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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Analyzing Multifaceted Scientific Data with Topological Analytics (Final Technical Report)

This final technical report describes the activities undertaken through Department of Energy, Office of Science, Advanced Scientific Computing Research Early Career award DE-SC-0019039, “Analyzing Multifaceted Scientific Data with Topological Analytics." This report summarizes contributions made toward the research of visualization, machine learning, and topological data analysis of complex simulation data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Public Health Analysis Transport Optimization Model v. 1.0

PHANTOM models logistic functions of national public health systems. The system enables public health officials to visualize and coordinate options for public health surveillance, diagnosis, response and administration in an integrated analytical environment. Users may simulate and analyze system performance applying scenarios that represent current conditions or future contingencies what-if analyses of potential systemic improvements. Public health networks are visualized as interactive maps, with graphical displays of relevant system performance metrics as calculated by the simulation modeling components.

Beyeler, Walt↗
DOE OSTI2019

Safety Analytics Forecasting and Evaluation Reporting (SAFER)

The SAFER Sandbox is a web application for testing various types of data visualization tools that analyze safety data across national laboratories. By enabling the user to visualize this data through visual and textual analyses, the SAFER Sandbox helps to facilitate the research and support for the National Nuclear Security Administration (NNSA) Office of Safety, Infrastructure and Operations (NA-50). Safety data across the national laboratory complex is not currently analyzed in one location. This prohibits administrators from viewing safety data trends and making large scale decisions based on a holistic view of the data. By implementing data analytics web services such as representative text summary, noun phrase extraction and graph exploration, users can view and explore safety data based on search queries. The data visualizations in the SAFER Sandbox reveal trends and point to items of concern based on safety data. The SAFER Sandbox is a testing ground for these visualizations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

GUIDE: A Scalable Information Directory Service to Collect, Federate, and Analyze Logs for Operational Insights into a Leadership HPC Facility

In this paper, we describe the GUIDE framework to collect, federate, and analyze log data from the Oak Ridge Leadership Computing Facility's (OLCF), and how we use it derive insights into center operations. We collectsystem logs and extract monitoring data at every level of the various OLCF subsystems, and have developed a suite of pre-processing tools to make the raw data consumable. The cleansed logs are then ingested and federated into a central, scalable data warehouse, Splunk, that offers storage, indexing, querying, and visualization capabilities. We have further developed and deployed a set of analytics tools to analyze these multiple disparate log streams in concert, and derive operational insights. We describe our experience from developing and deploying the GUIDE infrastructure, and deriving valuable insights on the various subsystems, based on two years of operations in the production OLCF environment.

Vazhkudai, Sudharshan↗
DOE OSTI2025

A Typology of Decision-Making Tasks for Visualization

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Z-checker: A framework for assessing lossy compression of scientific data

Because of the vast volume of data being produced by today’s scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. However, lossy compressor developers and users are missing a tool to explore the features of scientific data sets and understand the data alteration after compression in a systematic and reliable way. To address this gap, we have designed and implemented a generic framework called Z-checker. On the one hand, Z-checker combines a battery of data analysis components for data compression. On the other hand, Z-checker is implemented as an open-source community tool to which users and developers can contribute and add new analysis components based on their additional analysis demands. In this article, we present a survey of existing lossy compressors. Then, we describe the design framework of Z-checker, in which we integrated evaluation metrics proposed in prior work as well as other analysis tools. Specifically, for lossy compressor developers, Z-checker can be used to characterize critical properties (such as entropy, distribution, power spectrum, principal component analysis, and autocorrelation) of any data set to improve compression strategies. For lossy compression users, Z-checker can detect the compression quality (compression ratio and bit rate) and provide various global distortion analysis comparing the original data with the decompressed data (peak signal-to-noise ratio, normalized mean squared error, rate–distortion, rate-compression error, spectral, distribution, and derivatives) and statistical analysis of the compression error (maximum, minimum, and average error; autocorrelation; and distribution of errors). Z-checker can perform the analysis with either coarse granularity (throughout the whole data set) or fine granularity (by user-defined blocks), such that the users and developers can select the best fit, adaptive compressors for different parts of the data set. Z-checker features a visualization interface displaying all analysis results in addition to some basic views of the data sets such as time series. To the best of our knowledge, Z-checker is the first tool designed to assess lossy compression comprehensively for scientific data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Scalable Reasoning System (SRS)

