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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Characteristics of infrasound signals from North Korean underground nuclear explosions on 2016 January 6 and September 9

This study investigates infrasound signals from North Korean underground nuclear explosions on 2016 January 6 at 01:30:01 UTC (UNEJ16) and on 2016 September 9 at 00:30:01 UTC (UNES16). Infrasound observations from eight, seismo-acoustic arrays at distances of 300–600 km from the explosions and aligned approximately east–west across South Korea are used in the analysis. Progressive multichannel correlation was used followed by analyst review to identify the infrasound arrivals with estimates of azimuth, phase velocity, celerity, F-statistic, signal-to-noise ratio, correlation and signal duration. These observations are compared with model predictions of travel path, ray turning height and relative amplitudes, using Ground-to-Space atmospheric specifications at the time of each explosion. Stratospheric conditions at the time of the UNEJ16 are favourable to the Korean arrays when all arrays detected infrasound signals, while the few detections for UNES16 are consistent with unfavourable stratospheric winds and reduced amplitude thermospheric arrivals. Infrasound locations of the explosions were estimated using the Bayesian Infrasonic Source Location method (BISL), with wind-corrected backazimuth estimates based on the ray tracing calculations. Adding wind corrections to BISL resulted in infrasound locations up to 50 per cent closer to the seismic epicentre over uncorrected locations. The UNES16 infrasound location estimate using atmospheric corrections had larger differences (~67 km difference from seismic location) than the UNEJ16 location (~11 km difference from seismic location) as a result of the reduced number of observations and poor azimuth coverage for UNES16. Based on stratospheric phases recorded at six arrays and the empirical yield-scaling relations, wind corrected amplitudes from UNEJ16 lead to equivalent infrasound source energy estimates between 0.9 and 16.1 tons of TNT detonated in the atmosphere with an average of 6.4 tons and a standard deviation of 4.6 tons.

58 GEOSCIENCES↗
DOE OSTI2018

False alarms and the IMS infrasound network: Understanding the factors influencing the creation of false events

The International Monitoring System (IMS) infrasound network has been designed to acquire the necessary data to detect and locate explosions in the atmosphere with a yield equivalent to 1 kiloton of TNT anywhere on Earth. A major associated challenge is the task of automatically processing data from all IMS infrasound stations to identify possible nuclear tests for subsequent review by analysts. This paper is the first attempt to quantify the false alarm rate (FAR) of the IMS network, and in particular to assess how the FAR is affected by the numbers and distributions of detections at each infrasound station. To ensure that the results are sufficiently general, and not dependent entirely on one detection algorithm, the assessment is based on two detection algorithms that can be thought of as end members in their approach to the trade-off between missed detections and false alarms. The results show that the FAR for events formed at only two arrays is extremely high (ranging from 10’s to 100’s of false events per day across the IMS network, depending on the detector tuning). It is further shown that the FAR for events formed at three or more IMS arrays is driven by ocean-generated waves (microbaroms), despite efforts within both detection algorithms for avoiding these signals, indicating that further research into this issue is merited. Overall, the results highlight the challenge of processing data from a globally sparse network of stations to detect and form events. On the basis of these results, it is shown that further work on understanding signal and noise characteristics is needed before attempting to automatically form events detected at only two arrays on the global network. In particular, more work is required to reduce false alarms caused by the detection of microbarom signals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2012

Emergency Response Health Physics

Health physics is an important discipline with regard to understanding the effects of radiation on human health; however, there are major differences between health physics for research or occupational safety and health physics during a large-scale radiological emergency. The deployment of a U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) monitoring and assessment team to Japan in the wake of the March 2011 accident at Fukushima Daiichi Nuclear Power Plant yielded a wealth of lessons on these difference. Critical teams (CMOC (Consequence Management Outside the Continental U.S.) and CMHT (Consequence Management Home Team) ) worked together to collect, compile, review, and analyze radiological data from Japan to support the response needs of and answer questions from the Government of Japan, the U.S. military in Japan, the U.S. Embassy and U.S. citizens in Japan, and U.S. citizens in America. This paper addresses the unique challenges presented to the health physicist or analyst of radiological data in a large-scale emergency. A key lesson learned was that public perception and the availability of technology with social media requires a diligent effort to keep the public informed of the science behind the decisions in a manner that is meaningful to them.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

High-resolution coupled physics solvers for analysing fine-scale nuclear reactor design problems

