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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Real-Time Aerodynamic Flow and Data Visualization in an Interactive Virtual Environment

Significant advances have been made to non-intrusive flow field diagnostics in the past decade. Camera based techniques are now capable of determining physical qualities such as surface deformation, surface pressure and temperature, flow velocities, and molecular species concentration. In each case, extracting the pertinent information from the large volume of acquired data requires powerful and efficient data visualization tools. The additional requirement for real time visualization is fueled by an increased emphasis on minimizing test time in expensive facilities. This paper will address a capability titled LiveView3D, which is the first step in the development phase of an in depth, real time data visualization and analysis tool for use in aerospace testing facilities.

Schwartz, Richard J.↗
NASA NTRS2005

Mission leverage education: NSU/NASA innovative undergraduate model

The BEST Lab (Center for Excellence in Science Education), the Center for Materials Research (CMR), and the Chemistry, Mathematics, Physics, and Computer Science (CS) Departments at Norfolk State University (NSU) joined forces to implement MiLEN(2) IUM - an innovative approach tu integrate current and emerging research into the undergraduate curricula and train students on NASA-related fields. An Earth Observing System (EOS) mission was simulated where students are educated and trained in many aspects of Remote Sensing: detector physics and spectroscopy; signal processing; data conditioning, analysis, visualization; and atmospheric science. This model and its continued impact is expected to significantly enhance the quality of the Mathematics, Science, Engineering and Technology (MSET or SMET) educational experience and to inspire students from historically underrepresented groups to pursue careers in NASA-related fields. MiLEN(2) IUM will be applicable to other higher education institutions that are willing to make the commitment to this endeavor in terms of faculty interest and space.

Chaudhury, S. Raj↗
NASA NTRS2006

Impact of the Columbia Supercomputer on NASA Space and Exploration Mission

NASA's 10,240-processor Columbia supercomputer gained worldwide recognition in 2004 for increasing the space agency's computing capability ten-fold, and enabling U.S. scientists and engineers to perform significant, breakthrough simulations. Columbia has amply demonstrated its capability to accelerate NASA's key missions, including space operations, exploration systems, science, and aeronautics. Columbia is part of an integrated high-end computing (HEC) environment comprised of massive storage and archive systems, high-speed networking, high-fidelity modeling and simulation tools, application performance optimization, and advanced data analysis and visualization. In this paper, we illustrate the impact Columbia is having on NASA's numerous space and exploration applications, such as the development of the Crew Exploration and Launch Vehicles (CEV/CLV), effects of long-duration human presence in space, and damage assessment and repair recommendations for remaining shuttle flights. We conclude by discussing HEC challenges that must be overcome to solve space-related science problems in the future.

Biswas, Rupak↗
NASA NTRS2003

Algorithms for Haptic Rendering of 3D Objects

Algorithms have been developed to provide haptic rendering of three-dimensional (3D) objects in virtual (that is, computationally simulated) environments. The goal of haptic rendering is to generate tactual displays of the shapes, hardnesses, surface textures, and frictional properties of 3D objects in real time. Haptic rendering is a major element of the emerging field of computer haptics, which invites comparison with computer graphics. We have already seen various applications of computer haptics in the areas of medicine (surgical simulation, telemedicine, haptic user interfaces for blind people, and rehabilitation of patients with neurological disorders), entertainment (3D painting, character animation, morphing, and sculpting), mechanical design (path planning and assembly sequencing), and scientific visualization (geophysical data analysis and molecular manipulation).

Basdogan, Cagatay↗
NASA NTRS2017

Informal Statistics Help Desk

Back by popular demand, the JSC Biostatistics Laboratory and LSAH statisticians are offering an opportunity to discuss your statistical challenges and needs. Take the opportunity to meet the individuals offering expert statistical support to the JSC community. Join us for an informal conversation about any questions you may have encountered with issues of experimental design, analysis, or data visualization. Get answers to common questions about sample size, repeated measures, statistical assumptions, missing data, multiple testing, time-to-event data, and when to trust the results of your analyses.

Young, M.↗
NASA NTRS2018

STEM Engagement with NASA's Solar System Treks Portals for Lunar and Planetary Mapping and Modeling

This presentation will provide an overview of the uses and capabilities of NASA's Solar System Treks family of online mapping and modeling portals. While also designed to support mission planning and scientific research, this presentation will focus on the Science, Technology, Engineering, and Math (STEM) engagement and public outreach capabilities of these web based suites of data visualization and analysis tools.

