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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Framework and algorithms for illustrative visualizations of time-varying flows on unstructured meshes

Photo- and physically realistic techniques are often insufficient for visualization of fluid flow simulations, especially for 3D and time-varying studies. Substantial research effort has been dedicated to the development of non-photorealistic and illustration-inspired visualization techniques for compact and intuitive presentation of such complex datasets. However, a great deal of work has been reproduced in this field, as many research groups have developed specialized visualization software. Additionally, interoperability between illustrative visualization software is limited due to diverse processing and rendering architectures employed in different studies. In this investigation, a framework for illustrative visualization is proposed, and implemented in MarmotViz, a ParaView plug-in, enabling its use on a variety of computing platforms with various data file formats and mesh geometries. Region-of-interest identification and feature-tracking algorithms incorporated into this tool are described. Implementations of multiple illustrative effect algorithms are also presented to demonstrate the use and flexibility of this framework. Here, by providing an integrated framework for illustrative visualization of CFD data, MarmotViz can serve as a valuable asset for the interpretation of simulations of ever-growing scale.

42 ENGINEERING↗
DOE OSTI2013

MarmotViz

MarmotViz is an illustrative visualization plug-in for ParaView. It enables users to identify and track time-varying features in simulation datasets. It permits the application of illustrative visualization effects to these features including: selective visualization, feature coloring, boundary smoothing, haloing, silhouette outlining, speedlines, and strobe silhouettes. These techniques serve to assist in exploration and interpretation of simulation data and can be used to generate enhanced renderings for presentation. The MarmotViz plug-in is developed as a flexible framework, and can be easily extended to incorporate new region-of-interest (ROI) identification algorithms, feature matching and tracking algorithms, and illustrative visualization effects.

Rattner, Alexander↗
DOE OSTI2021

Analysis of conical slump shape reconstructed from stereovision images for yield stress prediction

Highlights: • Conical slump model tends to underpredict yield stress of material by slump height. • Profiles of slumped materials were reconstructed from stereovision images. • Actual profiles were compared to those predicted by cone and cylinder model. • The cause of deviation lies in the cylinder-like behaviour of conical slumping. • The model of equivalent cylinder could give an accurate yield stress value. Slump cones are widely utilised to evaluate the workability of cement materials owing to their simplicity. Analytical models have been established to relate this empirical value to the yield stress. However, they tend to be less effective for the cone and more valid for cylinder slumps. The cause of this deviation is yet to be fully understood. In this work, more details were sought from slump profiles using stereovision technology. A direct comparison of the predicted shapes based on conical model as well as cylindrical model to actual shapes was undertaken. The visual illustration clearly indicated that conical slumping shows a cylinder-like behaviour, and the slumped shapes in cone tests agree well with those predicted by an equivalent cylinder. By converting to cylindrical dimensionless units, the conical slump height can provide an accurate yield stress. The results suggest that the theory is valid if the slumping behaviour is effectively assumed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM

We report three-dimensional (3D) X-ray micro-computed tomography (μCT) has been widely used in petroleum engineering because it can provide detailed pore structural information for a reservoir rock, which can be imported into a pore-scale numerical model to simulate the transport and distribution of multiple fluids in the pore space. The partial volume blurring (PVB) problem is a major challenge in segmenting raw μCT images of rock samples, which impacts boundaries and small targets near the resolution limit. We developed a deep-learning (DL)-based workflow for accurate and fast partial volume segmentation. The DL model's performance depends primarily on the training data quality and model architecture. This study employed the entropy-based-masking indicator kriging (IK-EBM) to segment 3D Berea sandstone images as training datasets. The comparison between IK-EBM and manual segmentation using a 3D synthetic sphere pack, which had a known ground truth, showed that IK-EBM had higher accuracy on partial volume segmentation. We then trained and tested the UNet++ model, a state-of-the-art supervised encoder-decoder model, for binary (i.e., void and solid) and four-class segmentation. We compared the UNet++ with the commonly used U-Net and wide U-Net models and showed that the UNet++ had the best performance in terms of pixel-wise and physics-based evaluation metrics. Specifically, boundary-scaled accuracy demonstrated that the UNet++ architecture outperformed the regular U-Net architecture in the segmentation of pixels near boundaries and small targets, which were subjected to the PVB effect. Feature map visualization illustrated that the UNet++ bridged the semantic gaps between the feature maps extracted at different depths of the network, thereby enabling faster convergence and more accurate extraction of fine-scale features. The developed workflow significantly enhances the performance of supervised encoder-decoder models in partial volume segmentation, which has extensive applications in fundamental studies of subsurface energy, water, and environmental systems.

