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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 NTRS2020

Development and Comparison of an Artificial Gravity Concept for Human Spaceflight

This Internship served as a resource to find specific research about the many facets of artificial gravity. With three main areas to be addressed in detail: the negative health effects of microgravity and how current mitigation methods fall short, the knowns and unknowns of artificial gravity as a countermeasure, and past artificial gravity habitat concepts. This report primarily focuses on the negative health effects of microgravity and how current mitigation methods fall short. Through NASA resources such as the NTRS, and external sources such as the AIAA, research papers were gathered that were pertinent to the topic. Each paper was uploaded to a NASA SharePoint and abstracts were drafted to highlight the important information. The expected results were to build a library of easily accessible information that would allow for Ryan Joyce and Gabe Merrill to easily get up to speed on relevant information. This process should allow for a faster period of understanding with high relevancy material. The ability to quickly reference data will help to build an informed and detailed case for any future proposals on artificial gravity. This also prevents the need to spend many hours searching for relevant documentation, allowing for more time to be dedicated to the creation process. The project has successfully met expectations and is continuing to add pertinent documentation. Current research shows great potential for artificial gravity as a countermeasure on long duration missions. The gravity like forces created in an artificial gravity ship could help to prevent serious problems such as muscle loss, bone fractures, vision loss due to increased intracranial pressure, and would allow for transition to other gravity environments with little to no recovery period. These conditions are highly favorable for long duration missions as current mitigation methods have yet to be proven beyond one year in space and often only slow symptoms instead of preventing them.

Alice Elberfeld↗
NASA NTRS1990

SHAPA: An interactive software tool for protocol analysis applied to aircrew communications and workload

As modern transport environments become increasingly complex, issues such as crew communication, interaction with automation, and workload management have become crucial. Much research is being focused on holistic aspects of social and cognitive behavior, such as the strategies used to handle workload, the flow of information, the scheduling of tasks, the verbal and non-verbal interactions between crew members. Traditional laboratory performance measures no longer sufficiently meet the needs of researchers addressing these issues. However observational techniques are better equipped to capture the type of data needed and to build models of the requisite level of sophistication. Presented here is SHAPA, an interactive software tool for performing both verbal and non-verbal protocol analysis. It has been developed with the idea of affording the researchers the closest possible degree of engagement with protocol data. The researcher can configure SHAPA to encode protocols using any theoretical framework or encoding vocabulary that is desired. SHAPA allows protocol analysis to be performed at any level of analysis, and it supplies a wide variety of tools for data aggregation, manipulation. The output generated by SHAPA can be used alone or in combination with other performance variables to get a rich picture of the influences on sequences of verbal or nonverbal behavior.

James, Jeffrey M.↗
NASA NTRSDate not supplied

Using Time Series Data Products to Support ASHRAE’s Historic and Future Climate Data Needs through NASA’s POWER Web Services

To address the data needs for resilience and sustainability of building systems within ASHRAE, the NASA Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data on a long-term, global scale. Solar data from several NASA projects and meteorological data from NASA assimilation models have been reformatted and disseminated to the public via a user friendly web GIS-enabled based data portal (https://power.larc.nasa.gov) in selectable data formats, immediately amenable to key industry wide decision support tools such as EnergyPlus. Time series of potential climate conditions from downscaled climate simulations are being made available for similar statistics and formats. An emphasis is placed upon obtaining and utilizing time series data to conduct analysis of building system performance for current and planning building systems for energy savings and greenhouse gas emissions. This presentation has the learning following learning objectives for the ASHRAE community: 1) Learn how NASA POWER’s web service suite can provide supplementary solar and surface meteorological data parameters, including a discussion of uncertainty. 2) Learn how downscaled climate scenario information can be provided to evaluate potential future requirements. The presentation is part of a forum entitled, "Weather Data for Large-Scale Building Energy Modeling".

time series↗
NASA NTRS1980

Improving Building Construction Specifications in State and Local Governments

State and local governments can benefit from master specifications systems that centralize data on all types of building materials, products, and processes. Most of these systems are organized according to the MASTERFORMAT system, which, along with guide specifications that require the insertion or deletion of standardized information, resulted from the specific needs of users and providers. For jurisdictions preparing their own specifications, staff time and cost are reduced. For those subcontracting the preparation, master specifications provide a means of evaluating the specifications submitted. Current management specification systems described include SPECINTACT, OMSPEC, MASTERPEC, and the NAVFAC, Corps of Engineers, and GSA guide specifications.

