Results for “Visual analytics” — Page 3

NASA NTRS2012

Jupiter Environment Tool

The Jupiter Environment Tool (JET) is a custom UI plug-in for STK that provides an interface to Jupiter environment models for visualization and analysis. Users can visualize the different magnetic field models of Jupiter through various rendering methods, which are fully integrated within STK s 3D Window. This allows users to take snapshots and make animations of their scenarios with magnetic field visualizations. Analytical data can be accessed in the form of custom vectors. Given these custom vectors, users have access to magnetic field data in custom reports, graphs, access constraints, coverage analysis, and anywhere else vectors are used within STK.

Sturm, Erick J.↗

DOE OSTI2024

Architecture for Web-Based Visualization of Large-Scale Energy Domains: Preprint

With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.

grid modernization↗

NASA NTRS1981

Shaded computer graphic techniques for visualizing and interpreting analytic fluid flow models

Mathematical models which predict the behavior of fluid flow in different experiments are simulated using digital computers. The simulations predict values of parameters of the fluid flow (pressure, temperature and velocity vector) at many points in the fluid. Visualization of the spatial variation in the value of these parameters is important to comprehend and check the data generated, to identify the regions of interest in the flow, and for effectively communicating information about the flow to others. The state of the art imaging techniques developed in the field of three dimensional shaded computer graphics is applied to visualization of fluid flow. Use of an imaging technique known as 'SCAN' for visualizing fluid flow, is studied and the results are presented.

Parke, F. I.↗

NASA NTRS1970

An experimental and analytical study of visual detection in a spacecraft environment, 1 July 1968 - 1 July 1969

Predicting star magnitude which can be seen with naked eye or sextant through spacecraft window

Heinisch, R. P.↗

NASA NTRS1992

A study of the rotor wake of a small-scale rotor model in forward flight using laser light sheet flow visualization with comparisons to analytical models

An experimental investigation was conducted in the 14 by 22 ft subsonic tunnel at NASA Langley Research Center to quantify the rotor wake behind a scale model helicopter rotor in forward flight (mu = 0.15 and 0.23) at one thrust level (C sub T = 0.0064). The rotor system used in the present test consisted of a four-bladed, fully articulated hub and utilized blades of rectangular planform with a NACA-0012 airfoil section. A laser light sheet, seeded with propylene glycol smoke, was used to visualize the flow in planes parallel and perpendicular to the freestream flow. Quantitative measurements of vortex location, vertical skew angle, and vortex particle void radius were obtained for vortices in the flow; convective velocities were obtained for blade tip vortices. Comparisons were made between the experimental results and the wake geometry generated by computational predictions. The results of these comparisons show that the interaction between wake vortex structures is an important consideration for correctly predicting the wake geometry.

Ghee, Terence A.↗

DOE OSTI2024

Visualization Within the Department of Energy: NREL IEEE VIS Application Spotlight

This presentation highlights the role of advanced visualization techniques at the National Renewable Energy Laboratory (NREL) in supporting cutting-edge research across diverse energy domains. From immersive analytics and uncertainty visualization to high-resolution and real-time data analysis, NREL's visualization capabilities enable scientists to explore complex datasets more effectively. These tools are critical for advancing research in materials science, renewable energy technologies, biofuels, electric vehicle infrastructure, energy efficiency - from industrial processes to entire communities - and then bringing these innovations to practice through energy systems integration. NREL's visualization tools drive innovation across renewable energy and grid modernization efforts by providing deeper insights and improving decision-making.

