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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Vulnerabilities, Influences and Interaction Paths: Failure Data for Integrated System Risk Analysis

We describe graph-based analysis methods for identifying and analyzing cross-subsystem interaction risks from subsystem connectivity information. By discovering external and remote influences that would be otherwise unexpected, these methods can support better communication among subsystem designers at points of potential conflict and to support design of more dependable and diagnosable systems. These methods identify hazard causes that can impact vulnerable functions or entities if propagated across interaction paths from the hazard source to the vulnerable target. The analysis can also assess combined impacts of And-Or trees of disabling influences. The analysis can use ratings of hazards and vulnerabilities to calculate cumulative measures of the severity and importance. Identification of cross-subsystem hazard-vulnerability pairs and propagation paths across subsystems will increase coverage of hazard and risk analysis and can indicate risk control and protection strategies.

Malin, Jane T.↗
NASA NTRS1995

Impact damage in composite plates

The objective of this research paper was to link two computer codes, PDCOMP (for Progressive Damage Analysis for Laminated Composites) and 3DIMPACT (for the prediction of the extent of delaminations in laminated composites resulting from point impact loads), in order to predict impact damage by taking into account local damage and material degradation and to estimate residual stiffness of composites after impact. The resulting graphs and analysis versus test results are presented along with the conclusive results of the codes' performances.

Shahid, I.↗
NASA NTRS2010

Rocket-Plume Spectroscopy Simulation for Hydrocarbon-Fueled Rocket Engines

The UV-Vis spectroscopic system for plume diagnostics monitors rocket engine health by using several analytical tools developed at Stennis Space Center (SSC), including the rocket plume spectroscopy simulation code (RPSSC), to identify and quantify the alloys from the metallic elements observed in engine plumes. Because the hydrocarbon-fueled rocket engine is likely to contain C2, CO, CH, CN, and NO in addition to OH and H2O, the relevant electronic bands of these molecules in the spectral range of 300 to 850 nm in the RPSSC have been included. SSC incorporated several enhancements and modifications to the original line-by-line spectral simulation computer program implemented for plume spectral data analysis and quantification in 1994. These changes made the program applicable to the Space Shuttle Main Engine (SSME) and the Diagnostic Testbed Facility Thruster (DTFT) exhaust plume spectral data. Modifications included updating the molecular and spectral parameters for OH, adding spectral parameter input files optimized for the 10 elements of interest in the spectral range from 320 to 430 nm and linking the output to graphing and analysis packages. Additionally, the ability to handle the non-uniform wavelength interval at which the spectral computations are made was added. This allowed a precise superposition of wavelengths at which the spectral measurements have been made with the wavelengths at which the spectral computations are done by using the line-by-line (LBL) code. To account for hydrocarbon combustion products in the plume, which might interfere with detection and quantification of metallic elements in the spectral region of 300 to 850 nm, the spectroscopic code has been enhanced to include the carbon-based combustion species of C2, CO, and CH. In addition, CN and NO have spectral bands in 300 to 850 nm and, while these molecules are not direct products of hydrocarbon-oxygen combustion systems, they can show up if nitrogen or a nitrogen compound is present as an impurity in the propellants and/or these can form in the boundary layer as a result of interaction of the hot plume with the atmosphere during the ground testing of engines. Ten additional electronic band systems of these five molecules have been included into the code. A comprehensive literature search was conducted to obtain the most accurate values for the molecular and the spectral parameters, including Franck-Cordon factors and electronic transition moments for all ten band systems. For each elemental transition in the RPSSC, six spectral parameters - Doppler broadened line width at half-height, pressure-broadened line width at half-height, electronic multiplicity of the upper state, electronic term energy of the upper state, Einstein transition probability coefficient, and the atomic line center - are required. Input files have been created for ten elements of Ni, Fe, Cr, Co, Cu, Ca, Mn, Al, Ag, and Pd, which retain only relatively moderate to strong transitions in 300 to 430 nm spectral range for each element. The number of transitions in the input files is 68 for Ni; 148 for Fe; 6 for Cr; 87 for Co; 1 for Ca; 3 for Mn; 2 each for Cu, Al, and Ag; and 11 for Pd.

