SEARCH · Search NASA
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.
The Mars Pathfinder mission required new capabilities and adaptation of existing capabilities in order to support science analysis and flight operations requirements imposed by the in-situ nature of the mission.
Seven instrumented sensor plugs were installed on the Mars Science Laboratory heat shield in December 2008 as part of the Mars Science Laboratory Entry, Descent, and Landing Instrumentation (MEDLI) project. These sensor plugs contain four in-depth thermocouples and one Hollow aErothermal Ablation and Temperature (HEAT) sensor. The HEAT sensor follows the time progression of a 700 C isotherm through the thickness of a thermal protection system (TPS) material. The data can be used to infer char depth and, when analyzed in conjunction with the thermocouple data, the thermal gradient through the TPS material can also be determined. However, the uncertainty on the isotherm value is not well defined. To address this uncertainty, a team at NASA Ames Research Center is carrying out a HEAT sensor calibration test program. The scope of this test program is described, and initial results from experiments conducted in the laboratory to study the isotherm temperature of the HEAT sensor are presented. Data from the laboratory tests indicate an isotherm temperature of 720 C 60 C. An overview of near term arc jet testing is also given, including preliminary data from 30.48cm 30.48cm PICA panels instrumented with two MEDLI sensor plugs and tested in the NASA Ames Panel Test Facility. Forward work includes analysis of the arc jet test data, including an evaluation of the isotherm value based on the instant in time when it reaches a thermocouple depth.
This research program addresses astrophysics research with the Infrared Space Observatory's Long Wavelength Spectrometer (ISO-LWS), including efforts to supply ISO-LWS with superior metal mesh filters. This grant has, over the years, enabled Dr. Smith in his role as a Co-Investigator on the satellite, the PI (Principal Investigator) on the Extragalactic Science Team, and a member of the Calibration and performance working groups. The emphasis of the budget in this proposal is in support of Dr. Smith's Infrared Space Observatory research. This program began (under a different grant number) while Dr. Smith was at the Smithsonian's National Air and Space Museum, and was transferred to SAO with a change in number. While Dr. Smith was a visiting Discipline Scientist at NASA HQ the program was in abeyance, but it has resumed in full since his return to SAO. The Infrared Space Observatory mission was launched in November, 1996, and since then has successfully completed its planned lifetime mission. Data are currently being calibrated to the 2% level.
The analysis of space science data often requires researchers to work with many different types of data. For instance, correlative analysis can require data from multiple instruments on a single spacecraft, multiple spacecraft, and ground-based data. Typically, data from each source are available in a different format and have been written on a different type of computer, and so much effort must be spent to read the data and convert it to the computer and format that the researchers use in their analysis. The large and ever-growing amount of data and the large investment by the scientific community in software that require a specific data format make using standard data formats impractical. A format-independent approach to accessing and analyzing disparate data is key to being able to deliver data to a diverse community in a timely fashion. The system in use at the Planetary Plasma Interactions (PPI) node of the NASA Planetary Data System (PDS) is based on the object-oriented Distributed Inventory Tracking and Data Ordering Specification (DITDOS), which describes data inventories in a storage independent way. The specifications have been designed to make it possible to build DITDOS compliant inventories that can exist on portable media such as CD-ROM's. The portable media can be moved within a system, or from system to system, and still be used without modification. Several applications have been developed to work with DITDOS compliant data holdings. One is a windows-based client/server application, which helps guide the user in the selection of data. A user can select a data base, then a data set, then a specific data file, and then either order the data and receive it immediately if it is online or request that it be brought online if it is not. A user can also view data by any of the supported methods. DITDOS makes it possible to use already existing applications for data-specific actions, and this is done whenever possible. Another application is a stand-alone tool to assist in the extraction of data from portable media, such as CD-ROM's. In addition to the applications, there is a set of libraries that can facilitate building new DITDOS compliant applications.
The 2020 Multiscale Microbial Dynamics course is adapted from the virtual 2020 Mutliscale Microbial Dynamics Summer School that was hosted by Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy (DOE) science user facility located on the Pacific Northwest National Laboratory (PNNL) campus, in collaboration with the Joint Genome Institute (JGI) and the DOE Systems Biology Knowledgebase (KBase). The course course covers how to incorporate microbial metagenomic and environmental metabolite data from watershed ecosystems into metabolic and community modeling using computational frameworks, such as KBase and PFLOTRAN. The curriculum includes lectures and software and data analysis tutorials. All materials are freely accessible to the community as part of the 2020 Microbial Dynamics Summer School Organization in KBase.
