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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Transductive Graph Neural Network learning for Grid Resilience Analysis

Power grids are critical infrastructures that require robust resilience analysis to ensure reliable and uninterrupted electricity supply. Traditional simulation-based methods for grid resilience analysis suffer from computational complexity and limited ability to capture the full spectrum of potential disruptions. This paper presents a novel approach to enhance grid resilience by leveraging transductive graph neural network (GNN) learning to identify critical nodes and links. By leveraging the graph structure and system features, GNNs effectively learn resilience metrics and accurately identify critical nodes based on actual grid operational behavior. The efficacy of the proposed approach is demonstrated through case studies on node criticality scoring and critical node/line identification in cascading outage scenarios. The results highlight the advantages of learning-based methods over traditional simulation-based approaches and their potential to revolutionize grid resilience analysis. The contributions of this paper include a graph-based scalable approach for fast cascading analysis, an inductive formulation for training GNN models, and a transfer learning-based approach to scale the model to largescale power systems.

grid resilience, graph neural networks, transducti↗
DOE OSTI2022

Graph-component approach to defect identification in large atomistic simulations

In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Visual Comparison of Silent Error Propagation

High-performance computing (HPC) systems play a critical role in facilitating scientific discoveries. Their scale and complexity (e.g., the number of computational units and software stack) continue to grow as new systems are expected to process increasingly more data and reduce computing time. However, with more processing elements, the probability that these systems will experience a random bit-flip error that corrupts a program's output also increases, which is often recognized as silent data corruption. Analyzing the resiliency of HPC applications in extreme-scale computing to silent data corruption is crucial but difficult. An HPC application often contains a large number of computation units that need to be tested, and error propagation caused by error corruption is complex and difficult to interpret. Here, to accommodate this challenge, we propose an interactive visualization system that helps HPC researchers understand the resiliency of HPC applications and compare their error propagation. Our system models an application's error propagation to study a program's resiliency by constructing and visualizing its fault tolerance boundary. Coordinating with multiple interactive designs, our system enables domain experts to efficiently explore the complicated spatial and temporal correlation between error propagations. At the end, the system integrated a nonmonotonic error propagation analysis with an adjustable graph propagation visualization to help domain experts examine the details of error propagation and answer such questions as why an error is mitigated or amplified by program execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

ECoG Cluster Flow v1.0

ECoG ClusterFlow is a novel multi-scale visual analysis system for the detailed exploration of dynamic functional connectivity graphs obtained through electrocorticography.

MURUGESAN, SUGEERTH↗
DOE OSTI2017

ECoG Cluster Flow v1.0

ECoG ClusterFlow is a novel multi-scale visual analysis system for the detailed exploration of dynamic functional connectivity graphs obtained through electrocorticography.

MURUGESAN, SUGEERTH↗
DOE OSTI2013

Towards A Network-of-Networks Framework for Cyber Security

Networks-of-networks (NoN) is a graph-theoretic model of interdependent networks that have distinct dynamics at each network (layer). By adding special edges to represent relationships between nodes in different layers, NoN provides a unified mechanism to study interdependent systems intertwined in a complex relationship. While NoN based models have been proposed for cyber-physical systems, in this paper we build towards a three-layer NoN model for an enterprise cyber system. Each layer captures a different facet of a cyber system. We then discuss the potential benefits of graph-theoretic analysis enabled from such a model. Our goal is to provide a novel and powerful tool for modeling and analyzing problems in cyber security.

cyber security↗
DOE OSTI2021

Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks

In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate networks. Additionally, we show that utilizing a more centralized working relationship network rather than a strict administrative network further increases overall innovation reach. In fact, this more centralized structure in conjunction with management layers is essential to both reaching a plurality of nodes and creating a stable adopted community in the long time horizon. Further, we show that the selection of seed nodes affects the final stability of the adopted community, and while the most influential nodes often produce the highest peak adoption, this is not always the case. In some circumstances, seeding nodes near but not in the highest positions in the graph produces larger peak adoption and more stable long-time adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

