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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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The activities of the NASA Geodynamics Program in 1981 both in achieving improved measurement precision and in establishing the foundation for the acquisition and analysis of scientific data are discussed.
Understanding the ion chemistry and conditions leading to the formation of ionospheric depletions (ionospheric holes) was an important objective of the NASA active ionospheric experiment program. Millstone Hill radar observations were used to monitor the magnitude and temporal extent of the plasma holes produced under varying conditions. The major objective of the completed project was to provide radar diagnostic support for individual NASA rocket campaigns flown from Wallops Island. Two rocket programs, NICARE and REDAIR 2, were selected by NASA for radar support during the proposal period and pre-launch and in-flight radar observations were provided for each as well as basic reduction of the acquired data for scientific analysis. Radar operations and analysis for both of these experiments were performed as proposed and the work on these projects at M.I.T. was completed.
Goals for planetary exploration during the next decade include: (1) determine how our solar system formed, and understand whether planetary systems are a common phenomenon through out the cosmos; (2) explore the diverse changes that planets have undergone throughout their history and that take place at present, including those that distinguish Earth as a planet; (3) understand how life might have formed on Earth, whether life began anywhere else in the solar system, and whether life (including intelligent beings) might be a common cosmic phenomenon; (4) discover and investigate natural phenomena that occur under conditions not realizable in laboratories; (5) discover and inventory resources in the solar system that could be used by human civilizations in the future; and (6) make the solar system a part of the human experience in the same way that Earth is, and hence lay the groundwork for human expansion into the solar system in the coming century. The plan for solar system exploration is motivated by these goals as well as the following principle: The solar system exploration program will conduct flight programs and supporting data analysis and scientific research commensurate with United States leadership in space exploration. These programs and research must be of the highest scientific merit, they must be responsive to public excitement regarding planetary exploration, and they must contribute to larger national goals in technology and education. The result will be new information, which is accessible to the public, creates new knowledge, and stimulates programs of education to increase the base of scientific knowledge in the general public.
This paper details flight results from the PI-in-a-Box project. The project is an attempt to understand the issues involved in building automated real-time advisory systems to improve the conduct of science in remote laboratory settings. This is an important problem as examples of these situations can be found throughout aerospace. A knowledge-based approach is used to facilitate real-time reactions to experiment data as it is collected by a competent, but not expert, operator. The system was flown on the Space Shuttle in October, 1993. The PI-in-a-Box system was able to outperform humans in a variety of science-related tasks. These tasks include data integrity assurance, data analysis, and scientific model validation.
The goal of this proposal is to perform simultaneous x-ray spectral and millisecond timing observations of accreting neutron stars to further our understanding of their accretion dynamics and in the hope of using these systems as probes of the physics of strong gravitational fields. NAG5-9104 is the successor grant to NAG5-8408. Observations using the Rossi X-Ray Timing Explorer (RXTE) and BeppoSAX were performed of 4U1702-429, 4U1735-44, and Cyg X-2. Unfortunately, only a small fraction of the approved observing time was obtained for the first two targets and the data are of limited scientific value. Data analysis has been completed on the observations of Cyg X-2. We discovered a correlation between the frequency of the horizontal branch oscillations (HBO) and a soft, thermal component of the x-ray spectrum likely associated with emission from the accretion disk. This correlation may place constraints on models of the oscillations. A paper based on these results appeared in the Astrophysical Journal.
NASA's Earth Science Enterprise, working with its domestic and international partners, provides scientific data and analysis to improve life here on Earth. NASA provides science data products that cover a wide range of physical, geophysical, biochemical and other parameters, as well as services for interdisciplinary Earth science studies. Management and distribution of these products is administered through the Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs), which all hold data within a different Earth science discipline. This paper will highlight selected EOS datasets and will focus on how these observations contribute to the improvement of essential services such as weather forecasting, climate prediction, air quality, and agricultural efficiency. Emphasis will be placed on new data products derived from instruments on board Terra, Aqua and ICESat as well as new regional data products and field campaigns. A variety of data tools and services are available to the user community. This paper will introduce primary and specialized DAAC-specific methods for finding, ordering and using these data products. Special sections will focus on orienting users unfamiliar with DAAC resources, HDF-EOS formatted data and the use of desktop research and application tools.
