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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet [1], outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Cognitive Networking↗
NASA NTRS2022

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗
DOE OSTI2017

Reliable Broadcast under Cascading Failures in Interdependent Networks

Reliable broadcast is an essential tool to disseminate information among a set of nodes in the presence of failures. We present a novel study of reliable broadcast in interdependent networks, in which the failures in one network may cascade to another network. In particular, we focus on the interdependency between the communication network and power grid network, where the power grid depends on the signals from the communication network for control and the communication network depends on the grid for power. In this paper, we build a resilient solution to handle crash failures in the communication network that may cause cascading failures and may even partition the network. In order to guarantee that all the correct nodes deliver the messages, we use soft links, which are inactive backup links to non-neighboring nodes that are only active when failures occur. At the core of our work is a fully distributed algorithm for the nodes to predict and collect the information of cascading failures so that soft links can be maintained to correct nodes prior to the failures. In the presence of failures, soft links are activated to guarantee message delivery and new soft links are built accordingly for long term robustness. Our evaluation results show that the algorithm achieves low packet drop rate and handles cascading failures with little overhead.

cascading failures↗
DOE OSTI2022

The Evolution of the Peridynamics Co-Authorship Network

We report peridynamics is a relatively new field in continuum mechanics that has developed over the past 20 years. This paper studies the evolution of collaborations in the field of peridynamics since its inception using social network analysis. For this purpose, we construct a network for each year from 2000 to 2019 describing co-authorship between scientists in peridynamics. In these networks, each node represents a scientist and each link connects two co-authoring scientists with a link weight representing the frequency and strength of their collaboration; each network as a whole can be thought of as a graph representation of the peridynamics community for the given year. By constructing a network for each year, we are able to analyze the evolution of the network in time and discuss the implications of this evolution for the peridynamics community. Our study demonstrates that the peridynamics community has been growing exponentially in size in recent years. Centrality metrics are also used to identify the most collaborative scientists in the community. Moreover, we compute link recommendations based on both elevating a scientist’s position in the network with respect to certain centrality metrics or closing structural holes in the network identified with persistent homology. We further extend the analysis to higher-order networks whose nodes represent groups of scientists in the community and whose links connect collaborating groups. In some sense, our work studies the past, present, and future of the peridynamics community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events

Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges in this problem space, we propose a novel policy-guided tri-level optimization model applied to a proof-of-concept case study with fuel distribution and transportation networks – encompassing one realistic network; one fictitious, yet realistic network; as well as networks drawn from three synthetic distributions. Mathematically, our approach takes the form of a defender-attacker-defender (DAD) model—a multi-agent tri-level optimization, comprised of a defender, attacker, and an operator acting in sequence. Here, in this study, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations. A notional attacker aims to hypothetically disrupt normal operations by reducing supply at the supply terminals, and the notional defender aims to identify best proxy defense policy options which include hardening supply terminals or allowing alternative distribution methods such as trucking reserve supplies. We solve our DAD formulation at a metropolitan scale and present practical defense policy insights against hypothetical compound hazards. We demonstrate the generalizability of our framework by presenting results for a realistic network; a fictitious, yet realistic network; as well as for three networks drawn from synthetic distributions. Additionally, we demonstrate the scalability of the framework by investigating runtime performance as a function of the network size. Steps for future research are also discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Best effort broadcast under cascading failures in interdependent critical infrastructure networks

Here, we present a novel study of reliable broadcast in interdependent networks, in which the failures in one network may cascade to another network. In particular, we focus on the interdependency between a communication network and a power grid network, where the power grid depends on the communication network for control and the communication network depends on the grid for power. In this paper, we propose a best effort broadcast algorithm to handle crash failures in the communication network that may cause cascading failures. We guarantee that all the correct nodes, which operate correctly according to the protocol and do not experience any software or hardware or network failures, eventually deliver the message if the sender is correct. We provide a centralized algorithm and a fully distributed algorithm for nodes to analyze and handle cascading failures. At the core of our work is the fully distributed algorithm which enjoys great performance and scalability. Our evaluation results show that the algorithm handles cascading failures with low overhead.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Bifurcation behaviors of synchronized regions in logistic map networks with coupling delay

