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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Two Years Onboard the MER Opportunity Rover

The Autonomous Exploration for Gathering Increased Science (AEGIS) system provides automated data collection for planetary rovers. AEGIS is currently being used onboard the Mars Exploration Rover (MER) mission's Opportunity to provide autonomous targeting of the MER Panoramic camera. Prior to AEGIS, targeted data was collected in a manual fashion where targets were manually identified in images transmitted to Earth and the rover had to remain in the same location for one to several communication cycles. AEGIS enables targeted data to be rapidly acquired with no delays for ground communication. Targets are selected by AEGIS through the use of onboard data analysis techniques that are guided by scientist-specified objectives. This paper provides an overview of the how AEGIS has been used on the Opportunity rover, focusing on usage that occurred during a 21 kilometer historic trek to the Mars Endeavour crater.

Mars Exploration Rover (MER)↗
NASA NTRS1996

Automation of Command and Data Entry in a Glovebox Work Volume: An Evaluation of Data Entry Devices

The present study was designed to examine the human-computer interface for data entry while performing experimental procedures within a glovebox work volume in order to make a recommendation to the Space Station Biological Research Project for a data entry system to be used within the Life Sciences Glovebox. Test subjects entered data using either a manual keypad, similar to a standard computer numerical keypad located within the glovebox work volume, or a voice input system using a speech recognition program with a microphone headset. Numerical input and commands were programmed in an identical manner between the two systems. With both electronic systems, a small trackball was available within the work volume for cursor control. Data, such as sample vial identification numbers, sample tissue weights, and health check parameters of the specimen, were entered directly into procedures that were electronically displayed on a video monitor within the glovebox. A pen and paper system with a 'flip-chart' format for procedure display, similar to that currently in use on the Space Shuttle, was used as a baseline data entry condition. Procedures were performed by a single operator; eight test subjects were used in the study. The electronic systems were tested under both a 'nominal' or 'anomalous' condition. The anomalous condition was introduced into the experimental procedure to increase the probability of finding limitations or problems with human interactions with the electronic systems. Each subject performed five test runs during a test day: two procedures each with voice and keypad, one with and one without anomalies, and one pen and paper procedure. The data collected were both quantitative (times, errors) and qualitative (subjective ratings of the subjects).

Steele, Marianne K.↗
NASA NTRS2015

High Resolution Nature Runs and the Big Data Challenge

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

big data analysis↗
NASA NTRS2009

Demonstration of Effects on Tropical Cyclone Forecasts with a High Resolution Global Model from Variation in Cumulus Convection Parameterization

The Goddard Earth Observing System Model, Version 5 (GEOS-5) is a system of models that have been developed at Goddard Space Flight Center to support NASA's earth science research in data analysis, observing system modeling and design, climate and weather prediction, and basic research. The work presented used GEOS-5 with 0.25o horizontal resolution and 72 vertical levels (up to 0.01 hP) resolving both the troposphere and stratosphere, with closer packing of the levels close to the surface. The model includes explicit (grid-scale) moist physics, as well as convective parameterization schemes. Results will be presented that will demonstrate strong dependence in the results of modeling of a strong hurricane on the type of convective parameterization scheme used. The previous standard (default) option in the model was the Relaxed Arakawa-Schubert (RAS) scheme, which uses a quasi-equilibrium closure. In the cases shown, this scheme does not permit the efficient development of a strong storm in comparison with observations. When this scheme is replaced by a modified version of the Kain-Fritsch scheme, which was originally developed for use on grids with intervals of order 25 km such as the present one, the storm is able to develop to a much greater extent, closer to that of reality. Details of the two cases will be shown in order to elucidate the differences in the two modeled storms.

