SEARCH · Search NASA

Results for “Data Visualization and Analysis”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29

All sources NASA NTRS DOE OSTI

DOE OSTI2016

Statistical and sensitivity analysis of failing rods in EPR LBLOCA

A statistical fuel failure analysis procedure has been developed, and is now applied in full scale to a large break loss-of-coolant accident (LBLOCA) scenario in EPR-type nuclear power plant. The goal is to statistically determine the number of failing fuel rods. It is also important to bring out the underlying causes for rod failures, and therefore a sensitivity analysis procedure for the preceding complex calculation chain is outlined and adopted. In order to produce a statistically reliable estimation on the number of failing rods, a large number of simulations are required. In the analysis, single rods are simulated with the coupled fuel performance - thermal hydraulics code FRAPTRAN-GENFLO. The statistically varied factors are divided into global and local by their range of influence. Transient thermal hydraulic and power history boundary conditions and model parameters are global and affect all the rods. Fuel rod manufacturing parameters and other rod position related factors are local and are related to a certain rod. The number of global scenarios is 59 as dictated by the Wilks' formula, and 1000 randomly sampled rods are simulated in each scenario. The thermal hydraulic and transient power boundary conditions for the fuel performance code are calculated with a statistical version of the system code APROS. The steady-state irradiation histories are simulated with FRAPCON, and the steady-state power histories used as boundary conditions for FRAPCON are obtained from SIMULATE calculations. Thus, a multistage calculation chain is required to consolidate the procedure. As an outcome, in the worst global scenario, 1.4% of the simulated rods failed. It can be concluded that the Finnish safety regulations, i.e. max. 10% of the rods allowed to fail, are met. In order to find out which input parameters have significance to the outcome of the analysis, a sensitivity analysis is done. At the moment, only the worst global scenario is considered but later on it is possible to include also the sensitivities of the global factors. Due to complexity of the existing data, first the relevant input parameters for the sensitivity analysis have to be specified. Data visualization with a cobweb graph is used for the screening. Then, selected sensitivity measures are calculated between the chosen input and output parameters. The sensitivity indices calculated are the Borgonovo's delta measure, the first order Sobol sensitivity index, and squared Pearson correlation coefficients. The first mentioned is a novelty in this context. As an outcome, the most relevant parameters with respect to the cladding integrity were determined to be the decay heat power during the transient, the thermal hydraulic conditions in the rod's location in the reactor, and the steady-state irradiation history of the rod as represented in this analysis by the rod burnup. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRSDate not supplied

Web-based Visualization and Analytics of Petascale Data: Equity as a Tide that Lifts All Boats

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. This paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hid-ing the complexities of dealing with file systems or cloud services.We also optimize network utilization while streaming from petas-cale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any inter-net connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA’s use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We validate our approach by improving the ability of climate scientists to visually explore their data via two fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution.These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Data visualization↗

NASA NTRS2023

Addition of First-Person Visualization Capabilities to the Glenn Research Center Communication Analysis Suite (GCAS)

The ability to visualize large sets of data is important to giving an understanding of abstract ideas to large groups. With the ability to visualize the terrain of lunar surfaces in three dimensions from both an orbital and first-person view, the capabilities of the Glenn Research Center Communication Analysis Suite (GCAS) will have expanded to allow specific landing sites to be seen more realistically. GCAS will utilize GeoTIFF-based data to visualize the lunar terrain.

ECMAScript↗

DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗

NASA NTRS2009

DSN Data Visualization Suite

The DSN Data Visualization Suite is a set of computer programs and reusable Application Programming Interfaces (APIs) that assist in the visualization and analysis of Deep Space Network (DSN) spacecraft-tracking data, which can include predicted and actual values of downlink frequencies, uplink frequencies, and antenna-pointing angles in various formats that can include tables of values and polynomial coefficients. The data can also include lists of antenna-pointing events, lists of antenna- limit events, and schedules of tracking activities. To date, analysis and correlation of these intricately related data before and after tracking have been difficult and time-consuming. The DSN Data Visualization Suite enables operators to quickly diagnose tracking-data problems before, during, and after tracking. The Suite provides interpolation on demand and plotting of DSN tracking data, correlation of all data on a given temporal point, and display of data with color coding configurable by users. The suite thereby enables rapid analysis of the data prior to transmission of the data to DSN control centers. At the control centers, the same suite enables operators to validate the data before committing the data to DSN subsystems. This software is also Web-enabled to afford its capabilities to international space agencies.

