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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. Here, we devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Attribute-Aware RBFs: Interactive Visualization of Time Series Particle Volumes Using RT Core Range Queries

Smoothed-particle hydrodynamics (SPH) is a mesh-free method used to simulate volumetric media in fluids, astrophysics, and solid mechanics. Visualizing these simulations is problematic because these datasets often contain millions, if not billions of particles carrying physical attributes and moving over time. Radial basis functions (RBFs) are used to model particles, and overlapping particles are interpolated to reconstruct a high-quality volumetric field; however, this interpolation process is expensive and makes interactive visualization difficult. Existing RBF interpolation schemes do not account for color-mapped attributes and are instead constrained to visualizing just the density field. To address these challenges, we exploit ray tracing cores in modern GPU architectures to accelerate scalar field reconstruction. We use a novel RBF interpolation scheme to integrate per-particle colors and densities, and leverage GPU-parallel tree construction and refitting to quickly update the tree as the simulation animates over time or when the user manipulates particle radii. We also propose a Hilbert reordering scheme to cluster particles together at the leaves of the tree to reduce tree memory consumption. Finally, we reduce the noise of volumetric shadows by adopting a spatially temporal blue noise sampling scheme. Our method can provide a more detailed and interactive view of these large, volumetric, time-series particle datasets than traditional methods, leading to new insights into these physics simulations.

Particle Volumes↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
DOE OSTI2017

Toward a Visualization-Supported Workflow for Cyber Alert Management using Threat Models and Human-Centered Design

Cyber network analysts follow complex processes in their investigations of potential threats to their network. Much research is dedicated to providing automated tool support in the effort to make their tasks more efficient, accurate, and timely. This tool support comes in a variety of implementations from machine learning algorithms that monitor streams of data to visual analytic environments for exploring rich and noisy data sets. Cyber analysts, however, often speak of a need for tools which help them merge the data they already have and help them establish appropriate baselines against which to compare potential anomalies. Furthermore, existing threat models that cyber analysts regularly use to structure their investigation are not often leveraged in support tools. We report on our work with cyber analysts to understand they analytic process and how one such model, the MITRE ATT&CK Matrix [32], is used to structure their analytic thinking. We present our efforts to map specific data needed by analysts into the threat model to inform our eventual visualization designs. We examine data mapping for gaps where the threat model is under-supported by either data or tools. We discuss these gaps as potential design spaces for future research efforts. We also discuss the design of a prototype tool that combines machine-learning and visualization components to support cyber analysts working with this threat model.

Cyber Security↗
DOE OSTI2017

ISAVS: Interactive Scalable Analysis and Visualization System

Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. Here, we demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data---including electric line utilization, nodal price, and available generation flexibility---in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2015

Visual inspection reliability for precision manufactured parts

Sandia National Laboratories conducted an experiment for the National Nuclear Security Administration to determine the reliability of visual inspection of precision manufactured parts used in nuclear weapons. In addition visual inspection has been extensively researched since the early 20th century; however, the reliability of visual inspection for nuclear weapons parts has not been addressed. In addition, the efficacy of using inspector confidence ratings to guide multiple inspections in an effort to improve overall performance accuracy is unknown. Further, the workload associated with inspection has not been documented, and newer measures of stress have not been applied.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Visualization at exascale: Making it all work with VTK-m

The VTK-m software library enables scientific visualization on exascale-class supercomputers. Exascale machines are particularly challenging for software development in part because they use GPU accelerators to provide the vast majority of their computational throughput. Algorithmic designs for GPUs and GPU-centric computing often deviate from those that worked well on previous generations of high-performance computers that relied on traditional CPUs. Fortunately, VTK-m provides scientific visualization algorithms for GPUs and other accelerators. VTK-m also provides a framework that simplifies the implementation of new algorithms and adds a porting layer to work across multiple processor types. This paper describes the main challenges encountered when making scientific visualization available at exascale. Here, we document the surprises and obstacles faced when moving from pre-exascale platforms to the final exascale designs and the performance on those systems including scaling studies on Frontier, an exascale machine with over 37,000 AMD GPUs. We also report on the integration of VTK-m with other exascale software technologies. Finally, we show how VTK-m helps scientific discovery for applications such as fusion and particle acceleration that leverage an exascale supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Design and feasibility of a novel program of cervical screening in Nigeria: self-sampled HPV testing paired with visual triage

