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Results for “spatial data visualization”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Geothermal reservoir characterization using distributed temperature sensing at Brady Geothermal Field, Nevada

Distributed temperature sensing (DTS) systems provide near real-time data collection that captures borehole spatiotemporal temperature dynamics. For this study, temperature data was collected in an observation well at an active geothermal site for a period of eight days under geothermal production conditions. Collected temperature data showcase the ability of DTS systems to detect changes to the location of the steam-water interface, visualize borehole temperature recovery — following injection of a coldwater “slug” — and identify anomalously warm and/or cool zones. The high sampling rate and spatial resolution of DTS data also shows borehole temperature dynamics that are not captured by traditional pressure-temperature survey tools. Inversion of thermal recovery data using a finite-difference heat-transfer model produces a thermal-diffusivity profile that is consistent with laboratorymeasured values and correlates with identified lithologic changes within the borehole. Used alone or in conjunction with complementary data sets, DTS systems are useful tools for developing a better understanding of both reservoir rock thermal properties as well as within and near borehole fluid movement.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

AMM: Adaptive Multilinear Meshes

Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Additionally, recent research suggests that combining the two approaches, i.e., adapting both resolution and precision simultaneously, can offer significant gains over using them individually. However, there currently exist no practical solutions to creating and evaluating such representations at scale. In this work, we present a new resolution-precision-adaptive representation to support hybrid data reduction schemes and offer an interface to existing tools and algorithms. Through novelties in spatial hierarchy, our representation, Adaptive Multilinear Meshes (AMM), provides considerable reduction in the mesh size. AMM creates a piecewise multilinear representation of uniformly sampled scalar data and can selectively relax or enforce constraints on conformity, continuity, and coverage, delivering a flexible adaptive representation. AMM also supports representing the function using mixed-precision values to further the achievable gains in data reduction. We describe a practical approach to creating AMM incrementally using arbitrary orderings of data and demonstrate AMM on six types of resolution and precision datastreams. By interfacing with state-of-the-art rendering tools through VTK, we demonstrate the practical and computational advantages of our representation for visualization techniques. With an open-source release of our tool to create AMM, we make such evaluation of data reduction accessible to the community, which we hope will foster new opportunities and future data reduction schemes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit/hyperactivity disorder? Analysis with brain-image database

PURPOSE: To determine whether there is an association between the spatial distribution of lesions detected at magnetic resonance (MR) imaging of the brain in children after closed-head injury and the development of secondary attention-deficit/hyperactivity disorder (ADHD). MATERIALS AND METHODS: Data obtained from 76 children without prior history of ADHD were analyzed. MR images were obtained 3 months after closed-head injury. After manual delineation of lesions, images were registered to the Talairach coordinate system. For each subject, registered images and secondary ADHD status were integrated into a brain-image database, which contains depiction (visualization) and statistical analysis software. Using this database, we assessed visually the spatial distributions of lesions and performed statistical analysis of image and clinical variables. RESULTS: Of the 76 children, 15 developed secondary ADHD. Depiction of the data suggested that children who developed secondary ADHD had more lesions in the right putamen than children who did not develop secondary ADHD; this impression was confirmed statistically. After Bonferroni correction, we could not demonstrate significant differences between secondary ADHD status and lesion burdens for the right caudate nucleus or the right globus pallidus. CONCLUSION: Closed-head injury-induced lesions in the right putamen in children are associated with subsequent development of secondary ADHD. Depiction software is useful in guiding statistical analysis of image data.

NASA Discipline Neuroscience↗
DOE OSTI2019

Cumulative Spatial Impact Layers: A Novel Multivariate Spatio-Temporal Analytical Summarization Tool

Scientific inquiry often requires analysis of multiple spatio-temporal datasets, ranging in type and size, using complex multi-step processes demanding an understanding of GIS theory and software. Cumulative Spatial Impact Layers (CSIL) is a GIS-based tool that summarizes spatio-temporal datasets based on overlapping features and attributes. Leveraging a recursive quadtree method, and applying multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets by calculating data, record, or attribute density. Providing an efficient and robust method for summarizing disparate, multi-format, multi-source geospatial data, CSIL addresses the need for a new integration approach and resulting geospatial product. The built-in flexibility of the CSIL tool allows users to answer a range of spatially-driven questions. Example applications are provided in this paper to illustrate the versatility and variety of uses for this CSIL tool and method. Use cases include addressing regulatory decision-making needs, economic modeling, and resource management. Performance reviews for each use case are also presented, demonstrating how CSILs provides a more efficient and robust approach to assess a range of multivariate spatial data for a variety of uses.