URBAN-NET: A Network-based Infrastructure Monitoring and Analysis System for Emergency Management and Public Safety

Abstract Critical Infrastructures (CIs) such as energy, water, and transportation are complex networks that are crucial for sustaining day-to-day commodity flows vital to national security, economic stability, and public safety. The nature of these CIs is such that failures caused by an extreme weather event or a man-made incident can trigger widespread cascading failures, sending ripple effects at regional or even national scales. To minimize such effects, it is critical for emergency responders to identify existing or potential vulnerabilities within CIs during such stressor events in a systematic and quantifiable manner and take appropriate mitigating actions. We present here a novel critical infrastructure monitoring and analysis system named URBAN-NET. The system includes a software stack and tools for monitoring CIs, pre-processing data, interconnecting multiple CI datasets as a heterogeneous network, identifying vulnerabilities through graph-based topological analysis, and predicting consequences based on what-if simulations along with visualization. As a proof-of-concept, we present several case studies to show the capabilities of our system. We also discuss remaining challenges and future work.

Lee, Sangkeun↗
DOE OSTI2018

Domain configurations in dislocations embedded hexagonal manganite systems: From the view of graph theory

Topological defects and their interactions often arouse multiple types of emerging phenomena from edge states in Skyrmions to disclination pairs in liquid crystals. In hexagonal manganites, partial edge dislocations, a prototype topological defect, are ubiquitous and they significantly alter the topologically protected domains and their behaviors. In this work, combining electron microscopy experiment and graph theory analysis, we report a systematic study of the connections and configurations of domains in this dislocation embedded system. Rules for domain arrangement are established. The dividing line between domains, which can be attributed by the strain field of dislocations, is accurately described by a genus model from a higher dimension in the graph theory. In conclusion, our results open a door for the understanding of domain patterns in topologically protected multiferroic systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Attack detection and strategy optimization in game-theoretic trust models

Trust in a microelectronics-based systems can be characterized as the level of confidence that the system is free of subversive alterations inserted by a malicious adversary during system development. Outkin et al. recently developed GPLADD, a game-theoretic framework that enables trust analysis through a set of mathematical models that represent multi-step attack graphs and contention between system attackers and defenders. This paper extends GPLADD to include detection of attacks on development processes and defender decision processes that occur in response to detection events. The paper provides mathematical details for implementing attack detection and demonstrates the models on an example system. The authors further demonstrate how optimal defender strategies vary when solution concepts and objective functions are modified.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Sensor Network Demonstration for In Situ Decommissioning - 13332

