Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data visualizations”

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.

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

At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Superimposition, symbology, visual attention, and the head-up display

In two experiments we examined a number of related factors postulated to influence head-up display (HUD) performance. We addressed the benefit of reduced scanning and the cost of increasing the number of elements in the visual field by comparing a superimposed HUD with an identical display in a head-down position in varying visibility conditions. We explored the extent to which the characteristics of HUD symbology support a division of attention by contrasting conformal symbology (which links elements of the display image to elements of the far domain) with traditional instrument landing system (ILS) symbology. Together the two experiments provide strong evidence that minimizing scanning between flight instruments and the far domain contributes substantially to the observed HUD performance advantage. Experiment 1 provides little evidence for a performance cost attributable to visual clutter. In Experiment 2 the pattern of differences in lateral tracking error between conformal and traditional ILS symbology supports the hypothesis that, to the extent that the symbology forms an object with the far domain, attention may be divided between the superimposed image and its counterpart in the far domain.

Attention↗
NASA NTRS2025

NASA Open Science Data Repository: Open Science for Life in Space

Space biology and health data are critical for the success of deep space missions and sustainable human presence off-world. At the core of effectively managing biomedical risks is the commitment to open science principles, which ensure that data are findable, accessible, interoperable, reusable, reproducible and maximally open. The 2021 integration of the Ames Life Sciences Data Archive with GeneLab to establish the NASA Open Science Data Repository significantly enhanced access to a wide range of life sciences, biomedical-clinical, and mission telemetry data alongside existing ‘omics data from GeneLab. This paper describes the new database, its architecture, and new data streams supporting diverse data types and enhancing data submission, retrieval, and analysis. Features include the Biological Data Management Environment for improved data submission, a new user interface, controlled data access, an enhanced API, and comprehensive public visualization tools for environmental telemetry, radiation dosimetry data, and ‘omics analyses. By fostering global collaboration through its Analysis Working Groups and training programs, the Open Science Data Repository promotes widespread engagement in space biology, ensuring transparency and inclusivity in research. It supports the global scientific community in advancing our understanding of spaceflight's impact on biological systems, ensuring humans will thrive in future deep space missions.

OSDR↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
NASA NTRS2011

Coastal On-line Assessment and Synthesis Tool 2.0

COAST (Coastal On-line Assessment and Synthesis Tool) is a 3D, open-source Earth data browser developed by leveraging and enhancing previous NASA open-source tools. These tools use satellite imagery and elevation data in a way that allows any user to zoom from orbit view down into any place on Earth, and enables the user to experience Earth terrain in a visually rich 3D view. The benefits associated with taking advantage of an open-source geo-browser are that it is free, extensible, and offers a worldwide developer community that is available to provide additional development and improvement potential. What makes COAST unique is that it simplifies the process of locating and accessing data sources, and allows a user to combine them into a multi-layered and/or multi-temporal visual analytical look into possible data interrelationships and coeffectors for coastal environment phenomenology. COAST provides users with new data visual analytic capabilities. COAST has been upgraded to maximize use of open-source data access, viewing, and data manipulation software tools. The COAST 2.0 toolset has been developed to increase access to a larger realm of the most commonly implemented data formats used by the coastal science community. New and enhanced functionalities that upgrade COAST to COAST 2.0 include the development of the Temporal Visualization Tool (TVT) plug-in, the Recursive Online Remote Data-Data Mapper (RECORD-DM) utility, the Import Data Tool (IDT), and the Add Points Tool (APT). With these improvements, users can integrate their own data with other data sources, and visualize the resulting layers of different data types (such as spatial and spectral, for simultaneous visual analysis), and visualize temporal changes in areas of interest.

