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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

In-situ visualization of corrosion behavior of Al CoCrFeNi high-entropy alloys during electrochemical polarization

In this study, the corrosion behavior of the Al x CoCrFeNi (x = 0.3, 0.5, 0.7) high-entropy alloys (HEAs) is investigated by an in-situ visualization system. Surface morphology changes on both micro-spatial and temporal scales are monitored in the 3.5 wt% NaCl solution during the potentiodynamic polarization. The microstructures of the Al x CoCrFeNi HEAs are characterized by SEM, EBSD, and EDS. The results show that the microstructure evolves from singe face-centered cubic (FCC) phase to multi-phases by increasing the Al content. The in-situ observations directly reveal the different localized corrosion processes in the AlxCoCrFeNi HEAs. In the single FCC phase Al 0.3 CoCrFeNi HEA, localized corrosion shows in the form of pitting. In the Al 0.5 CoCrFeNi and Al 0.7 CoCrFeNi alloys, which possess FCC and body-centered cubic (BCC) phases, the localized corrosion initiates and propagates in the Cr-depleted BCC phase. In addition, the in-situ visualization uncovers the evolution of current serration, which represents the propagation and repassivation of metastable pit during the polarization. The frequent occurrence of metastable pits indicates distinct repassivation ability of the Al 0.3 CoCrFeNi HEA.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Insights into the rapid two-phase transport dynamics in different structured porous transport layers of water electrolyzers through high-speed visualization

In proton exchange membrane electrolyzer cells (PEMECs), maintaining efficient two-phase transport is one of the most important functions of porous transport layers (PTLs). To enhance the two-phase transport in PTLs, thin/titanium liquid/gas diffusion layers (TT-LGDLs) are introduced in PEMECs, and their difference from the conventional Ti felt PTLs are analyzed in-situ through high-speed and microscale visualization and electrochemical characterizations. The visualization results show that unfavorable large slugs can be greatly reduced in the PEMEC with a TT-LGDL compared to the PEMEC with a Ti felt PTL. More importantly, the recovery capability of water starvation with different PTLs is studied. After water starvation, the PEMEC with the TT-LGDL can recover the water starvation much more rapidly than the Ti felt cell, benefiting from its short and straight-through flow paths. Furthermore, the TT-LGDL tends to generate oxygen bubbles that are almost six times smaller and 236 times more frequently than the Ti felt PTL, indicating significantly boosted removal efficiency of produced oxygen and PEMEC performance. Finally, this study offers new insights into the dynamic processes of two-phase transport and the recovery capability of water starvation for different PTLs, which will provide valuable guidance for further optimization of PTLs and performance enhancement of PEMECs.

08 HYDROGEN↗
DOE OSTI2018

On visualizing material flow and precipitate evolution during probeless friction stir spot welding of an Al-Li alloy

Highlights: • Severe thermal cycle and deformation refined grains and intermetallic particles in SZ. • T{sub 1} phases fully dissolved in SZ followed by re-precipitation of GP zone and δ′ phases. • The predominant flow direction remained in spiral-like pattern during P-FSSW. • Local abrasion existed in the lap-weld interface due to flow difference, forming joining. - Abstract: The precipitate evolution and material flow visualization during probeless friction stir spot welding (P-FSSW) of an Al-Li alloy has been investigated experimentally and numerically in this work. Due to severe heat input during welding, the main strengthening phases of T{sub 1} fully dissolved in the stir zone followed by the re-precipitation of GP zone and δ′ phases, assisted with the refinement of the intermetallic particles and grains. The history of material flow was three-dimensionally visualized with a tracer, studied with X-ray tomography. The experiments and the simulation results provided a consistent interpretation of a circumferential material flow which moved inwards radially near the top surface. Upward flow in the bottom sheet improved with dwell time, causing the interface to bend. Local abrasion between the two sheets due to flow difference led to the disruption of oxide layer, and formed a connection at the interface.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evaluating adaptive and predictive power management strategies for optimizing visualization performance on supercomputers

