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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

Execution on massively threaded processors is one of the most critical challenges for high-performance computing (HPC) scientific visualization. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Moreover, our current production scientific visualization software is not designed for these new types of architectures. In order to address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Damaris: Addressing performance variability in data management for post-petascale simulations

With exascale computing on the horizon, reducing performance variability in data management tasks (storage, visualization, analysis, etc.) is becoming a key challenge in sustaining high performance. Here, this variability significantly impacts the overall application performance at scale and its predictability over time. In this article, we present Damaris, a system that leverages dedicated cores in multicore nodes to offload data management tasks, including I/O, data compression, scheduling of data movements, in situ analysis, and visualization. We evaluate Damaris with the CM1 atmospheric simulation and the Nek5000 computational fluid dynamic simulation on four platforms, including NICS’s Kraken and NCSA’s Blue Waters. Our results show that (1) Damaris fully hides the I/O variability as well as all I/O-related costs, thus making simulation performance predictable; (2) it increases the sustained write throughput by a factor of up to 15 compared with standard I/O approaches; (3) it allows almost perfect scalability of the simulation up to over 9,000 cores, as opposed to state-of-the-art approaches that fail to scale; and (4) it enables a seamless connection to the VisIt visualization software to perform in situ analysis and visualization in a way that impacts neither the performance of the simulation nor its variability. In addition, we extended our implementation of Damaris to also support the use of dedicated nodes and conducted a thorough comparison of the two approaches—dedicated cores and dedicated nodes—for I/O tasks with the aforementioned applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

XRFtomo

Based on [1], XRFtomo brings together a variety of image processing tools used for preparing, processing and reconstructing 3D renditions of samples imaged using a synchrotron-based x-ray fluorescence (XRF) microprobe or nanoprobe at the Advanced Photon Source (APS). This software bridges the gap between data acquisition and visualization by providing tools to adjust for misalignment in the imaging process and tools for running reconstructions[2], all with the use of a GUI or command line. XRFtomo accepts .h5 files processed through MAPS[3] and it outputs reconstructed cross-sections, projections, sinograms, metadata, and a configuration file with last-used parameters for rapid processing the next time the program is launched. These images serve as inputs for other 3D visualization software for generating volumetric renderings and animations.

Marin, Fabrico↗
DOE OSTI2014

Development of an Advanced Simulator to Model Mobility Control and Geomechanics during CO 2 Floods

The simulator is an isothermal, three-dimensional, four-phase, compositional, equation-of–state (EOS) simulator. We have named the simulator UTDOE-CO2 capable of simulating various recovery processes (i.e., primary, secondary waterflooding, and miscible and immiscible gas flooding). We include both the Peng-Robinson EOS and the Redlich-Kwong EOS models. A Gibbs stability test is also included in the model to perform a phase identification test to consistently label each phase for subsequent property calculations such as relative permeability, viscosity, density, interfacial tension, and capillary pressure. Our time step strategy is based on an IMPEC-type method (implicit pressure and explicit concentration). The gridblock pressure is solved first using the explicit dating of saturation-dependent terms. Subsequently, the material balance equations are solved explicitly for the total concentration of each component. The physical dispersion term is also included in the governing equations. The simulator includes (1) several foam model(s) for gas mobility control, (2) compositional relative permeability models with the hysteresis option, (3) corner point grid and several efficient solvers, (4) geomechanics module to compute stress field as the result of CO 2 injection/production, (5) the format of commercial visualization software, S3graf from Science-soft Ltd., was implemented for user friendly visualization of the simulation results. All tasks are completed and the simulator was fully tested and delivered to the DOE office including a user’s guide and several input files and the executable for Windows Pcs. We have published several SPE papers, presented several posters, and one MS thesis is completed (V. Pudugramam, 2013) resulting from this DOE funded project.