Abstract We present a scalable, cloud-based science platform solution designed to enable next-to-the-data analyses of terabyte-scale astronomical tabular data sets. The presented platform is built on Amazon Web Services (over Kubernetes and S3 abstraction layers), utilizes Apache Spark and the Astronomy eXtensions for Spark for parallel data analysis and manipulation, and provides the familiar JupyterHub web-accessible front end for user access. We outline the architecture of the analysis platform, provide implementation details and rationale for (and against) technology choices, verify scalability through strong and weak scaling tests, and demonstrate usability through an example science analysis of data from the Zwicky Transient Facility’s 1Bn+ light-curve catalog. Furthermore, we show how this system enables an end user to iteratively build analyses (in Python) that transparently scale processing with no need for end-user interaction. The system is designed to be deployable by astronomers with moderate cloud engineering knowledge, or (ideally) IT groups. Over the past 3 yr, it has been utilized to build science platforms for the DiRAC Institute, the ZTF partnership, the LSST Solar System Science Collaboration, and the LSST Interdisciplinary Network for Collaboration and Computing, as well as for numerous short-term events (with over 100 simultaneous users). In a live demo instance, the deployment scripts, source code, and cost calculators are accessible. 4 4 http://hub.astronomycommons.org/
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.
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is the home of processing, archiving, and distribution services for NASA sounders: the present Aqua AIRS mission and the succeeding SNPP CrIS mission. The AIRS mission is entering its 15th year of global observations of the atmospheric state, including temperature and humidity profiles, outgoing longwave radiation, cloud properties, and trace gases. The GES DISC, in collaboration with the AIRS Project, released product from the version 6 algorithm in early 2013. Giovanni, a Web-based application developed by the GES DISC, provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data without having to download the data. Most important variables from version 6 AIRS product are available in Giovanni. We are developing a climatology product using 14-year AIRS retrievals. The study can be a good start for the long term climatology from NASA sounders: the AIRS and the succeeding CrIS. This presentation will show the impacts to the climatology product from different aggregation methods. The climatology can serve climate science and application communities in data visualization and analysis, which will be demonstrated using a variety of functions in version 4 Giovanni. The highlights of these functions include user-defined monthly and seasonal climatology, inter annual seasonal time series, anomaly analysis.
Experiments evaluating the uniformity and intensity of rainfall produced by the NGEE Arctic Rainfall Simulator (NARS) were conducted at Los Alamos National Laboratory, New Mexico, over summer 2022. Petri dishes were placed in a grid within the NARS plot. Simulated rainfall was collected in each petri dish and the intensity and uniformity of the simulator was subsequently calculated. This data package contains two .csv files, one that summarizes the rainfall intensity and uniformity for each experiment, the other that contains individual petri dish water volume and intensity for each plot location and experiment. The Python scripts to control NARS are also included. The NGEE Arctic Rainfall Simulator (NARS) is a variable intensity rainfall simulator (RFS) with a frame design based on the Humphry et al. (2002) RFS and a water delivery system based on the Walnut Gulch (Paige et al., 2004) RFS. The NARS uses an aluminum frame that is fully deconstructable for transportation to field locations and a water system that enables variable rain intensity. Rain intensity control and data collection are automated using a Raspberry Pi microcomputer. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
The Space Science and Technology Partnership Forum was established in 2015 to identify synergistic efforts and technologies across the government. In-Space Assembly (iSA) is the focus of the topic area that NASA is currently coordinating with other government agencies. This paper focuses on the data collection process, the data analysis of that information, and preliminary insights gleaned from the data. The goal of the analysis is to understand the linkages within the collected data, identifying synergies and gaps, and provide visualization of the current state of iSA needs and capability development across the government. Capability roadmaps, Venn diagrams, bubble charts, and scorecards (an overview of each individual iSA capability) are used to visualize the results of this analysis, which reveals areas of possible inter-agency collaboration, investment gaps in capabilities relative to the need, and capabilities that warrant engagement across multiple agencies to eliminate potential inefficiencies.
