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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Cross-linked structure of network evolution

We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

A Gaussian graphical model approach to climate networks

Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

In this paper, we examine the behaviour of basic autocatalytic feedback modules involving a species catalyzing its own production, either directly or indirectly. We first perform a systematic study of the autocatalytic feedback module in isolation, examining the effect of different factors, showing how this module is capable of exhibiting monostable threshold and bistable switch-like behaviour. We then study the behaviour of this module embedded in different kinds of basic networks including (essentially) irreversible cycles, open and closed reversible chains, and networks with additional feedback. We study the behaviour of the networks deterministically and also stochastically, using simulations, analytical work, and bifurcation analysis. We find that (i) there are significant differences between the behaviour of this module in isolation and in a network: thresholds may be altered or destroyed and bistability may be destroyed or even induced, even when the ambient network is simple. The global characteristics and topology of this network and the position of the module in the ambient network can play important and unexpected roles. (ii) There can be important differences between the deterministic and stochastic dynamics of the module embedded in networks, which may be accentuated by the ambient network. This provides new insights into the functioning of such enzymatic modules individually and as part of networks, with relevance to other enzymatic signalling modules as well.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Evaluating conducting network based transparent electrodes from geometrical considerations

Conducting nanowire networks have been developed as viable alternative to existing indium tin oxide based transparent electrode (TE). The nature of electrical conduction and process optimization for electrodes have gained much from the theoretical models based on percolation transport using Monte Carlo approach and applying Kirchhoff's law on individual junctions and loops. While most of the literature work pertaining to theoretical analysis is focussed on networks obtained from conducting rods (mostly considering only junction resistance), hardly any attention has been paid to those made using template based methods, wherein the structure of network is neither similar to network obtained from conducting rods nor similar to well periodic geometry. Here, we have attempted an analytical treatment based on geometrical arguments and applied image analysis on practical networks to gain deeper insight into conducting networked structure particularly in relation to sheet resistance and transmittance. Many literature examples reporting networks with straight or curvilinear wires with distributions in wire width and length have been analysed by treating the networks as two dimensional graphs and evaluating the sheet resistance based on wire density and wire width. The sheet resistance values from our analysis compare well with the experimental values. Our analysis on various examples has revealed that low sheet resistance is achieved with high wire density and compactness with straight rather than curvilinear wires and with narrower wire width distribution. Similarly, higher transmittance for given sheet resistance is possible with narrower wire width but of higher thickness, minimal curvilinearity, and maximum connectivity. For the purpose of evaluating active fraction of the network, the algorithm was made to distinguish and quantify current carrying backbone regions as against regions containing only dangling or isolated wires. The treatment can be helpful in predicting the properties of a network simply from image analysis and will be helpful in improvisation and comparison of various TEs and better understanding of electrical percolation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Effects of Beta-Amyloid on Resting State Functional Connectivity Within and Between Networks Reflect Known Patterns of Regional Vulnerability

In Alzheimer's disease (AD), Beta-amyloid (Aβ) deposition is one of the hallmarks. However, it is also present in some cognitively normal elderly adults and may represent a preclinical disease state. While AD patients exhibit disrupted functional connectivity (FC) both within and between resting-state networks, studies of preclinical cases have focused primarily on the default mode network (DMN). The extent to which Aβ-related effects occur outside of the DMN and between networks remains unclear. In the present study, we examine how within- and between-network FC are related to both global and regional Aβ deposition as measured by [ 11 C]PIB-PET in 92 cognitively normal older people. We found that within-network FC changes occurred in multiple networks, including the DMN. Changes of between-network FC were also apparent, suggesting that regions maintaining connections to multiple networks may be particularly susceptible to Aβ-induced alterations. Cortical regions showing altered FC clustered in parietal and temporal cortex, areas known to be susceptible to AD pathology. These results likely represent a mix of local network disruption, compensatory reorganization, and impaired control network function. They indicate the presence of Aβ-related dysfunction of neural systems in cognitively normal people well before these areas become hypometabolic with the onset of cognitive decline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Sign Problem in Tensor-Network Contraction