Tejwani, Gopal D.↗
NASA NTRSDate not supplied

Nasa Genelab - Knowledge Graph Fabric Enables Deep Biomedical Analysis of Multi-Omics Datasets

The limited number of astronauts and human samples from long-duration space missions pose significant challenges for studying the health risks associated with spaceflight and developing new treatments. As a result, much of our understanding of the biological impact of space travel relies on samples from model organisms. NASA GeneLab, integrated into Open Science Data Repository (OSDR) is a centralized multi-omics resource containing almost 1000 datasets from over 500 space-related studies from human and model organism samples. Previous studies have demonstrated that human phenotypes and physiological changes caused by spaceflight can be identified by connecting gene expression data from model organisms flown in space to a biomedical knowledge graph (SPOKE). In this work, we present a data fabric connecting OSDR datasets to SPOKE that empowers biomedical analyses through the GeneLab visualization portal. This collaboration is funded by NSF’s Proto-OKN program.

data fabric↗
NASA NTRS2011

Antenna Near-Field Probe Station Scanner

A miniaturized antenna system is characterized non-destructively through the use of a scanner that measures its near-field radiated power performance. When taking measurements, the scanner can be moved linearly along the x, y and z axis, as well as rotationally relative to the antenna. The data obtained from the characterization are processed to determine the far-field properties of the system and to optimize the system. Each antenna is excited using a probe station system while a scanning probe scans the space above the antenna to measure the near field signals. Upon completion of the scan, the near-field patterns are transformed into far-field patterns. Along with taking data, this system also allows for extensive graphing and analysis of both the near-field and far-field data. The details of the probe station as well as the procedures for setting up a test, conducting a test, and analyzing the resulting data are also described.

Zaman, Afroz J.↗
NASA NTRS2014

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of-the- art cloud computing and crowdsourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters.

CyberFlood↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.

Ryan T Scott↗
NASA NTRSDate not supplied

Increasing Discovery and Usability of Earth Science Satellite Data with My NASA Data

For 20 years, the My NASA Data project at NASA Langley Research Center has developed innovative approaches to increase the use of NASA’s satellite data by learners. My NASA Data offers a variety of authentic Earth Science datasets and a data visualization tool, eliminating the need for educators and/or learners to obtain specialized knowledge of GIS data formats and software to access and use authentic Earth Science data. While there is no shortage of available data, as federal government agencies such as NASA house petabytes of freely accessible Earth Science datasets, much of the data are only available for download and visualization in specialized formats and software, limiting their accessibility to educators and learners, especially those in primary and secondary school. Using the Google Earth Engine platform, the My NASA Data team has recently reinvented their data visualization tool, called the Earth System Data Explorer (ESDE). The ESDE gives users the capability to explore over 60 Earth Science satellite datasets in a multitude of formats such as maps, graphs, and data table Its new and improved user interface design was developed based on the preferences of educators, whom the My NASA Data project has over 20 years’ experience working with. Earth Science and GIS Subject Matter Experts (SMEs) structured the data in a professional and scientific manner. During Fiscal Year 2023, the My NASA Data website received over 1 million digital engagements, with over one-third being visitors to the data visualization tool. These metrics highlight the interest in a visualization tool that is simple and free to use with reliable and trusted datasets. The ESDE empowers users to readily relate and analyze NASA Earth Science data within their area of interest. The team used a user-centered design (UCD) framework to receive and incorporate feedback into the application’s design. Core requested features include the ability to create time series graphs, comparative analysis of maps, and download the data as CSV file. Responses indicate that advances in data visualization tools such as the ESDE make authentic Earth Science data more accessible. This presentation will cover how the My NASA Data project develops tools to enhance data discovery and accessibility, as well as how SME and user suggestions are incorporated.