02 PETROLEUM↗
DOE OSTI2013

Role of electron-electron interference in ultrafast time-resolved imaging of electronic wavepackets

Ultrafast time-resolved x-ray scattering is an emerging approach to image the dynamical evolution of the electronic charge distribution during complex chemical and biological processes in real-space and real-time. Recently, the differences between semiclassical and quantum-electrodynamical (QED) theory of light-matter interaction for scattering of ultrashort x-ray pulses from the electronic wavepacket were formally demonstrated and visually illustrated by scattering patterns calculated for an electronic wavepacket in atomic hydrogen [G. Dixit, O. Vendrell, and R. Santra, Proc. Natl. Acad. Sci. U.S.A. 109, 11636 (2012)]. In this work, we present a detailed analysis of time-resolved x-ray scattering from a sample containing a mixture of non-stationary and stationary electrons within both the theories. In a many-electron system, the role of scattering interference between a non-stationary and several stationary electrons to the total scattering signal is investigated. In general, QED and semiclassical theory provide different results for the contribution from the scattering interference, which depends on the energy resolution of the detector and the x-ray pulse duration. The present findings are demonstrated by means of a numerical example of x-ray time-resolved imaging for an electronic wavepacket in helium. It is shown that the time-dependent scattering interference vanishes within semiclassical theory and the corresponding patterns are dominated by the scattering contribution from the time-independent interference, whereas the time-dependent scattering interference contribution do not vanish in the QED theory and the patterns are dominated by the scattering contribution from the non-stationary electron scattering.

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

Manipulating waves with LEGO{sup ®} bricks: A versatile experimental platform for metamaterial architectures

In this letter, we discuss a versatile, fully reconfigurable experimental platform for the investigation of phononic phenomena in metamaterial architectures. The approach revolves around the use of 3D laser vibrometry to reconstruct global and local wavefield features in specimens obtained through simple arrangements of LEGO{sup ®} bricks on a thin baseplate. The agility by which it is possible to reconfigure the brick patterns into a nearly endless spectrum of topologies makes this an effective approach for rapid experimental proof of concept, as well as a powerful didactic tool, in the arena of phononic crystals and metamaterials engineering. We use our platform to provide a compelling visual illustration of important spatial wave manipulation effects (waveguiding and seismic isolation), and to elucidate fundamental dichotomies between Bragg-based and locally resonant bandgap mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Development of a video image-based QA system for the positional accuracy of dynamic tumor tracking irradiation in the Vero4DRT system

