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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

MCNPy

SAND2026-20425O MCNPy runs and analyzes simulations from MCNP, a software that models radiation transport of neutrons and gamma rays. MCNPy uses Python to start MCNP, retrieve event data files, and convert them into graph structures for detailed analysis. It offers visualization tools, including 2D views of particle histories, making complex simulation data easier to interpret for researchers and engineers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Nowack, Aaron [Sandia National Lab. (SNL-CA), Live↗
NASA NTRS1973

Interdisciplinary applications and interpretations of ERTS data within the Susquehanna River Basin; resources inventory, land use and pollution

The author has identified the following significant results. Identification and mapping of three major kinds of coal refuse targets based on spectral signatures in channels four through seven of the ERTS-1 MSS were conducted. Correlation of the placement of the coal refuse targets with an existing map of their location was accomplished. Digital processing of ERTS-1 data permitted identification of stripped areas including ones that are not discernible by visual analysis of ERTS imagery. Combined visual and digital techniques of analyzing ERTS-1 data for geologic formations have been tried on selected areas of Pennsylvania. Mapping of two major agriculture counties to show land forms, drainage patterns, water, and urban areas were made using positive transparencies of MSS data. Two frames of the same central Pennsylvania area were brought into registration by translation and then merged even though the frames were obtained 71 days apart.

Mcmurtry, G. J.↗
NASA NTRS1981

Texture transforms of remote sensing data

Tone and texture are fundamental interrelated visual concepts. The concepts are used for the digital analysis of remotely sensed image data. The reported investigation had the objective to develop software for the quantification of image texture and to apply the texture information to both image enhancement and thematic classification of remotely sensed data. The quantitative texture information was applied to the analysis of Landsat-2 Multispectral Scanner Subsystem (MSS) data. Attention is given to the characterization of image texture, textured transformations, the subtext program, and a description of methods and results. It is pointed out that the inability to use the texture transforms of the Landsat MSS data for the thematic mapping of the study area's land cover contrasts sharply with the reported results of the textural analysis of digitized aerial photography by Hsu (1978).

Irons, J. R.↗
NASA NTRS2002

The D3 Middleware Architecture

DARWIN is a NASA developed, Internet-based system for enabling aerospace researchers to securely and remotely access and collaborate on the analysis of aerospace vehicle design data, primarily the results of wind-tunnel testing and numeric (e.g., computational fluid-dynamics) model executions. DARWIN captures, stores and indexes data; manages derived knowledge (such as visualizations across multiple datasets); and provides an environment for designers to collaborate in the analysis of test results. DARWIN is an interesting application because it supports high-volumes of data. integrates multiple modalities of data display (e.g., images and data visualizations), and provides non-trivial access control mechanisms. DARWIN enables collaboration by allowing not only sharing visualizations of data, but also commentary about and views of data. Here we provide an overview of the architecture of D3, the third generation of DARWIN. Earlier versions of DARWIN were characterized by browser-based interfaces and a hodge-podge of server technologies: CGI scripts, applets, PERL, and so forth. But browsers proved difficult to control, and a proliferation of computational mechanisms proved inefficient and difficult to maintain. D3 substitutes a pure-Java approach for that medley: A Java client communicates (though RMI over HTTPS) with a Java-based application server. Code on the server accesses information from JDBC databases, distributed LDAP security services, and a collaborative information system. D3 is a three tier-architecture, but unlike 'E-commerce' applications, the data usage pattern suggests different strategies than traditional Enterprise Java Beans - we need to move volumes of related data together, considerable processing happens on the client, and the 'business logic' on the server-side is primarily data integration and collaboration. With D3, we are extending DARWIN to handle other data domains and to be a distributed system, where a single login allows a user transparent access to test results from multiple servers and authority domains.