Law, E. S.↗
NASA NTRS2018

Biocene 2018 - Welcome to the Period of New Life

Biocene is the period of new life. When our descendants look back at this period in time, they will see evidence, in the geologic and electronic record, of anthropic climate change, growing population, and scarcity of resources. But they will also see the rebirth of human ingenuity as we overcame the challenges that faced us through nature-inspired exploration. The Periodic Table of Life (PeTaL) is a proposed tool and open source framework that uses artificial intelligence to aid in the systematic inquiry of biology for its application to human systems. This presentation defines the PeTaL concept and workflow. Biomimicry, biophysics, biomimetics, bionics and numerous other terms refer to the use of biology and biological principles to inform practices in other disciplines. For the most part, the domain of inquiry in these fields have been confined to extant biological models with the proponents of biomimicry often citing the evolutionary success of extant organisms relative to extinct ones. The primary objective of this paper is to expand the domain of inquiry for human processes that seek to model those that are, were or could be found in nature with examples that relate to the field of aerospace and to spur development of tools that can work together to accelerate the use of artificial intelligence in problem solving. Specifically specialized fields such as paleomimesis, anthropomimesis and physioteleology are proposed in conjunction with artificial evolution. Blockchain technology may be vital in allowing open source design tools such as PeTaL to democratize design and yet protect intellectual property. The overarching philosophy outlined here can be thought of as physiomimetics, a holistic and systematic way of learning from natural history. The backbone of PeTaL integrates an unstructured database with an ontological model consisting of function, morphology, environment, state of matter and ecosystem. Tools include text classification, thesaurus, data visualization, and analysis. Applications of PeTaL include guiding human space exploration, understanding human and geological history, and discovering new or extinct life.

Biocene↗
NASA NTRS2019

Lunar Science for Landed Missions Sites Visualized with NASA's Moon Trek Portal

The Lunar Science for Landed Missions Workshop, held in January 2018 at NASA Ames, asked the important question, “Where should we explore next on the Moon?” Representatives from NASA, other international space agencies, commercial exploration companies, and the international lunar science and exploration communities gathered to present and debate the scientific and exploration relevance of a range of potential future lunar landing sites. From this workshop, a list of representative high-value sites was reported. In this presentation, we will present the sites and summarize their individual merits as described in the workshop report. We will augment this with detailed visualizations of each of the sites through the capabilities of NASA’s Moon Trek (https://trek.nasa.gov/moon) data visualization and analysis portal with the goal of highlighting each site’s outstanding geomorphological features along with of the scientific and exploration contexts of each site.

Day, Brian↗
NASA NTRS2021

Noise Exposure Maps of Urban Air Mobility

A noise exposure map is “a scaled geographic depiction of an airport, its noise exposure contours, noise-sensitive facilities, and land uses in the airport surrounding area” developed in accordance with the FAA’s 14 Code of Federal Regulation Part 150. This paper is the first to explore the applicability of airport noise exposure maps to Urban Air Mobility (UAM). The FAA’s airport noise compatibility planning program is first described. Then the applicability of the noise exposure map to Urban Air Mobility (UAM) is explored. Finally, new airspace infrastructure, including vertiport locations and UAM routes from NASA’s UAM engineering simulations, and local noise-sensitive facility locations and land use information were collected and processed to develop the noise exposure maps of UAM near the Dallas-Fort Worth area. The DNL noise contours resulting from a six-passenger electric quadrotor prototype vehicle are predicted using NASA’s AIRNOISEUAM software. The noise exposure maps of UAM are generated automatically using Python’s data analysis and visualization libraries. The results have applications for UAM’s noise compatibility planning, noise-reducing route planning, and vertiport location selection

Urban Air Mobility↗
NASA NTRS2021

Noise Exposure Map of Urban Air Mobility

A noise exposure map is “a scaled geographic depiction of an airport, its noise exposure contours, noise-sensitive facilities, and land uses in the airport surrounding area” developed in accordance with the FAA’s 14 Code of Federal Regulation Part 150. This paper is the first to explore the applicability of airport noise exposure maps to Urban Air Mobility (UAM). The FAA’s airport noise compatibility planning program is first described. Then the applicability of the noise exposure map to Urban Air Mobility (UAM) is explored. Finally, new airspace infrastructure, including vertiport locations and UAM routes from NASA’s UAM engineering simulations, and local noise-sensitive facility locations and land use information were collected and processed to develop the noise exposure maps of UAM near the Dallas-Fort Worth area. The DNL noise contours resulting from a six-passenger electric quadrotor prototype vehicle are predicted using NASA’s AIRNOISEUAM software. The noise exposure maps of UAM are generated automatically using Python’s data analysis and visualization libraries. The results have applications for UAM’s noise compatibility planning, noise-reducing route planning, and vertiport location selection. (To hear the voice please utilize the video uploaded to the record)