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Provided is an overview of emerging technologies in miniaturized roving vehicles that enable new types of low-cost planetary missions, those which are aimed at detailed survey of extended surface areas, collection and analysis of interesting samples, and in-situ analysis of scientific data. New types of planetary surface data can be acquired at low-cost through the use of innovative technologies.
We present indigenous knowledge narratives and explore their connections to documented temperature and other climate changes and observed climate change impact studies. We then propose a framework for enhancing integration of these indigenous narratives of observed climate change with global assessments. Our aim is to contribute to the thoughtful and respectful integration of indigenous knowledge with scientific data and analysis, so that this rich body of knowledge can inform science, and so that indigenous and traditional peoples can use the tools and methods of science for the benefit of their communities if they choose to do so. Enhancing ways of understanding such connections are critical as the Intergovernmental Panel on Climate Change Fifth Assessment process gets underway.
Humanitarian assistance to developing countries has long focused on countries that have political, economic and strategic interest to the United States. Recent changes in global security concerns have heightened the perception that humanitarian action is becoming increasingly politicized. This is seen to be largely driven by the 'global war on terror' along with a push by donors and the United Nations for closer integration between humanitarian action and diplomatic, military and other spheres of engagement in conflict and crisis-affected states (HPG 2010). As we enter an era of rising commodity prices and increasing uncertainty in global food production due to a changing climate, scientific data and analysis will be increasingly important to improve the targeting of humanitarian assistance. Earth science data enables appropriate humanitarian response to complex food emergencies that arise in regions outside the areas of current strategic and security focus. As the climate changes, new places will become vulnerable to food insecurity and will need emergency assistance. Earth science data and multidisciplinary models will enable an information-based comparison of need that goes beyond strategic and political considerations to identify new hotspots of food insecurity as they emerge. These analyses will improve aid targeting and timeliness while reducing strategic risk by highlighting new regions at risk of crisis in a rapidly changing world. Improved targeting with respect to timing and location could reduce cost while increasing the likelihood that those who need aid get it.
The Seventh Annual Earth System Grid Federation (ESGF) Face-to-Face (F2F) Conference held December 4–8, 2017, in San Francisco, California, USA, assembled together a collection of independently funded national and international projects comprised of government agencies, institutions, and companies dedicated to the creation, management, analysis, and distribution of extreme-scale scientific data. The purpose of the conference was to discuss sustaining and enhancing the resilient ESGF data infrastructure with friendlier tools for the expanding global scientific community—this year’s emphasis was placed on the preparedness of the Coupled Model Intercomparison Project, phase 6 (CMIP6). It also focused on new tools that fulfill important and strategic capability gaps in scientific data archiving, access, analysis, and knowledge discovery. As the Executive Committee Chair of ESGF, I would like to personally thank each of conference attendees and those who could not attend but contributed to and/or supported the development of the ESGF software stack. It is an exciting time for the ESGF consortium as we continue to grow and adjust, remaining always adaptable, motivated, and responsive to our growing base of community projects. As we move forward, our ESGF organization is confronting and addressing many changes during a time of larger national and international commitment with fewer community resources. That said, our commitment to our sponsors and the community remains strong as we continue to meet the challenges before us and bring inspired developers and the scientific community together through forums like this conference, ensuring our ESGF organization remains robust and at the cutting edge of technology.