Source record↗
NASA NTRS2003

NASA's Earth Observatory and Visible Earth: Imagery and Science on the Internet

The purpose of NASA s Earth Observatory and Visible Earth Web sites is to provide freely-accessible locations on the Internet where the public can obtain new satellite imagery (at resolutions up to a given sensor's maximum) and scientific information about our home planet. Climatic and environmental change are the sites main foci. As such, they both contain ample data visualizations and time-series animations that demonstrate geophysical parameters of particular scientific interest, with emphasis on how and why they vary over time. An Image Composite Editor (ICE) tool will be added to the Earth Observatory in October 2002 that will allow visitors to conduct basic analyses of available image data. For example, users may produce scatter plots to correlate images; or they may probe images to find the precise unit values per pixel of a given data product; or they may build their own true-color and false-color images using multi- spectral data. In particular, the sites are designed to be useful to the science community, public media, educators, and students.

King, Michael D.↗
NASA NTRS2000

Coupling Satellite and Ground-Based Instruments to Map Climate Forcing by Anthropogenic Aerosols

Climate forcing by anthropogenic aerosols is a significant but highly uncertain factor in global climate change. Only satellites can offer the global coverage essential to reducing this uncertainty; however, satellite measurements must be coupled with correlative, in situ measurements both to constrain the aerosol optical properties required in satellite retrieval algorithms and to provide chemical identification of aerosol sources. This grant funded the first two years of a three-year project which seeks to develop methodologies for combining spaceborne lidar with in-situ aerosol data sets to improve estimates of direct aerosol climate forcing. Progress under this two-year grant consisted in the development and deployment of a new in-situ capability for measuring aerosol 180' backscatter and the extinction-to-backscatter ratio. This new measurement capacity allows definitive lidar/in-situ comparisons and improves our ability to interpret lidar data in terms of climatically relevant quantities such as the extinction coefficient and optical depth. Measurements were made along the coast of Washington State, in Central Illinois, over the Indian Ocean, and in the Central Pacific. Thus, this research, combined with previous measurements by others, is rapidly building toward a global data set of extinction-to-backscatter ratio for key aerosol types. Such information will be critical to interpreting lidar data from the upcoming PICASSO-CENA, or P-C, satellite mission. Another aspect of this project is to investigate innovative ways to couple the lidar-satellite signal with targeted in-situ measurements toward a direct determination of aerosol forcing. This aspect is progressing in collaboration with NASA Langley's P-C lidar simulator and radiative transfer modeling by the University of Lille, France.

Charlson, Robert J.↗
NASA NTRS2004

Improvements in Electron-Probe Microanalysis: Applications to Terrestrial, Extraterrestrial, and Space-Grown Materials

Improvement in the accuracy of electron-probe microanalysis (EPMA) has been accomplished by critical assessment of standards, correction algorithms, and mass absorption coefficient data sets. Experimental measurement of relative x-ray intensities at multiple accelerating potential highlights errors in the absorption coefficient. The factor method has been applied to the evaluation of systematic errors in the analysis of semiconductor and silicate minds. Accurate EPMA of Martian soil stimulant is necessary in studies that build on Martian rover data in anticipation of missions to Mars.

Carpenter, Paul↗
NASA NTRS2017

GeoDash: Assisting Visual Image Interpretation in Collect Earth Online by Leveraging Big Data on Google Earth Engine

Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.