An integrated multi-physics simulation capability for the design and analysis of current and future nuclear reactor models is being investigated, to tightly couple neutron transport and thermal-hydraulics physics under the SHARP framework. Over several years, high-fidelity, validated mono-physics solvers with proven scalability on petascale architectures have been developed independently. Based on a unified component-based architecture, these existing codes can be coupled with a mesh-data backplane and a flexible coupling-strategy-based driver suite to produce a viable tool for analysts. The goal of the SHARP framework is to perform fully resolved coupled physics analysis of a reactor on heterogeneous geometry, in order to reduce the overall numerical uncertainty while leveraging available computational resources. Finally, the coupling methodology and software interfaces of the framework are presented, along with verification studies on two representative fast sodium-cooled reactor demonstration problems to prove the usability of the SHARP framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Role of the likelihood for elastic scattering uncertainty quantification

In the last decade, uncertainty quantification (UQ) for optical model potentials (OMPs) has become a focal point for nuclear reaction theory, and several competing approaches for OMP UQ have recently been developed. Here, we clarify recent efforts to compare frequentist and Bayesian approaches in the context of OMP UQ [G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019)]. We replicate a portion of that OMP UQ study but use independent statistical tools. Specifically, we compare two methods for OMP parameter inference from elastic scattering data: the Levenberg-Marquardt algorithm for χ 2 minimization on one hand and Markov chain Monte Carlo (MCMC) sampling on the other. Separately, we assess the common practice of using a renormalized likelihood (χ 2 /N), N being the number of data points, instead of the canonical weighted-least-squares likelihood (χ 2 ), as a way of accounting for unknown data correlations. Here, we show that for a generic linear model and for a five-parameter OMP analysis, frequentist and uniform-prior Bayesian approaches recover the same optimum and uncertainty estimates—not systematically larger uncertainties for the Bayesian approach, as was concluded in G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019). Further, we show that if an additional, near-degenerate parameter is introduced into the same OMP analysis such that the parameter posterior becomes non-Gaussian, then covariance-based estimates of uncertainty become unreliable. Finally, we show that regardless of optimization approach, if χ 2 /N is used for the likelihood, the resulting parametric uncertainties increase by $\sqrt{N}$, and that this is responsible for the conclusions drawn in the revisited study. Based on our replication results, we find that a fortuitous cancellation of unreported errors and the renormalization factor can lead to improvement in empirical coverages, as was the case in the original comparative study. We emphasize that developing and applying a realistic likelihood function is an essential task in a UQ analysis, and that several recent UQ studies that employed a renormalized likelihood (i.e., including a 1/N factor) may have yielded unrealistically large uncertainties for elastic-scattering observables. If the parameter posterior deviates from multivariate-normal, a sampling-based approach like MCMC has a clear advantage over methods that assume the Laplace approximation holds. We note that empirical coverage can serve as an important internal check for the analyst whose model or data may have additional, unaccounted-for uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

X-ray detectors at the Linac Coherent Light Source

Free-electron lasers (FELs) present new challenges for camera development compared with conventional light sources. At SLAC a variety of technologies are being used to match the demands of the Linac Coherent Light Source (LCLS) and to support a wide range of scientific applications. In this paper an overview of X-ray detector design requirements at FELs is presented and the various cameras in use at SLAC are described for the benefit of users planning experiments or analysts looking at data. Features and operation of the CSPAD camera, which is currently deployed at LCLS, are discussed, and the ePix family, a new generation of cameras under development at SLAC, is introduced.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Hybrid Attack Graph Generation with Graph Convolutional Deep-Q Learning

Critical infrastructures such as power grids have become increasingly complex, connected, and vulnerable to adverse scenarios, including cyber and physical attacks and faults. Effective risk mitigation for such cyber-physical energy systems (CPES), requires preemptive knowledge of likely adversarial attack scenarios. Hybrid Attack Graph (HAG) is a structured way to represent an adversarial scenario as an attack sequence using a threat model. However, the scarcity of documented attack sequences hinders analysts and CPES planners’ ability to identify credible attack scenarios for a given CPES. We propose a data-driven Graph Convolutional Deep-Q Network (GCDQ) to address this data challenge through generating HAGs. By leveraging limited real-world observations from the MITRE ATT&CK knowledge base, our GCDQ model synthesizes realistic graphs with the targeted attribute of minimum detectability via reinforcement learning. This generative model is the first step in creating a tool to substantially boost the attack sequence dataset and enhance the performance of CPS defense-related tasks by providing insights into likely attack sequences with given attributes.

deep learning, artificial intelligence↗
DOE OSTI2012

Shopping For Danger: E-commerce techniques applied to collaboration in cyber security