58 GEOSCIENCES↗
DOE OSTI2024

Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization

Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.

Chae, Junghoon↗
NASA NTRS2002

A Regional Monitoring and Visualization System for Decision Support and Disaster Management Applications for the Mesoamerican Biological Corridor and Beyond

The Mesoamerican Biological Corridor (MBC)-a network of managed and protected areas extending from Mexico to Columbia-is a crucial initiative for the Mesoamerican region, with a central development concept of integrating conservation and sustainable use of biodiversity within the framework of sustainable economic development. The MBC is of particular importance to the Central American Commission for Environment and Development (CCAD), which is comprised of the environmental ministers from the seven Central American countries. Responsible for determining priority areas for action in the corridor, CCAD decision makers require current and accurate information, and access to the dynamic knowledge of the changes in the MBC such as deforestation hotspots, fires, and the effects of natural disasters. Currently this information is not integrated and in disparate locations throughout the region and the world. Leveraging NASA technology, satellite data, and capability, we propose to team with the World Bank and the CCAD to develop a regional monitoring and visualization system-with central nodes at the NASA/Marshall Space Flight Center and at CCAD headquarters. This system will assimilate NASA spatial datasets (e.g. MODIS, Landsat, etc.), spatial data from other sources (commercial and public-domain), and ancillary data developed in each of the seven Central American countries (soils, transportation networks, biodiversity indicator maps, etc.). The system will function as a "virtual dashboard" for monitoring the MBC and provide the critical decision support tools for CCAD decision makers. The CCAD central node will also serve as a high-tech showcase for the corridor among the international community, other decision-makers, the media, and students.

Irwin, Daniel↗
NASA NTRS1989

Visualizing large data sets in the earth sciences

The authors describe the capabilities of McIDAS, an interactive visualization system that is vastly increasing the ability of earth scientists to manage and analyze data from remote sensing instruments and numerical simulation models. McIDAS provides animated three-dimensionsal images and highly interactive displays. The software can manage, analyze, and visualize large data sets that span many physical variables (such as temperature, pressure, humidity, and wind speed), as well as time and three spatial dimensions. The McIDAS system manages data from at least 100 different sources. The data management tools consist of data structures for storing different data types in files, libraries of routines for accessing these data structures, system commands for performing housekeeping functions on the data files, and reformatting programs for converting external data to the system's data structures. The McIDAS tools for three-dimensional visualization of meteorological data run on an IBM mainframe and can load up to 128-frame animation sequences into the workstations. A highly interactive version of the system can provide an interactive window into data sets containing tens of millions of points produced by numerical models and remote sensing instruments. The visualizations are being used for teaching as well as by scientists.

Hibbard, William↗
DOE OSTI2018

Dataset for Biorefinery-specific feedstock price variability, Part 1: Corn stover

This dataset was utilized in a report to highlight parameters that affect near-term sustainable supply of corn stover and forest resources at $56 and $74 per dry ton delivered. While the report focus is restricted to 2018, the modeling runs are available from 2016-2022. In the 2016 Billion-ton Report (BT16), two stover cases were presented. In this dataset, we vary technical levels of those assumptions to measure stover supply response and to evaluate the major determinants of stover supply. In each of these cases, the supply is modeled first at the farmgate at prices up to $80 per dry ton for five deterministic scenarios. Building on this dataset, a supplementary dataset of delivered supply was modeled for 800k dry ton per year capacity facilities in two facility siting approaches. Results were summarized across delivered supply curves for twelve scenarios. The resulting supply curves are highly elastic, resulting in a range of potential supplies across scenarios at specified prices. Interactive visualization of these data allows exploration into any specified nth plant supply sensitivity to key variables and spatial distribution of stover resources. The analysis is economic supply risk and doesn’t account for disruptions from competing demands, namely livestock feed and bedding markets. Scenario ending in _BC3080 is a reference scenario consistent with BT16 Basecase (BC1), but with corn stover price isolation. Scenario ending in _OHB080 includes high operational efficiency constraints for corn stover. Scenario ending in _OLB080 includes low operational efficiency constraints for corn stover. Scenario ending in _PLB080 includes low opportunity cost. Scenario ending in _PHB080 includes high opportunity cost. No Land Rental costs are applied to these scenarios. All scenarios were under an exogenous price simulation using POLYSYS (v2017).