Desiray Wilson↗
NASA NTRS2008

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Tilton, James C.↗
NASA NTRS2009

Decomposition Algorithm for Global Reachability Analysis on a Time-Varying Graph with an Application to Planetary Exploration

Hot air (Montgolfiere) balloons represent a promising vehicle system for possible future exploration of planets and moons with thick atmospheres such as Venus and Titan. To go to a desired location, this vehicle can primarily use the horizontal wind that varies with altitude, with a small help of its own actuation. A main challenge is how to plan such trajectory in a highly nonlinear and time-varying wind field. This paper poses this trajectory planning as a graph search on the space-time grid and addresses its computational aspects. When capturing various time scales involved in the wind field over the duration of long exploration mission, the size of the graph becomes excessively large. We show that the adjacency matrix of the graph is block-triangular, and by exploiting this structure, we decompose the large planning problem into several smaller subproblems, whose memory requirement stays almost constant as the problem size grows. The approach is demonstrated on a global reachability analysis of a possible Titan mission scenario.

Dijkstra's algorithm↗
NASA NTRS2000

Solar Power System Evaluated for the Human Exploration of Mars

The electric power system is a crucial element of any mission for the human exploration of the Martian surface. The bulk of the power generated will be delivered to crew life support systems, extravehicular activity suits, robotic vehicles, and predeployed in situ resource utilization (ISRU) equipment. In one mission scenario, before the crew departs for Mars, the ISRU plant operates for 435 days producing liquefied methane and oxygen for ascent-stage propellants and water for crew life support. About 200 days after ISRU production is completed, the crew arrives for a 500-day surface stay. In this scenario, the power system must operate for a total of 1130 days (equivalent to 1100 Martian "sols"), providing 400 MW-hr of energy to the ISRU plant and up to 18 kW of daytime user power. A photovoltaic power-generation system with regenerative fuel cell (RFC) energy storage has been under study at the NASA Glenn Research Center at Lewis Field. The conceptual power system is dominated by the 4000- m2 class photovoltaic array that is deployed orthogonally as four tent structures, each approximately 5 m on a side and 100-m long. The structures are composed of composite members deployed by an articulating mast, an inflatable boom, or rover vehicles, and are subsequently anchored to the ground. Array panels consist of thin polymer membranes with thin-film solar cells. The array is divided into eight independent electrical sections with solar cell strings operating at 600 V. Energy storage is provided by regenerative fuel cells based on hydrogen-oxygen proton exchange membrane technology. Hydrogen and oxygen reactants are stored in gaseous form at 3000 psi, and the water produced is stored at 14.7 psi. The fuel cell operating temperature is maintained by a 40-m2 deployable pumped-fluid loop radiator that uses water as the working fluid. The power management and distribution (PMAD) architecture features eight independent, regulated 600-Vdc channels. Power management and distribution power cables use various gauges of copper conductors with ethylene tetrafluoroethylene insulation. To assess power system design options and sizing, we developed a dedicated Fortran code to predict detailed power system performance and estimate system mass. This code also modeled the requisite Mars surface environments: solar insolation, Sun angles, dust storms, dust deposition, and thermal and ultraviolet radiation. Using this code, trade studies were performed to assess performance and mass sensitivities to power system design parameters (photovoltaic array geometry and orientation) and mission parameters (landing date and landing site latitude, terrain slope, and dust storm activity). Mission analysis cases were also run. Power results are shown in this graph for an analysis case with a September 1, 2012, landing date; 18.95 North latitude landing site; two seasonal dusts storms; and tent arrays. To meet user load requirements and the ISRU energy requirement, an 8-metric ton (MT) power system and 4000-m2 photovoltaic array area were required for the assumed advanced CuInS2 thin-film solar cell technology. In this figure, the top curve is the average daytime photovoltaic array power, the middle curve is average daytime user load power, and the bottom curve is nighttime power. At mission day 1, daytime user power exceeds 120 kW before falling off to 80 kW at the end of the mission. Throughout the mission, nighttime user power is set to the nighttime power requirement. In this analysis, "nighttime" is defined as the 13- to 15-hr period when array power output is below the daytime power requirement. During dust storms, power system capability falls off dramatically so that by mission day 900, a daily energy balance cannot be maintained. Under these conditions, the ISRU plant is placed in standby mode, and the regenerative fuel cell energy storage is gradually discharged to meet user loads.

Kerslake, Thomas W.↗