Urban Air Mobility↗
NASA NTRSDate not supplied

The Future of NASA Earth Science in the Commercial Cloud: Challenges and Opportunities

NASA produces a large volume and variety of data products that are used every day to support research, decision making, and education. The widespread use of NASA’s Earth Science data is enabled by NASA’s Earth Science Data System (ESDS) program, which oversees the archiving and distribution of these data and invests in the development of new data systems and tools. However, NASA’s current approach to Earth Science data distribution — based on distributed institutional archives with individual on-premises high-performance computing capabilities — faces some significant challenges, including massive increases in data volume from upcoming missions, a greater need for transdisciplinary science that synthesizes many different kinds of observations, and a push to make science more open, inclusive, and accessible. To address these challenges, NASA is aggressively migrating its Earth Science data and related tools and services into the commercial cloud. Migration of data into the commercial cloud can significantly improve NASA’s existing data system capabilities by (1) providing more flexible options for storage and compute (including rapid, as-needed access to state-of-the-art capabilities); (2) by centralizing and standardizing data access, which gives all of NASA’s institutional data centers access to all of each other’s datasets; and (3) by facilitating “analysis-in-place”, whereby users can bring their own computational workflows and tools to the data rather than having to maintain their own copies of NASA datasets. However, migration to the commercial cloud also poses some significant challenges, including (1) managing costs under a “pay-as-you-go” model; (2) incompatibility with existing tools and data formats with object-based storage and network access; (3) vendor lock-in; (4) challenges with data access for workflows that mix on-premise and cloud computing; and (5) standardization for highly diverse data as is present in NASA’s data archive. I conclude with two examples of recent NASA activities showcasing capabilities enabled by the commercial cloud: An interactive analysis and development platform for analyzing airborne imaging spectroscopy data, and a new collection of tools and services for data discovery, analysis, publication, and data-driven storytelling (Visualization, Exploration, and Data Analysis, VEDA).

Alexey N Shiklomanov↗
DOE OSTI2023

Topological Guided Detection of Extreme Wind Phenomena: Implications for Wind Energy: Preprint

Extreme wind phenomena play a crucial role in the efficient operation of wind farms for renewable energy generation. However, existing detection methods are computationally expensive, limited to specific coordinate. In real-world scenarios, understanding the occurrence of these phenomena over a large area is essential. Therefore, there is a significant demand for a fast and accurate approach to forecast such events. In this paper, we propose a novel method for detecting wind phenomena using topological analysis, leveraging the gradient of wind speed or critical points in a topological framework. By extracting topological features from the wind speed profile within a defined region, we employ topological distance to identify extreme wind phenomena. Our results demonstrate the effectiveness of utilizing topological features derived from regional wind speed profiles. We validate our approach using high-resolution simulations with the Weather Research and Forecasting model (WRF) over a month in the US East Coast.

MATHEMATICS AND COMPUTING,WIND ENERGY↗
DOE OSTI2019

COMBIgor: Data-Analysis Package for Combinatorial Materials Science

Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially-resolved characterization of structure and properties. Here, due to maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment, designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data sets. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data analysis and visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Practical Solver for Scalar Data Topological Simplification

This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field f and a set of “signal” persistence pairs to maintain, our approaches produces an output field g that is close to f and which optimizes (i) the cancellation of “non-signal” pairs, while (ii) preserving the “signal” pairs. In contrast to pre-existing simplification algorithms, our approach is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our approach enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. Here, we also show how our approach can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Exploring and Analyzing Climate Variations Online by Using NASA MERRA-2 Data at GES DISC

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) (http:giovanni.sci.gsfc.nasa.govgiovanni) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, preprocessing, and learning data. Example data include climate reanalysis data from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning in 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS), which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM), which provides data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

knowledge base↗
NASA NTRS2007

Exploring NASA and ESA Atmospheric Data Using GIOVANNI, the Online Visualization and Analysis Tool

Giovanni, the NASA Goddard online visualization and analysis tool (http://giovanni.gsfc.nasa.gov) allows users explore various atmospheric phenomena without learning remote sensing data formats and downloading voluminous data. Using NASA MODIS (Terra and Aqua) and ESA MERIS (ENVISAT) aerosol data as an example, we demonstrate Giovanni usage for online multi-sensor remote sensing data comparison and analysis.

Leptoukh, Gregory↗
NASA NTRS1993

DataHub: Science Data Management in Support of Interactive Exploratory Analysis

The DataHub addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, videlicet knowledge-based expert systems, science visualization, and science data management. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system.

science↗