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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ascribe XR v0.1.0

Ascribe XR is an immersive visualization software designed for scientists and engineers working with 3D data sets. Its key features include interactive exploration, multi-user collaboration, and flexible data import capabilities, supporting various formats such as meshes, volumes, and terrain maps. The software utilizes Godot, OpenXR and PC-VR technology to provide an immersive experience. Ascribe XR is used for data analysis, visualization, and collaboration in various fields, enabling users to gain deeper insights into complex data sets. Its advantages over similar technologies include its flexibility, customizability, and ease of use. Ascribe XR's interactive and immersive environment facilitates collaboration and accelerates the discovery process. Compared to traditional 2D visualization tools, Ascribe XR offers a more engaging and intuitive experience, allowing users to explore complex data sets in a more natural and interactive way. Its ability to support multi-user collaboration and flexible data import capabilities make it a versatile tool for various applications. Overall, Ascribe XR provides a unique combination of features, usability, and performance, making it an attractive solution for scientists and engineers working with 3D data sets.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗
DOE OSTI2020

Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. Additionally, these techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

3D visualization↗
DOE OSTI2026

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys

As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., E ( B − V ) < 0.04 ). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in good and bad categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.

Luo, Yufeng (ORCID:0000000246230683)↗
DOE OSTI2021

Volumetric Density-Equalizing Reference Map with Applications

The density-equalizing map, a technique developed for cartogram creation, has been widely applied to data visualization but only for 2D applications. Here, we propose a novel method called the volumetric density-equalizing reference map (VDERM) for computing density-equalizing map for volumetric domains. Given a prescribed density distribution in a volumetric domain in $\mathbb{R}^3$, the proposed method continuously deforms the domain, with different volume elements enlarged or shrunk according to the density distribution. With the aid of the proposed method, medical and sociological data can be visualized via deformations of 3D objects. The method can also be applied to adaptive remeshing and shape modeling. Furthermore, by exploiting the time-dependent nature of the proposed method, applications to shape morphing can be easily achieved. Experimental results are presented to demonstrate the effectiveness of the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Turbulent Processes that Influence Boundary-Layer Cloud Structure (Final Technical Report)

Many models of the atmosphere simulate vertical profiles of observable fields, such as temperature, moisture, and vertical velocity. To assess the realism of such model output, it is important to evaluate the modeled profiles against observations. However, many extant evaluations consist of comparing time averaged profiles from the model and the observations. Such time averages can smooth out unrealistic features that appear in instantaneous profiles. In this way, the time averages may obscure certain model errors from detection. For instance, many models misrepresent turbulent processes, leading to profiles that are too smoothed out or too noisy. E.g., if a model has coarse vertical resolution and overly strong diffusion, then the model may be unable to produce observed sharp peaks. On the other hand, a model may have artificial approximations that produce spurious jumps in the vertical. Much can be learned by browsing through instantaneous snapshots of model output. But the volume of output produced by a global model is far too great to rely purely on visual inspection. In addition, we need a way to statistically characterize the profiles, so that anomalies can be detected more conveniently.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantification of morphological change in materials based on image data utilizing machine learning techniques

Computed tomography (CT) resolution has become high enough to monitor morphological changes due to aging in materials in long-term applications. We explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images visually reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect. Both critic and morphological metrics analysis showed image differentiation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Treatment of Tilted Sonar Data for Salt Cavern Analysis

Structural modeling and visualization of salt caverns requires three-dimensional representations. These representations are typically produced from sonar surveys conducted by companies that then produce a report of depths, distances, and volumes. There are multiple formats that are vendor dependent, and, as technology improves, there have been changes from only horizontal surveys to inclined shots for ceilings and floors to mid-cavern inclined shots. For geomechanical modeling, leaching predictions, and cavern stability visualizations, Sandia has previously written in-house software, called SONAR8, that created a consistent geometry format from the processed sonar reports. However, the increase in the need for mid-cavern inclined surveys led to the discovery of certain limitations in that code. This report describes methods used to process the multiple different formats to handle inclined shots in a consistent and accurate manner in our modeling efforts. A set of file formats and a database schema that was developed for this work is also documented in the appendices.