Network synchronized regions play an extremely important role in network synchronization according to the master stability function framework. This paper focuses on network synchronous state stability via studying the effects of nodal dynamics, coupling delay, and coupling way on synchronized regions in Logistic map networks. Theoretical and numerical investigations show that (1) network synchronization is closely associated with its nodal dynamics. Particularly, the synchronized region bifurcation points through which the synchronized region switches from one type to another are in good agreement with those of the uncoupled node system, and chaotic nodal dynamics can greatly impede network synchronization. (2) The coupling delay generally impairs the synchronizability of Logistic map networks, which is also dominated by the parity of delay for some nodal parameters. (3) A simple nonlinear coupling facilitates network synchronization more than the linear one does. The results found in this paper will help to intensify our understanding for the synchronous state stability in discrete-time networks with coupling delay.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Thermoelectric properties of semiconductor nanowire networks

To examine the thermoelectric (TE) properties of a semiconductor nanowire (NW) network, we propose a theoretical approach mapping the TE network on a two-port network. In contrast to a conventional single-port (i.e., resistor)network model, our model allows for large scale calculations showing convergence of TE figure of merit, ZT, with an increasing number of junctions. Using this model, numerical simulations are performed for the Bi 2 Te 3 branched nanowire (BNW) and Cayley tree NW (CTNW) network. We find that the phonon scattering at the network junctions plays a dominant role in enhancing the network ZT. Specifically, disordered BNW and CTNW demonstrate an order of magnitude higher ZT enhancement compared to their ordered counterparts. Formation of preferential TE pathways in CTNW makes the network effectively behave as its BNW counterpart. In conclusion, we provide formalism for simulating large scale nanowire networks hinged upon experimentally measurable TE parameters of a single T-junction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Designing monitoring networks for local earthquakes

Abstract Seismic networks are essential for monitoring local earthquakes in connection to industrial activities, including wastewater injection and CO2 sequestration. Because these networks are typically deployed for short periods of time at specific sites, it is beneficial to develop best practices for efficient and effective installation and monitoring. Such standards are available for regional earthquake and microseismic monitoring, but not readily available for local scale (tens of kilometer scale) monitoring. Once the region of interest has been determined, the key parameters for establishing a network are available monitoring station locations, site noise, and site utility. Networks should be designed based on the project monitoring goals for earthquake magnitude and density. Herein, a network was established at the Patterson and Hartland Oil Fields in western Kansas in association with a US Department of Energy CarbonSAFE carbon capture and sequestration research project. We employed observations from an analog local network, the Wellington Earthquake Monitoring Network in south-central Kansas, to assess site noise and estimate earthquake detection thresholds for the Patterson and Hartland fields. Noise from oil production facilities was evaluated, concluding that oil lease sites are suitable for monitoring small local earthquakes (M1-M3). The network was designed to have a magnitude of completeness of M1 while using station locations on existing field operator leases. Fifteen months of continuous network operation demonstrates reliable and efficient local earthquake monitoring and provides best practices recommendations for similar operations.

58 GEOSCIENCES↗
DOE OSTI2020

Differentially Private Generation of Social Networks via Exponential Random Graph Models

Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. In this work, we propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis

The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network behavior and thereby optimize performance. Previous work implemented a software package for bottleneck structure analysis, but applied it only to toy examples. In this work, we introduce the first software package capable of scaling bottleneck structure analysis to production-size networks. Here, we benchmark our system using logs from ESnet, the Department of Energy's high-performance data network that connects research institutions in the U.S. Using the previously published tool as a baseline, we demonstrate that our system achieves vastly improved performance, constructing the bottleneck structure graphs in 0.21 s and calculating link derivatives in 0.09 s on average. We also study the asymptotic complexity of our core algorithms, demonstrating good scaling properties and strong agreement with theoretical bounds. These results indicate that our new software package can maintain its fast performance when applied to even larger networks. They also show that our software is efficient enough to analyze rapidly changing networks in real time. Overall, we demonstrate the feasibility of applying bottleneck structure analysis to solve practical problems in large, real-world data networks.

benchmark↗
DOE OSTI2018

ProgLIMI: Programmable LInk Metric Identification in Software-Defined Networks

In this paper, we propose the Programmable LInk Metric Identification (ProgLIMI) infrastructure for software-defined networking (SDN) networks. ProgLIMI identifies round-trip link metrics (RTLMs) from accumulated end-to-end metrics of selected measurement paths by leveraging the flexible routing control capability of SDN networks. ProgLIMI mainly solves three sub-problems: (1) monitor placement; (2) linearly independent measurement path construction; and (3) flow rule design. To reduce measurement cost, ProgLIMI tries to minimize the number of required monitors and flow rules. In this paper, we address the three sub-problems for both full and hybrid SDN networks. For full SDN networks, ProgLIMI can achieve full RTLM identification using only one monitor and two flow rules in each SDN switch. In contrast, the RTLM identification in hybrid SDN networks is more complicated due to the routing constraint of hybrid SDN networks. We first prove that the monitor placement problem in hybrid SDN networks is NP-hard. We then formulate the monitor placement and measurement path selection problem in hybrid SDN networks and propose a greedy heuristic algorithm to solve the problem efficiently. Our evaluations on both physical testbed and simulation platform reveal that ProgLIMI can accurately identify the RTLMs (delay and loss rate). Besides, ProgLIMI is also resource efficient, i.e., it only requires two flow rules in each SDN switch and a small number of monitors, and the extra probing traffic load incurred by ProgLIMI is also low.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Quantum neural networks: Current status and prospects for development