Background: Accelerated global control of cervical cancer would require primary prevention with human papillomavirus (HPV) vaccination in addition to novel screening program strategies that are simple, inexpensive, and effective. We present the feasibility and outcome of a community-based HPV self-sampled screening program. Methods: In Ile Ife, Nigeria, 9406 women aged 30–49 years collected vaginal self-samples, which were tested for HPV in the local study laboratory using Hybrid Capture-2 (HC2) (Qiagen). HPV-positive women were referred to the colposcopy clinic. Gynecologist colposcopic impression dictated immediate management; biopsies were taken when definite acetowhitening was present to produce a histopathologic reference standard of precancer (and to determine final clinical management). Retrospective linkage to the medical records identified 442 of 9406 women living with HIV (WLWH). Results: With self-sampling, it was possible to screen more than 100 women per day per clinic. Following an audio-visual presentation and in-person instructions, overall acceptability of self-sampling was very high (81.2% women preferring self-sampling over clinician collection). HPV positivity was found in 17.3% of women. Intensive follow-up contributed to 85.9% attendance at the colposcopy clinic. Of those referred, 8.2% were initially treated with thermal ablation and 5.6% with large loop excision of transformation zone (LLETZ). Full visibility of the squamocolumnar junction, necessary for optimal visual triage and ablation, declined from 68.5% at age 30 to 35.4% at age 49. CIN2+ and CIN3+ (CIN- Cervical intraepithelial neoplasia), including five cancers, were identified by histology in 5.9 and 3.2% of the HPV-positive women, respectively (0.9 and 0.5% of the total screening population), leading to additional treatment as indicated. The prevalences of HPV infection and CIN2+ were substantially higher (40.5 and 2.5%, respectively) among WLWH. Colposcopic impression led to over- and under-treatment compared to the histopathology reference standard. Conclusion: A cervical cancer screening program using self-sampled HPV testing, with colposcopic immediate management of women positive for HPV, proved feasible in Nigeria. Based on the collected specimens and images, we are now evaluating the use of a combination of partial HPV typing and automated visual evaluation (AVE) of cervical images to improve the accuracy of the screening program.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data

Background: Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge. Results: We present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented. Conclusions: VizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/ VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Computational Science - Scientific Visualization: Closeout Certifications; CSSV Newsletter Volume I; CSSV Newsletter Volume II; CSSV Activity Journal; CSSV Final Financial Report (Final Technical Progress Report)

This Project's third year of implementation in 2007-2008, the final year, as designated by Elizabeth City State University (ECSU), in cooperation with the National Association of Mathematicians (NAM) Inc., in an effort to promote research and research training programs in computational science & scientific visualization (CSSV). A major goal of the Project was to attract the energetic and productive faculty, graduate and upper division undergraduate students of diverse ethnicities to a program that investigates science and computational science issues of long-term interest to the Department of Energy (DoE) and the nation. The breadth and depth of computational science & scientific visualization and the magnitude of resources available are enormous for permitting a variety of research activities. ECSU's Computational Science-Science Visualization Center will serve as a conduit for directing users to these enormous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Experiments at Scale with In-Situ Visualization Using ParaView/Catalyst in RAGE

In this paper I describe some numerical experiments performed using the ParaView/Catalyst in-situ visualization infrastructure deployed in the Los Alamos RAGE radiation-hydrodynamics code to produce images from a running large scale 3D ICF simulation on the Cielo supercomputer at Los Alamos. The detailed procedures for the creation of the visualizations using ParaView/Catalyst are discussed and several images sequences from the ICF simulation problem produced with the in-situ method are presented. My impressions and conclusions concerning the use of the in-situ visualization method in RAGE are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Graph Sampling for Visual Analytics

Effectively visualizing large graphs and capturing the statistical properties are two challenging tasks. To aid in these two tasks, many sampling approaches for graph simplification have been proposed, falling into three categories: node sampling, edge sampling, and traversal-based sampling. It is still unknown which approach is the best. We evaluate commonly used graph sampling methods through a combined visual and statistical comparison of graphs sampled at various rates. We conduct our evaluation on three graph models: random graphs, small-world graphs, and scale-free graphs. Initial results indicate that the effectiveness of a sampling method is dependent on the graph model, the size of the graph, and the desired statistical property. This benchmark study can be used as a guideline in choosing the appropriate method for a particular graph sampling task, and the results presented can be incorporated into graph visualization and analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Novel Analysis and Visualization of Chemical Events for Public Health Surveillance