02 PETROLEUM↗
DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Volumetric Rendering on Wavelet-Based Adaptive Grid

Numerical modeling of physical phenomena frequently involves processes across a wide range of spatial and temporal scales. In the last two decades, the advancements in wavelet-based numerical methodologies to solve partial differential equations, combined with the unique properties of wavelet analysis to resolve localized structures of the solution on dynamically adaptive computational meshes, make it feasible to perform large-scale numerical simulations of a variety of physical systems on a dynamically adaptive computational mesh that changes both in space and time. Volumetric visualization of the solution is an essential part of scientific computing, yet the existing volumetric visualization techniques do not take full advantage of multi-resolution wavelet analysis and are not fully tailored for visualization of a compressed solution on the wavelet-based adaptive computational mesh. Our objective is to explore the alternatives for the visualization of time-dependent data on space-time varying adaptive mesh using volume rendering while capitalizing on the available sparse data representation. Two alternative formulations are explored. The first one is based on volumetric ray casting of multi-scale datasets in wavelet space. Rather than working with the wavelets at the finest possible resolution, a partial inverse wavelet transform is performed as a preprocessing step to obtain scaling functions on a uniform grid at a user-prescribed resolution. As a result, a solution in physical space is represented by a superposition of scaling functions on a coarse regular grid and wavelets on an adaptive mesh. An efficient and accurate ray casting algorithm is based just on these coarse scaling functions. Additional details are added during the ray tracing by taking an appropriate number of wavelets into account based on support overlap with the interpolation point, wavelet coefficient magnitude, and other characteristics, such as opacity accumulation (front to back ordering) and deviation from frontal viewing direction. The second approach is based on complementing of wavelet-based adaptive mesh to the traditional Adaptive Mesh Refinement (AMR) mesh. Both algorithms are illustrated and compared to the existing volume visualization software for Rayleigh-Benard thermal convection and electron density data sets in terms of rendering time and visual quality for different data compression of both wavelet-based and AMR adaptive meshes.

Vezolainen, Alexei V.↗
DOE OSTI2019

COMBIgor: Data-Analysis Package for Combinatorial Materials Science

Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially-resolved characterization of structure and properties. Here, due to maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment, designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data sets. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data analysis and visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Minimizing development costs for efficient many-core visualization using MCD3

Scientific visualization software increasingly needs to support many-core architectures. However, development time is a significant challenge due to the breadth and diversity of both visualization algorithms and architectures. With this work, we introduce a development environment for visualization algorithms on many-core devices that extends the traditional data-parallel primitive (DPP) approach with several existing constructs and an important new construct: meta-DPPs. We refer to our approach as MCD 3 — Meta-DPPs, Convenience routines, Data management, DPPs, and Devices. The twin goals of MCD 3 are to reduce developer time and to deliver efficient performance on many-core architectures, and our evaluation considers both of these goals. For development time, we study 57 algorithms implemented in the VTK-m software library and determine that MCD 3 leads to significant savings. For efficient performance, we survey ten studies looking at individual algorithms and determine that the MCD 3 hardware-agnostic approach leads to performance comparable to hardware-specific approaches: sometimes better, sometimes worse, and better in the aggregate. In total, we find that MCD 3 is an effective approach for scientific visualization libraries to support many-core architectures.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2025

Paraview-MCP

This project provides a streamlined way for users to interact with and control powerful scientific visualization software (ParaView) through a conversational interface. By developing an automated "Model Context Protocol" (MCP) server with a Python-based ParaView manager, the system allows users to seamlessly load and visualize complex datasets, explore visualization options with AI assistance, and optimize visualization output in a close loop. This is achieved by issuing intuitive, natural-language commands. The result is a user-friendly interface that integrates high-level conversation and scriptable data visualization, making scientific visualization tools more accessible to a broad audience.

Liu, Shusen [Lawrence Livermore National Laborator↗
DOE OSTI2018

PROTEUS-MOC User Manual

The PROTEUS-MOC code is a three-dimensional (3D) neutron transport code based on the finite element mesh and the method of characteristics (MOC) which combines a 2D MOC with the discontinuous Galerkin finite element method for the axial direction. Thus, for PROTEUS-MOC, a 3D geometry is represented with by the implicit extrusion of a single 2D planar geometry with different material assignments allowable on each plane. The code requires four input files for a steady-state calculation without the thermal feedback: a driver input, a mesh input, a cross section input, and a material assignment input. With thermal feedback, a T/H input file is needed. For kinetics, a kinetics driver input file is required. The PROTEUS-MOC software produces a text output file as well as a data file which includes integral and average quantities such on fluxes, powers, temperatures, densities, etc. The detailed output file can be converted to the VTK format using the post processing code so that the outputs can be visualized using a visualization software such as VisIt.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

