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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Review of Edge Computing Technology and Its Applications in Power Systems

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Material Resilience in Harsh Service Conditions

Resilience describes the attributes of a material that allow it to withstand or resist detrimental environmental effects degrading properties and performance. In service, materials may experience harsh or extreme conditions, but even modest thermal or load conditions experienced over a long period can degrade performance. Thus, the National Nuclear Security Administration mission requires predictive understanding of materials performance in harsh and extreme conditions over long periods. This is particularly true for applications in which replacement is impractical, impossible, or costly. This area of leadership addresses the evolution of material properties in environments that include static and dynamic stress, radiation, and chemical or thermal extremes. A particular focus is on situations when environments coexist or for which collection of experimental data is challenging or impossible. The capability to predict and control the nature and evolution of properties to allow designing resilience is a crucial aspect of mission success in national nuclear, global, and energy security.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Material Resilience in Harsh Service Conditions

Resilience describes the attributes of a material that allow it to withstand or resist detrimental environmental effects degrading properties and performance. In service, materials may experience harsh or extreme conditions, but even modest thermal or load conditions experienced over a long period can degrade performance. Thus, the National Nuclear Security Administration mission requires predictive understanding of materials performance in harsh and extreme conditions over long periods. This performance is particularly relevant for applications in which replacement is impractical, impossible, or costly. This area of leadership addresses the evolution of material properties in environments that include static and dynamic stress, radiation, and chemical or thermal extremes. A particular focus is on situations when environments coexist or for which collection of experimental data is challenging or impossible. The capability to predict and control the nature and evolution of properties to allow designing resilience is a crucial aspect of mission success in national nuclear, global, and energy security.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale Modeling of the Mechanical Response of Silicon Carbide Composite Within the Accelerated Fuel Qualification Framework

The accelerated fuel qualification (AFQ) framework has been used for the initial development of multiscale modeling of silicon carbide (SiC) fiber reinforced composite (SiC-SiC). The AFQ framework provides a methodology to leverage physics-informed multiscale modeling along with a reduced set of empirical test data to reduce the time and cost of licensing and qualification of new nuclear fuel systems while maintaining the overall nuclear power plant safety case. SiC-SiC is being proposed for in-core applications, most notably fuel cladding, for current and next-generation nuclear reactors because of its high temperature stability, irradiation tolerance, and ability to withstand many accident conditions. As these composites exhibit multiscale architectures and complex microstructure-based fracture mechanics, it is an appealing use case for the AFQ methodology. While the end goal of this work is a single multiscale model that can be used for predictive in-core performance, current focus is on the individual various length scale models. Four individual models have been initially developed from microscale to engineering system level to capture key physics-based effects across different length scales. These models include a microscale homogenized tow model, a mesoscale fast Fourier transform–based weave model that integrates the homogenized tow model, a mesoscale finite element–based weave model, and a system-level BISON fuel performance model. Results of these models have undergone an initial comparison with separate-effects test data showing a good match to experimental results. By using the AFQ framework during model development, several near-term benefits have been secured including a reduction in development time for the SiC-SiC cladding, more targeted irradiation testing, and a better understanding of uncertainty.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A Review of Machine Learning Applications in Power System Resilience

The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.

Deep Learning↗
DOE OSTI2015

Developing an Ontology for Cyber Security Knowledge Graphs

In this paper we describe an ontology developed for a cyber security knowledge graph database. This is intended to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe areas for future work.

Iannacone, Michael↗
DOE OSTI2023

A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena

Abstract Climate variability and weather phenomena can cause extremes and pose significant risk to society and ecosystems, making continued advances in our physical understanding of such events of utmost importance for regional and global security. Advances in machine learning (ML) have been leveraged for applications in climate variability and weather, empowering scientists to approach questions using big data in new ways. Growing interest across the scientific community in these areas has motivated coordination between the physical and computer science disciplines to further advance the state of the science and tackle pressing challenges. During a recently held workshop that had participants across academia, private industry, and research laboratories, it became clear that a comprehensive review of recent and emerging ML applications for climate variability and weather phenomena that can cause extremes was needed. This article aims to fulfill this need by discussing recent advances, challenges, and research priorities in the following topics: sources of predictability for modes of climate variability, feature detection, extreme weather and climate prediction and precursors, observation–model integration, downscaling, and bias correction. This article provides a review for domain scientists seeking to incorporate ML into their research. It also provides a review for those with some ML experience seeking to broaden their knowledge of ML applications for climate variability and weather.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Risk assessment for Industrial Control Systems quantifying availability using mean failure cost (MFC)