Analyzing data sets collected in experiments or by observations is a Core scientific activity. Typically, experimentd and observational data are &aught with uncertainty, and the analysis is based on a statistical model of the conjectured underlying processes, The large data volumes collected by modern instruments make computer support indispensible for this. Consequently, scientists spend significant amounts of their time with the development and refinement of the data analysis programs. AutoBayes [GF+02, FS03] is a fully automatic synthesis system for generating statistical data analysis programs. Externally, it looks like a compiler: it takes an abstract problem specification and translates it into executable code. Its input is a concise description of a data analysis problem in the form of a statistical model as shown in Figure 1; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Internally, however, it is quite different: AutoBayes derives a customized algorithm implementing the given model using a schema-based process, and then further refines and optimizes the algorithm into code. A schema is a parameterized code template with associated semantic constraints which define and restrict the template s applicability. The schema parameters are instantiated in a problem-specific way during synthesis as AutoBayes checks the constraints against the original model or, recursively, against emerging sub-problems. AutoBayes schema library contains problem decomposition operators (which are justified by theorems in a formal logic in the domain of Bayesian networks) as well as machine learning algorithms (e.g., EM, k-Means) and nu- meric optimization methods (e.g., Nelder-Mead simplex, conjugate gradient). AutoBayes augments this schema-based approach by symbolic computation to derive closed-form solutions whenever possible. This is a major advantage over other statistical data analysis systems which use numerical approximations even in cases where closed-form solutions exist. AutoBayes is implemented in Prolog and comprises approximately 75.000 lines of code. In this paper, we take one typical scientific data analysis problem-analyzing planetary nebulae images taken by the Hubble Space Telescope-and show how AutoBayes can be used to automate the implementation of the necessary anal- ysis programs. We initially follow the analysis described by Knuth and Hajian [KHO2] and use AutoBayes to derive code for the published models. We show the details of the code derivation process, including the symbolic computations and automatic integration of library procedures, and compare the results of the automatically generated and manually implemented code. We then go beyond the original analysis and use AutoBayes to derive code for a simple image segmentation procedure based on a mixture model which can be used to automate a manual preproceesing step. Finally, we combine the original approach with the simple segmentation which yields a more detailed analysis. This also demonstrates that AutoBayes makes it easy to combine different aspects of data analysis.
The primary objective of this project was to provide a preliminary investigation of data collected during the MARCUS/MICRE field experiments and to produce products and analysis for the scientific community that will enable future scientific investigations and hypothesis testing. Further, the goal was to assist those in the scientific community to understand the strengths and caveats of the MARCUS/MICRE data so that members of the scientific community could use the data in their publications.
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.
Distributed data structures are key to implementing scalable applications for scientific simulations and data analysis. In this paper we look at two implementation styles for distributed data structures: remote direct memory access (RDMA) and remote procedure call (RPC). We focus on operations that require individual accesses to remote portions of a distributed data structure, e.g., accessing a hash table bucket or distributed queue, rather than global operations in which all processors collectively exchange information. We look at the trade-offs between the two styles through microbenchmarks and a performance model that approximates the cost of each. The RDMA operations have direct hardware support in the network and therefore lower latency and overhead, while the RPC operations are more expressive but higher cost and can suffer from lack of attentiveness from the remote side. We also run experiments to compare the real-world performance of RDMA- and RPC-based data structure operations with the predicted performance to evaluate the accuracy of our model, and show that while the model does not always precisely predict running time, it allows us to choose the best implementation in the examples shown. We believe this analysis will assist developers in designing data structures that will perform well on current network architectures, as well as network architects in providing better support for this class of distributed data structures.
This final report provides a final report on a NASA sponsored project involving data reduction and scientific analysis related to the Comprehensive Energetic Particle Pitch Angle Distribution (CEPPAD) experiment of POLAR. Dr. D.N. Baker, the chief scientist, has focused primarily on the calibration of CEPPAD sensors and the interpretation of data from the sensors which has led to discoveries regarding storm-substorm relationships in the earth's magnetosphere. The report contains approximately 190 bibliographic references to the activities of Baker and others involved.
The analysis of scientific simulation data enables scientists to derive insights from their simulations. This analysis of the simulation output can be performed at the same execution site as the simulation using the same resources or can be done at a different site. The optimal output frequency is challenging to decide and is often chosen empirically. We propose a mathematical formulation for choosing the optimal frequency of data transfer for analysis and the feasibility of performing the analysis, under the given resource constraints such as network bandwidth, disk space, available memory, and computation time. We propose formulations for two cases of co-analysis -- local and remote. We consider various analyses features such as computation time, input data and memory requirement, importance of the analysis and minimum frequency required for performing the analysis. We demonstrate the effectiveness of our approach using molecular dynamics applications on the Mira and Edison supercomputers.
Analysis and data requirements for solving potential aerospace electromagnetic compatibility problems