02 PETROLEUM↗
DOE OSTI2021

LLEPE

LLEPE (liquid-liquid equilibrium parameter estimator) is a tool for estimating thermodynamic parameters like standard enthalpies, standard molar volumes and Pitzer coefficients, by combining Gibbs minimization with regression to multiphase equilibrium isotherm data. It also includes visualization tools to access data quality and prediction model performance.

ILOEJE, CHUKWUNWIKE↗
DOE OSTI2025

Prelude to Simulation Diagnostics in MOOSE: Diagnosing Meshes

As finite element simulations increase in complexity, so does the need for high quality meshes. Without proper diagnostics, issues pertaining to poor mesh quality or improper mesh construction may go undetected, causing simulations to fail unexpectedly. This paper details the new mesh diagnostic system available in MOOSE, which provides users with the tools to inspect mesh quality for both externally and internally generated meshes. This system can detect issues arising from element volume size, non-conformal elements, intersecting edges, missing sidesets, as well as other common mesh issues. As meshes increase in size and complexity, such issues become more difficult to diagnose through visual inspection alone.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Time‐And‐Space Averaging Applied to Intermittent Multiphase Flow Experiments

Abstract Various researchers have studied fluctuations in pore‐scale phase occupancy during multiphase flow in porous media using synchrotron‐based X‐ray microcomputed tomography (micro‐CT). However, the impact of these fluctuations on the concept of a representative volume is not yet fully understood. In this study, we performed spatial and temporal averaging of multiphase flow experiments visualized with synchrotron‐based micro‐CT, focusing on oil saturation as the key parameter to determine a representative time‐and‐space average. Our findings revealed that a saturation value representative of both time and space was achieved during fractional flow experiments in drainage mode with fractional flows of 0.8, 0.5, and 0.3. Furthermore, we computed a range of relative permeabilities on the basis of whether momentaneous saturation or time‐and‐space averaged saturation was utilized for direct simulation. Our results highlighted the importance of time‐and‐space averaging in determining a representative relative permeability and indicated that the temporal and spatial scales covered in a typical micro‐CT flow experiment were sufficient to obtain a representative saturation value for sandstone rock under intermittent flow conditions.

Environmental Sciences & Ecology↗
DOE OSTI2023

Optical Imaging Methods for Volumetric Additive Manufacturing

Tomographic volumetric additive manufacturing (VAM) is a technique that enables light-induced curing of photoresins into complex 3D end use objects within a single step. This is made possible through projecting tomographically patterned light energy into a photo-curable resin volume within a rotating container. In order to monitor, quantify, and control curing during tomographic VAM, researchers need to visualize the curing parts in real-time. This may enable advancements toward dynamic, controlled and closedloop VAM methods in the future, as some researchers have shown. Herein we briefly review various optical imaging methods used to monitor printing in real-time, as well as provide some perspectives for future needs and considerations for imaging methods in VAM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Root system architecture and environmental flux analysis in mature crops using 3D root mesocosms

Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new “3D Root Mesocosms” and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Oxidation performance and limitations of additively manufactured SiC/Si-O-C composites at elevated temperatures

Here, SiC preforms were infiltrated with polycarbosiloxane preceramic polymer to create low-cost SiC—Si x O y C z matrix composites for possible high temperature application. The effect of specimen annealing conditions on the steam and air oxidation resistance was analyzed. Air exposures from 900–1500°C showed stability of the Si x O y C z matrix phase with moderate oxidation resistance. After 900°C exposures, oxidation of the composite is primarily limited to the matrix phase, and only at 1200 and 1500°C was SiC consumption visualized. Steam cycle exposures at 1200°C resulted in rapid linear oxidation of the composites with induced specimen swelling from volume expansion associated with internal oxidation. Specimens annealed in argon were found to be the most oxidation resistant due to increased matrix crystallinity and carbon retention. The results of this work were used to develop clear strategies for improving the high-temperature properties of SiC—Si x O y C z composites.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Managing negative values is reservoir inflow computation: A case study

Reservoir inflow is conventionally estimated using the water balance method, which involves the reservoir release and the change in storage during the period considered. As a result, the estimated inflow may sometimes be negative as the errors involved in each input variable build-up to the output. In our study, the fleet data was provided by the Tennessee Valley Authority (TVA) for their Norris Hydropower facility. Unlike the flow release data, which was readily accessible, the change in storage had to be calculated using the reservoir elevation and volume relationship. The original inflow estimates produced a wide range of negative values with large outliers, making it difficult to visualize the current trends. This paper describes a methodology to remove the negative values encountered during the inflow computation, and the results were analyzed by correlating with the nearby streamflow gaging stations.

