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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
NASA NTRS1995

Scientific visualization tools for the ISTP project: Mission planning, data analysis and model interpretation

Visualization tools are being developed to meet the challenges of mission planning and data analysis presented by the International Solar-Terrestrial Physics (ISTP) program. ISTP encompasses a large number of spacecraft, multiple ground-based observatories, and several theoretical investigations, with the goal of understanding the global behavior of the solar wind/magnetosphere/ionosphere system. The tools include three-dimensional displays of key boundaries in geospace along with spacecraft trajectories, which can be animated and synchronized to universal time. Magnetic field models and MHD simulation results can be invoked to reveal the magnetic topology or to identify magnetic conjunctions between spacecraft and/or ground-based facilities. Simultaneous displays of satellite trajectories, spacecraft-borne observations, and model predictions are available to facilitate data processing and interpretation efforts. The current status of these tools is described, and their implementation at the ISTP Science Planning and Operations Facility and distribution to the entire ISTP community are discussed.

Peredo, M.↗
NASA NTRS1996

The State of Scientific Visualization with Regard to the NASA EOS Mission to Planet Earth

In support of the mission to better understand the dynamics of the global atmosphere, John R. Christy and Nathaniel D. Reynolds investigated a wide range of topics. Christy worked closely with NASA scientist Roy Spencer to develop a data set of precision temperature measurements using the NASA built Microwave Sounding Unit. The data from this effort has received international recognition as they provide a source of precise information for the most difficult of environmental issues in the global climate change arena. In addition, Christy coordinated modeling research with NASA scientist Franklin Robertson with research focusing on the validation of global model output using various satellite data with sophisticated statistical techniques. Reynolds worked with NASA scientist Timothy Miller on idealized flows in a rotating annulus and the application of the results to the general circulation of the atmosphere. Additional work was carried out in investigation of stratospheric ozone fluctuations due to dynamical causes.

Christy, John R.↗
NASA NTRS1994

Scientific Visualization of Large Scale Unsteady Fluid Flow

In a numerical flow simulation. it is common to generate several thousand time steps of unsteady (time-dependent) flow data. Each time step may require tens to hundreds of megabytes for disk storage, and the total disk requirement for storing the unsteady flow data may be hundreds of gigabytes. Interactive visualization of unsteady flow data of this magnitude is presently impossible with the current hardware technology. This chapter describes the current approaches for unsteady flow visualization. An effective particle tracing technique for unsteady flow is also described. First, the life cycle of a typical numerical flow simulation is outlined. Several unsteady flow data sets from real-world problems are then given. The current approaches for visualizing unsteady flow are then described. There are many existing systems for flow visualization, and some of them are discussed. Streaklines depict time-varying phenomena that are sometimes difficult or impossible to see with other visualization techniques. The algorithms for computing streaklines are described. Several unsteady flow data sets have been visualized using streaklines, and the results are presented. Finally, some current issues in unsteady flow visualization are discussed.

Lane, David A.↗
NASA NTRS2011

Scientific Visualization to Study Flux Transfer Events at the Community Coordinated Modeling Center

In this paper we present results of modeling of reconnection at the dayside magnetopause with subsequent development of flux transfer event signatures. The tools used include new methods that have been added to the suite of visualization methods that are used at the Community Coordinated Modeling Center (CCMC). Flux transfer events result from localized reconnection that connect magnetosheath magnetic field and plasma with magnetospheric fields and plasma and results in flux rope structures that span the dayside magnetopause. The onset of flux rope formation and the three-dimensional structure of flux ropes are studied as they have been modeled by high-resolution magnetohydrodynamic simulations of the dayside magnetosphere of the Earth. We show that flux transfer events are complex three-dimensional structures that require modern visualization and analysis techniques. Two suites of visualization methods are presented and we demonstrate the usefulness of those methods through the CCMC web site to the general science user.

Rastatter, Lutz↗
NASA NTRS1995

Interactive access and management for four-dimensional environmental data sets using McIDAS

This grant has fundamentally changed the way that meteorologists look at the output of their atmospheric models, through the development and wide distribution of the Vis5D system. The Vis5D system is also gaining acceptance among oceanographers and atmospheric chemists. Vis5D gives these scientists an interactive three-dimensional movie of their very large data sets that they can use to understand physical mechanisms and to trace problems to their sources. This grant has also helped to define the future direction of scientific visualization through the development of the VisAD system and its lattice data model. The VisAD system can be used to interactively steer and visualize scientific computations. A key element of this capability is the flexibility of the system's data model to adapt to a wide variety of scientific data, including the integration of several forms of scientific metadata.