Industrial Control Systems (ICS) are commonly used in industries such as oil and natural gas, transportation, electric, water and wastewater, chemical, pharmaceutical, pulp and paper, food and beverage, as well as discrete manufacturing (e.g., automotive, aerospace, and durable goods.) SCADA systems are generally used to control dispersed assets using centralized data acquisition and supervisory control. Originally, ICS implementations were susceptible primarily to local threats because most of their components were located in physically secure areas (i.e., ICS components were not connected to IT networks or systems). The trend toward integrating ICS systems with IT networks (e.g., efficiency and the Internet of Things) provides significantly less isolation for ICS from the outside world thus creating greater risk due to external threats. Albeit, the availability of ICS/SCADA systems is critical to assuring safety, security and profitability. Such systems form the backbone of our national cyber-physical infrastructure. We extend the concept of mean failure cost (MFC) to address quantifying availability to harmonize well with ICS security risk assessment. This new measure is based on the classic formulation of Availability combined with Mean Failure Cost (MFC). The metric offers a computational basis to estimate the availability of a system in terms of the loss that each stakeholder stands to sustain as a result of security violations or breakdowns (e.g., deliberate malicious failures).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Observed Inefficiency of Explosions to Produce Large Aftershocks: Båth’s Law for Explosions is 2.5

Underground explosions are observed to produce fewer and smaller aftershocks than similar size earthquakes. The seismic magnitude difference $Δm_x$ between an explosion and its largest aftershock is an expression of Båth’s law for explosions. Based on an analysis of a compilation of aftershock studies from Soviet testing at the Semipalatinsk test site in Kazakhstan and observations from American testing at the Nevada National Security Site (NNSS), here we find that the average magnitude difference for explosions $\overline{Δm_x}$ is about 2.5. Based on the NNSS data, two standard deviations of $Δm_x$ is about 1.5. In all the cases studied, from ton to megaton yield, from shallow to overburied depth, and chemical or nuclear source, no explosion aftershock has been larger than the explosion that preceded it. In fact, the two events at the NNSS with the largest aftershock magnitudes relative to the explosion are associated with the collapse of the cavity created by the explosion. This is similar to observations from North Korean testing at the Punggye-ri Test Site, where the largest seismic event following the test is attributed to the collapse after the 2017 explosion and is from 0.8 to 2 magnitude units less than the mainshock.

58 GEOSCIENCES↗
DOE OSTI2016

Multi-channel probes to understand fission dynamics

Explaining the origin of the elements is a major outstanding question in nuclear astrophysics. Observed elemental abundance distribution shows strong nuclear physics effects. In conclusion, neutron-induced reactions are important for nuclear astrophysics and applied fields in nuclear energy and security. LANSCE has a program to address many of these questions directly with neutron beams on (near-)stable nuclei. Increasing demand for correlated data to test details of fission models poses additional challenges. Possibilities exist to extend existing experimental efforts to radioactive beam facilities. Kinematic focusing from using inverse kinematics has potential to circumvent some challenges associated with measuring correlations between fission output channels.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Impact of Micro-to Meso-scale Fractures on Sealing Behavior of Argillaceous Cap Rocks For CO 2 Sequestration

This multi-disciplinary project evaluated seal lithologies for the safety and security of long-term geosequestration of CO 2 . We used integrated studies to provide qualitative risk for potential seal failure; we integrated data sets from outcrop, core, geochemical analysis, rock failure properties from mechanical testing, geophysical wireline log analysis, and geomechanical modeling to understand the effects of lithologic heterogeneity and changing mechanical properties have on the mechanical properties of the seal. The objectives of this study were to characterize cap rock seals using natural field analogs, available drillhole logging data and whole-rock core, geochemical and isotopic analyses. Rock deformation experiments were carried out on collected samples to develop better models of risk estimation for potential cap rock seal failure. We also sampled variably faulted and fractured cap rocks to examine the impacts of mineralization and/or alteration on the mechanical properties. We compared CO 2 reacted systems to non-CO 2 reacted seal rock types to determine response of each to increased pore fluid pressures and potential for the creation of unintentional hydrofractures at depth.

