Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Visualization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
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 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↗
DOE OSTI2017

Visualization of the microwave beam generated by a plasma relativistic microwave amplifier

A method based on the detection of emission of a dielectric screen with metal microinclusions in open air is applied to visualize the transverse structure of a high-power microwave beam. In contrast to other visualization techniques, the results obtained in this work provide qualitative information not only on the electric field strength, but also on the structure of electric field lines in the microwave beam cross section. The interpretation of the results obtained with this method is confirmed by numerical simulations of the structure of electric field lines in the microwave beam cross section by means of the CARAT code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Visual Analytics of Heterogeneous Data Using Hypergraph Learning

For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. Thesemodels represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach forthe classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets.To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. Here, we demonstrate the capability of our approach via two real-world cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Visualizing urban microclimate and quantifying its impact on building energy use in San Francisco

© 2019 Association for Computing Machinery. Weather data at nearby airports are usually used in building energy simulation to estimate energy use in buildings or evaluate building design or retrofit options. However, due to urbanization and geography characteristics, local weather conditions can differ significantly from those at airports. This study presents the visualization of 10-year hourly weather data measured at 27 sites in San Francisco, aiming to provide insights into the urban microclimate and urban heat island effect in San Francisco and how they evolve during the recent decade. The 10-year weather data are used in building energy simulations to investigate its influence on energy use and electrical peak demand, which informs the city's policy making on building energy efficiency and resilience. The visualization feature is implemented in CityBES, an open web-based data and computing platform for urban building energy research.

Hong, T↗
DOE OSTI2020

Parallel Embeddings: a Visualization Technique for Contrasting Learned Representations

We introduce ``Parallel Embeddings'', a new technique that generalizes the classical Parallel Coordinates visualization technique from a sequence of features within a single matrix to a sequence of multiple feature matrices. This is accomplished by separately embedding each matrix in the sequence into a 2-D frame, juxtaposing these 2-D representations, then connecting points between frames. We outline how this visualization can be used for several ``model comparison'' tasks in machine learning. We compare user performance with Parallel Embeddings to a baseline using the TensorFlow Embedding Projector for estimating model accuracy and understanding conceptual model differences. We found that users were more accurate with Parallel Embeddings for model comparison and that users learned how to use Parallel Embeddings more quickly than TensorFlow Embedding Projector. Furthermore, users' analytical process using Embedding Projector was positively affected by using Parallel Embeddings beforehand.

Model comparison, machine learing, machine learnin↗
DOE OSTI2023

VTK-m: Visualization for the Exascale Era and Beyond

A recent trend in modern high-performance computing is the increasing use of hybrid architectures, where the vast majority of performance comes from accelerators. Modern accelerators are based on Graphics Processing Units (GPU) that contain many low power cores that in their aggregate provides an extremely high computation rate. Current and future CPU processors are requiring more explicit parallelism as each successive version of the hardware packs in more cores, and technologies like hyperthreading and vector operations require even more parallel processing to leverage each core’s full potential. As an example, the Frontier supercomputer installed at Oak Ridge National Laboratories recently hit a record breaking 1.1 exaflops1 on the LINPACK HPC benchmark [Shoemaker 2022]. The system contains 37632 AMD MI250x GPUs which requires more than half a billion threads to keep the system fully utilized [Khizeran 2022].VTK-m is a toolkit of scientific visualization algorithms for these emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures.

Bolstad, Mark↗
DOE OSTI2023

Advancing Comprehension of Quantum Application Outputs: A Visualization Technique

Noise in quantum computers presents a challenge for the users of quantum computing despite the rapid progress we have seen in the past few years in building quantum computers. Existing works have addressed the noise in quantum computers using a variety of mitigation techniques since error correction requires a large number of qubits which is infeasible at present. One of the consequences of quantum computing noise is that users are unable to reproduce similar output from the same quantum computer at different times, let alone from various quantum computers. In this work, we have made initial attempts to visualize quantum basis states for all the circuits that were used in quantum machine learning from various quantum computers and noise-free quantum simulators. We have opened up a pathway for further research into this field where we will be able to isolate noisy states from non-noisy states leading to efficient error mitigation. This is where our work provides an important step in the direction of efficient error mitigation. Our work also provides a ground for quantum noise visualization in the case of large numbers of qubits.

Senapati, Priyabrata↗
DOE OSTI2024

A Visual Analytic Platform for Interactive Validation of Human Mobility Simulations

Human mobility insights guide domain experts in an array of decisions, including critical infrastructure design, disaster response, epidemic modeling, national security, and policy making. Due to the inherent noise and privacy concerns in real-world individual-level mobility data, it is often preferred to leverage simulators that generate synthetic mobility data instead. However, it is critical to inspect and validate the output of such simulators to ensure the synthetic data is aligned with the characteristics of the population and the area of interest known to domain experts. While there exist many quantitative approaches for validating synthetic data, we argue it is also important to also validate such data qualitatively to capture aspects that are known to domain experts but difficult to quantify. In this work, we demonstrate a visual analytic platform that empowers domain experts to interact with their simulation outputs along spatial and temporal dimensions. By augmenting automated techniques and human skills, our visual analytic platform is a step towards interactive capabilities for model steering and quality control of mobility simulators.

Monadjemi, Shayan↗
DOE OSTI2025

Neutron-Friendly Li-Ion Battery Coin Cell for In Situ 3D Visualization of Li Plating

Advanced battery characterization using in situ/operando neutron imaging is critical for uncovering degradation modes such as lithium (Li) plating in Li-ion batteries (LIBs). However, conventional LIBs hinder operando neutron radiography (NR) and in situ neutron micro-computed tomography (N-μCT) for visualizing Li plating near the graphite-separator interface due to strong attenuation from hydrogen-rich components like PP–PE–PP separators, electrolyte, and Fe-based spacers. In this work, we designed and tested a neutron-friendly battery (NFB) optimized for in situ Li detection during extreme fast charging (XFC). Guided by neutron attenuation cross-sections and material transmission, the NFB enables clear visualization at the graphite–separator interface, which is typically opaque in standard LIBs. Electrochemical tests show the NFB exhibits voltage/current responses like standard cells for up to 50 XFC cycles. However, its lower reversibility and capacity are likely due to Cu-coated Al spacer degradation from delamination or corrosion. We propose titanium spacers as a more stable alternative, albeit requiring custom machining. Using this optimized cell, we achieved simultaneous neutron tomography of multiple cells, capturing in situ 3D images of dead Li accumulation, particularly near graphite edges. These heterogeneous deposits and disconnected Li clusters suggest localized current density hotspots during XFC.

25 ENERGY STORAGE↗