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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Visualizations for Real-time Pricing Demonstration

In this paper, the visualization tools created for monitoring the operations of a real-time pricing demonstration system that runs at a distribution feeder level are presented. The information these tools provide gives insights into demand behavior from automated price responsive devices, distribution feeder characteristics, impact of weather on system’s development, and other significant dynamics. Given the large number of devices that bid into a feeder-level real-time electricity market, new techniques are explored to summarize the present state of the system and contrast that with previous trends as well as future projections. To better understand the system behavior and correctly inform decision-making procedures, effective visualization of the data is imperative.

Marinovici, Maria C.↗
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 OSTI2025

The Origins of the ParaView and VisIt Scientific Visualization Tools

ParaView and VisIt play a key role in the visual understanding of scientific simulation data. These tools are open source, designed to handle extremely large datasets, and can run on supercomputers and large-scale display walls. They are in daily use by scientists, practitioners, and students at supercomputing centers, in industry, and at universities. In conclusion, this article gives personal accounts of the origins of these visualization tools.

Ahrens, James [Los Alamos National Laboratory (LAN↗
DOE OSTI2018

Visualization and Simulation of Carbon Structures with Higher Genus

The Computational Science Initiative (CSI) department at Brookhaven National Laboratory involves varied and interdisciplinary work through out all sciences. In particular, we worked within the realm of computational chemistry through the means of studying carbon nanotori structures via their visualization and simulation. Compounds like graphene and other carbon allotropes,such as the nanotorus, have been in the eye of scientists for several years due to their unique polyhex structures which provide unique topologies and therefore possibly unique material properties. C++ code to generate Cartesian coordinates for an arbitrary torus was utilized to allow the use of molecular editing and modeling programs, such as Avogadro or Visual Molecular Dynamics (VMD) to design multiple genus nanocarbon toroidal structures. Several files were generated that contained multiple genus to them, namely a double torus, triple torus, and quadruple torus, all based off of a single genus carbon nanotorus from previous said code. Bond angle information was then utilized through .report files generated from structures files (.xyz or .cml) to allow for vertices and thereby neighboring atoms to the vertices to be identified. With these identified atoms for the above multiple genus structures, as well as for the C240 buckyball fullerene and carbon nanotorus, we wrote C++ code to generate an adjacency matrix for each respective one. These matrices were then subsequently used to generate eigenvalues and eigenvectors through a Mathmatica function for each structure. Using these values we can study the energies of these systems and thereby its functionalities and properties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗
DOE OSTI2016

Visual Data-Analytics of Large-Scale Parallel Discrete-Event Simulations

Parallel discrete-event simulation (PDES) is an important tool in the codesign of extreme-scale systems because PDES provides a cost-effective way to evaluate designs of highperformance computing systems. Optimistic synchronization algorithms for PDES, such as Time Warp, allow events to be processed without global synchronization among the processing elements. A rollback mechanism is provided when events are processed out of timestamp order. Although optimistic synchronization protocols enable the scalability of large-scale PDES, the performance of the simulations must be tuned to reduce the number of rollbacks and provide an improved simulation runtime. To enable efficient large-scale optimistic simulations, one has to gain insight into the factors that affect the rollback behavior and simulation performance. We developed a tool for ROSS model developers that gives them detailed metrics on the performance of their large-scale optimistic simulations at varying levels of simulation granularity. Model developers can use this information for parameter tuning of optimistic simulations in order to achieve better runtime and fewer rollbacks. In this work, we instrument the ROSS optimistic PDES framework to gather detailed statistics about the simulation engine. We have also developed an interactive visualization interface that uses the data collected by the ROSS instrumentation to understand the underlying behavior of the simulation engine. The interface connects real time to virtual time in the simulation and provides the ability to view simulation data at different granularities. We demonstrate the usefulness of our framework by performing a visual analysis of the dragonfly network topology model provided by the CODES simulation framework built on top of ROSS. The instrumentation needs to minimize overhead in order to accurately collect data about the simulation performance. To ensure that the instrumentation does not introduce unnecessary overhead, we perform a scaling study that compares instrumented ROSS simulations with their noninstrumented counterparts in order to determine the amount of perturbation when running at different simulation scales.