Florida International University's (FIU's) Applied Research Center is currently supporting the Department of Energy's (DOE) Environmental Management Office of D and D and Facility Engineering program. FIU is supporting DOE's initiative to improve safety, reduce technical risks, and limit uncertainty within D and D operations by identifying technologies suitable to meet specific facility D and D requirements, assessing the readiness of those technologies for field deployment, and conducting feasibility studies and large scale demonstrations of promising technologies. During FY11, FIU collaborated with Savannah River National Laboratory in the development of an experimental test site for the demonstration of multiple sensor systems for potential use in the in situ decommissioning process. In situ decommissioning is a process in which the above ground portion of a facility is dismantled and removed, and the underground portion is filled with a cementious material such as grout. In such a scenario, the question remains on how to effectively monitor the structural health of the grout (cracking, flexing, and sinking), as well as track possible migration of contaminants within and out of the grouted monolith. The right types of sensors can aid personnel in better understanding the conditions within the entombed structure. Without sensors embedded in and around the monolith, it will be very difficult to estimate structural integrity and contaminant transport. Yet, to fully utilize the appropriate sensors and the provided data, their performance and reliability must be evaluated outside a laboratory setting. To this end, a large scale experimental setup and demonstration was conducted at FIU. In order to evaluate a large suite of sensor systems, FIU personnel designed and purchased a pre-cast concrete open-top cube, which served as a mock-up of an in situ DOE decommissioned facility. The inside of the cube measures 10 ft x 10 ft x 8 ft. In order to ensure that the individual sensors would be immobilized during the grout pouring activities, a set of nine sensor racks were designed. The 270 sensors provided by Idaho National Laboratory (INL), Mississippi State University (MSU), University of Houston (UH), and University of South Carolina (USC) were secured to these racks based on predetermined locations. Once sensor racks were installed inside the test cube, connected and debugged, approximately 32 cubic yards of special grout material was used to entomb the sensors. MSU provided and demonstrated four types of fiber loop ring-down (FLR) sensors for detection of water, temperature, cracks, and movement of fluids. INL provided and demonstrated time differenced 3D electrical resistivity tomography (ERT), advanced tensiometers for moisture content, and thermocouples for temperature measurements. University of Houston provided smart aggregate (SA) sensors, which detect crack severity and water presence. An additional UH sensor system demonstrated was a Fiber Bragg Grating (FBG) fiber optic system measuring strain, presence of water, and temperature. USC provided a system which measured acoustic emissions during cracking, as well as temperature and pH sensors. All systems were connected to a Sensor Remote Access System (SRAS) data networking and collection system designed, developed and provided by FIU. The purpose of SRAS was to collect and allow download of the raw sensor data from all the sensor system, as well as allow upload of the processed data and any analysis reports and graphs. All this information was made available to the research teams via the Deactivation and Decommissioning Knowledge Management and Information Tool (D and D KM-IT). As a current research effort, FIU is performing an energy analysis, and transferring several sensor systems to a Photovoltaic (PV) System to continuously monitor energy consumption parameters and overall power demands. Also, One final component of this research is focusing on developing an integrated data network to capture, log and analyze sensor system data in near real time from a single interface. FIU personnel and DOE Fellows monitored the progress and condition of the sensors for a period of six months. During this time, the sensors recorded data pertaining to strain, compression, temperature, crack detection, moisture presence, fluid mobility, shock resistance, monolith movement, and electrical resistivity. In addition, FIU regularly observed the curing process of the grout and documented the cube condition via the nine racks of sensors. The sensors held up throughout the curing process, withstood the natural elements for six months, and monitored the integrity of the grout. The large scale experiment and demonstration conducted at FIU was the first of its kind to demonstrate the feasibility of state of the art sensors for in situ decommissioning applications. These efforts successfully measured the durability, performance, and precision of the sensors in question as well as monitored and recorded the curing process of the selected grout material under natural environmental conditions. The current energy analysis work is resulting in data on the constraints placed by some of the sensor systems on a power network that requires high reliability and low losses. In addition, a sensor system demonstration has determined that it is feasible to develop an integrated data network where data can be accessed in near real-time from all systems, thereby allowing for larger-scale integrated system testing to be performed. Information collected during the execution of this research project will aid decision makers in the identification of sensors to be used in nuclear facilities selected for in situ decommissioning. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Optimal Sizing of Movable Energy Resources for Enhanced Resilience in Distribution Systems: A Techno-Economic Analysis

This article introduces a techno-economic analysis aimed at identifying the optimal total size of movable energy resources (MERs) to enhance the resilience of electric power supply. The core focus of this approach is to determine the total size of MERs required within the distribution network to expedite restoration after extreme events. Leveraging distribution line fragility curves, the proposed methodology generates numerous line outage scenarios, with scenario reduction techniques employed to minimize computational burden. For each reduced multiple line outage scenario, a systematic reconfiguration of the distribution network, represented as a graph, is executed using tie-switches within the system. To evaluate each locational combination of MERs for a specific number of these resources, the expected load curtailment (ELC) is calculated by summing the load curtailment within microgrids formed due to multiple line outages. This process is repeated for all possible locational combinations of MERs to determine minimal ELC for each MER total size. For every MER total size, the minimal ELCs are determined. Finally, a techno-economic analysis is performed using power outage cost and investment cost of MERs to pinpoint an optimal total size of MERs for the distribution system. To demonstrate the effectiveness of the proposed approach, case studies are conducted on the 33-node and the modified IEEE 123-node distribution test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Applications of the Dulmage–Mendelsohn decomposition for debugging nonlinear optimization problems