Purpose: To develop and evaluate a new video image-based QA system, including in-house software, that can display a tracking state visually and quantify the positional accuracy of dynamic tumor tracking irradiation in the Vero4DRT system. Methods: Sixteen trajectories in six patients with pulmonary cancer were obtained with the ExacTrac in the Vero4DRT system. Motion data in the cranio–caudal direction (Y direction) were used as the input for a programmable motion table (Quasar). A target phantom was placed on the motion table, which was placed on the 2D ionization chamber array (MatriXX). Then, the 4D modeling procedure was performed on the target phantom during a reproduction of the patient’s tumor motion. A substitute target with the patient’s tumor motion was irradiated with 6-MV x-rays under the surrogate infrared system. The 2D dose images obtained from the MatriXX (33 frames/s; 40 s) were exported to in-house video-image analyzing software. The absolute differences in the Y direction between the center of the exposed target and the center of the exposed field were calculated. Positional errors were observed. The authors’ QA results were compared to 4D modeling function errors and gimbal motion errors obtained from log analyses in the ExacTrac to verify the accuracy of their QA system. The patients’ tumor motions were evaluated in the wave forms, and the peak-to-peak distances were also measured to verify their reproducibility. Results: Thirteen of sixteen trajectories (81.3%) were successfully reproduced with Quasar. The peak-to-peak distances ranged from 2.7 to 29.0 mm. Three trajectories (18.7%) were not successfully reproduced due to the limited motions of the Quasar. Thus, 13 of 16 trajectories were summarized. The mean number of video images used for analysis was 1156. The positional errors (absolute mean difference + 2 standard deviation) ranged from 0.54 to 1.55 mm. The error values differed by less than 1 mm from 4D modeling function errors and gimbal motion errors in the ExacTrac log analyses (n = 13). Conclusions: The newly developed video image-based QA system, including in-house software, can analyze more than a thousand images (33 frames/s). Positional errors are approximately equivalent to those in ExacTrac log analyses. This system is useful for the visual illustration of the progress of the tracking state and for the quantification of positional accuracy during dynamic tumor tracking irradiation in the Vero4DRT system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

System and method for the identification of radiation in contaminated rooms

Devices and methods for the characterization of areas of radiation in contaminated rooms are provided. One such device is a collimator with a collimator shield for reducing noise when measuring radiation. A position determination system is provided that may be used for obtaining position and orientation information of the detector in the contaminated room. A radiation analysis method is included that is capable of determining the amount of radiation intensity present at known locations within the contaminated room. Also, a visual illustration system is provided that may project images onto the physical objects, which may be walls, of the contaminated room in order to identify the location of radioactive materials for decontamination.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

A Simulation Approach for Modeling High-Resolution Daytime Commuter Travel Flows and Distributions of Worker Subpopulations

High-resolution daytime travel flows and population distribution data are critical for addressing important issues ranging from business planning to emergency preparedness and response. Though dasymetric modeling approaches and Census datasets have been developed to capture daytime populations and travel flows, they often lack the spatial resolution, demographic detail and uncertainty measures needed by decision-makers. In this paper, we propose a high-resolution simulation approach for estimating and quantifying the uncertainty of worker subpopulation distributions and their commuter travel flows in the United States (U.S.). An early case study and novel visualization illustrate the model’s potential utility and impact.

Morton, April↗
DOE OSTI2025

SynopFrame: Multiscale time-dependent visual abstraction framework for analyzing DNA nanotechnology simulations

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback.

Abstraction space↗
DOE OSTI2024

A manifold learning perspective on surrogate modeling of nitrate concentration in the Kansas River

Abstract A non-linear surrogate model of nitrate concentration in the Kansas River (USA) is described. The model is an (almost) Piece-wise Linear response surface that provides a mean field approximation to the dynamics of the measured data for nitrate plus nitrite (target product) correlations to turbidity and chlorophyll-a concentrations (input variables). The method extends the United States Geological Survey’s linear procedures for surrogate data modeling allowing for better approximations for river systems exhibiting algal blooms due to nutrient-rich source waters. The model and visualization procedures illustrated in the Kansas River example should be generally applicable to many medium-size rivers in agricultural regions.