The Space Science and Technology Partnership Forum was established in 2015 to identify synergistic efforts and technologies across the government. In-Space Assembly (iSA) is the focus of the topic area that NASA is currently coordinating with other government agencies. This paper focuses on the data collection process, the data analysis of that information, and preliminary insights gleaned from the data. The goal of the analysis is to understand the linkages within the collected data, identifying synergies and gaps, and provide visualization of the current state of iSA needs and capability development across the government. Capability roadmaps, Venn diagrams, bubble charts, and scorecards (an overview of each individual iSA capability) are used to visualize the results of this analysis, which reveals areas of possible inter-agency collaboration, investment gaps in capabilities relative to the need, and capabilities that warrant engagement across multiple agencies to eliminate potential inefficiencies.
Projected goals include the following: (1) Participate in and provide analysis of laboratory and in-flight calibration of LTV sensors used for space observations of backscattered LTV radiation; (2) Provide support to the TOMS Science Operations Center, including generating instrument command lists and analysis of TOMS health and safety data; (3) Develop and maintain software and algorithms designed to capture and process raw spacecraft and instrument data, convert the instrument output into measured radiance and irradiances, and produce scientifically valid products; (4) Process the TOMS data into Level 1, Level 2, and Level 3 data products; (5) Provide analysis of the science data products in support of NASA GSFC Code 916's research.
No abstract available
This Final Progress Report covers the specific technical activities of Hughes STX Corporation for the last contract triannual period of 1 June through 30 Sep. 1993, in support of assigned task activities at Goddard Space Flight Center (GSFC). It also provides a brief summary of work throughout the contract period of performance on each active task. Technical activity is presented in Volume 1, while financial and level-of-effort data is presented in Volume 2. Technical support was provided to all Division and Laboratories of Goddard's Space Sciences and Earth Sciences Directorates. Types of support include: scientific programming, systems programming, computer management, mission planning, scientific investigation, data analysis, data processing, data base creation and maintenance, instrumentation development, and management services. Mission and instruments supported include: ROSAT, Astro-D, BBXRT, XTE, AXAF, GRO, COBE, WIND, UIT, SMM, STIS, HEIDI, DE, URAP, CRRES, Voyagers, ISEE, San Marco, LAGEOS, TOPEX/Poseidon, Pioneer-Venus, Galileo, Cassini, Nimbus-7/TOMS, Meteor-3/TOMS, FIFE, BOREAS, TRMM, AVHRR, and Landsat. Accomplishments include: development of computing programs for mission science and data analysis, supercomputer applications support, computer network support, computational upgrades for data archival and analysis centers, end-to-end management for mission data flow, scientific modeling and results in the fields of space and Earth physics, planning and design of GSFC VO DAAC and VO IMS, fabrication, assembly, and testing of mission instrumentation, and design of mission operations center.
The objectives of this work are twofold. First, to provide real-time meteorological satellite guidance to airborne field missions for NASA's Upper Atmosphere Research Program, the Global Tropospheric Experiment, and the Atmospheric Effects of Aviation Project. Extensive meteorological satellite datasets were provided for use by the mission scientist and by the science team. These same data were then archived for postdeployment data analysis by the science team. Second, to provide scientific analysis of the data from the airborne field missions supported. The results of these analyses were made public through peer-reviewed publications.
Rovers are the state of the art for the exploration and detection of past habitability and life on other worlds. One of the most basic functions of a rover is terrain navigation. Information collected by the rover is used autonomously to mitigate terrain hazards such large rocks, while humans qualitatively assess hazardous geologic terrain such as soil type and degree of rock cover. Planetary scientists use the same information to select targets such as drill sites, and for basic scientific analysis such as characterization of rock outcrops. Although the data is complementary, data from terrain analysis for navigation and terrain analysis for scientific investigations are poorly integrated. The lack of integration creates science and operation inefficiencies that limit exploration of habitable environments. As new modes of exploration come online, such as unmanned aerial systems (UAS) (e.g., the Mars Helicopter Scout and Titan Dragonfly), a need exists to integrate terrain data and science analysis to improve operational and scientific outcomes during exploration. We present an overview of a project aimed at evaluating the effectiveness and capability rover and UAS-based semi-automated terrain analysis using the Automated Soil Assessment Systems (ASAS) developed by Mission Control Space Services for navigating, selecting targets for sampling, and characterizing mafic detrital sediments along glacio-fluvial-aeolian sand transport pathways in Iceland. We describe recent advances in automated terrain analysis in sandy environments and scientific uses of terrain assessment from sandy environments. We assess fluvial and aeolian terrains in Iceland and show how terrain analysis data can inform scientific characterization of these environments.
Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5-10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.