Miller, Timothy L.↗
NASA NTRS2007

Smart Payload Development for High Data Rate Instrument Systems

This slide presentation reviews the development of smart payloads instruments systems with high data rates. On-board computation has become a bottleneck for advanced science instrument and engineering capabilities. In order to improve the computation capability on board, smart payloads have been proposed. A smart payload is a Localized instrument, that can offload the flight processor of extensive computing cycles, simplify the interfaces, and minimize the dependency of the instrument on the flight system. This has been proposed for the Mars mission, Mars Atmospheric Trace Molecule Spectroscopy (MATMOS). The design of this system is discussed; the features of the Virtex-4, are discussed, and the technical approach is reviewed. The proposed Hybrid Field Programmable Gate Array (FPGA) technology has been shown to deliver breakthrough performance by tightly coupling hardware and software. Smart Payload designs for instruments such as MATMOS can meet science data return requirements with more competitive use of available on-board resources and can provide algorithm acceleration in hardware leading to implementation of better (more advanced) algorithms in on-board systems for improved science data return

Virtex-4↗
NASA NTRS2009

An Intelligent Archive Testbed Incorporating Data Mining

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by U.S. and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a value chain for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival. The trend in the community today is towards many distributed sets of providers of data and services. Despite this, visions for the future include users being able to locate, fuse and utilize data with location transparency and high degree of interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004).

Ramapriyan, H.↗
NASA NTRS2015

Magnetospheric Multiscale Instrument Suite Operations and Data System

The four Magnetospheric Multiscale (MMS) spacecraft will collect a combined volume of approximately 100 gigabits per day of particle and field data. On average, only 4 gigabits of that volume can be transmitted to the ground. To maximize the scientific value of each transmitted data segment, MMS has developed the Science Operations Center (SOC) to manage science operations, instrument operations, and selection, downlink, distribution, and archiving of MMS science data sets. The SOC is managed by the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, Colorado and serves as the primary point of contact for community participation in the mission. MMS instrument teams conduct their operations through the SOC, and utilize the SOC's Science Data Center (SOC) for data management and distribution. The SOC provides a single mission data archive for the housekeeping and science data, calibration data, ephemerides, attitude and other ancillary data needed to support the scientific use and interpretation. All levels of data products will reside at and be publicly disseminated from the SDC. Documentation and metadata describing data products, algorithms, instrument calibrations, validation, and data quality will be provided. Arguably, the most important innovation developed by the SOC is the MMS burst data management and selection system. With nested automation and 'Scientist-in-the-Loop' (SITL) processes, these systems are designed to maximize the value of the burst data by prioritizing the data segments selected for transmission to the ground. This paper describes the MMS science operations approach, processes and data systems, including the burst system and the SITL concept.

Baker, D. N.↗
NASA NTRS1990

NASA Information Sciences and Human Factors Program

Fiscal year 1989 descriptions of technical accomplishments in seven sections are presented: automation and robotics; communications; computer sciences; controls and guidance; data systems; human factors; and sensor technology.

Holcomb, Lee↗
NASA NTRS1991

NASA information sciences and human factors program

The FY-90 descriptions of technical accomplishments are contained in seven sections: Automation and Robotics, Communications, Computer Sciences, Controls and Guidance, Data Systems, Human Factors, and Sensor Technology.

Lee Holcomb↗
NASA NTRS2001

Global Gridded Data from the Goddard Earth Observing System Data Assimilation System (GEOS-DAS)

The Goddard Earth Observing System Data Assimilation System (GEOS-DAS) timeseries is a globally gridded atmospheric data set for use in climate research. This near real-time data set is produced by the Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center in direct support of the operational EOS instrument product generation from the Terra (12/1999 launch), Aqua (05/2002 launch) and Aura (01/2004 launch) spacecrafts. The data is archived in the EOS Core System (ECS) at the Goddard Earth Sciences Data and Information Services Center/Distributed Active Archive Center (GES DISC DAAC). The data is only a selection of the products available from the GEOS-DAS. The data is organized chronologically in timeseries format to facilitate the computation of statistics. GEOS-DAS data will be available for the time period January 1, 2000, through present.