Tufillaro, Nicholas (ORCID:0009000628968832)↗
DOE OSTI2022

Deep Active Learning for Solvability Prediction in Power Systems

Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism and handle limited types of power flow models. In this letter, we propose a deep active learning framework for solvability prediction in power systems. Compared with passive learning where the training is performed after all instances are labeled, active learning selects most informative instances to be labeled and therefore significantly reduces the size of the labeled dataset for training. In the active learning framework, the acquisition functions, which correspond to different sampling strategies, are defined in terms of the on-the-fly posterior probability from the classifier. First, the IEEE 39-bus system is employed to validate the proposed framework, where a two-dimensional case is illustrated to visualize the effectiveness of the sampling method followed by the high-dimensional numerical experiments. Then, the Northeast Power Coordinating Council (NPCC) 140-bus system is used to validate the performance on large-scale power systems.

42 ENGINEERING↗
DOE OSTI2016

Interpretation of Simultaneous Mechanical-Electrical-Thermal Failure in a Lithium-Ion Battery Module: Preprint

Lithium-ion batteries are currently the state-of- the-art power sources for electric vehicles, and their safety behavior when subjected to abuse, such as a mechanical impact, is of critical concern. A coupled mechanical-electrical-thermal model for simulating the behavior of a lithium-ion battery under a mechanical crush has been developed. We present a series of production-quality visualizations to illustrate the complex mechanical and electrical interactions in this model.

25 ENERGY STORAGE↗
DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

The Molten Salt Reactor (MSR) represents a significant innovation in nuclear technology, offering several operational and safety benefits over traditional solid-fuel reactors. However, MSRs face uncertainties in waste management due to their flexible designs and variable waste compositions. To address these challenges, we propose a visualization platform that illustrates solutions and performance predictions for various waste management strategies, enhancing user experience and improving strategy and communication. Immersive visualizations are widely used in the nuclear industry for training, simulation, and safety enhancement. Our project aims to develop a visualization platform incorporating virtual reality (VR) technologies to illustrate MSR characteristics immediately following reactor shutdown. This immersive simulation will allow users to interact, explore, and understand different waste management strategies. The platform will display MSR reactor characterizations, including nuclide decay, salt solidification, and corrosion, which are crucial for assessing and selecting backend management strategies. Using the Meta Quest 3 VR headset with Unity software, our platform will provide real-scale visualizations, enabling users to experience and evaluate designs and plans as if they were physically present. This user-friendly interface will make complex data accessible and understandable for non-domain experts, aiding in decision-making for MSR waste management. Our proposed visualization workflow can be applied to other nuclear reactors, assisting in the design and planning of waste management strategies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Modeling and evaluating user behavior in exploratory visual analysis

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, however, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This paper presents a methodology for modeling and evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis, and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Quantitative Reflection Imaging for the Morphology and Dynamics of Live Aplysia californica Pedal Ganglion Neurons Cultured on Nanostructured Plasmonic Crystals

We describe a reflection imaging system that consists of a plasmonic crystal, a common laboratory microscope, and band-pass filters for use in the quantitative imaging and in situ monitoring of live cells and their substrate interactions. Surface plasmon resonance (SPR) provides a highly sensitive method to monitor changes in physicochemical properties occurring at metal-dielectric interfaces. Polyelectrolyte thin films deposited using the layer-by-layer (LBL) self-assembly method provide a reference system for calibrating the reflection contrast changes that occur when the polyelectrolyte film thickness changes and provide insight into the optical responses that originate from the multiple plasmonic features supported by this imaging system. Finitedifference time-domain (FDTD) simulations of the optical responses measured experimentally from the polyelectrolyte reference system are used to provide a calibration of the optical system for subsequent use in quantitative studies investigating live cell dynamics in cultures supported on a plasmonic crystal substrate. Live Aplysia californica pedal ganglion neurons cultured in artificial seawater were used as a model system through which to explore the utility of this plasmonic imaging technique. Here, the morphology of cellular peripheral structures ≲80 nm in thickness were quantitatively analyzed, and the dynamics of their trypsin-induced surface detachment were visualized. These results illustrate the capacities of this system for use in investigations of the dynamics of ultrathin cellular structures within complex bioanalytical environments.

47 OTHER INSTRUMENTATION↗