Pacific Northwest National Laboratory hosted an intern-based web application development contest in the summer of 2016 centered around developing novel chemical surveillance applications to aid in health situational awareness. Making up the three teams were three graduate students (n=9) from various US schools majoring in non-public health domains, such as computer science and user design. The interns successfully developed three applications that demonstrated a value-add to chemical surveillance—ChemAnalyzer (text analytics), RetroSpect (retrospective analysis of chemical events),and ToxicBusters (geo-based trend analytics). These applications will be the basis for the first chemical surveillance application to be incorporated into the DTRA Biosurveillance Ecosystem (BSVE).Pacific Northwest National Laboratory (PNNL), on behalf the Defense Threat Reduction Agency (DTRA; project number CB10190), hosts an annual intern- based web app development contest. Previous competitions have focused on mobile biosurveillance. The 2016 competition pivoted away from biosurveillance to focus on addressing challenges within the field of chemical surveillance and increasing public health chemical situational awareness. The result of the app will be integrated within the DTRA BSVE. PNNL hosted nine graduate interns for a 10-week period in the summer of 2016 as participants in a summer web application development contest. Students were drawn from such fields as software engineering and user experience and design and placed into three teams of three students. The challenge presented to the interns was to design and develop a fully-functional web application that would address a critical need within the chemical surveillance community. The interns developed their own ideas (vetted by PNNL and DTRA), discovered and integrated their own data sources, and produced their own visualizations and analytics, independent of any assistance outside of that provided in an advisory capacity. The competition end with a judging event with a panel of subject matter experts and cash awards were distributed to the teams. Each team produced a unique application. Although there was mild overlap between some of the ideas, the applications were developed independently and each reflected the unique contributions of the teams. ChemAnalyzer is a text-analytics platform designed to facilitate more data-driven decision, given a corpus of text data about a chemical event. Their platform provided the ability to automatically identify and highlight key words in documents related to chemical events. The keywords are drawn from an ontology installed with the system, as well as any user-identified keywords. The ChemAnalyzer team finished in third place. The RetroSpect team developed a visual analytic tool for performing retrospective analysis and monitoring of chemical events. Their app provided the ability to search and analyze past events, as well as visualization of state and county information for the recorded chemical events. The RetroSpect team finished in second place. The Toxic busters team - the winners of the competition - created a geo-based situational awareness tool for tracking chemical events. Their app featured an updateable map overlay, search functionality for finding specific or related events, incident and city/state/national-level statistics and trends, as well as news and social media integration based on keywords related to chemical surveillance. Each of the apps developed by the teams provides value to an analyst tasked with monitoring chemical events. The apps integrated unique data sources to provides a full picture of a chemical event, and its effects upon the surrounding population. This integrated analytics provides a valuable benefit over existing workflows, where analysts must monitor news, social, and other information sources manually for real-time information. The apps developed by these interns are designed to enable identification and analysis of the incident as quickly as possible, allowing for more timely assessments of the incident and its impacts. The web app development contest provided a unique opportunity for students to learn about the emerging needs in chemical surveillance as it relates to health situational awareness. Students were drawn from a variety of fields and were tasked with developing novel web apps addressing some of the most pressing challenges in the field of chemical surveillance. The ideas generated by the students will help form the basis for future chemical surveillance application development to be integrated with the DTRABSVE.

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

Immersive Visualization for Materials Science Data Analysis using the Oculus Rift

In this paper, we propose strategies and objectives for immersive data visualization with applications in materials science using the Oculus Rift virtual reality headset. We provide background on currently available analysis tools for neutron scattering data and other large-scale materials science projects. In the context of the current challenges facing scientists, we discuss immersive virtual reality visualization as a potentially powerful solution. We introduce a prototype immersive visual- ization system, developed in conjunction with materials scientists at the Spallation Neutron Source, which we have used to explore large crystal structures and neutron scattering data. Finally, we offer our perspective on the greatest challenges that must be addressed to build effective and intuitive virtual reality analysis tools that will be useful for scientists in a wide range of fields.

analytics↗
DOE OSTI2016

EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data

We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based on ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.

ensemble simulation↗
DOE OSTI2018

Experts’ Familiarity Versus Optimality of Visualization Design: How Familiarity Affects Perceived and Objective Task Performance

Experts, cutting across domains like biology, climate science, cyber, energy, etc., frequently use visualizations as the principal medium for making analytical judgments or for presenting the results of their analysis. However, scientists are often skeptical about adopting new visualization methods over familiar ones, although the latter might be perceptually sub-optimal. This is due to the use of the familiarity heuristic, where the perceived cognitive ease in processing familiar representations of information leads scientists to undermine the effect of visualization best practices. Recent studies have shown that this often results in a discrepancy between scientists’ perceived and actual performance quality. It has also been shown that in some cases, participatory design sessions and qualitative and quantitative user studies are able to mitigate the effects of such bias. In this paper, we discuss the potential causes and effects of familiarity related biases with examples from recent studies and reflect on the related research questions.

visualization, climate science, cognitive bias, fa↗