Brown, Richard↗
NASA NTRSDate not supplied

Digital Lunar Exploration Sites (DLES)

It has been almost 50 years since humans last set foot on the Moon. With NASA’s Artemis program, the United States and its international and commercial partners are embarking on a new endeavor to explore the lunar surface. Before we return, we will have simulated every aspect of these future missions. Many of these models and simulations (M&S) will rely on well-known and commonly-used technologies, some of which trace their origins back to the Apollo program. However, M&S has advanced significantly, as have the underlying computational capabilities. As a result, we are able to model many more aspects of the Artemis vehicles and support systems with significantly improved detail and confidence. Accurately and efficiently modeling the lunar environment will be critical to simulating the Artemis elements and mission activities. This includes characterizing and modeling lunar topography, smaller craters, exposed surface rocks, lunar regolith, surface lighting, and ambient thermal environment. These are all necessary for understanding fundamental behaviors and performance of vehicles and support systems in the lunar environment and are often determining factors in the selection of exploration sites and defining mission profiles. The Astromaterials Research and Exploration Sciences (ARES) team in the Exploration Integration and Science Directorate at NASA’s Johnson Space Center (JSC) and the NASA Exploration Systems Simulations (NExSyS) team in the Simulation and Graphics Branch at JSC are developing and maintaining the Digital Lunar Exploration Sites (DLES) data, documentation, and software packages. This data is being fed directly into a diverse collection of graphics and simulation environments, where it is used to construct the closest known truth for numerous potential Lunar South Pole landing sites. DLES is available to projects across NASA and particularly the Artemis program to support coordinated digital representations of the lunar environment. This paper describes the fundamental need for DLES, the science data sets that are going into DLES, some of the processes used to integrate this data into DLES products, the basic products that constitute DLES, and some examples of DLES in use.

Lunar↗
NASA NTRS1991

The design of electronic map displays

This paper presents a cognitive analysis of a pilot's navigation task and describes an experiment comparing a new map display that employs the principle of visual momentum with the two traditional approaches, track-up and north-up. The data show that the advantage of a track-up alignment is its congruence with the egocentered forward view; however, the inconsistency of the rotating display hinders development of a cognitive map. The stability of a north-up alignment aids the acquisition of a cognitive map, but there is a cost associated with the mental rotation of the display to a track-up alignment for tasks involving the ego-centered forward view. The data also show that the visual momentum design captures the benefits and reduces the costs associated with the two traditional approaches.

Aretz, Anthony J.↗
NASA NTRS2004

Picturing Data With Uncertainty

NASA is in the business of creating maps for scientific purposes to represent important biophysical or geophysical quantities over space and time. For example, maps of surface temperature over the globe tell scientists where and when the Earth is heating up; regional maps of the greenness of vegetation tell scientists where and when plants are photosynthesizing. There is always uncertainty associated with each value in any such map due to various factors. When uncertainty is fully modeled, instead of a single value at each map location, there is a distribution expressing a set of possible outcomes at each location. We consider such distribution data as multi-valued data since it consists of a collection of values about a single variable. Thus, a multi-valued data represents both the map and its uncertainty. We have been working on ways to visualize spatial multi-valued data sets effectively for fields with regularly spaced units or grid cells such as those in NASA's Earth science applications. A new way to display distributions at multiple grid locations is to project the distributions from an individual row, column or other user-selectable straight transect from the 2D domain. First at each grid cell in a given slice (row, column or transect), we compute a smooth density estimate from the underlying data. Such a density estimate for the probability density function (PDF) is generally more useful than a histogram, which is a classic density estimate. Then, the collection of PDFs along a given slice are presented vertically above the slice and form a wall. To minimize occlusion of intersecting slices, the corresponding walls are positioned at the far edges of the boundary. The PDF wall depicts the shapes of the distributions very dearly since peaks represent the modes (or bumps) in the PDFs. We've defined roughness as the number of peaks in the distribution. Roughness is another useful summary information for multimodal distributions. The uncertainty of the multi-valued data can also be interpreted by the number of peaks and the widths of the peaks as shown by the PDF walls.

Kao, David↗
NASA NTRS2007

Boundary Layer Development on a Turbine Blade in a Linear Cascade

Several different boundary-layer development patterns for flow over the suction surface of a turbine airfoil in a linear cascade were studied and documented using a sliding surface hot-film sensor. The state of the boundary layer, whether laminar, transitional or turbulent, was determined at numerous locations along the airfoil suction surface from leading to trailing edge. Boundary-layer transition from laminar to turbulent flow through laminar separation and turbulent reattachment, or through a combination of bypass transition and strong and weak separation and turbulent reattachment, or through solely bypass transition without separation, was observed and benchmark data were recorded. Surface flow visualization and numerical boundary-layer analysis results are consistent with the hot-film data. Flow and geometry information necessary for nmerical code operation is available.