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

The “curse of dimensionality” has limited the applicability and expansion of tabulated combustion models. While the tabulated flamelet model and other multi-dimensional manifold approaches have shown predictive capability, the associated tabulation involves the storage of large lookup tables, requiring large memory as well as multi-dimensional interpolation subroutines, all implemented during runtime. As such, this work investigates the use of deep artificial neural networks to replace lookup tables in order to reduce the memory footprint and increase the computational speed of tabulated flamelets and related approaches. Specifically, different strategic approaches to training the artificial neural network models are explored and a grouped multi-target artificial neural network is introduced, which takes advantage of the ability of artificial neural networks to map an input space to multiple targets by classifying the species based on their correlation to one another. The grouped multi-target artificial neural network approach is validated by applying it to an n-dodecane spray flame using conditions of the Spray A flame from the Engine Combustion Network and comparing global flame characteristics for different ambient conditions using a well-established large-eddy simulation framework. The same framework is then extended to the simulations of methyl decanoate combustion in a compression ignition engine. The validation studies show that the grouped multi-target artificial neural networks are able to accurately capture flame liftoff, autoignition, two-stage heat release and other quantitative trends over a range of conditions. The use of neural networks in conjunction with the grouping mechanism as performed in the grouped multi-target artificial neural network produces a significant reduction in the memory footprint and computational costs for the code and, thus, widens the operating envelope for higher fidelity engine simulations with detailed mechanisms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A resilient network recovery framework against cascading failures with deep graph learning

Because of the increasing importance and dependencies of infrastructure networks and the potential for massive cascading failures in real-world network systems, maintenance optimization to effectively reduce system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. In this work, a new recovery framework was developed to rapidly identify important system components for maintenance to improve network resilience against cascading failures. Here this work provides distinct advantages to determine an optimal maintenance priority by combining real-time network structure importance with other maintenance prioritization based on customer preference. This approach adopts structural graph embedding and deep reinforcement learning to extract real-time network topology information (such as minimum vertex cover) to update the maintenance priority during the recovery process. Based on the case studies on synthetic networks and a US airport network, the proposed recovery framework with real-time network topology awareness shows better performance than other maintenance prioritization strategies regarding resilience enhancement. This work improves the understanding of how the changing network structure influences maintenance effects. It also provides insights of the practical usefulness of advanced deep learning on helping optimal maintenance prioritization to effectively reduce the intensity and extent of cascading failures.

42 ENGINEERING↗
DOE OSTI2020

On Transfer Learning Of Neural Networks Using Bi-Fidelity Data For Uncertainty Propagation

Due to their high degree of expressiveness, neural networks have recently been used as surrogate models for mapping inputs of an engineering system to outputs of interest. Once trained, neural networks are computationally inexpensive to evaluate and remove the need for repeated evaluations of computationally expensive models in uncertainty quantification applications. However, given the highly parameterized construction of neural networks, especially deep neural networks, accurate training often requires large amounts of simulation data that may not be available in the case of computationally expensive systems. In this paper, to alleviate this issue for uncertainty propagation, we explore the application of transfer learning techniques using training data generated from both high- and low-fidelity models. We explore two strategies for coupling these two datasets during the training procedure, namely, the standard transfer learning and the bi-fidelity-weighted learning. In the former approach, a neural network model mapping the inputs to the outputs of interest is trained based on the low-fidelity data. The high-fidelity data are then used to adapt the parameters of the upper layer(s) of the low-fidelity network, or train a simpler neural network to map the output of the low-fidelity network to that of the high-fidelity model. In the latter approach, the entire low-fidelity network parameters are updated using data generated via a Gaussian process model trained with a small high-fidelity dataset. The parameter updates are performed via a variant of stochastic gradient descent with learning rates given by the Gaussian process model. Using three numerical examples, we illustrate the utility of these bi-fidelity transfer learning methods where we focus on accuracy improvement achieved by transfer learning over standard training approaches.

97 MATHEMATICS AND COMPUTING↗