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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Creating a Prototype Web Application for Spacecraft Real-Time Data Visualization on Mobile Devices

Mobile devices (smart phones, tablets) have become commonplace among almost all sectors of the workforce, especially in the technical and scientific communities. These devices provide individuals the ability to be constantly connected to any area of interest they may have, whenever and wherever they are located. The Huntsville Operations Support Center (HOSC) is attempting to take advantage of this constant connectivity to extend the data visualization component of the Payload Operations and Integration Center (POIC) to a person's mobile device. POIC users currently have a rather unique capability to create custom user interfaces in order to view International Space Station (ISS) payload health and status telemetry. These displays are used at various console positions within the POIC. The Software Engineering team has created a Mobile Display capability that will allow authenticated users to view the same displays created for the console positions on the mobile device of their choice. Utilizing modern technologies including ASP.net, JavaScript, and HTML5, we have created a web application that renders the user's displays in any modern desktop or mobile web browser, regardless of the operating system on the device. Additionally, the application is device aware which enables it to render its configuration and selection menus with themes that correspond to the particular device. The Mobile Display application uses a communication mechanism known as signalR to push updates to the web client. This communication mechanism automatically detects the best communication protocol between the client and server and also manages disconnections and reconnections of the client to the server. One benefit of this application is that the user can monitor important telemetry even while away from their console position. If expanded to the scientific community, this application would allow a scientist to view a snapshot of the state of their particular experiment at any time or place. Because the web application renders the displays that can currently be created with the POIC ground system, the user can tailor their displays for a particular device using tools that they are already trained to use.

Lang, Jeremy S.↗
NASA NTRS1995

Data handling and visualization for NASA's science programs

Advanced information systems capabilities are essential to conducting NASA's scientific research mission. Access to these capabilities is no longer a luxury for a select few within the science community, but rather an absolute necessity for carrying out scientific investigations. The dependence on high performance computing and networking, as well as ready and expedient access to science data, metadata, and analysis tools is the fundamental underpinning for the entire research endeavor. At the same time, advances in the whole range of information technologies continues on an almost explosive growth path, reaching beyond the research community to affect the population as a whole. Capitalizing on and exploiting these advances are critical to the continued success of space science investigations. NASA must remain abreast of developments in the field and strike an appropriate balance between being a smart buyer and a direct investor in the technology which serves its unique requirements. Another key theme deals with the need for the space and computer science communities to collaborate as partners to more fully realize the potential of information technology in the space science research environment.

Bredekamp, Joseph H.↗
NASA NTRS1994

Experimenter's Laboratory for Visualized Interactive Science

ELVIS (Experimenter's Laboratory for Visualized Interactive Science) is an interactive visualization environment that enables scientists, students, and educators to visualize and analyze large, complex, and diverse sets of scientific data. It accomplishes this by presenting the data sets as 2-D, 3-D, color, stereo, and graphic images with movable and multiple light sources combined with displays of solid-surface, contours, wire-frame, and transparency. By simultaneously rendering diverse data sets acquired from multiple sources, formats, and resolutions and by interacting with the data through an intuitive, direct-manipulation interface, ELVIS provides an interactive and responsive environment for exploratory data analysis.

Hansen, Elaine R.↗
DOE OSTI2022

Heterogeneous Visualization: A Call for Empirically Driven Technology Advancement and Adoption; Preprint

Our ability to generate data from simulations and experimental platforms is generally outpacing our ability to analyze those data efficiently and effectively. There are a growing number and variety of possible visualization display modalities, from commodity head-mounted displays to large-scale immersive environments, from desktop and touch displays to high-resolution display walls. Many of these visualization technologies and visual environments are rapidly evolving and could revolutionize analysis for certain classes of complex scientific and engineering data. However, the choice of what technology or combination of technologies to use has historically been largely ad hoc and lacks a carefully validated understanding of how to best utilize different visualization technologies to advance the science. To maximize the impact of our data and analyses, it is imperative that we use the most effective visualization environments for the analysis task at hand.

heterogeneous visualization↗
DOE OSTI2023

Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

Abstract Critical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

Visual observations from space

A visual observations project was established as part of the Skylab experimental program in order to optimize the collection of scientific data during the 84-day Skylab 4 mission. One objective of this project was the determination of the capabilities of the crewmen in the visual identification of various types of surface, air, and water phenomena of the earth which may be observed from the Skylab orbit. Another objective was a study of the possibilities provided by visual observations, supplemented by photographs, in the support of scientific investigations. Aspects of premission preparations are considered along with details concerning the equipment to be used and questions of mission operations. The results of the project show that the presence of man in an earth-observations program contributes a significant additional capability which is not available with a totally automated program.