Walton, Joan↗
DOE OSTI2023

ExaWind at NREL: Upping the Ante

The objective of the ExaWind component of the Exascale Computing Project is to deliver many-turbine blade-resolved simulations in complex terrain. These simulations bring new challenges to both compute and analysis of the resulting data. In this paper/video, we visually explore the impact of ExaWind on wind simulations through two studies of a small wind farm under two atmospheric conditions. We then turn to analysis and review tools that visualization researchers at NREL use to answer the challenges that ExaWind brings.

collaborative visualization↗
DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗
NASA NTRS2019

NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research

The lack of observations over vast tropical oceans is a major challenge for tropical cyclone research. Satellite observations and model reanalysis data play an important role in filling these- gaps. Established in the mid-1980's, the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA data centers, archives and distributes data from several Earth science disciplines such as precipitation, atmospheric dynamics, atmospheric composition, hydrology, including well-known NASA satellite missions (e.g. TRMM, GPM) and model assimilation projects (MERRA-2). Acquiring datasets suitable for tropical cyclone research in a large data archive is a challenge for many, especially for those who are not familiar with satellite or model data. Over the years, the GES DISC has developed user-friendly data services. For example, Giovanni is an online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data and software. In this chapter, we will describe data and services at the GES DISC with emphasis on tropical cyclone research. We will also present two case studies and discuss future plans.

Liu, Zhong↗
NASA NTRS2011

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

Leptoukh, Gregory↗
NASA NTRSDate not supplied

"Giovanni at 20: Consistency and Persistency in Making Earth Remote Sensing Data Available (and Useful) to the Earth Science Community"

Since its creation in the year 2000, the NASA Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) has been an exemplar of how to provide remotely-sensed satellite data and related Earth science datasets to a broad and globally diverse researcher community. One of the hallmarks of Giovanni system usage is that it is common to see practitioners of many branches of science – particularly those in fields not traditionally associated with remote-sensing data, such as animal behavior and paleooceanography – both accessing and employing datasets which the system provides. This usage pattern is attested to by the diversity of published science citing Giovanni. Giovanni has evolved through several versions, each of which increased analytical and visualization options while also enhancing ease-of-use. Information resources supporting Giovanni from the Goddard Earth Sciences Data and Information Services Center (GES DISC), which hosts the system, start with basic mapping and plotting functions and extend to “How-to” recipes demonstrating interusability with other data analysis systems and software. Giovanni is now being developed for use in a cloud environment, potentially expanding datasets the system can be applied to, and also improving performance for data having high spatial and temporal resolution. This presentation covers the system’s historical success with interesting examples of Giovanni-citing research, and will apply its current capabilities to a multi-dataset examination of derecho events associated with mesoscale convective phenomena in the summer of 2020, indicating how Giovanni is prepared for ongoing support of Earth science in a changing world.

Giovanni Derecho visualization↗
NASA NTRS1983

Research and analysis of head-directed area-of-interest visual system concepts

An analysis and survey with conjecture supporting a preliminary data base design is presented. The data base is intended for use in a Computer Image Generator visual subsystem for a rotorcraft flight simulator that is used for rotorcraft systems development, not training. The approach taken was to attempt to identify the visual perception strategies used during terrain flight, survey environmental and image generation factors, and meld these into a preliminary data base design. This design is directed at Data Base developers, and hopefully will stimulate and aid their efforts to evolve such a Base that will support simulation of terrain flight operations.

Sinacori, J. B.↗
NASA NTRS1990

Flight set 360L007 (STS-33) insulation component. Volume 3: Final release

Volume 3 of this postfire report deals with the insulation component of the RSRM. The report is released twice for each flight set. The interim release contract date is on or before 60 days after the last field joint or nozzle to case joint is disassembled at KSC and contain the results of the KSC visual evaluation. The data contained in Volume 3 interim release supersedes the insulation data presented in the KSC 10 day report. The final release contract data is on or before 60 days after the last factory joint is disassembled at the Clearfield H-7 facility and contains the results of all visual evaluations and a thermal safety factor analysis. The data contained in the Volume 3 final release supersedes the interim release and the insulation data presented in the Clearfield 10 day report.