Collaboration among cyber security analysts is essential to a successful protection strategy on the Internet today, but it is uncommonly practiced or encouraged in operating environments. Barriers to productive collaboration often include data sensitivity, time and effort to communicate, institutional policy, and protection of domain knowledge. We propose an ambient collaboration framework, Vulcan, designed to remove the barriers of time and effort and mitigate the others. Vulcan automated data collection, collaborative filtering, and asynchronous dissemination, eliminating the effort implied by explicit collaboration among peers. We instrumented two analytic applications and performed a mock analysis session to build a dataset and test the output of the system.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Linking Threat Agents to Targeted Organizations: A Pipeline for Enhanced Cybersecurity Risk Metrics

In this study, we present a methodology leveraging Large Language Models (LLMs) to transform Cybersecurity Threat Intelligence (CTI) narratives into actionable insights for individual organizations. Our approach automates the extraction of machine-readable adversary SKRAM (Skills, Knowledge, Resources, Authorities, and Motivation) attributes from open-source reports, extending LLM utility beyond typical interactions. This innovation enables precise, automated assessments of cybersecurity risks posed by various adversaries. Using a chain-of-thought and multi-shot prompting strategy, our methodology advances the automation of cybersecurity feature extraction for new machine-learning models that predict the risk of adversary targeting. This approach is refined using a substantial dataset of over 150 analyst-validated threat reports and synthetic organizational data from 900 companies. Here, by bootstrapping the training data with a rule-based heuristic over synthetic data, we have developed a high-accuracy machine-learning model that allows entities to dynamically prioritize threats and defensive actions.

Cyber Threat Intelligence↗
DOE OSTI2018

A Comparison of Machine Learning Techniques to Extract Human Settlements from High Resolution Imagery

Two machine learning techniques were developed to extract human settlements from very high resolution (VHR) satellite images of 3 provinces in Afghanistan: Logar, Panjsher, and Wardak. The results were then compared with analyst verified reference data information known as the LandScan Settlement Layer (LandScan SL).[1] This study attempts to compare settlement mapping results from a support vector machine (SVM) classifier specifically integrated in a current settlement mapping framework and a deep learner utilizing a convolutional neural network (CNN) approach. By comparing the results from the SVM and the CNN to the reference data information we demonstrate that the CNN yields more accurate results overall, in terms of overall pixel cells, and the SVM performs more accurately in omission, based on derived statistics against the reference data information.

Weaver, Jeanette E↗
DOE OSTI2017

High-Performance Data Analytics Beyond the Relational and Graph Data Models with GEMS

Graphs represent an increasingly popular data model for data-analytics, since they can naturally represent relationships and interactions between entities. Relational databases and their pure table-based data model are not well suitable to store and process sparse data. Consequently, graph databases have gained interest in the last few years and the Resource Description Framework (RDF) became the standard data model for graph data. Nevertheless, while RDF is well suited to analyze the relationships between the entities, it is not efficient in representing their attributes and properties. In this work we propose the adoption of a new hybrid data model, based on attributed graphs, that aims at overcoming the limitations of the pure relational and graph data models. We present how we have re-designed the GEMS data-analytics framework to fully take advantage of the proposed hybrid data model. To improve analysts productivity, in addition to a C++ API for applications development, we adopt GraQL as input query language. We validate our approach implementing a set of queries on net-flow data and we compare our framework performance against Neo4j. Experimental results show significant performance improvement over Neo4j, up to several orders of magnitude when increasing the size of the input data.

Data Analytics↗
DOE OSTI2013

The CommonGround Visual Paradigm for Biosurveillance

Biosurveillance is a critical area in the intelligence community for real-time detection of disease outbreaks. Identifying epidemics enables analysts to detect and monitor disease outbreaks that might be spread from natural causes or from possible biological warfare attacks. Containing these events and disseminating alerts requires the ability to rapidly find, classify and track harmful biological signatures. In this paper, we describe a novel visual paradigm to conduct biosurveillance using an Infectious Disease Weather Map. Our system provides a visual common ground in which users can view, explore and discover emerging concepts and correlations such as symptoms, syndromes, pathogens, and geographic locations.

Livnat, Yarden↗
DOE OSTI2013

LINEBACkER: Bio-inspired Data Reduction Toward Real Time Network Traffic Analysis

Abstract—One essential component of resilient cyber applications is the ability to detect adversaries and protect systems with the same flexibility adversaries will use to achieve their goals. Current detection techniques do not enable this degree of flexibility because most existing applications are built using exact or regular-expression matching to libraries of rule sets. Further, network traffic defies traditional cyber security approaches that focus on limiting access based on the use of passwords and examination of lists of installed or downloaded programs. These approaches do not readily apply to network traffic occurring beyond the access control point, and when the data in question are combined control and payload data of ever increasing speed and volume. Manual analysis of network traffic is not normally possible because of the magnitude of the data that is being exchanged and the length of time that this analysis takes. At the same time, using an exact matching scheme to identify malicious traffic in real time often fails because the lists against which such searches must operate grow too large. In this work, we introduce an alternative method for cyber network detection based on similarity-measuring algorithms for gene sequence analysis. These methods are ideal because they were designed to identify similar but nonidentical sequences. We demonstrate that our method is generally applicable to the problem of network traffic analysis by illustrating its use in two different areas both based on different attributes of network traffic. Our approach provides a logical framework for organizing large collections of network data, prioritizing traffic of interest to human analysts, and makes it possible to discover traffic signatures without the bias introduced by expert-directed signature generation. Pattern recognition on reduced representations of network traffic offers a fast, efficient, and more robust way to detect anomalies.