grid modernization↗

DOE OSTI2026

Three-Dimensional Grid Visualization for Planning Activities: A Dubai Case Study

National Laboratory of the Rockies (NLR), in collaboration with the Dubai Electricity and Water Authority (DEWA) and Infra-X, has undertaken the Energy Visualization Analysis Project. The aim of this project is to enhance analytical and 3D visualization capabilities for distribution network planning and renewable energy integration. As modern grid continues to evolve with large-scale solar PV deployment and emerging distributed energy resources (DERs), the ability to effectively analyze, visualize, and communicate complex grid behaviors has become increasingly critical. The project focuses on developing empirical use cases based on real distribution feeder data and engineering workflows, ensuring the outcomes are directly aligned with operational environment. Through time-series power flow simulations and nodal hosting capacity analysis, the study quantifies the impacts of high PV penetration on voltage and thermal limits within representative 11 kV feeders. These analyses identify specific nodes and conditions where DER integration challenges arise. Furthermore, a Battery Energy Storage System (BESS) optimization algorithm was applied to determine the optimal size and placement of storage systems that can mitigate network constraints and enhance hosting capacity. The comparative results between base-case and BESS-augmented scenarios clearly demonstrate improvements in network stability and load management efficiency. In parallel, the NLR team developed an immersive 3D visualization framework, enabling interactive exploration of grid simulations using commodity head-mounted display (HMD) systems. This framework transforms conventional 2D simulation data into spatially intuitive visual environments - allowing engineers to analyze feeder conditions, PV hosting potential, and BESS effects in real time. This report represents the first foundational phase in establishing a visualization-driven analytical ecosystem. It provides a methodological foundation for data integration, visualization architecture, and simulation-based decision support, paving the way for large-scale adoption of immersive visualization across DEWA's Smart Grid Initiative, R&D activities, and future network resilience studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2019

Sherlock Data Warehouse

This slide deck provides an overview of the data and resources available in the Sherlock Data Warehouse. Sherlock was developed and is currently maintained by the Aviation Systems Division at NASA Ames Research Center. Sherlock contains a valuable collection of flight, air traffic management, and weather data. But Sherlock is not just a data archive. Sherlock also includes tools and resources to access, download, and visualize data, as well as resources to process the data. This overview summarizes Sherlock data sources, demonstrates data analytics and visualization with MicroStrategy, illustrates disparate data integration using the ATM Knowledge graph, and presents a machine learning use case using the Big Data system.

data warehouse↗

NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: An Application to XCO2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

Calle, Leonardo↗

NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: an Application to Xco2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

segmentation algorithm↗

NASA NTRSDate not supplied

NASA ESTO Advanced Information Systems Technology (AIST)

"(Only Talk/No Publication) NASA’s Advanced Information Systems Technology (AIST) Program identifies, develops, and supports adoption of software and information systems, as well as novel computer science technologies expected to be needed by the Earth Science Division in the 5-10-year timeframe. This presentation gives an overview of the AIST Program. AIST’s previous thrusts have been New Observing Strategies (NOS) and Analytic Collaborative Frameworks (ACF). The current vision is to connect these two thrusts and integrate them into the larger concept of Earth System Digital Twins (ESDT). To implement this new vision, the AIST Program is focusing on technologies and innovative concepts with three main objectives: O1. Enable new observation measurements and new observing systems design and operations through intelligent, timely, dynamic, and coordinated distributed sensing; O2. Enable agile science investigations that fully utilize the large amount of diverse observations using advanced analytic tools, visualizations, and computing environments, and that interact seamlessly with relevant observing systems; O3. Enable the development of integrated Earth Science frameworks that mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. This thrust will provide technology for enabling near- and long-term science and policy decisions (“science decisions” including planning for the acquisition of new measurements; the development of new models or science analysis; the integration of Earth observations in novel ways; applications to inform choices, support decisions, and guide actions for societal benefit; etc.)."

Mathematical and Computer Sciences (General)↗

NASA NTRS2023

Advanced Information Systems Technology (AIST) Overview & Current and Recent Projects

"NASA's Advanced Information Systems Technology (AIST) Program identifies, develops, and supports adoption of software and information systems, as well as novel computer science technologies expected to be needed by the Earth Science Division in the 5-10-year timeframe. Overall, the AIST Program is focusing on advanced technologies and innovative concepts with three main objectives: O1. Enable new observation measurements and new observing systems design and operations through intelligent, timely, dynamic, and coordinated distributed sensing; O2. Enable agile science analyses that fully utilize the large amount of diverse observations using advanced analytic tools, visualizations, and computing environments, and that interact seamlessly with relevant observing systems; O3. Enable the development of integrated Earth Science frameworks that mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. This thrust will provide technology for enabling near- and long-term science and policy decisions (""""science decisions"""" including planning for the acquisition of new measurements; the development of new models or science analysis; the integration of Earth observations in novel ways; applications to inform choices, support decisions, and guide actions for societal benefit; etc.). AIST objectives aim at optimizing Earth Science mission return – NOS from an observation point of view and ACF/ESDT from an information analysis and utilization point of view. The assets and data accessed and utilized in these systems may come from NASA and non-NASA sources. "