Ross, Caitlin↗
DOE OSTI2023

VISILIENCE: An Interactive Visualization Framework for Resilience Analysis using Control-Flow Graph

Soft errors have become one of the major concerns for the error resilience of HPC applications, as those errors can cause HPC applications to generate serious outcomes such as Silent Data Corruptions (SDCs). A large body of approaches has been proposed to analyze the resilience of HPC applications. However, existing studies rarely address the challenges of the analysis result perception. Specifically, resilience analysis techniques often produce a massive volume of unstructured data, making it difficult for programmers to conduct the resilience analysis due to non-intuitive raw data. Furthermore, different analysis models produce diverse results with multiple levels of details, which may create hurdles to compare and explore the resilience of HPC program execution. To this end, we present VISILIENCE, an interactive VISual resILIENCE analysis framework to allow programmers to facilitate the resilience analysis of HPC applications. In particular, VISILIENCE leverages an effective visualization approach Control Flow Graph (CFG) to present a function execution. In addition, three widely-used models for resilience analysis (i.e., Y-Branch, IPAS, and TRIDENT) are seamlessly embedded into the framework for resilience analysis and result comparison. Multiple case studies have been conducted to demonstrate the effectiveness of our proposed framework VISILIENCE.

Jiang, Hailong↗
DOE OSTI2021

VAINE: Visualization and AI for Natural Experiments

Natural experiments are observational studies where the assignment of treatment conditions to different populations occur by chance ``in the wild''. Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson’s paradox through two use cases.

Guo, Grace↗
DOE OSTI2017

Visualizing the Uncertainty of Graph-based 2D Segmentation with Min-path Stability

Here, this paper presents a novel approach to visualize the uncertainty in graph-based segmentations of scalar data. Segmentation of 2D scalar data has wide application in a variety of scientific and medical domains. Typically, a segmentation is presented as a single unambiguous boundary although the solution is often uncertain due to noise or blur in the underlying data as well as imprecision in user input. Our approach provides insight into this uncertainty by computing the “min-path stability”, a scalar measure analyzing the stability of the segmentation given a set of input constraints. Our approach is efficient, easy to compute, and can be generally applied to either graph cuts or live-wire (even partial) segmentations. In addition to its general applicability, our new approach to graph cuts uncertainty visualization improves on the time complexity of the current state-of-the-art with an additional fast approximate solution. We also introduce a novel query enabled by our approach which provides users with alternate segmentations by efficiently extracting local minima of the segmentation optimization. Finally, we evaluate our approach and demonstrate its utility on data from scientific and medical applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid-latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling

In this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state‐of‐the‐art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state‐of‐the‐art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human‐brain HARDI datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Coherent Image Layout using an Adaptive Visual Vocabulary

When querying a huge image database containing millions of images, the result of the query may still contain many thousands of images that need to be presented to the user. We consider the problem of arranging such a large set of images into a visually coherent layout, one that places similar images next to each other. Image similarity is determined using a bag-of-features model, and the layout is constructed from a hierarchical clustering of the image set by mapping an in-order traversal of the hierarchy tree into a space-filling curve. This layout method provides strong locality guarantees so we are able to quantitatively evaluate performance using standard image retrieval benchmarks. Performance of the bag-of-features method is best when the vocabulary is learned on the image set being clustered. Because learning a large, discriminative vocabulary is a computationally demanding task, we present a novel method for efficiently adapting a generic visual vocabulary to a particular dataset. We evaluate our clustering and vocabulary adaptation methods on a variety of image datasets and show that adapting a generic vocabulary to a particular set of images improves performance on both hierarchical clustering and image retrieval tasks.