Bui, Bach X.↗

DOE OSTI2018

Understanding Dynamical Systems in High-Dimensional Parameter Spaces (Final Report)

In the last few decades, computational models, especially of the complex dynamical systems of greatest interest, i.e., climate, high energy density science, combustion, etc. have become increasingly accurate and sophisticated. Consequently, they are now routinely used to predict outcomes of experiments or to inform design decisions. However, many such simulations depend on a (large) number of parameters in order to, for example, approximate physical phenom whose exact solution is unknown or describe material properties in situations with little or no experimental data. Choosing these parameters and understanding how the outcomes of such a simulation changes with respect to the inputs are rapidly becoming some of the primary challenges in many application areas. Simulations have long been too complex for humans to fully comprehend and thus are often treated as black-box systems. The inputs to the systems are typically a collection of parameters with expert-selected ranges, and the outputs are various quantities of interest (QoIs) describing the performance of the system. Therefore, the fundamental challenge can be described as one of understanding a high-dimensional (many input parameters), multi-variate (many QoIs) function. Unfortunately, existing analysis tools are only of limited use for this task as most techniques can neither deal with many input dimensions nor offer opportunities to jointly study several QoIs. Furthermore, data analysis has long relied on an exploratory process in which scientist first visualize the available data, for example, using scatter plots or 3D renderings, form new hypotheses on some phenomena of interest, and subsequently test these hypotheses using dedicated analysis or statistics approaches. However, for high-dimensional functions this exploration is difficult if not impossible using existing tools as there are too many QoIs to simultaneously observe, and visualizations of high-dimensional spaces are abstract and difficult to understand if they exist at all. The goal of this project is to build new analysis techniques and tool that offer high level insights for exploring high-dimensional scientific data. And to extend existing approach beyond single scalar functions and instead consider multiple QoIs simultaneously.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1995

A Unified Air-Sea Visualization System: Survey on Gridding Structures

The goal is to develop a Unified Air-Sea Visualization System (UASVS) to enable the rapid fusion of observational, archival, and model data for verification and analysis. To design and develop UASVS, modelers were polled to determine the gridding structures and visualization systems used, and their needs with respect to visual analysis. A basic UASVS requirement is to allow a modeler to explore multiple data sets within a single environment, or to interpolate multiple datasets onto one unified grid. From this survey, the UASVS should be able to visualize 3D scalar/vector fields; render isosurfaces; visualize arbitrary slices of the 3D data; visualize data defined on spectral element grids with the minimum number of interpolation stages; render contours; produce 3D vector plots and streamlines; provide unified visualization of satellite images, observations and model output overlays; display the visualization on a projection of the users choice; implement functions so the user can derive diagnostic values; animate the data to see the time-evolution; animate ocean and atmosphere at different rates; store the record of cursor movement, smooth the path, and animate a window around the moving path; repeatedly start and stop the visual time-stepping; generate VHS tape animations; work on a variety of workstations; and allow visualization across clusters of workstations and scalable high performance computer systems.

Anand, Harsh↗

DOE OSTI2019

Bringing Molecular Dynamics Simulation Data into View

Molecular Dynamics (MD) simulations monitor time-resolved motions of macromolecules. While visualization of MD trajectories allows an instant and intuitive understanding of dynamics and function, so far mainly static representations are provided in published literature. Recent advances in browser technology may allow for the sharing of trajectories through interactive visualization on the web. We believe that by providing intuitive and interactive visualization along with related protocols and analysis data promotes understanding, reliability, and re-usability of MD simulations. Existing barriers for sharing MD simulations are discussed and emerging solutions are highlighted. We predict that interactive visualization of MD trajectories will quickly be adopted by researchers, research consortiums, journals, and funding agencies to gather and distribute results from MD simulations via the web.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Analysis and Visualization of Discrete Fracture Networks Using a Flow Topology Graph

We present an analysis and visualization prototype using the concept of a flow topology graph (FTG) for characterization of flow in constrained networks, with a focus on discrete fracture networks (DFN), developed collaboratively by geoscientists and visualization scientists. Our method allows users to understand and evaluate flow and transport in DFN simulations by computing statistical distributions, segment paths of interest, and cluster particles based on their paths. The new approach enables domain scientists to evaluate the accuracy of the simulations, visualize features of interest, and compare multiple realizations over a specific domain of interest. Geoscientists can simulate complex transport phenomena modeling large sites for networks consisting of several thousand fractures without compromising the geometry of the network. However, few tools exist for performing higher-level analysis and visualization of simulated DFN data. The prototype system we present addresses this need. Here, we demonstrate its effectiveness for increasingly complex examples of DFNs, covering two distinct use cases - hydrocarbon extraction from unconventional resources and transport of dissolved contaminant from a spent nuclear fuel repository.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

COMBIgor (A data assistant for combinatorial scientists in Igor Pro) [SWR-19-16]

Combinatorial materials science experiments involve synthesis and spatially-resolved characterization of thin film sample libraries. As a result, the modern combinatorial materials science laboratory produces large, diverse, and complex data sets, requiring automation of analysis and visualization techniques. COMBIgor is a software package aimed to assist a combinatorial material scientists with large volumes of experimental data. This package, written in Igor Pro, offers a systematic approach to the loading, processing, and visualizing combinatorial data sets. COMBIgor is designed to be expanded on a laboratory, group, or individual basis to adopt additional loading, processing and visualization capabilities. By utilizing COMBIgor software package the burden of data management in combinatorial materials science experiments can be significantly reduced.