The easiest and often most useful way to work with experimentally determined or computationally predicted structures of biomolecules is by viewing their three-dimensional (3D) shapes using a molecular visualization tool. Mol* was collaboratively developed by RCSB Protein Data Bank (RCSB PDB, RCSB.org) and Protein Data Bank in Europe (PDBe, PDBe.org) as an open-source, web-based, 3D visualization software suite for examination and analyses of biostructures. It is capable of displaying atomic coordinates and related experimental data of biomolecular structures together with a variety of annotations, facilitating basic and applied research, training, education, and information dissemination. Across RCSB.org, the RCSB PDB research-focused web portal, Mol* has been implemented to support single-mouse-click atomic-level visualization of biomolecules (e.g., proteins, nucleic acids, carbohydrates) with bound cofactors, small-molecule ligands, ions, water molecules, or other macromolecules. RCSB.org Mol* can seamlessly display 3D structures from various sources, allowing structure interrogation, superimposition, and comparison. Using influenza A H5N1 virus as a topical case study of an important pathogen, we exemplify how Mol* has been embedded within various RCSB.org tools—allowing users to view polymer sequence and structure-based annotations integrated from trusted bioinformatics data resources, assess patterns and trends in groups of structures, and view structures of any size and compositional complexity. In addition to being linked to every experimentally determined biostructure and Computed Structure Model made available at RCSB.org, Standalone Mol* is freely available for visualizing any atomic-level or multi-scale biostructure at rcsb.org/3d-view.

3D biostructure↗
DOE OSTI2015

Characterization of the Uranium-Bearing Products of the Ammonia Injection Remediation Method - 15707

Commissioned in 1943, the Hanford Site is the home of the world's first full-scale nuclear production facility. After more than 40 years of processing, improper discharge and failed storage tanks have resulted in the contamination of the Hanford vadose zone with radiologically contaminated waste. Remediation methods considered for the area include sequestration of the mobile uranium by injection of ammonia (NH{sub 3}) gas. Injection of this reactive gas increases the pore water pH, promoting the dissolution of soil minerals; a subsequent re-establishment of natural conditions is believed to result in the recrystallization of those soil minerals and the co-precipitation of uranium phases. Characterization of the precipitates formed when the ammonia (NH{sub 3}) injection method, proposed for the Hanford vadose zone, is applied to synthetic pore water on a laboratory scale. This involves: Identification of the uranium-bearing phases; A study of the effect of pore water constitution on the phases produced. Synthetic pore water solutions were prepared to mimic selected constituents of the pore water from the Hanford 200 Area: Varying constituent concentrations; Observations led to modifications to increase relative uranium content: 200 ppm → 500 ppm U; Substituted sodium silicate for silicic acid. Dried samples were analyzed by SEM-EDS and powder X-ray diffraction (XRD). A suspension in ethanol was prepared and analyzed by Transmission Electron Microscope (TEM) with Selective Area Electron Diffraction (SAED). Studied and applied geochemical modeling software (Visual Minteq and Geo-chemist's Workbench) for speciation and prediction of potential species formed. Discussion/Conclusions: - SEM-EDS Analysis: Scanning electron microscopy revealed areas of high average atomic weight which would be confirmed to be uranium-rich by energy dispersive spectroscopy analysis. The samples prepared with a more than two-fold increase in uranium concentration did not show the crystal-like forms spotted in the lower [U] samples. The samples prepared with silicic acid substituted in showed little to no uranium precipitation [not shown]. - Diffraction Analysis: Powder XRD analysis of the low [U] samples confirmed the presence of repeating crystalline patterns in the precipitate samples. Nitratine (NaNO{sub 3}) showed a massive presence, potentially obscuring significant peaks in the low [U] samples. Though it was not predicted, there was a tentative match for cejkaite (Na{sub 4}(UO{sub 2})(CO{sub 3}){sub 3}). Elevated uranium samples showed none of the needle-like crystalline shapes [diffraction data pending]. TEM - SAED confirmed the presence of a polycrystalline phase. Data analysis is complex but a comparison of average d-spacings with those of reference minerals could further support a match with cejkaite. - Geochemical Modeling: The system conditions were input into geochemical modeling software to predict the forms present throughout the reaction process. The model was used to demonstrate that the pH of the system could be increased using [NH{sub 4}{sup +}] in solution to better represent the system. Using the [NH{sub 4}{sup +}] increase, the changes in significant species with increasing pH were observed. Future Work: Repeat samples will be prepared and analyzed to bolster any potential identification. Promising samples will be submitted to the PNNL's EMSL facility, which is better equipped to perform the required analyses. An expanded study on the impact of carbonate (CO{sub 3}{sup 2-}) on the phases produced.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Automated Transformation of Lignin Topologies into Atomic Structures with LigninBuilder