Agricultural residues, biomass resources, 2018, bi↗
DOE OSTI2026

Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute

As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

Characterization of LANDSAT-4 TM and MSS Image Quality for the Interpretation of California's Agricultural Resources

The quality of LANDSAT-4 MSS and TM data was determined by analyzing TM spectral and spatial performance in terms of spectral variability of natural targets and the TM-ground instantaneous field-of-view (IFOV) variability in level and mountainous terrain; and by assessing the suitability of TM and MSS image products for characterizing renewable resourse features. The TM data should be extremelly valuable for crop type and area proportion estimation; undating agricultural land use survey maps at 1:24,000 scale and smaller, field boundary definition; and determining the size and location of individual farmsteads. Ongoing research activities are focused on making spectral and spatial analyses of both MSS and TM analytical film products. The improved spectral, spatial, and radiometric quality of the TM data, should promote a renewed emphasis and interest in direct visual interpretation of these image products, both for updating and improving land stratification in support of resource inventory and for enhancing the image analyst's contribution to computer-assisted analysis procedures.

Degloria, S. D.↗
NASA NTRS1992

Involuntary attentional capture by abrupt onsets

Five experiments were carried out to examine the extent to which brief abrupt-onset visual stimuli involuntarily capture spatial attention. A fundumantal limitation on the conscious control of spatial attention is demonstrated. Data obtained reveal conditions under which the control of spatial attention is completely involuntary: attention is captured by an irrelevant event despite subjects' intentions to ignore the event. The paradigm used provided strong incentives to ignore the distracting abrupt onset, but these were insufficient to prevent capture. Results suggest that voluntary control of attention is limited to focusing attention in advance on locations, objects, or properties of interest. Under appropriate conditions, spatial attention can be involantarily drawn to abrupt-onset events despite the intention of subjects' to ignore them.

Remington, Roger W.↗
NASA NTRS2016

Analyzing and Visualizing Precipitation and Soil Moisture in ArcGIS

Precipitation and soil moisture are among the most important parameters in many land GIS (Geographic Information System) research and applications. These data are available globally from NASA GES DISC (Goddard Earth Science Data and Information Services Center) in GIS-ready format at 10-kilometer spatial resolution and 24-hour or less temporal resolutions. In this presentation, well demonstrate how rainfall and soil moisture data are used in ArcGIS to analyze and visualize spatiotemporal patterns of droughts and their impacts on natural vegetation and agriculture in different parts of the world.

precipitation↗
DOE OSTI2025

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2017

Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our system detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system's effectiveness. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Subband And Transform Compression Of Video Signals

Class of hierarchical subband coders developed primarily for compression of image data at video rates. Offers good performance with limited computational complexity and with flexibility inherent in subband decomposition. Particular subband decomposition chosen for coders appears to hide large quantitative errors effectively, largely because decomposition occurs along two-dimensional spatial-frequency-domain boundaries resembling spatial-frequency-domain curves of constant sensitivity of human visual system. Curves found approximately diamond-shaped: thus, low-pass filtering for reduction of data ideally involves nonrectangular passbands.

Sauer, Ken↗
NASA NTRS1986

A laser-induced-fluorescence visualization study of transverse, sonic fuel injection in a nonreacting supersonic combustor

The present paper reports work which has been conducted in the first phase of a research program which is to provide a data base of spatially-resolved measurements in nonreacting supersonic combustors. In the measurements, a nonintrusive diagnostic technique based on the utilization of laser-induced fluorescence (LIF) is employed. The reported work had the objective to conduct LIF visualization studies of the injection of a simulated fuel into a Mach 2.07 airstream for comparison with corresponding numerical calculations. Attention is given to injection from a single orifice into a constant-area duct, injection from a single orifice behind a rearward-facing step, and injection from staged orifices behind a rearward-facing step.

Mcdaniel, J. C.↗
NASA NTRS2007

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

Leptoukh, Gregory↗