Power is becoming an increasingly scarce resource on the next generation of supercomputers, and should be used wisely to improve overall performance. One strategy for improving power usage is hardware overprovisioning, i.e., systems with more nodes than can be run at full power simultaneously without exceeding the system-wide power limit. With this study, we compare two strategies for allocating power throughout an overprovisioned system – adaptation and prediction – in the context of visualization workloads. While adaptation has been suitable for workloads with more regular execution behaviors, it may not be as suitable on visualization workloads, since they can have variable execution behaviors. This study considers a total of 104 experiments, which vary the rendering workload, power budget, allocation strategy, and node concurrency, including tests processing data sets up to 1 billion cells and using up to 18,432 cores across 512 nodes. Overall, we find that prediction is a superior strategy for this use case, improving performance up to 27% compared to an adaptive strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

IrrigationViz: A Geospatial Visualization Application to Facilitate Irrigation Modernization

Irrigation water delivery infrastructure, such as canals and pipelines, are essential to agriculture in the Western U.S., yet many of these systems are reaching the end of their useful lives. Reinvestment can achieve a wide variety of benefits, from water conservation to energy savings or renewable energy generation. Modernization requires significant planning, design, and implementation funding which can be a challenge for many irrigation districts. Here, this paper introduces IrrigationViz, a web-based mapping application designed to help irrigation districts visualize and generate high-level cost and benefit estimates for infrastructure modernization projects. These estimates can help water managers identify projects that benefit from additional engineering resources and ultimately obtain funding. IrrigationViz includes map interactions, graphs, and visualization components to facilitate planning and communication to stakeholders and funders. IrrigationViz combines user-provided information about a water-delivery system with public datasets and basic engineering formulas to generate estimates of the benefits of reinvestment. Estimates include the amount of water seepage in earthen canals, potential hydropower generation associated with replacing a canal with a pressurized pipe, and pipe size recommendations. We found that hydropower generation estimates compared favorably with real world projects, but seepage loss estimates showed high variability relative to on-the-ground measurements

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Alternatives to Contour Visualizations for Power Systems Data

Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system - Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗
DOE OSTI2016

Reducing the Analytical Bottleneck for Domain Scientists: Lessons from a Climate Data Visualization Case Study

The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, etc. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data, and communicate their findings effectively to a broad audience. In this paper, by reflecting on a set of successful collaborative research efforts between with a group of climate scientists and visualization researchers, we introspect how interactive visualization can help reduce the analytical bottleneck for domain scientists.

climate↗
DOE OSTI2016

A Survey of Colormaps in Visualization

Colormaps are a vital method for users to gain insights into data in a visualization. With a good choice of colormaps, users are able to acquire information in the data more effectively and efficiently. In this survey, we attempt to provide readers with a comprehensive review of colormap generation techniques and provide readers a taxonomy which is helpful for finding appropriate techniques to use for their data and applications. Specifically, we first briefly introduce the basics of color spaces including color appearance models. In the core of our paper, we survey colormap generation techniques, including the latest advances in the field by grouping these techniques into four classes: procedural methods, user-study based methods, rule-based methods, and data-driven methods; we also include a section on methods that are beyond pure data comprehension purposes. We then classify colormapping techniques into a taxonomy for readers to quickly identify the appropriate techniques they might use. Furthermore, a representative set of visualization techniques that explicitly discuss the use of colormaps is reviewed and classified based on the nature of the data in these applications. Lastly, our paper is also intended to be a reference of colormap choices for readers when they are faced with similar data and/or tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models

With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

TTHRESH: Tensor Compression for Multidimensional Visual Data

Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. In this paper, we introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Visual Analytics of Multivariate Networks With Representation Learning and Composite Variable Construction

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This article presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding

Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. In this work, we proposed F-Hash, a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization. Experiments show the F-Hash achieves state-of-the-art convergence speed in training various time-varying volumetric datasets for diverse features. We also proposed an adaptive ray marching algorithm to optimize the sample streaming for faster rendering of the time-varying neural representation.

deep learning↗
DOE OSTI2021

Visualization of Jet Impingement and Ignition in a Piston-Cylinder Chamber

The transportation sector accounts for almost a third of the United States' energy consumption. We are developing predictive simulations of the complex in-cylinder processes of internal combustion engines to improve performance and decrease pollutant emissions. We provide a first look at the jet impingement and ignition inside a piston-cylinder chamber using volume rendering. Through the visualization, we constructed a complete 3D picture of the jet impingement on the piston-bowl walls leading to the formation of pockets of auto-ignition of the fuel-air mixture. These auto-ignition pockets subsequently lead to flame stabilization, releasing the chemical energy for conversion to useful work. A detailed physical picture of flame stabilization, as provided by the visualization techniques presented in this study, help in the design of next-generation internal combustion engines.

91 SC - Advanced Scientific Computing Reseach (ASC↗
DOE OSTI2016

In Situ Visualization and Production of Extract Databases

Simulations running at high concurrency on HPC systems generate large volumes of data that are impractical to write to disk due to time and storage constraints. Applications often adapt by saving data infrequently, resulting in datasets with poor temporal resolution. This can make datasets difficult to interpret during post hoc visualization and analysis, or worse, it can lead to lost science. In Situ visualization and analysis can enable efficient production of small data products such as rendered images or surface extracts that consist of polygonal geometry plus fields. These data products are far smaller than their source data and can be processed much more economically in a traditional post hoc workflow using far fewer computational resources. We used the SENSEI and Libsim in situ infrastructures to implement rendering workflow and surface data extraction workflows in the AVF-LESLIE combustion code. Finally, these workflows were then demonstrated at high levels of concurrency and showed significant data reductions and limited impact on the simulation runtime.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

In-Situ Visualization Experiments with ParaView Cinema in RAGE

A previous paper described some numerical experiments performed using the ParaView/Catalyst in-situ visualization infrastructure deployed in the Los Alamos RAGE radiation-hydrodynamics code to produce images from a running large scale 3D ICF simulation. One challenge of the in-situ approach apparent in these experiments was the difficulty of choosing parameters likes isosurface values for the visualizations to be produced from the running simulation without the benefit of prior knowledge of the simulation results and the resultant cost of recomputing in-situ generated images when parameters are chosen suboptimally. A proposed method of addressing this difficulty is to simply render multiple images at runtime with a range of possible parameter values to produce a large database of images and to provide the user with a tool for managing the resulting database of imagery. Recently, ParaView/Catalyst has been extended to include such a capability via the so-called Cinema framework. Here I describe some initial experiments with the first delivery of Cinema and make some recommendations for future extensions of Cinema’s capabilities.

in-situ visualization↗
DOE OSTI2016

Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis

An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different case studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Scalable Data Management, Analysis, and Visualization (SDAV) Institute

The purpose of the SDAV institute is to provide tools and expertise in scientific data management, analysis, and visualization to DOE’s application scientists. Our goal is to actively work with application teams to assist them in achieving breakthrough science, and to provide technical solutions in the data management, analysis, and visualization regimes that are broadly used by the computational science community. Over the last 5 years members of our institute worked directly with application scientists and DOE leadership-class facilities to assist them by applying the best tools and technologies at our disposal. We also enhanced our tools based on input from scientists on their needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A Visual Evaluation Study of Graph Sampling Techniques

We evaluate a dozen prevailing graph-sampling techniques with an ultimate goal to better visualize and understand big and complex graphs that exhibit different properties and structures. The evaluation uses eight benchmark datasets with four different graph types collected from Stanford Network Analysis Platform and NetworkX to give a comprehensive comparison of various types of graphs. The study provides a practical guideline for visualizing big graphs of different sizes and structures. The paper discusses results and important observations from the study.

Big graphs↗