Earth Science Remote Sensing; Information Systems↗

NASA NTRS1991

Experimental and analytical studies of flow through a ventral and axial exhaust nozzle system for STOVL aircraft

Flow through a combined ventral and axial exhaust nozzle system was studied experimentally and analytically. The work is part of an ongoing propulsion technology effort at NASA Lewis Research Center for short takeoff, vertical landing (STOVL) aircraft. The experimental investigation was done on the NASA Lewis Powered Lift Facility. The experiment consisted of performance testing over a range of tailpipe pressure ratios from 1 to 3.2 and flow visualization. The analytical investigation consisted of modeling the same configuration and solving for the flow using the PARC3D computational fluid dynamics program. The comparison of experimental and analytical results was very good. The ventral nozzle performance coefficients obtained from both the experimental and analytical studies agreed within 1.2 percent. The net horizontal thrust of the nozzle system contained a significant reverse thrust component created by the flow overturning in the ventral duct. This component resulted in a low net horizontal thrust coefficient. The experimental and analytical studies showed very good agreement in the internal flow patterns.

Esker, Barbara S.↗

NASA NTRS1991

Experimental and analytical studies of flow through a ventral and axial exhaust nozzle system for STOVL aircraft

Flow through a combined ventral and axial exhaust nozzle system was studied experimentally and analytically. The work is part of an ongoing propulsion technology effort at NASA Lewis Research Center for short takeoff, vertical landing (STOVL) aircraft. The experimental investigation was done on the NASA Lewis Powered Lift Facility. The experiment consisted of performance testing over a range of tailpipe pressure ratios from 1 to 3.2 and flow visualization. The analytical investigation consisted of modeling the same configuration and solving for the flow using the PARC3D computational fluid dynamics program. The comparison of experimental and analytical results was very good. The ventral nozzle performance coefficients obtained from both the experimental and analytical studies agreed within 1.2 percent. The net horizontal thrust of the nozzle system contained a significant reverse thrust component created by the flow overturning in the ventral duct. This component resulted in a low net horizontal thrust coefficient. The experimental and analytical studies showed very good agreement in the internal flow patterns.

Esker, Barbara S.↗

DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗

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↗

DOE OSTI2026

Ten questions concerning low-cost indoor air quality sensors: Perspectives from research and practice

Low-cost indoor air quality (IAQ) sensors are increasingly being used in homes and commercial and public buildings, driven by growing concerns about the impact of air on health, cognitive performance, and occupant wellbeing. These sensors offer a potentially transformative opportunity to increase spatial and temporal coverage of IAQ monitoring at a fraction of the cost of conventional reference instruments. However, their widespread use raises questions around accuracy, calibration, placement, data handling and interpretation, and integration into existing standards and workflows. This paper presents ten critical questions concerning the use of low-cost IAQ sensors in buildings, drawing on the latest empirical research, field deployments, and emerging practice. It discusses potential frameworks for deployment and evaluation, examines current sensor capabilities for measuring common pollutants, identifies methodological gaps in validation and uncertainty quantification, and outlines the extent to which existing IAQ standards can accommodate sensor-based evidence. The paper also explores how monitoring needs and deployment models vary by building type, the potential of real-time IAQ data to support building operations, and the ethical and legal implications of widespread sensor use. While significant challenges remain in ensuring data quality and building stakeholder trust, new applications are emerging through open data initiatives and advances in analytics and visualization. As the technology, science, and standards co-evolve, low-cost IAQ sensors are poised to become integral to routine building operation, building science, and environmental health research.

Parkinson, Thomas↗

DOE OSTI2024

A New Default Colormap for ParaView

ParaView is one of the most prominent software tools for scientific visualization used by scientists around the world. Color is a primary conduit to visually map data to its representation and, thus, enable investigation and interpretation of the data. Colormap selection has a significant impact on the data revealed; its design and selection is a critical aspect of scientific data visualization. A common choice for a user is the program’s default colormap, so careful consideration of this default is consequential. Although the current default colormap in ParaView, a succession of hues from cool blue to warm red, has served the community well, research shows that more nuanced colormap configurations increase discriminability while maintaining other critical metrics. These findings inspire us to revisit and update the default colors in ParaView. Here, in this study, we present a new ParaView default colormap, the criteria and methods of development, and example visualizations and analytic metrics.

97 MATHEMATICS AND COMPUTING↗