Multimedia Analytics, image clustering, content ba↗
DOE OSTI2016

Subsampling-based compression and flow visualization

As computational capabilities increasingly outpace disk speeds on leading supercomputers, scientists will, in turn, be increasingly unable to save their simulation data at its native resolution. One solution to this problem is to compress these data sets as they are generated and visualize the compressed results afterwards. We explore this approach, specifically subsampling velocity data and the resulting errors for particle advection-based flow visualization. We compare three techniques: random selection of subsamples, selection at regular locations corresponding to multi-resolution reduction, and introduce a novel technique for informed selection of subsamples. Furthermore, we explore an adaptive system which exchanges the subsampling budget over parallel tasks, to ensure that subsampling occurs at the highest rate in the areas that need it most. We perform supercomputing runs to measure the effectiveness of the selection and adaptation techniques. Overall, we find that adaptation is very effective, and, among selection techniques, our informed selection provides the most accurate results, followed by the multi-resolution selection, and with the worst accuracy coming from random subsamples.

Agranovsky, Alexy↗
DOE OSTI2014

TH-C-17A-07: Visualizing and Quantifying Radiation Therapy in Real-Time Using a Novel Beam Imaging Technique

Purpose: Radiation therapy uses invisible high energy X-rays to treat an invisible tumor. Proper positioning of the treatment beam relative to the patient's anatomy during dose delivery is critically important to the success of treatment. We develop and characterize a novel radiation therapy beam visualization technique for real-time monitoring of patient treatment. Methods: Custom made flexible scintillator sheets were fabricated from gadolinium oxysulfide (GOS) particles that had been doped with terbium within a silicone elastomer matrix. Sheets of several thicknesses ranging from 0.3 to 1mm were prepared and tested. Sheets were exposed to megavoltage X-ray and electron beams from a Varian linac and the resulting optical signal was collected by multiple CMOS cameras placed in the treatment room. Real-time images were collected for different beam energies and dose rates. Signal intensity and SNR were calculated by processing the acquired images. Results: All signals were detectable in the presence of full room lighting and at an integration time of 45ms. Average signal intensity and SNR increased with both sheet thickness and dose rate and decreased with beam energy and incident light. For a given sheet thickness and beam energy the correlation between dose rate and signal intensity was highly linear. Increased sheet thickness or dose rate results in a linear increase in the detected signal. All results are consistent with analytical approximations. Conclusion: The technique offers a means of accurately visualizing a radiation therapy beam shape and fluence in real time. The effects of salient parameters have been characterized and will enable further optimization of the technique as it is implemented into the clinical workflow. The project described was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health through UL1 TR001085.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-C-17A-09: Direct Visualization and Monitoring of Medical Radiation Beams in Air

Purpose: Radiation therapy errors are rare but potentially catastrophic. Recent fatal incidents could have been avoided by utilizing real-time methods of monitoring delivery of radiation during treatment. However, few existing methods are practical enough to be used routinely. The study presents the first experimental demonstration of a novel non-perturbing method of monitoring radiation therapy through the phenomena of air scintillation. Methods: Monitoring of radiation delivery was devised by leveraging the phenomena of nitrogen excitation in air by ionizing radiation. The excitation induced weak luminescence in the 300–400 nm range, a process called air scintillation. An electron-multiplication charge-coupled device camera (f/0.95 lens; 440 nm shortpass) was set-up in a clinical treatment vault and was used to capture air scintillation images of kilovoltage and megavoltage beams. Monte Carlo simulations were performed to determine the correlation of radiation dose to air scintillation. Results: Megavoltage beams from a Varian Clinac 21EX and kilovoltage beams from an orthovoltage unit (50 kVp, 30 mA) were visualized with a relatively short exposure time (10 s). Cherenkov luminescence produced in a plastic transparent phantom did not interfere with detection of air scintillation. The image intensity displayed an inverse intensity falloff (r{sup 2} = 0.89) along the central axis and was proportional to dose rate (r{sup 2} = 0.9998). As beam energy increased, the divergence of the imaged beam decreased. Last, air scintillation was visualized during a simulated total skin irradiation electron treatment. Conclusion: Air scintillation can be clinically detected to monitor a radiation beam in an inexpensive and non-perturbing manner. This new method is advantageous in monitoring for gross delivery and uniquely capable of wide area in a single acquisition, such as the case for online verification of total body / skin / lymphoid irradiation treatments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-303-10: Impact of Visual Biofeedback On Respiratory Reproducibility in 4DMRI