SERVI↗
NASA NTRS1992

Evaluation Of Threshold Functions For Searches Among Data

Paper presents information-theoretical analysis of problem of building system of inferences from large collection of data (data base). More specifically, problem to design searches of data base for informative conjunctive production rules. Results of analysis may lead to design of more-economical and more-effective electronic neural networks drawing inferences from data bases.

Goodman, Rodney M.↗
NASA NTRS2010

Sonic Booms on Big Structures (SonicBOBS) Phase I Database; NASA Dryden Sensors

This DVD contains 13 channels of microphone and up to 22 channels of pressure transducer data collected in September, 2009 around several buildings located at Edwards Air Force Base. These data were recorded by NASA Dryden. Not included are data taken by NASA Langley and Gulfstream. Each day's data is in a separate folder and each pass is in a file beginning with "SonicBOBS_" (for microphone data) or "SonicBOBSBB_" (for BADS and BASS data) followed by the month, day, year as two digits each, followed by the hour, minute, sec after midnight GMT. The filename time given is for the END time of the raw recording file. In the case of the microphone data, this time may be several minutes after the sonic boom, and is according to the PC's uncalibrated clock. The Matlab data files have the actual time as provided by a GPS-based IRIG-B signal recorded concurrently with the data. Microphone data is given for 5 seconds prior to 20 seconds after the sonic boom. BADS and BASS data is given for the full recording, 6 seconds for the BADS and 10 seconds for the BASS. As an example of the naming convention, file "SonicBOBS_091209154618.mat" is from September 12, 2009 at 15:46:18 GMT. Note that data taken on September 12, 2009 prior to 01:00:00 GMT was of the Space Shuttle Discovery (a sonic boom of opportunity), which was on September 11, 2009 in local Pacific Daylight Time.

Haering, Edward A., Jr.↗
NASA NTRS2015

Computational and Experimental Study of Supersonic Nozzle Flow and Shock Interactions

This study focused on the capability of NASA Tetrahedral Unstructured Software System's CFD code USM3D capability to predict the interaction between a shock and supersonic plume flow. Previous studies, published in 2004, 2009 and 2013, investigated USM3D's supersonic plume flow results versus historical experimental data. This current study builds on that research by utilizing the best practices from the early papers for properly capturing the plume flow and then adding a wedge acting as a shock generator. This computational study is in conjunction with experimental tests conducted at the Glenn Research Center 1'x1' Supersonic Wind Tunnel. The comparison of the computational and experimental data shows good agreement for location and strength of the shocks although there are vertical shifts between the data sets that may be do to the measurement technique.

Carter, Melissa B.↗
NASA NTRSDate not supplied

Onboard Hyperspectral Image Classification via Transfer Learning for Communication-Limited Spacecraft

Employing deep-learning and artificial-intelligence (AI) techniques onboard spacecraft can dramatically improve priority data selection to ensure more effective use of the available downlink. However, deployment of effective deep-learning models requires significant training on the ground, which may not be feasible, due to limited data available in an unexplored environment. Therefore, this research explores building robust classification models for onboard data processing where training data is highly limited using transfer-learning techniques. In this paper, we focus on the use case of hyperspectral imaging for remote sensing, a domain where the high dimensionality of the data from the sensor can rapidly saturate the downlink bandwidth. With this bottleneck, there is an impending need to autonomously and robustly classify data onboard to optimize downlink of high-impact measurements, thus maximizing the scientific utility per bit transmitted to the ground. This paper examines the use of deep neural networks onboard for hyperspectral image classification in a communication-limited scenario to analyze how the models perform with limited training data. The use of transfer learning can ameliorate the issue of poor generalization by transferring features learned from training on a large source dataset for one classification task to the target classification task with limited training data. For two deep-learning models from literature, we compare the accuracy of the models trained using transfer learning to models trained from scratch using a random weight initialization with varying amounts of training data. We demonstrate the feasibility and performance of running inference of the deep-learning models on representative flight-like hardware.