We investigate how the computational difficulty of contracting tensor networks depends on the sign structure of the tensor entries. Using results from computational complexity, we observe that the approximate contraction of tensor networks with only positive entries has lower computational complexity as compared to tensor networks with general real or complex entries. This raises the question of how this transition in computational complexity manifests itself in the hardness of different tensor-network-contraction schemes. We pursue this question by studying random tensor networks with varying bias toward positive entries. First, we consider contraction via Monte Carlo sampling and find that the transition from hard to easy occurs when the tensor entries become predominantly positive; this can be understood as a tensor-network manifestation of the well-known negative-sign problem in quantum Monte Carlo. Second, we analyze the commonly used contraction based on boundary tensor networks. The performance of this scheme is governed by the number of correlations in contiguous parts of the tensor network (which by analogy can be thought of as entanglement). Remarkably, we find that the transition from hard to easy—i.e., from a volume-law to a boundary-law scaling of entanglement—already occurs for a slight bias of the tensor entries toward a positive mean, scaling inversely with the bond dimension D , and thus the problem becomes easy the earlier the larger D occurs. This is in contrast both to expectations and to the behavior found in Monte Carlo contraction, where the hardness at fixed bias increases with the bond dimension. To provide insight into this early breakdown of computational hardness and the accompanying entanglement transition, we construct an effective classical statistical-mechanical model that predicts a transition at a bias of the tensor entries of 1 / D , confirming our observations. We conclude by investigating the computational difficulty of computing expectation values of tensor-network wave functions (projected entangled-pair states, PEPSs) and find that in this setting, the complexity of entanglement-based contraction always remains low. We explain this by providing a local transformation that maps PEPS expectation values to a positive-valued tensor network. This not only provides insight into the origin of the observed boundary-law entanglement scaling but also suggests new approaches toward PEPS contraction based on positive decompositions. Published by the American Physical Society 2025

Chen, Jielun (ORCID:0000000178411545)↗
DOE OSTI2014

SWIM: A Switch Manager for Datacenter Networks

As modern datacenter networks (DCNs) grow to support hundreds of thousands of servers and beyond, managing network equipment - such as routers, firewalls, and load balancers - becomes increasingly complex. Network attributes such as IP address allocations and BGP neighbor relations are scattered among various network engineering groups, which makes troubleshooting the network a cumbersome task. In addition, network vendor diversity leads to an explosion of vendor-specific management systems or single-use automation scripts, limiting network scalability while increasing the time required to perform management tasks. In this article, the authors propose a unified network management system, Switch Manager (SWIM), to cope with the growth by standardizing the language for describing network attributes and unifying the interface for executing management actions on the network equipment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

IEEE 342 Node Low Voltage Networked Test System

The IEEE Distribution Test Feeders provide a benchmark for new algorithms to the distribution analyses community. The low voltage network test feeder represents a moderate size urban system that is unbalanced and highly networked. This is the first distribution test feeder developed by the IEEE that contains unbalanced networked components. The 342 node Low Voltage Networked Test System includes many elements that may be found in a networked system: multiple 13.2kV primary feeders, network protectors, a 120/208V grid network, and multiple 277/480V spot networks. This paper presents a brief review of the history of low voltage networks and how they evolved into the modern systems. This paper will then present a description of the 342 Node IEEE Low Voltage Network Test System and power flow results.

Schneider, Kevin P.↗
DOE OSTI2023

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes.

16S rRNA↗
DOE OSTI2021

Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification

Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity model, which suppresses noise in the gradients and mitigates local minima. Furthermore, the velocity model generated by neural networks is input to the same partial differential equation (PDE) solvers used in conventional FWI. The gradients of the neural networks and PDEs are calculated using automatic differentiation, which back propagates gradients through the acoustic PDEs and neural network layers to update the weights of the generative neural network. Experiments on 1D velocity models, the Marmousi model, and the 2004 BP model determine that NNFWI can mitigate local minima, especially for imaging high-contrast features such as salt bodies, and it significantly improves the inversion in the presence of noise. Adding dropout layers to the neural network model also allows analyzing the uncertainty of the inversion results through Monte Carlo dropout. NNFWI opens a new pathway to combine deep learning and FWI for exploiting the characteristics of deep neural networks and the high accuracy of PDE solvers. Because NNFWI does not require extra training data and optimization loops, it provides an attractive and straightforward alternative to conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2013

RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks

The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need to capture uncertainty and risk using appropriate probabilistic, statistical and optimization concepts. The main difficulty arising in addressing these issues is the dramatic increase in the computational complexity of the resulting optimization problems. This project studied novel models and methodologies for risk-averse network optimization- specifically, network design, network flows and cluster detection problems under uncertainty. The approach taken was to incorporate a quantitative risk measure known as conditional value-at-risk that is widely used in financial applications. This approach presents a viable alternate modeling and optimization framework to chance-constrained optimization and mean-variance optimization, one that also facilitates the detection of risk-averse solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Anomaly Detection in Dynamic Networks

Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the communication counts. In a sequential analysis, anomalous behavior is then identified from outlying behavior with respect to the fitted predictive probability models. Seasonality is again incorporated into the model and is treated as a changepoint model on the transition probabilities of a discrete time Markov process. Second stage analytics are then developed which combine anomalous edges to identify anomalous substructures in the network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

2022 Peer Review Project Summary: Advanced Protection for Microgrids and DER in Secondary Networks and Meshed Distribution Systems

Although there are increasing numbers of distributed energy resources (DERs) and microgrids being deployed, current IEEE and utility standards generally strictly limit their interconnection inside secondary networks. Secondary networks are low-voltage meshed (non-radial) distribution systems that create redundancy in the path from the main grid source to each load. This redundancy provides a high level of immunity to disruptions in the distribution system, and thus extremely high reliability of electric power service. There are two main types of secondary networks, called grid and spot secondary networks, both of which are used worldwide. In the future, primary networks in distribution systems that might include looped or meshed distribution systems at the primary-voltage (mediumvoltage) level may also become common as a means for improving distribution reliability and resilience. The objective of this multiyear project is to increase the adoption of microgrids in secondary networks and meshed distribution systems by developing novel protection schemes that allow for safe reliable operation of DERs in secondary networks. We will address these challenges by working with the appropriate stakeholders of secondary network operators, protection vendors, and standards committee. The outcomes of this project include: a) development and/or demonstration of candidate methods for enabling protection of secondary networks containing high levels of DER; b) development of modeling and testing tools for protection systems designed for use with secondary networks including DERs; and c) development of new industrial partnerships to facilitate widespread results dissemination and eventual commercialization of results as appropriate.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Game-Theoretic Strategies for Quantum-Conventional Network Infrastructures

Fundamentally and practically, quantum networks and conventional networks are inextricably tied, since the basic quantum protocols such as teleportation require both networks and the conventional network fiber is also used for the quantum network. A Recursive System of Systems (RSOS) model is developed for quantum-conventional (QC) networks by modeling the correlations at various levels based on the failure and attack modes of quantum, conventional and hybrid components and the propagative effects across QC boundaries. A game-theoretic formulation is developed to capture the cost-benefit trade-offs of the provider in defending against component attacks, using sum-form utility functions. By applying the Nash Equilibrium results, the conditions and sensitivity functions of the survivalprobabilities of a QC network at different levels are derived using the strong dependencies between quantum and conventional infrastructures. The results provide insights into the dependenciesbetween conventional and quantum networks, including cross QC boundary effects in terms of disruption impact of conventional networks on quantum networks, and vice versa.

Rao, Nageswara↗
DOE OSTI2017

Photometric Properties of Network and Faculae Derived from HMI Data Compensated for Scattered Light

We report on the photometric properties of faculae and network, as observed in full-disk, scattered-light-corrected images from the Helioseismic Magnetic Imager. We use a Lucy–Richardson deconvolution routine that corrects an image in less than one second. Faculae are distinguished from network through proximity to active regions. This is the first report that full-disk observations, including center-to-limb variations, reproduce the photometric properties of faculae and network observed previously only in sub-arcsecond-resolution; small field-of-view studies, i.e. that network, as defined by distance from active regions, exhibit higher photometric contrasts. Specifically, for magnetic flux values larger than approximately 300 G, the network is brighter than faculae and the contrast differences increase toward the limb, where the network contrast is about twice the facular one. For lower magnetic flux values, network appear darker than faculae. Contrary to reports from previous full-disk observations, we also found that network exhibits a higher center-to-limb variation. Our results are in agreement with reports from simulations that indicate magnetic flux alone is a poor proxy of the photometric properties of magnetic features. We estimate that the contribution of faculae and network to Total Solar Irradiance variability of the current Cycle 24 is overestimated by at least 11%, due to the photometric properties of network and faculae not being recognized as different. This estimate is specific to the method employed in this study to reconstruct irradiance variations, so caution should be paid when extending it to other techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Representativeness assessment of the pan-Arctic eddy covariance site network and optimized future enhancements