Hibbard, William L.↗
NASA NTRS1990

Scientific data visualization software - Trends and directions

Scientific data visualization has finally come of age as an important and accepted discipline. While scientists have been using computer graphics to visualize experimental data and computational results for at least 30 years, recent improvements in cost/performance of graphics workstations, more readily available software, and a new-found identity based on the report "Visualization in Scientific Computing" (McCormick, DeFanti, and Brown, 1987) have solidified the discipline. The thesis here is that scientists are forced to work too hard to create these visualizations, but that the evolving set of visualization tools can greatly reduce the requisite effort. The architecture of a new class of software can lead to a more widespread availability of interactive visualization tools, which can make the process of creating a visualization as simple as doing interactive chart and graph layout.

Foley, James D.↗
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

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↗
NASA NTRS1993

Visualization techniques to aid in the analysis of multispectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists. Twenty-one examples of the use of visualization for astrophysical data are included with this report. Sixteen publications related to efforts performed during or initiated through work on this project are listed at the end of this report.

Brugel, E. W.↗
NASA NTRS1993

Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions, and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists.

Brugel, Edward W.↗
DOE OSTI2023

GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). Furthermore, the results demonstrate the overall advantage of GMT in translation quality and generalization ability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Report for the ASCR Workshop on Visualization for Scientific Discovery, Decision-Making, and Communication

Visualization—the use of visual elements to explore data, form hypotheses, or convey conclusions—is an integral part of the scientific process. Starting from an initial exploration of new data to illustrating outcomes for the general public, visualization is one of the most intuitive and powerful modes of communication. With the explosion of new data sources and types, unprecedented volumes of data, and new technologies, such as virtual reality (VR) and artificial intelligence (AI), visualization has become increasingly essential but also ever more challenging. The Department of Energy’s (DOE) Office of Advanced Scientific Computing Research (ASCR) sponsored a Basic Research Needs workshop in January 2022 to understand the major opportunities and grand challenges in visualization tools and technologies for scientific computing as well as for DOE-relevant applications and goals in general. The workshop identified five priority research directions (PRDs) for visualization to support scientific discovery, decision making, and communication. The first three PRDs describe interconnected research themes addressing the need for new techniques to deal with complex data, uncertainty, and interpretability (PRD 1); the need for scalable and interoperable software stacks (PRD 2); and the challenges and opportunities inherent in new technologies, such as VR, cloud, or exascale computing (PRD 3). The remaining two PRDs describe foundational research themes that recognize the potential of visualizations to provide equitable access to information and to strengthen the scientific discourse (PRD 4); and the need to consider human factors when designing visualizations (PRD 5). Collectively, these PRDs form the pillars for a coherent, long-term research and development strategy in Visualization for Scientific Discovery, Decision-Making, and Communication in the context of the Office of Science’s mission scope.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Immersive Visualization for Scientific Data Analysis

We will present the use of immersive visualization at the National Renewable Energy Laboratory (NREL), showcasing how immersive visualization is advancing scientific research and engineering practices and transforming our day-to-day operations. We are leveraging immersive visualization to support scientific discovery and engineering in various domains, including material design, computational fluid dynamics, immersive analytics, grid modernization, digital twins, and situated visualization. We have observed several benefits across four key areas: enhanced spatial judgments, improved understanding through interaction, increased capacity to embed high-dimensional data, and improved collaboration.

immersive analytics↗
DOE OSTI2023

Visualization for Scientific Discovery, Decision-Making, and Communication

Visualization–the use of visual elements to explore data, form hypotheses, or convey conclusions–is an integral part of the scientific process. Starting from an initial exploration of new data to illustrating outcomes to the general public, visualization is one of the most intuitive and powerful modes of communication. With the explosion of new data sources and types, unprecedented volumes of data, and new technologies, such as virtual reality and AI, visualization has become increasingly essential but also ever more challenging. Department of Energy’s (DOE) Office of Advanced Scientific Computing Research (ASCR) sponsored a Basic Research Needs workshop in January 2022 to understand the major opportunities and grand challenges in visualization tools and technologies for scientific computing, with a special focus on DOE-relevant applications and goals. The workshop identified five priority research directions (PRDs) for visualization to support scientific discovery, decision-making, and communication.

97 MATHEMATICS AND COMPUTING↗