Lenoir, W. B.↗
NASA NTRS1995

Remote Visualization and Remote Collaboration On Computational Fluid Dynamics

A new technology has been developed for remote visualization that provides remote, 3D, high resolution, dynamic, interactive viewing of scientific data (such as fluid dynamics simulations or measurements). Based on this technology, some World Wide Web sites on the Internet are providing fluid dynamics data for educational or testing purposes. This technology is also being used for remote collaboration in joint university, industry, and NASA projects in computational fluid dynamics and wind tunnel testing. Previously, remote visualization of dynamic data was done using video format (transmitting pixel information) such as video conferencing or MPEG movies on the Internet. The concept for this new technology is to send the raw data (e.g., grids, vectors, and scalars) along with viewing scripts over the Internet and have the pixels generated by a visualization tool running on the viewer's local workstation. The visualization tool that is currently used is FAST (Flow Analysis Software Toolkit).

Watson, Val↗
NASA NTRS1994

Data Communications Requirements for CAS Applications

This paper discusses the long-haul data communications requirements needed to support visualization of Computational Aeroscience (CAS) simulations. A scientific computing model is presented, from which current communications requirements are derived. This model is then expanded to describe the CAS Collaborative Visualization Environment, in which scientists from multiple NASA centers will collaborate on multidisciplinary, time-accurate simulations. An analysis of this model, along with data from the TFCC Workload Model, shows that CAS data communications requirements will increase to 10-100 megabytes/second by 1996.

McCabe, James D.↗
NASA NTRS2020

LaRC SmartLab Apps For Instrument Control And Data Processing: Optical Micrometer Data Visualizer

The LaRC Smart Lab applications are a series of software tools to greatly enhance researcher efficiency by streamlining and automating workflows. Python scripts and applications are increasingly being used in scientific workflows, including for instrument control and data processing. Interactive Python scripting environments such as Jupyter Lab provide powerful tools for using Python. In some use cases, the development of standalone applications with dedicated graphical user interfaces (GUIs) can enhance the utility of the code and open it up to more users, including non-programmers. Here, we describe a GUI based optical micrometer data visualization application developed as part of the LaRC SmartLab project. We highlight its use in visualizing experimental data and briefly discuss its implementation to give pointers to programmers who wish develop work based on this application's or similar co de.

LaRC SmartLab↗
NASA NTRS2003

Direct Manipulation in Virtual Reality

Virtual Reality interfaces offer several advantages for scientific visualization such as the ability to perceive three-dimensional data structures in a natural way. The focus of this chapter is direct manipulation, the ability for a user in virtual reality to control objects in the virtual environment in a direct and natural way, much as objects are manipulated in the real world. Direct manipulation provides many advantages for the exploration of complex, multi-dimensional data sets, by allowing the investigator the ability to intuitively explore the data environment. Because direct manipulation is essentially a control interface, it is better suited for the exploration and analysis of a data set than for the publishing or communication of features found in that data set. Thus direct manipulation is most relevant to the analysis of complex data that fills a volume of three-dimensional space, such as a fluid flow data set. Direct manipulation allows the intuitive exploration of that data, which facilitates the discovery of data features that would be difficult to find using more conventional visualization methods. Using a direct manipulation interface in virtual reality, an investigator can, for example, move a data probe about in space, watching the results and getting a sense of how the data varies within its spatial volume.

Bryson, Steve↗
DOE OSTI2024

Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past

September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.

Johnson, Andrew E.↗
DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1995

Envision: An interactive system for the management and visualization of large geophysical data sets

Envision is a software project at the University of Illinois and Texas A&M, funded by NASA's Applied Information Systems Research Project. It provides researchers in the geophysical sciences convenient ways to manage, browse, and visualize large observed or model data sets. Envision integrates data management, analysis, and visualization of geophysical data in an interactive environment. It employs commonly used standards in data formats, operating systems, networking, and graphics. It also attempts, wherever possible, to integrate with existing scientific visualization and analysis software. Envision has an easy-to-use graphical interface, distributed process components, and an extensible design. It is a public domain package, freely available to the scientific community.

Searight, K. R.↗
DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