Bauers, Sage↗

NASA NTRS1974

Study of the characteristics of seismic signals generated by natural and cultural phenomena

Seismic data recorded at the Tonto Forest Seismological Observatory in Arizona and the Uinta Basin Seismological Observatory in Utah were used to compare the frequency of occurrence, severity, and spectral content of ground motions resulting from earthquakes, and other natural and man-made sources with the motions generated by sonic booms. A search of data recorded at the two observatories yielded a classification of over 180,000 earthquake phase arrivals on the basis of frequency of occurrence versus maximum ground velocity. The majority of the large ground velocities were produced by seismic surface waves from moderate to large earthquakes in the western United States, and particularly along the Pacific Coast of the United States and northern Mexico. A visual analysis of raw film seismogram data over a 3-year period indicates that local and regional seismic events, including quarry blasts, are frequent in occurrence, but do not produce ground motions at the observatories comparable to either the large western United States earthquakes or to sonic booms. Seismic data from the Nevada Test Site nuclear blasts were used to derive magnitude-distance-sonic boom overpressure relations.

Goforth, T. T.↗

DOE OSTI2019

9Cr steel visualization and predictive modeling

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Supporting Weather Data

Since its founding in 1992, Global Science & Technology, Inc. (GST), of Greenbelt, Maryland, has been developing technologies and providing services in support of NASA scientific research. GST specialties include scientific analysis, science data and information systems, data visualization, communications, networking and Web technologies, computer science, and software system engineering. As a longtime contractor to Goddard Space Flight Center s Earth Science Directorate, GST scientific, engineering, and information technology staff have extensive qualifications with the synthesis of satellite, in situ, and Earth science data for weather- and climate-related projects. GST s experience in this arena is end-to-end, from building satellite ground receiving systems and science data systems, to product generation and research and analysis.

Source record↗

DOE OSTI2017

Z-checker: A framework for assessing lossy compression of scientific data

Because of the vast volume of data being produced by today’s scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. However, lossy compressor developers and users are missing a tool to explore the features of scientific data sets and understand the data alteration after compression in a systematic and reliable way. To address this gap, we have designed and implemented a generic framework called Z-checker. On the one hand, Z-checker combines a battery of data analysis components for data compression. On the other hand, Z-checker is implemented as an open-source community tool to which users and developers can contribute and add new analysis components based on their additional analysis demands. In this article, we present a survey of existing lossy compressors. Then, we describe the design framework of Z-checker, in which we integrated evaluation metrics proposed in prior work as well as other analysis tools. Specifically, for lossy compressor developers, Z-checker can be used to characterize critical properties (such as entropy, distribution, power spectrum, principal component analysis, and autocorrelation) of any data set to improve compression strategies. For lossy compression users, Z-checker can detect the compression quality (compression ratio and bit rate) and provide various global distortion analysis comparing the original data with the decompressed data (peak signal-to-noise ratio, normalized mean squared error, rate–distortion, rate-compression error, spectral, distribution, and derivatives) and statistical analysis of the compression error (maximum, minimum, and average error; autocorrelation; and distribution of errors). Z-checker can perform the analysis with either coarse granularity (throughout the whole data set) or fine granularity (by user-defined blocks), such that the users and developers can select the best fit, adaptive compressors for different parts of the data set. Z-checker features a visualization interface displaying all analysis results in addition to some basic views of the data sets such as time series. To the best of our knowledge, Z-checker is the first tool designed to assess lossy compression comprehensively for scientific data sets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July-2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1973

Reflectance measurements for the detection and mapping of soil limitations

During 1971 and 1972 research was conducted on two fallow fields in the proposed Oahe Irrigation Project to investigate the relationship between the tonal variations observed on aerial photographs and the principal soil limitations of the area. A grid sampling procedure was used to collected detailed field data during the 1972 growing season. The field data was compared to imagery collected on May 14, 1971 at 3050 meters altitude. The imagery and field data were initially evaluated by a visual analysis. Correlation and regression analysis revealed a highly significant correlation and regression analysis revealed a highly significant correlation between the digitized color infrared film data and soil properties such as organic matter content, color, depth to carbonates, bulk density and reflectivity. Computer classification of the multiemulsion film data resulted in maps delineating the areas containing claypan and erosion limitations. Reflectance data from the red spectral band provided the best results.

Benson, L. A.↗

DOE OSTI2022

Interactively Assessing Disentanglement in GANs

Abstract Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo‐realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute‐based semantic scoring of images produced by GANs. Through use‐cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.

Jeong, Sangwon↗

NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