Halstead, Dave↗
NASA NTRS1990

ISOLEV - A level surface cutting plane program for fluid flow data

A computer program, ISOLEV, allows interactive visualization of CFD scalar and vector functions by means of user-specified sections of the data. The application is based on table lookups which govern both isosurface generation on hexahedral grid cells and recursive subdivision of the cells. The program supports Gouraud-shaded color maps of the data, surface-on-surface maps, and deformation surfaces of vector fields. The execution of the code for animated sweeps is improved by resorting to the cells in the database and maintaining an active set of cells to be rendered.

Kerlick, G. David↗
NASA NTRS1992

Hand/Eye Coordination For Fine Robotic Motion

Fine motions of robotic manipulator controlled with help of visual feedback by new method reducing position errors by order of magnitude. Robotic vision subsystem includes five cameras: three stationary ones providing wide-angle views of workspace and two mounted on wrist of auxiliary robot arm. Stereoscopic cameras on arm give close-up views of object and end effector. Cameras measure errors between commanded and actual positions and/or provide data for mapping between visual and manipulator-joint-angle coordinates.

Lokshin, Anatole M.↗
NASA NTRS1992

Statistical discrete particle simulation studies

A particle simulation code was developed that is suited to Multiple Instruction Multiple Data multiprocessor computers. The resulting code is designed to be portable between a large class of multiprocessor computer architectures, including hypercubes (e.g., Intel iPSC/860), shared memory machines (e.g., Cray, SGI, DASH), and simple uniprocessors (e.g., SUN, VAX, IBM PC). This code provides a powerful general framework that can be adapted to specific applications, such as thermomechanical modeling, general 3-D geometry support, or visualization support via a codeveloped CPlot data reduction and visualization system. Chemistry models were implemented and validated with comparison to continuum solutions of thermochemically relaxing gas mixtures and with experimental results from the high speed flow about a circular cylinder. Furthermore, the exchange of energy between translational and internal modes was modeled. New models were developed, combining greater efficiency than earlier phenomenological models. They offer greater physical detail by addressing vibrational energy as a discretely distributed quantity.

Mcdonald, Jeffrey D.↗
NASA NTRS1978

Effects of visual and motion simulation cueing systems on pilot performance during takeoffs with engine failures

Data are presented that show the effects of visual and motion during cueing on pilot performance during takeoffs with engine failures. Four groups of USAF pilots flew a simulated KC-135 using four different cueing systems. The most basic of these systems was of the instrument-only type. Visual scene simulation and/or motion simulation was added to produce the other systems. Learning curves, mean performance, and subjective data are examined. The results show that the addition of visual cueing results in significant improvement in pilot performance, but the combined use of visual and motion cueing results in far better performance.

Parris, B. L.↗
NASA NTRS1995

Scientific Visualization and Computational Science: Natural Partners

Scientific visualization is developing rapidly, stimulated by computational science, which is gaining acceptance as a third alternative to theory and experiment. Computational science is based on numerical simulations of mathematical models derived from theory. But each individual simulation is like a hypothetical experiment; initial conditions are specified, and the result is a record of the observed conditions. Experiments can be simulated for situations that can not really be created or controlled. Results impossible to measure can be computed.. Even for observable values, computed samples are typically much denser. Numerical simulations also extend scientific exploration where the mathematics is analytically intractable. Numerical simulations are used to study phenomena from subatomic to intergalactic scales and from abstract mathematical structures to pragmatic engineering of everyday objects. But computational science methods would be almost useless without visualization. The obvious reason is that the huge amounts of data produced require the high bandwidth of the human visual system, and interactivity adds to the power. Visualization systems also provide a single context for all the activities involved from debugging the simulations, to exploring the data, to communicating the results. Most of the presentations today have their roots in image processing, where the fundamental task is: Given an image, extract information about the scene. Visualization has developed from computer graphics, and the inverse task: Given a scene description, make an image. Visualization extends the graphics paradigm by expanding the possible input. The goal is still to produce images; the difficulty is that the input is not a scene description displayable by standard graphics methods. Visualization techniques must either transform the data into a scene description or extend graphics techniques to display this odd input. Computational science is a fertile field for visualization research because the results vary so widely and include things that have no known appearance. The amount of data creates additional challenges for both hardware and software systems. Evaluations of visualization should ultimately reflect the insight gained into the scientific phenomena. So making good visualizations requires consideration of characteristics of the user and the purpose of the visualization. Knowledge about human perception and graphic design is also relevant. It is this breadth of knowledge that stimulates proposals for multidisciplinary visualization teams and intelligent visualization assistant software. Visualization is an immature field, but computational science is stimulating research on a broad front.