Nonlinear modeling and optimization is a valuable tool for aiding decisions by engineering practitioners, but programming an optimization problem based on a complex electrical, mechanical, or chemical process is a time-consuming and error-prone activity. Therefore, there is a need for model analysis and debugging tools that can detect and diagnose modeling errors. One such tool is the Dulmage–Mendelsohn decomposition, which identifies structurally under- and over-determined subsets in systems of equations and variables by partitioning the bipartite graph of the system. This work provides the necessary background to understand the Dulmage–Mendelsohn decomposition and its application to the analysis of nonlinear optimization problems, demonstrates its use in diagnosing a variety of modeling errors, and introduces software implementations for analyzing nonlinear optimization problems in the Pyomo and JuMP algebraic modeling languages.

42 ENGINEERING↗
DOE OSTI2017

DAGSENS: Directed Acyclic Graph Based Direct and Adjoint Transient Sensitivity Analysis for Event-Driven Objective Functions

We present DAGSENS, a new theory for parametric transient sensitivity analysis of Differential Algebraic Equation systems (DAEs), such as SPICE-level circuits. The key ideas behind DAGSENS are, (1) to represent the entire sequence of computations, starting from DAE parameters, all the way up to the objective function whose sensitivity is needed, as a Directed Acyclic Graph (DAG) called the "sensitivity DAG", and (2) to compute the required sensitivities efficiently (with time complexity linear in the size of the sensitivity DAG) by leveraging dynamic programming techniques to traverse the DAG. DAGSENS is simple, elegant, and easy-to-understand compared to existing sensitivity analysis approaches; for example, in DAGSENS, one can switch between direct and adjoint transient sensitivities just by changing the direction of DAG traversal (i.e., topological order vs. reverse topological order). Also, DAGSENS is significantly more powerful than existing sensitivity analysis approaches because it allows one to compute the sensitivities of a much more general class of objective functions, including those defined based on "events" that occur during a transient simulation (e.g., a node voltage crossing a particular threshold, a phase-locked loop (PLL) achieving lock, a signal reaching its maximum/minimum value during a transient run, etc.). In this paper, we apply DAGSENS to compute the sensitivities of important event-driven performance metrics in several real-world electronic and biological applications, including high-speed communication (featuring sub-systems such as I/0 links and PLLs), statistical cell library characterization, and gene expression in Drosophila embryos.

42 ENGINEERING↗
DOE OSTI2012

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism’s genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotypeexpressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http:// freescience.org/cs/NIBBS.

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

ARCADES: analysis of risk from cyberattack against defensive strategies for the power grid

In this study, the authors introduce ARCADES (Analysis of Risk from Cyberattack against DEfensive Strategy), a technique to systematically explore the cybersecurity defence strategies based on contingency rankings in power systems. While cybersecurity defensive standards exist, these approaches are primarily based on expert opinions, rather than systematic studies of risk. ARCADES presents an approach to identify improved cybersecurity defensive strategies based on a graph‐based cyber‐physical security model that is evaluated by resistance‐distance metrics and then prioritised based on contingency analysis studies of the system. This study also proposed a technique that identifies the most critical cybersecurity mechanisms to protect the power grid. Planning and operation: those two applications for cybersecurity on power grid mainly explored in this study. For planning, a systematic method is developed to verify the effectiveness of security strategies and for operation, prioritised the security mechanism for auditing and monitoring purpose. As a case study, they analyse the IEEE‐14 bus, IEEE‐30 bus, and IEEE‐118 bus systems, observe the defensive strategies, and calculate substation criticality ranking by using the authors’ proposed method.

Touhiduzzaman, Md↗