Hicken, Steve↗
NASA NTRS2006

Advanced Visualization of Experimental Data in Real Time Using LiveView3D

LiveView3D is a software application that imports and displays a variety of wind tunnel derived data in an interactive virtual environment in real time. LiveView3D combines the use of streaming video fed into a three-dimensional virtual representation of the test configuration with networked communications to the test facility Data Acquisition System (DAS). This unified approach to real time data visualization provides a unique opportunity to comprehend very large sets of diverse forms of data in a real time situation, as well as in post-test analysis. This paper describes how LiveView3D has been implemented to visualize diverse forms of aerodynamic data gathered during wind tunnel experiments, most notably at the NASA Langley Research Center Unitary Plan Wind Tunnel (UPWT). Planned future developments of the LiveView3D system are also addressed.

Schwartz, Richard J.↗
NASA NTRS2010

Influence of Averaging Method on the Evaluation of a Coastal Ocean Color Event on the U.S. Northeast Coast

Application of appropriate spatial averaging techniques is crucial to correct evaluation of ocean color radiometric data, due to the common log-normal or mixed log-normal distribution of these data. Averaging method is particularly crucial for data acquired in coastal regions. The effect of averaging method was markedly demonstrated for a precipitation-driven event on the U.S. Northeast coast in October-November 2005, which resulted in export of high concentrations of riverine colored dissolved organic matter (CDOM) to New York and New Jersey coastal waters over a period of several days. Use of the arithmetic mean averaging method created an inaccurate representation of the magnitude of this event in SeaWiFS global mapped chl a data, causing it to be visualized as a very large chl a anomaly. The apparent chl a anomaly was enhanced by the known incomplete discrimination of CDOM and phytoplankton chlorophyll in SeaWiFS data; other data sources enable an improved characterization. Analysis using the geometric mean averaging method did not indicate this event to be statistically anomalous. Our results predicate the necessity of providing the geometric mean averaging method for ocean color radiometric data in the Goddard Earth Sciences DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni).

Acker, James G.↗
NASA NTRS2022

An Earth System Digital Twin for Flood Prediction and Analysis

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observation data, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA’s Advanced Information Systems Technology (AIST)’s Integrated Digital Earth Analysis System (IDEAS) project is to establish an extensible architectural solution to develop digital twins of our physical environment for Earth Science. IDEAS delivers a formal system architecture with mechanisms for the outputs of one model to feed into others; for driving models with observation data; and for harmonizing observation data and model outputs for analysis. To validate and demonstrate the IDEAS architecture, this project collaborates with the Space Climate Observatory (SCO)’s FloodDAM project and the Centre National d’Etudes Spatiales (CNES) to focus on floods detection, prediction and their impacts.

Kettig, Peter↗
DOE OSTI2018

In Situ Analysis and Visualization of Fusion Simulations: Lessons Learned

The trends in high performance computing, where far more data can be computed that can ever be stored, have made in situ techniques an important area of research and development. Simulation campaigns, where domain scientists work with computer scientists to run a simulation and perform in situ analysis and visualization are important, and complex undertakings. In this paper we report our experiences performing in situ analysis and visualization on two campaigns. The two campaigns were related, but had important differences in terms of the codes that were used, the types of analysis and visualization required, and the visualization tools used. Further, we report the lessons learned from each campaign.