Advanced Avionics, Machine Learning, Data Processi↗
NASA NTRSDate not supplied

Experimental Characterization of Non-Associative Plasticity Flow Rule Coefficients and Post-Peak Stress Degradation for the LS-DYNA MAT213 Model

This project is focused on developing an experimental framework for characterizing non-associative plasticity flow rule coefficients through coupon-scale tests for the LS-DYNA MAT213 model. The main objective is to characterize these coefficients based on the multi-scale (i.e., both microscopic and macroscopic) full-field measurement of the evolution of strain and stress fields. This paper focuses on presenting the experimental work on characterizing the full-scale stress-strain curves of T700/LM-PAEK composites under tension, compression, and shear loads. The experimental data set was intended to build a deformation sub-model in the MAT213 model for the material. The strain data were collected using both microscopic and macroscopic digital image correlation techniques. The microscopic technique was particularly useful for fracture cases under small strains. A preliminary simulation result obtained from the MAT213 model is also presented in the paper. The experimental framework herein will be extended to characterize post-peak stress degradation in the composite material and to develop a damage sub-model for the material. This project will contribute to developing a simulation tool based on the MAT213 model for simulating the rate-dependent impact damage in composites under multi-axial loading.

MAT213↗
NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗
NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗
NASA NTRS2003

[Development and Use of Hidrosig]

The NASA portion of this joint NSF-NASA grant consists of objective 2 and a part of objective 3. A major effort was made on objective 2, and it consisted of developing a numerical GIs environment called Hidrosig. This major research tool is being developed by the University of Colorado for conducting river-network-based scaling analyses of coupled water-energy-landform-vegetation interactions including water and energy balances, and floods and droughts, at multiple space-time scales.Objective 2: To analyze the relevant remotely sensed products from satellites, radars and ground measurements to compute the transported water mass for each complete Strahler stream using an 'assimilated water balance equation' at daily and other appropriate time scales. This objective requires analysis of concurrent data sets for Precipitation (PPT), Evapotranspiration (ET) and stream flows (Q) on river networks. To solve this major problem, our decision was to develop Hidrosig, a new Open-Source GIs software. A research group in Colombia, South America, developed the first version of Hidrosig, and Ricardo Mantilla was part of this effort as an undergraduate student before joining the graduate program at the University of Colorado in 2001. Hydrosig automatically extracts river networks from large DEMs and creates a "link-based" data structure, which is required to conduct a variety of analyses under objective 2. It is programmed in Java, which is a multi-platform programming language freely distributed by SUN under a GPL license. Some existent commercial tools like Arc-Info, RiverTools and others are not suitable for our purpose for two reasons. First, the source code is not available that is needed to build on the network data structure. Second, these tools use different programming languages that are not most versatile for our purposes. For example, RiverTools uses an IDL platform that is not very efficient for organizing diverse data sets on river networks. Hidrosig establishes a clear data organization framework that allows a simultaneous analysis of spatial fields along river network structures involving Horton- Strahler framework. Software tools for network extraction from DEMs and network-based analysis of geomorphologic and topologic variables were developed during the first year and a part of second year.

Gupta, Vijay K.↗
NASA NTRS2010

Individualized Cognitive Modeling for Close-Loop Task Mitigation

An accurate real-time operator functional state assessment makes it possible to perform task management, minimize risks, and improve mission performance. In this paper, we discuss the development of an individualized operator functional state assessment model that identifies states likely leading to operational errors. To address large individual variations, we use two different approaches to build a model for each individual using its data as well as data from subjects with similar responses. If a subject's response is similar to that of the individual of interest in a specific functional state, all the training data from this subject will be used to build the individual model. The individualization methods have been successfully verified and validated with a driving test data set provided by University of Iowa. With the individualized models, the mean squared error can be significantly decreased (by around 20%).

Zhang, Guangfan↗