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 feasibility of the MST radar as a tool for investigating turbulence spectra is discussed. Power spectral measurements using radar data are discussed. The characteristics of stratospheric turbulence are described. A model of the mesoscale turbulent process is developed.
The TOPEX/Poseidon Project Satellite Performance Analysis Team's (SPAT) roles and responsibilities have grown to include functions that are typically performed by other teams on JPL Flight Projects. In particular, SPAT Telecommunication's role has expanded beyond the nominal function of monitoring, assessing, characterizing, and trending the spacecraft (S/C) RF/Telecom subsystem to one of End-to-End Information Systems (EEIS) monitoring. This has been accomplished by taking advantage of the spacecraft and ground data system structures and protocols. By processing both the received spacecraft telemetry minor frame ground generated CRC flags and NASCOM block poly error flags, bit error rates (BER) for each link segment can be determined. This provides the capability to characterize the separate link segments, determine science data recovery, and perform fault/anomaly detection and isolation. By monitoring and managing the links, TOPEX has successfully recovered approximately 99.9 percent of the science data with an integrity (BER) of better than 1 x 10(exp 8). This paper presents the algorithms used to process the above flags and the techniques used for EEIS monitoring.
Dependability evaluation involves the study of failures and errors. The destructive nature of a crash and long error latency make it difficult to identify the causes of failures in the operational environment. It is particularly hard to recreate a failure scenario for a large, complex system. To identify and understand potential failures, we use an experiment-based approach for studying the dependability of a system. Such an approach is applied not only during the conception and design phases, but also during the prototype and operational phases. To take an experiment-based approach, we must first understand a system's architecture, structure, and behavior. Specifically, we need to know its tolerance for faults and failures, including its built-in detection and recovery mechanisms, and we need specific instruments and tools to inject faults, create failures or errors, and monitor their effects.
Performance degradation in scientific computing on parallel and distributed computer systems can be caused by numerous factors. In this half-day tutorial we explain what are the important methodological issues involved in obtaining codes that have good performance potential. Then we discuss what are the possible obstacles in realizing that potential on contemporary hardware platforms, and give an overview of the software tools currently available for identifying the performance bottlenecks. Finally, some realistic examples are used to illustrate the actual use and utility of such tools.
This research project was designed as part of a larger effort to help Human Factors (HF) implementers, and others in the aviation maintenance community, understand, evaluate, and validate the impact of Maintenance Resource Management (MRM) training programs, and other MRM interventions; on participant attitudes, opinions, behaviors, and ultimately on enhanced safety performance. It includes research and development of evaluation methodology as well as examination of psychological constructs and correlates of maintainer performance. In particular, during 2001, three issues were addressed. First a prototype process for measuring performance was developed and used. Second an automated calculator was developed to aid the HF implementer user in analyzing and evaluating local survey data. These results include being automatically compared with the experience from all MRM programs studied since 1991. Third the core survey (the Maintenance Resource Management Technical Operations Questionnaire, or 'MRM/TOQ') was further developed and tested to include topics of added relevance to the industry.
This viewgraph presentation provides information on NASA's geographically dispersed computing resources, and the various methods by which the disparate technologies are integrated within a nationwide computational grid. Many large-scale science and engineering projects are accomplished through the interaction of people, heterogeneous computing resources, information systems and instruments at different locations. The overall goal is to facilitate the routine interactions of these resources to reduce the time spent in design cycles, particularly for NASA's mission critical projects. The IPG (Information Power Grid) seeks to implement NASA's diverse computing resources in a fashion similar to the way in which electric power is made available.
No abstract available
The TOPEX/Poseidon Project Satellite Performance Analysis Team's roles and responsibilities have grown to include functions that are typically performed by other teams on JPL flight projects.
The TOPEX/Poseidon Project Satellite Performance Analysis Team's (SPAT) roles and responsibilities have grown to include functions that are typically performed by other teams on JPL flight projects. In particular, SPAT Telecommunication's role has expanded beyond the nominal function of monitoring, assessing, characterizing, and trending the spacecraft RF/telecom subsystem to one of End-to-End Information Systems (EEIS) monitoring. This has been accomplished by taking advantage of the spacecraft and ground data system structures and protocols.
CIS’ Mission Engagement team is hosting its first-ever forum for government, academia, and industry audiences. This event will detail the Near Space Network’s service acquisition process and the services provided by the network for pre-launch planning.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
We have designed, built, and tested a sampling kit (Fig. 1) to aseptically collect microbiological samples from exterior surfaces on the ISS (International Space Station). The kit was flown to ISS as part of the NG-19 commercial cargo mission in August of 2023. Astronauts will use the kit to collect samples from six exterior surfaces on ISS. These samples will be frozen at -80°C after collection and returned to the ground for next generation DNA sequencing. The results of this experiment will help inform planetary protection requirements for crewed missions. We hypothesize that there are detectable microbial communities outside ISS and that these communities originate from inside ISS. Current life support systems do not include any components for reducing microbial leakage. Gases are vented from inside ISS without filtration and there are currently no protocols in place to minimize bioburden on space suit exteriors prior to use. It is important to quantify the bioburden on current vehicles so that achievable limits can be set for crewed missions to astrobiologically relevant locations like Mars.
Explore the source record for details and available documents.
This is a presentation of plenary session to be included in the conference proceedings.
Explore the source record for details and available documents.
Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).
Goddard Space Flight Center's Flight Projects Directorate employed a Business Change Initiative (BCI) to infuse a series of activities coordinated to drive improved cost and schedule performance across Goddard's missions. This sustaining change framework provides a platform to manage and implement cost and schedule control techniques throughout the project portfolio. The BCI concluded in December 2014, deploying over 100 cost and schedule management changes including best practices, tools, methods, training, and knowledge sharing. The new business approach has driven the portfolio to improved programmatic performance. The last eight launched GSFC missions have optimized cost, schedule, and technical performance on a sustained basis to deliver on time and within budget, returning funds in many cases. While not every future mission will boast such strong performance, improved cost and schedule tools, management practices, and ongoing comprehensive evaluations of program planning and control methods to refine and implement best practices will continue to provide a framework for sustained performance. This paper will describe the tools, techniques, and processes developed during the BCI and the utilization of collaborative content management tools to disseminate project planning and control techniques to ensure continuous collaboration and optimization of cost and schedule management in the future.