58 GEOSCIENCES↗
DOE OSTI2017

Scope Complexity Options Risks Excursions (SCORE) Factor Mathematical Description.

The purpose of the Scope, Complexity, Options, Risks, Excursions (SCORE) model is to estimate the relative complexity of design variants of future warhead options, resulting in scores. SCORE factors extend this capability by providing estimates of complexity relative to a base system (i.e., all design options are normalized to one weapon system). First, a clearly defined set of scope elements for a warhead option is established. The complexity of each scope element is estimated by Subject Matter Experts (SMEs), including a level of uncertainty, relative to a specific reference system. When determining factors, complexity estimates for a scope element can be directly tied to the base system or chained together via comparable scope elements in a string of reference systems that ends with the base system. The SCORE analysis process is a growing multi-organizational Nuclear Security Enterprise (NSE) effort, under the management of the NA-12 led Enterprise Modeling and Analysis Consortium (EMAC). Historically, it has provided the data elicitation, integration, and computation needed to support the out-year Life Extension Program (LEP) cost estimates included in the Stockpile Stewardship Management Plan (SSMP).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2019

Operational History of the Area 3 Radioactive Waste Management Site

Report presents information based on characterization and monitoring data, historical documentation and records, modeling studies, evaluations, and process knowledge for the Area 3 Radioactive Waste Management Site on the Nevada National Security Site.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗
DOE OSTI2015

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving net- works spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with promi- nent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a “Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named “Relative Selectivity" that is used to se- lect between different query processing strategies. Our experiments performed on real online news, network traffic stream and a syn- thetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.

graphs, cybersecurity↗
NASA NTRS1994

The design and implementation of EPL: An event pattern language for active databases

The growing demand for intelligent information systems requires closer coupling of rule-based reasoning engines, such as CLIPS, with advanced data base management systems (DBMS). For instance, several commercial DBMS now support the notion of triggers that monitor events and transactions occurring in the database and fire induced actions, which perform a variety of critical functions, including safeguarding the integrity of data, monitoring access, and recording volatile information needed by administrators, analysts, and expert systems to perform assorted tasks; examples of these tasks include security enforcement, market studies, knowledge discovery, and link analysis. At UCLA, we designed and implemented the event pattern language (EPL) which is capable of detecting and acting upon complex patterns of events which are temporally related to each other. For instance, a plant manager should be notified when a certain pattern of overheating repeats itself over time in a chemical process; likewise, proper notification is required when a suspicious sequence of bank transactions is executed within a certain time limit. The EPL prototype is built in CLIPS to operate on top of Sybase, a commercial relational DBMS, where actions can be triggered by events such as simple database updates, insertions, and deletions. The rule-based syntax of EPL allows the sequences of goals in rules to be interpreted as sequences of temporal events; each goal can correspond to either (1) a simple event, or (2) a (possibly negated) event/condition predicate, or (3) a complex event defined as the disjunction and repetition of other events. Various extensions have been added to CLIPS in order to tailor the interface with Sybase and its open client/server architecture.

Giuffrida, G.↗
NASA NTRS1997

Emergency Operations Center at Johnson Space Center

In June 1966, at the start of the Gulf Coast hurricane season, the Johnson Space Center (JSC) celebrated the opening of its new 4,000-square foot, state-of-the-art Emergency Operations Center (EOC). The new EOC has been upgraded and enhanced to support a wide spectrum of emergencies affecting JSC and neighboring communities. One of the main features of the EOC is its premier computerized dispatch center. The new system unites many of JSC's critical emergency functions into one integrated network. It automatically monitors fire alarms, security entrances, and external cameras. It contains the JSC inventory of hazardous materials, by building and room, and can call up Material Safety Data Sheets for most of the generic hazardous materials used on-site. The EOC is available for community use during area emergencies such as hurricanes and is a welcome addition to the Clear Lake/Galveston Bay Area communities' emergency response resources.

Caylor, Gary C.↗
NASA NTRS2003

Thermal Studies of Near Earth Objects

In this proposal, we seek to apply the optical/thermal method to the measurement of the diameters and albedos of a large sample of Near Earth Objects (NEOs). Whereas main-belt asteroids have been studied in large numbers, principally using thermal detections from the IRAS satellite, relatively few thermal observations of NEOs have been secured. This program capitalizes on our access to large telescopes and imaging thermal IR detectors in pursuit of the definitive set of albedo data on the NEOs.

Jewitt, David↗