Abstract. Large changes in the Arctic carbon balance are expected as warming linked to climate change threatens to destabilize ancient permafrost carbon stocks. The eddy covariance (EC) method is an established technique to quantify net losses and gains of carbon between the biosphere and atmosphere at high spatiotemporal resolution. Over the past decades, a growing network of terrestrial EC tower sites has been established across the Arctic, but a comprehensive assessment of the network's representativeness within the heterogeneous Arctic region is still lacking. This creates additional uncertainties when integrating flux data across sites, for example when upscaling fluxes to constrain pan-Arctic carbon budgets and changes therein. This study provides an inventory of Arctic (here > = 60∘ N) EC sites, which has also been made available online (https://cosima.nceas.ucsb.edu/carbon-flux-sites/, last access: 25 January 2022). Our database currently comprises 120 EC sites, but only 83 are listed as active, and just 25 of these active sites remain operational throughout the winter. To map the representativeness of this EC network, we evaluated the similarity between environmental conditions observed at the tower locations and those within the larger Arctic study domain based on 18 bioclimatic and edaphic variables. This allows us to assess a general level of similarity between ecosystem conditions within the domain, while not necessarily reflecting changes in greenhouse gas flux rates directly. We define two metrics based on this representativeness score: one that measures whether a location is represented by an EC tower with similar characteristics (ER1) and a second for which we assess if a minimum level of representation for statistically rigorous extrapolation is met (ER4). We find that while half of the domain is represented by at least one tower, only a third has enough towers in similar locations to allow reliable extrapolation. When we consider methane measurements or year-round (including wintertime) measurements, the values drop to about 1/5 and 1/10 of the domain, respectively. With the majority of sites located in Fennoscandia and Alaska, these regions were assigned the highest level of network representativeness, while large parts of Siberia and patches of Canada were classified as underrepresented. Across the Arctic, mountainous regions were particularly poorly represented by the current EC observation network. We tested three different strategies to identify new site locations or upgrades of existing sites that optimally enhance the representativeness of the current EC network. While 15 new sites can improve the representativeness of the pan-Arctic network by 20 %, upgrading as few as 10 existing sites to capture methane fluxes or remain active during wintertime can improve their respective ER1 network coverage by 28 % to 33 %. This targeted network improvement could be shown to be clearly superior to an unguided selection of new sites, therefore leading to substantial improvements in network coverage based on relatively small investments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Reliable Communication Models in Interdependent Critical Infrastructure Networks

Modern critical infrastructure networks are becoming increasingly interdependent where the failures in one network may cascade to other dependent networks, causing severe widespread national-scale failures. A number of previous efforts have been made to analyze the resiliency and robustness of interdependent networks based on different models. However, communication network, which plays an important role in today's infrastructures to detect and handle failures, has attracted little attention in the interdependency studies, and no previous models have captured enough practical features in the critical infrastructure networks. In this paper, we study the interdependencies between communication network and other kinds of critical infrastructure networks with an aim to identify vulnerable components and design resilient communication networks. We propose several interdependency models that systematically capture various features and dynamics of failures spreading in critical infrastructure networks. We also discuss several research challenges in building reliable communication solutions to handle failures in these models.

Lee, Sangkeun↗
DOE OSTI2023

Combustion control using spiking neural networks

A system that controls a combustion engine stores network vectors in a memory that represent diverse and distinct spiking neural networks. The system decodes the network vectors and trains and evaluates the spiking neural networks. The system duplicates selected network vectors and crosses-over the duplicated network vectors that represent modified spiking neural networks. The system mutates the crossed-over duplicated network vectors by randomly modifying one or more portions of the crossing-over duplicated network vectors. The system meter exhaust gas into an intake manifold when an engine temperature exceeds a threshold, an engine load exceeds a threshold, an engine's rotation-per-minute rate exceeds a threshold, and a fuel flow exceeds a threshold. The system modifies fuel flow into an engine's combustion chamber on a cycle-to-cycle basis by the trained spiking neural network.

Puente, Brian P. Maldonado↗