Purpose: Precise radiation therapy (RT) for abdominal lesions is complicated by respiratory motion and suboptimal soft tissue contrast in 4DCT. 4DMRI offers improved contrast. However, long scan times and irregular breathing patterns can be limiting. To address this, we introduced visual biofeedback (VBF) into 4DMRI. Methods: Eight healthy volunteers were consented to an IRB-approved protocol. Prospective respiratory-triggered, T2-weighted coronal 4DMRIs were acquired on an open 1.0T MR- SIM. VBF was integrated using an MR-compatible interactive breath-hold control system. Subjects visually monitored their breathing patterns to stay within pre-determined tolerances. 4DMRIs were acquired with and without VBF for 2–8 phase acquisitions. Normalized respiratory waveforms were evaluated for scan time, duty cycle (programmed/acquisition time), breathing period, end-inhale (EI) amplitude, and breathing variability (coefficient of variation, EI-COV). B-spline-based deformable image registration propagated contours from end-exhale (EE) to EI phases. Respiration-induced liver motion was calculated via centroid analysis and compared. Results: Incorporating VBF reduced 2-phase acquisition time (4.7±0.6 and 5.6±1.4 minutes with and without VBF, respectively) while reducing the amplitude EI-COV by 53.0±8.1%. On average, incorporating VBF reduced 8-phase 4DMRI acquisition times by 1.7±1.2 minutes and EI-COVs by 46.0±15.8%. Using VBF yielded higher duty cycles than free breathing (34.7% versus 28.3%, respectively). 4DMRI acquisition time was reduced for the cohort with VBF despite breathing rate remaining similar (10.5±4.0 with vs. 10.6±3.3 BPM without). Respiratory waveforms showed higher EI amplitude with VBF (0.84±0.05 a.u.) as compared to 0.72±0.06 a.u. without. This translated to differences in liver excursions, where superior-inferior, anterior-posterior, and left-right EE-EI displacements were 14.3±3.6, 4.8±2.1, and 1.6±1.0 mm, respectively, with VBF compared to 13.0±6.2, 3.8±2.4, and 1.2±1.4 mm without. Conclusion: Incorporating VBF system into 4DMRI substantially reduced acquisition time and breathing variability. While VBF reduced liver motion variability, differences in excursion were observed, suggesting that implementation will be required throughout the RT workflow. Research supported in part by a grant from Philips HealthCare (Best, Netherlands) and an equipment evaluation agreement with MedSpira.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Synchrotron x-ray imaging visualization study of capillary-induced flow and critical heat flux on surfaces with engineered micropillars

Over the past several decades, phenomena related to critical heat flux (CHF) on structured surfaces have received a large amount of attention from the research community. The purpose of such research has been to enhance the safety and efficiency of a variety of thermal systems. A number of theories have been put forward to explain the key CHF enhancement mechanisms on structured surfaces. However, these theories have not been confirmed experimentally due to limitations in the available visualization techniques and the complexity of the phenomena. To overcome the limitations of the previous visualization techniques and elucidate the CHF enhancement mechanism on the structured surfaces, we introduce synchrotron X-ray imaging with high spatial (~2 μm) and time (~20,000 Hz) resolutions. Lastly, this technique has enabled us to confirm that capillary-induced flow is the key CHF enhancement mechanism on structured surfaces.

42 ENGINEERING↗
DOE OSTI2021

Visualizing broken symmetry and topological defects in a quantum Hall ferromagnet

The interaction between electrons in graphene under high magnetic fields drives the formation of a rich set of quantum Hall ferromagnetic (QHFM) phases with broken spin or valley symmetry. Visualizing atomic-scale electronic wave functions with scanning tunneling spectroscopy (STS), we resolved microscopic signatures of valley ordering in QHFM phases and spectral features of fractional quantum Hall phases of graphene. At charge neutrality, we observed a field-tuned continuous quantum phase transition from a valley-polarized state to an intervalley coherent state, with a Kekulé distortion of its electronic density. Mapping the valley texture extracted from STS measurements of the Kekulé phase, we could visualize valley skyrmion excitations localized near charged defects. Our techniques can be applied to examine valley-ordered phases and their topological excitations in a wide range of materials.

valley symmetry↗