Shibu, Asha↗
DOE OSTI2021

TangibleData: Interactive Data Visualization with Mid-Air Haptics

In this paper, we investigate the effects of mid-air haptics in interactive 3D data visualization. We build an interactive 3D data visualization tool that adapts hand gestures and mid-air haptics to provide tangible interaction in VR using ultrasound haptic feedback on 3D data visualization. We consider two types of 3D visualization datasets and provide different data encoding methods for haptic representations. Two user experiments are conducted to evaluate the effectiveness of our approach. The first experimental results show that adding a mid-air haptic modality can be beneficial regardless of noise conditions and useful for handling occlusion or discerning density and volume information. The second experiment results further show the strengths and weaknesses of direct touch and indirect touch modes. Our findings can shed light on designing and implementing a tangible interaction on 3D data visualization with mid-air haptic feedback.

Bhardwaj, Ayush↗
DOE OSTI2022

Continuous Emulation and Multiscale Visualization of Traffic Flow Using Stationary Roadside Sensor Data

With the advent of the next-generation traffic monitoring systems, there has been a significant increase in the spatial-temporal resolution of vehicle mobility data in many cities. Effective analysis and visualization of such data can provide transportation planners with data-driven insights, which can facilitate the understanding of multiscale traffic dynamics. In this paper, we present a web-based traffic emulator for emulating and visualizing near-real-time and historical traffic flows on highways using data from road-side sensors. To construct a continuous traffic flow, the emulator adopts an analytical pipeline that can (a) integrate traffic data collected from discrete road-side radar detection sensors, (b) interpolate traffic conditions (vehicle speed and volume) on unmeasured road segments based on traffic flow theory, and (c) generate lane-specific vehicle trajectories and movements using a mathematically optimized representation of the road network. Our app also provides an integrated visual workflow that allows users to explore the interconnected traffic dynamics using an appropriate traffic flow visualization selected based on the level of detail. We devise two innovative geo-visualization techniques that utilize an animated strips-network representation and a lane usage matrix to visualize lane performances. To ensure a smooth emulation of large-scale traffic flow in an easy-to-access web environment, we implement the emulator using client-side GPU-accelerated techniques. Lastly, we close with a case study that visualizes traffic dynamics of two scenarios - an afternoon peak hour and a traffic accident - in Chattanooga, Tennessee. Our app visualizes the responses of traffic dynamics during different traffic conditions, and to the presence of the traffic accident at different spatial scales.

42 ENGINEERING↗
DOE OSTI2022

Soot and PAH formation in high pressure spray pyrolysis of gasoline and diesel fuels

Time-resolved soot and PAH formation from gasoline and diesel spray pyrolysis are visualized and quantified using diffuse back illumination (DBI) and laser induced fluorescence (LIF) at 355 nm, respectively, in a constant-volume vessel at 60 bar from 1400 to 1700 K for up to 30 ms. The delay, maximum formation rate, and yield of soot and PAHs are compared across fuels and temperatures and correlated with the yield sooting indices on either the mass or mole basis. The delays generally decrease with increasing temperature, and the formation rates of both PAHs and soot generally increase with temperature. The apparent PAH-LIF yield may decrease with temperature due to PAH growth and conversion into larger species, signal trapping, and thermal quneching. Soot yield generally increases with temperature. The mass-based YSI correlates reasonably well with soot delay, but YSI does not correlate well with soot yield. Here, the mass-based YSI is a more appropriate predictor of sooting propensity than the mole-based YSI.

42 ENGINEERING↗