Teuton, Jeremy R.↗
DOE OSTI2020

An Integrated Platform for Collaborative Data Analytics

While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management “how can organizations use knowledge more effectively?”. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.

Oesch, T↗
DOE OSTI2019

A Multidimensional Holistic Framework for the Security of Distributed Energy and Control Systems

The digitization of smart grid distributed generation and industrial control systems has prompted utilities to deploy tools with ubiquitous communications that potentially widen the attack surface. The utilities still continue to rely on the traditional cybersecurity technologies, such as firewalls, anti-malware tools, and passwords that do not ensure security across all dimensions of the information assurance model required for a strong cybersecurity business process. This paper proposes a multidimensional holistic framework that addresses this gap through advanced technologies, intelligent algorithms, and continued assessments. To show proof, the layered defense model, a solution dimension of the framework, is integrated into the National Renewable Energy Laboratory's Security and Resilience Testbed to replicate a utility's enterprise and substation networks. The model is used to evaluate the security and resilience of microgrid control systems, and, based on the insights gathered, recommend best practices for utility cybersecurity analysts for a strong business process. Here, with this baseline, the paper conceptually introduces intelligence-driven solutions comprising contextual data analysis and machine learning to respond to advanced persistent threats sponsored by campaign efforts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Evaluating the Efficacy of Wavelet Configurations on Turbulent-Flow Data

I/O is increasingly becoming a significant constraint for simulation codes and visualization tools on modern supercomputers. Data compression is an attractive workaround, and, in particular, wavelets provide a promising solution. However, wavelets can be applied in multiple configurations, and the variations in configuration impact accuracy, storage cost, and execution time. While the variation in these factors over wavelet configurations have been explored in image processing, they are not well understood for visualization and analysis of scientific data. To illuminate this issue, we evaluate multiple wavelet configurations on turbulent-flow data. Our approach is to repeat established analysis routines on uncompressed and lossy-compressed versions of a data set, and then quantitatively compare their outcomes. Our findings show that accuracy varies greatly based on wavelet configuration, while storage cost and execution time vary less. Overall, our study provides new insights for simulation analysts and visualization experts, who need to make tradeoffs between accuracy, storage cost, and execution time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Transfer Learning approach to parking lot classification in aerial imagery

The importance of satellite imagery analysis has increased dramatically over the last several years, keeping pace with the rapid improvements seen in both remote sensing platforms and sensors. As this field expands, so too does the interest in using machine learning methods to automate parts of the imagery analyst’s workflow. In this paper we address one aspect of this challenge: the development of a method for the automatic extraction of parking lots from aerial imagery. To the best of our knowledge, there has been no prior work conducted on the development of an end-to-end pipeline for this particular task. Due to the limited size of our dataset and to accommodate the potentially limited size of future datasets, we propose a deep learning approach using transfer learning. This process hinges upon the use of state of the art Convolutional Neural Networks (CNNs), trained on general image classification datasets. These networks were then fine-tuned on our custom dataset, to establish a comprehensive benchmark for this task. Our method exhibits promising results for automatic parking lot extraction, and is generalizable enough to work with different input types, including high resolution aerial orthoimagery, satellite imagery, full motion video (FMV), and UAV imagery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Using Co-Simulation to Model Interconnect-Scale Power Systems from Loads to Generators

Co-simulation is a modeling technique that allows analysts to combine simulation tools and their corresponding models to exchange data during run-time, allowing the creation of larger and more complex models across heterogeneous domains. HELICS is a co-simulation platform developed over the past six years that has been shown to be effective for these multi-domain analysis. Recently, a HELICS-based analysis was completed where the ERCOT electrical interconnect in the United States was modeled in high detail from bulk power system generation to individual customer loads. This model was used to evaluate a flat-rate and transactive energy tariff with integrated wholesale and retail real-time and day-ahead energy markets. This modeling allows detailed analysis showing how the operations of the power system under these tariffs impact all actors in the power system, from individual customers to bulk power system operators.

co-simulation, HELICS, transactive energy system, ↗