Uselton, Samuel P.↗
NASA NTRS1978

Skylab/EREP application to ecological, geological, and oceanographic investigations of Delaware Bay

Skylab/EREP S190A and S190B film products were optically enhanced and visually interpreted to extract data suitable for; (1) mapping coastal land use; (2) inventorying wetlands vegetation; (3) monitoring tidal conditions; (4) observing suspended sediment patterns; (5) charting surface currents; (6) locating coastal fronts and water mass boundaries; (7) monitoring industrial and municipal waste dumps in the ocean; (8) determining the size and flow direction of river, bay and man-made discharge plumes; and (9) observing ship traffic. Film products were visually analyzed to identify and map ten land-use and vegetation categories at a scale of 1:125,000. Digital tapes from the multispectral scanner were used to prepare thematic maps of land use. Classification accuracies obtained by comparison of derived thematic maps of land-use with USGS-CARETS land-use maps in southern Delaware ranged from 44 percent to 100 percent.

Klemas, V.↗
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 NTRS2017

Quantitative Image Analysis Techniques with High-Speed Schlieren Photography

Optical flow visualization techniques such as schlieren and shadowgraph photography are essential to understanding fluid flow when interpreting acquired wind tunnel test data. Output of the standard implementations of these visualization techniques in test facilities are often limited only to qualitative interpretation of the resulting images. Although various quantitative optical techniques have been developed, these techniques often require special equipment or are focused on obtaining very precise and accurate data about the visualized flow. These systems are not practical in small, production wind tunnel test facilities. However, high-speed photography capability has become a common upgrade to many test facilities in order to better capture images of unsteady flow phenomena such as oscillating shocks and flow separation. This paper describes novel techniques utilized by the authors to analyze captured high-speed schlieren and shadowgraph imagery from wind tunnel testing for quantification of observed unsteady flow frequency content. Such techniques have applications in parametric geometry studies and in small facilities where more specialized equipment may not be available.

Pollard, Victoria J.↗
NASA NTRS2004

Average Characteristics of Triggered and Nontriggered Substorms

Magnetic field data from ground stations, geosynchronous orbit, and magnetotail are examined to study the response to substorm activity with and without apparent interplanetary magnetic field (IMF) perturbations. Global substorms are identified using a sudden, persistent decrease in the AL index. The onset of this global expansion is taken to be the time of the Pi2 burst nearest to the beginning of the AL decrease. IMF triggers were identified subjectively through visual scanning of the data. Both northward turnings of the IMF B, and decreases in the amplitude of the By component were considered as possible triggers. Two different solar wind monitors were used in the investigation: IMP 8 in a circular orbit with a distance between approx.12 and approx.35 R(sub E) from the Earth-Sun line and ISEE 2 in an elliptical orbit with a distance of only approx.5- 10 R(sub E) from the Earth-Sun line. The results of superposed epoch analysis show that the temporal response from ground stations, geosynchronous orbit, and magnetotail are nearly identical for triggered (with apparent IMF perturbation) and nontriggered (without apparent IMF perturbation) substorms. It is therefore concluded that the nontriggered substorms are not a different form of activity than triggered substorms. However, we demonstrate that the magnitude of the response is different for the two types of substo&. By every measure considered, triggered substorm are systematically larger than nontriggered substorms. We interpret the fact that nearly 40% of all substorms cannot be associated with an IMF trigger as evidence that substorms are caused by an internal instability. However, the fact that so many appear to be triggered suggests that this internal instability is susceptible to external perturbations by the IMF. The fact that triggered substorms are larger than nontriggered substorms is counterintuitive, and we have no explanation for the observation.

Hsu, Tung-Shin↗
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↗