SRS is a web-based and mobile toolkit for information visualization and collaboration. It includes server-side components in the form of web services that provide analytical functions such as data clustering and temporal analysis. The SRS client is accessed via a web browser and uses asynchronous javascript and XML (AJAX) to interact with and visualize the results of these web services. Users of SRS can query data sources that are connected to the SRS backend, and query results are presented in the form of coordinated visual displays in a web browser that depict temporal, topical, and spatial relationships in the results. SRS also includes an early stage graphical reasoning diagram interface that helps analysts express and test hypotheses. This software was developed with funding from the Science and Technology Directorate of the U.S. Department of Homeland Security.

Pike, William↗
DOE OSTI2015

Mechanical Serial-Sectioning Data Assistant

Mechanical Serial-Sectioning Data Assistant (MECH-SSDA) is a real-time data analytics software with graphical user-interface that; 1) tracks and visualizes material removal rates for mechanical serial-sectioning experiments using at least two height measurement methods; 2) tracks process time for specific segments of the serial-sectioning experiment; and 3) alerts the user to anomalies in expected removal rate, process time or unanticipated operational pauses

Poulter, Gregory A.↗
DOE OSTI2021

Bridging Scales in Bioenergy and Catalysis: A Review of Mesoscale Modeling Applications, Methods, and Future Directions

Between the molecular and reactor scales, which are familiar to the chemical engineering community, lies an intermediate regime, here termed the “mesoscale,” where transport phenomena and reaction kinetics compete on similar time scales. Bioenergy and catalytic processes offer particularly important examples of mesoscale phenomena owing to their multiphase nature and the complex, highly variable porosity characteristic of biomass and many structured catalysts. In this review, we overview applications and methods central to mesoscale modeling as they apply to reaction engineering of biomass conversion and catalytic processing. A brief historical perspective is offered to put recent advances in context. Applications of mesoscale modeling are described, and several specific examples from biomass pyrolysis and catalytic upgrading of bioderived intermediates are highlighted. Methods including reduced order modeling, finite element and finite volume approaches, geometry construction and import, and visualization of simulation results are described; in each category, recent advances, current limitations, and areas for future development are presented. Owing to improved access to high-performance computational resources, advances in algorithm development, and sustained interest in reaction engineering to sustainably meet societal needs, we conclude that a significant upsurge in mesoscale modeling capabilities is on the horizon that will accelerate design, deployment, and optimization of new bioenergy and catalytic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work here focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. In conclusion, our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Digital Twin: Visualizing the Future of the Power Grid [Slides]

The energy systems are generating data at a scale and complexity that increasingly outpaces our ability to analyze it. This talk argues that rigorous, large-scale visualization is a critical tool for meeting that challenge. Drawing on recent work in the Computational Science Center at the National Laboratory of the Rockies, we trace a progression of visualization research culminating in an immersive digital twin of a real campus power grid. Along the way, we examine why visualization matters at all, where widely-used techniques quietly fail, and what becomes analytically possible when you match the display environment to the scale of the problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Bringing Molecular Dynamics Simulation Data into View

Molecular Dynamics (MD) simulations monitor time-resolved motions of macromolecules. While visualization of MD trajectories allows an instant and intuitive understanding of dynamics and function, so far mainly static representations are provided in published literature. Recent advances in browser technology may allow for the sharing of trajectories through interactive visualization on the web. We believe that by providing intuitive and interactive visualization along with related protocols and analysis data promotes understanding, reliability, and re-usability of MD simulations. Existing barriers for sharing MD simulations are discussed and emerging solutions are highlighted. We predict that interactive visualization of MD trajectories will quickly be adopted by researchers, research consortiums, journals, and funding agencies to gather and distribute results from MD simulations via the web.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