Lignin is an abundant aromatic heteropolymer found in secondary plant cell walls and is a potential feedstock for conversion into bioderived fuels and chemicals. Lignin chemical diversity complicates traditional structural studies, and so, relatively little experimental evidence exists for how lignin structure exists in aqueous solution or how lignin polymers respond to changes in their chemical environment. Molecular modeling can address these concerns; however, prior computational structural lignin models typically did not capture lignin heterogeneity, as only a few polymers were considered. LigninBuilder creates a framework for building structural libraries for lignin from existing topological libraries, permitting significantly greater diversity of lignin structures to be sampled at atomic detail. As a demonstration of its capabilities, LigninBuilder was applied to three libraries of lignin from hardwood, softwood, and grass, and the resulting polymer structures were simulated in an aqueous environment. The lignins adopted compact globular structures, as would be expected for polymers in poor solvents. The differences between the libraries were largest when quantifying the packing of nonadjacent aromatic residues, with greater branching within the polymer resulting in poorer aromatic packing. Individual lignin polymers were also found to undergo rapid conformational changes, with the dwell time within a state growing as the square of the molecular weight. This first application of LigninBuilder demonstrates the potential for atomic-level insight into lignin interactions. LigninBuilder is distributed as a plugin to the visualization software VMD, lowering the barrier for modeling lignin structure in diverse environments.

09 BIOMASS FUELS↗
DOE OSTI2014

An automated miniature robotic vehicle inspection system

A novel, autonomous reconfigurable robotic inspection system for quantitative NDE mapping is presented. The system consists of a fleet of wireless (802.11g) miniature robotic vehicles, each approximately 175 × 125 × 85 mm with magnetic wheels that enable them to inspect industrial structures such as storage tanks, chimneys and large diameter pipe work. The robots carry one of a number of payloads including a two channel MFL sensor, a 5 MHz dry coupled UT thickness wheel probe and a machine vision camera that images the surface. The system creates an NDE map of the structure overlaying results onto a 3D model in real time. The authors provide an overview of the robot design, data fusion algorithms (positioning and NDE) and visualization software.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. Here, in this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Materials Informatics ChemVis

A Browser-based visualization of morphology, material, and chemical data extracted from chemical papers. This UI is focused for specifically nanomaterial synthesis papers. The visual analytic environment allows the user to explore a large number of papers quickly, through an interactive, intuitive, and visual way that helps see possible trends in the data. This exploration-oriented systems allows domain experts who may not have an extensive background in writing analysis code or database queries to easily express their idea to experiment on the data and view the results. The visualization software requires the data in JSON format. The data packaged with the software is the result of 35k nanomaterial synthesis papers queried from Elsevier, created through a separate software pipeline. The data contains information such as the title, authors, chemicals, and created material and morphology.

Liu, Shusen↗
DOE OSTI2018

pnnl/Haskell-IDF-Executable

Executables for working with EnergyPlus Input Files (IDF) This project aims to provide a suite of executables for working with EnergyPlus Input Files (IDF). The project is developed using the Haskell programming language (c.f., https://www.haskell.org/). The current release (version 0.1.0.0) includes a utility that "pretty prints" the input file (i.e., that decodes and then, immediately, re-encodes and displays the input file, viz., that sanitizes the input) and a utility that converts the input file into a labeled graph in the DOT language (c.f., https://www.graphviz.org/doc/info/lang.html) for subsequent rendering using Graphviz Visualization Software (c.f., https://www.graphviz.org/). An example workflow is to pipe the input file into the pretty printer, to pipe the result into the labeled graph converter, and then to pipe the result into Graphviz, yielding an image file that depicts the hierarchical and referential relationships between EnergyPlus Data Dictionary (IDD) classes that are detected within the input file.

Borkum, Mark↗