, and events confined to the network can even mimic those in coronal ARs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

NuGraph2 with explainability: post-hoc explanations for geometric neural network predictions

With the growing popularity of artificial intelligence (AI) used for scientific applications, the ability of attribute a result to a reasoning process from the network is in high demand for robust scientific generalizations to hold. In this work we aim to motivate the need for and demonstrate the use of post-hoc explainability methods when applied to AI methods used in scientific applications. To this end, we introduce explainability add-ons to the existing graph neural network (GNN) for neutrino tagging, NuGraph2. The explanations take the form of a suite of techniques examining the output of the network (node classifications) and the edge connections between them, and probing of the latent space using novel general-purpose tools applied to this network. We show how none of these methods are singularly sufficient to show network ‘understanding’, but together can give insights into the processes used in classification. While these methods are tested on the NuGraph2 application, they can be applied to a broad range of networks, not limited to GNNs. The code for this work is publicly available on GitHub at https://github.com/voetberg/XNuGraph.

Voetberg, Margaret [Fermilab] (ORCID:0009000527154↗
DOE OSTI2021

Efficient Sampling of Complex Interdependent and Multiplex Networks

Efficient sampling of interdependent and multiplex infrastructure networks is critical for effectively applying failure and recovery algorithms in real-world settings, as well as to generate property-preserving reduced-order graph-based ensembles that address topological uncertainties. In this paper, we first explore the performance, i.e. the success in preserving graph properties, of graph sampling algorithms for interdependent and multiplex networks with synthetic and real-world graphs. We simulate sampling algorithms under different parameter settings. These settings include probabilistic graph generators, coupling patterns, and various performance metrics. Our results show that while Random Node and Random Walk sampling algorithms perform best for interdependent networks, Random Edge and Forest Fire sampling algorithms perform best for multiplex networks. Second, we propose and implement a novel similarity-based sampling algorithm for multiplex networks that samples only log(N) number of layers of an N-layer multiplex network while yielding computational savings with performance guarantees. Experimental results show that similarity sampling outperforms complete sampling of all layers while decreasing performance costs from a linear scale to a logarithmic one. Our results also indicate that similarity-based sampling outperforms complete sampling and random selection in nearly all scenarios when tested with real-world data.

Subasi, Omer↗
DOE OSTI2025

Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design

SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.

58 GEOSCIENCES↗