Source record↗
NASA NTRS1999

CSPICE - A C Version of JPL's SPICELIB Toolkit

The Navigation Ancillary Information Facility (NAIF), under the direction of NASA's Office of Space Science, built the SPICE data system to assist scientists with planning and interpretation of scientific observations from space borne-instruments.

ancillary data viewing geometry bernel files↗
NASA NTRS2006

Achieving Operability via the Mission System Paradigm

In the past, flight and ground systems have been developed largely-independently, with the flight system taking the lead, and dominating the development process. Operability issues have been addressed poorly in planning, requirements, design, I&T, and system-contracting activities. In many cases, as documented in lessons-learned, this has resulted in significant avoidable increases in cost and risk. With complex missions and systems, operability is being recognized as an important end-to-end design issue. Never-the-less, lessons-learned and operability concepts remain, in many cases, poorly understood and sporadically applied. A key to effective application of operability concepts is adopting a 'mission system' paradigm. In this paradigm, flight and ground systems are treated, from an engineering and management perspective, as inter-related elements of a larger mission system. The mission system consists of flight hardware, flight software, telecom services, ground data system, testbeds, flight teams, science teams, flight operations processes, procedures, and facilities. The system is designed in functional layers, which span flight and ground. It is designed in response to project-level requirements, mission design and an operations concept, and is developed incrementally, with early and frequent integration of flight and ground components.

ground systems↗
NASA NTRS2012

Restoration and Reexamination of Data from the Apollo 11, 12, 14, and 15 Dust, Thermal and Radiation Engineering Measurements Experiments

As part of an effort by the Lunar Data Node (LDN) we are restoring data returned by the Apollo Dust, Thermal, and Radiation Engineering Measurements (DTREM) packages emplaced on the lunar surface by the crews of Apollo 11, 12, 14, and 15. Also commonly known as the Dust Detector experiments, the DTREM packages measured the outputs of exposed solar cells and thermistors over time. They operated on the surface for up to nearly 8 years, returning data every 54 seconds. The Apollo 11 DTREM was part of the Early Apollo Surface Experiments Package (EASEP), and operated for a few months as planned following emplacement in July 1969. The Apollo 12, 14, and 15 DTREMs were mounted on the central station as part of the Apollo Lunar Surface Experiments Package (ALSEP) and operated from deployment until ALSEP shutdown in September 1977. The objective of the DTREM experiments was to determine the effects of lunar and meteoric dust, thermal stresses, and radiation exposure on solar cells. The LDN, part of the Geosciences Node of the Planetary Data System (PDS), operates out of the National Space Science Data Center (NSSDC) at Goddard Space Flight Center. The goal of the LDN is to extract lunar data stored on older media and/or in obsolete formats, restore the data into a usable digital format, and archive the data with PDS and NSSDC. For the DTREM data we plan to recover the raw telemetry, translate the raw counts into appropriate output units, and then apply calibrations. The final archived data will include the raw, translated, and calibrated data and the associated conversion tables produced from the microfilm, as well as ancillary supporting data (metadata) packaged in PDS format.

McBride, Marie J.↗
NASA NTRS2018

NASA's EOSDIS Near Term Challenges

Given the long-term requirements, and the rapid pace of information technology and changing expectations of the user community, the ESDIS Project has had to evolve EOSDIS continually over the past three decades. However, many challenges remain. One near-term challenge is the enormous quantity of new data that will need to be managed by the EOSDIS. With the upcoming launch of the latest NASA missions coupled with existing operational missions and field campaigns, EOSDIS can expect to handle as much as 50 petabytes of data per year. In perspective, this is twice the size of the current existing archive, which took over 21 years to collect. Another continuing challenge is the disparate requirements of a diverse science community. Maintaining rigorous long-term data preservation, supporting ease of discovery and access, incorporating user feedback, enabling reanalysis/ reprocessing, and agile integration of new data sources, continue to be the Project's objectives.

Remote Sensing↗
NASA NTRS2018

New Metadata Capabilities within NASA's Common Metadata Repository (CMR)

This talk will convey the new capabilities and features of the UMM-Variables and the UMM-Services metadata model within the Common Metadata Repository (CMR) and how community engagement through the ESDIS Standards Office (ESO) review shaped the new versions of the models. The Unified Metadata Model (UMM) provides a common metadata model to unify legacy systems (i.e. GCMD (Global Change Master Directory), ECHO (Earth Observing System (EOS) Clearinghouse)) with new systems (i.e. CMR). The rationale and migration process of the Service Entry Resource Format (SERF) to the UMM-S will also be conveyed. The talk will conclude with discussing issues and lessons learned from the review process and how future reviews will be conducted to ensure a more targeted, meaningful review with a faster turn-around time for triage and implementation.

Earth Observing System↗