Kim, Mark B.↗
DOE OSTI2020

GIS Visualization of Transportation Energy Consumption

Transportation is witnessing unprecedented transformation, where emerging technologies are disrupting the way we travel. Be it sharing economy, or micro-mobility, the landscape of urban transportation is undergoing a much-needed paradigm shift. In order to capture and model these shifts, researchers need to be agile in their studies of the current and future transportation landscape. The need for agility in turn calls for sophisticated, intuitive, and reliable means to visualize and share transportation data. Travel is inherently spatial, so methods of visualizing transportation information should also be rooted in spatial analysis. Geographic Information Systems (GIS) are the premier technological framework to analyze and display spatial data. GIS tools have been used in the past to depict flow of vehicles, and display of network conditions (speed, congestion, etc.). However, in an ever-changing technological landscape, spanning advancements in vehicle as well as information systems, depicting vehicle flows falls short of providing a comprehensive picture of the impact these advancements have on travel related energy consumption. To address this issue, this research effort presents a web-based mapping application to visualize how travel related energy flows across a city. The application is being developed by National Renewable Energy Laboratory researchers to integrate various transportation energy consumption models within a GIS schema. This has the goal of enabling rapid analysis of energy impacts of the dramatically evolving transportation environment. The application is being developed using a Python framework for ArcGIS Online. Using road network data from the City of Columbus, Ohio and traffic data from CATT Laboratory's Regional Integrated Transportation Information System, transportation energy consumption will be modeled at a macro level. The application aims to provide highly accurate data while still maintaining the flexibility needed to adapt the model when new technologies arise. The methodology presented through this effort is expected to provide insights into how light-duty vehicles use energy on a large scale based on the road network they use. The application will also provide data exports to enable sharing and collaboration. " need for agility in turn calls for sophisticated, intuitive, and reliable means to visualize and share transportation data. Travel is inherently spatial, so methods of visualizing transportation information should also be rooted in spatial analysis. Geographic Information Systems (GIS) are the premier technological framework to analyze and display spatial data. GIS tools have been used in the past to depict flow of vehicles, and display of network conditions (speed, congestion, etc.). However, in an ever-changing technological landscape, spanning advancements in vehicle as well as information systems, depicting vehicle flows falls short of providing a comprehensive picture of the impact these advancements have on travel related energy consumption. To address this issue, this research effort presents a web-based mapping application to visualize how travel related energy flows across a city. The application is being developed by National Renewable Energy Laboratory researchers to integrate various transportation energy consumption models within a GIS schema. This has the goal of enabling rapid analysis of energy impacts of the dramatically evolving transportation environment. The application is being developed using a Python framework for ArcGIS Online. Using road network data from the City of Columbus, Ohio and traffic data from CATT Laboratory's Regional Integrated Transportation Information System, transportation energy consumption will be modeled at a macro level. The application aims to provide highly accurate data while still maintaining the flexibility needed to adapt the model when new technologies arise. The methodology presented through this effort is expected to provide insights into how light-duty vehicles use energy on a large scale based on the road network they use. The application will also provide data exports to enable sharing and collaboration.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1980

Appendix E: Research papers. Manual versus digital LANDSAT analysis for modeling river flooding

The comparative value of manual versus digital image analysis for determining flood boundaries is being examined in a study of the use of LANDSAT data for modeling flooding of the Black River, in northern New York. The work is an extension of an earlier study in which Black River flooding was assessed through visually interpreted, multi-date LANDSAT band 7 images. Based on the results to date, it appears that neither color-additive viewing nor digital analysis of LANDSAT data provide improvement in accuracy over visual analysis of band 7 images, for delineating the boundaries of flood-affected areas.

Philipson, W. R.↗
NASA NTRS1981

Manual versus digital Landsat analysis for modeling river flooding

The comparative value of manual versus digital image analysis for determining flood boundaries is being examined in a study of the use of Landsat data for modeling flooding of the Black River, in northern New York. The work is an extension of an earlier study in which Black River flooding was assessed through visually interpreted, multi-date Landsat band 7 images. Based on the results to date, it appears that neither color-additive viewing nor digital analysis of Landsat data provide improvement in accuracy over visual analysis of band 7 images, for delineating the boundaries of flood-affected areas.

Philipson, W. R.↗