McClay, Wilbert↗
DOE OSTI2019

XScan: An Integrated Tool for Understanding Open Source Community-Based Scientific Code

Many scientific communities have adopted community-based models that integrate multiple components to simulate whole system dynamics. The community software projects’ complexity, stems from the integration of multiple individual software components that were developed under different application requirements and various machine architectures, has become a challenge for effective software system understanding and continuous software development. The paper presents an integrated software toolkit called X-ray Software Scanner (in abbreviation, XScan) for a better understanding of large-scale community-based scientific codes. Our software tool provides support to quickly summarize the overall information of scientific codes, including the number of lines of code, programming languages, external library dependencies, as well as architecture-dependent parallel software features. The XScan toolkit also realizes a static software analysis component to collect detailed structural information and provides an interactive visualization and analysis of the functions. We use a large-scale community-based Earth System Model to demonstrate the workflow, functions and visualization of the toolkit. We also discuss the application of advanced graph analytics techniques to assist software modular design and component refactoring.

Zheng, Weijian↗
DOE OSTI2019

Abstract for CRADA between VariGrid Explorations Inc. and NETL (AGMT-0863)

As the energy industry continues to invest in data, big data, and advanced analytics to improve production, reduce operation and maintenance costs, improve safety, and reduce risks, uncertainty will serve as a limiting factor in the success of these operations. Intuitive communication of uncertainty can provide more transparency in the decision-making process; offering the ability to more accurately evaluate the risk and impact of decisions, identify data and knowledge gaps, and evaluate trade-offs. The issue of uncertainty will thus play a large role in the success of investments that rely on big data and data analytics. However, no robust method for representation of uncertainty in spatial data visuals currently exists. As such, uncertainty is, at best, communicated using multiple independent visuals and aggregated in final displays, or frequently omitted altogether. Failure to accurately and effectively quantify and communicate uncertainty poses a barrier, limiting critical information that would better allow businesses to make more accurate and intelligent decisions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Lift Equivalence and Cancellation for Airfoil Surge–Pitch–Plunge Oscillations

A NACA 0018 airfoil in freestream velocity is oscillated in longitudinal, transverse, and angle-of-attack directions with respect to the freestream velocity, known as surge, plunge, and pitch. The lift-based equivalence method introduces phase shifts between these three motions to construct in-phase sinusoidal components for maximum lift, waveform construction. Lift cancellation is also determined with the exact negative pitch and plunge motion amplitudes found from the equivalence method to achieve out-of-phase wave destruction. Lift cancellation occurs when a combination of these motions is sought to obtain a constant lift magnitude throughout the oscillation cycle. To achieve both equivalence and cancellation of lift, a prescribed pure pitch amplitude through the Theodorsen theory equates the corresponding equivalent plunge amplitude and pitch–plunge phase shift. These Theodorsen, linear superposition findings of pitch–plunge are leveraged toward the Greenberg theory to determine a closed-form, surge–pitch–plunge solution through the addition of a surge–plunge phase shift and optimal surge amplitude for lift cancellation. The lift cancellation surge–pitch–plunge amplitudes define the equivalence amplitude investigated here and theoretically limit the experiment to combinations of the first lift harmonic of the Greenberg theory. The analytical results are then compared with experimental lift force measurements and dye visualization. Finally, the normalized lift differences due to unsteady wake and boundary-layer behavior are examined to explore the extents of the Greenberg theory for these cases of lift-based equivalence and cancellation.

42 ENGINEERING↗
DOE OSTI2020

Advanced User Interface Capabilities [Slides]

This tutorial will review the data plotting and geometry visualization capabilities in the Fulcrum user interface. This tutorial will help you become familiar with Fulcrum’s 2D plot, and 2D and 3D geometry visualization features. You will learn how to identify plottable data items, compose and export plot and plot data for SCALE plot formats (SDF, Ampx MG/CE, PLT, F71, PTP, SPF, ORIGEN Gamma data, etc.) and visualize, navigate, cut, hide, and export the geometry and spatial data (fission-, dose-map, etc.) overlays in 2D and 3D. No prior experience with SCALE is required. Attendees can follow along using 6.3.0-beta.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