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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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Further to the development of a model analysis framework suitable for calibration and validation of complex multivariate multiphysics models, this report presents an approach to data collection and characterization which can work in parallel with the above-mentioned model analysis framework and support the calibration and validation of the case-study subcooled flow boiling model.
5-minute energy data from January 1, 2008 - December 31, 2014 for the City of Ellensburg, WA, U.S.A. This data was collected under the Pacific Northwest Smart Grid Demonstration Project under the American Recovery and Reinvestment Act (ARRA).
The focus of the NASA 'Space Flight Risk Data Collection and Analysis' project was to acquire and evaluate space flight data with the express purpose of establishing a database containing measurements of specific risk assessment - reliability - availability - maintainability - supportability (RRAMS) parameters. The developed comprehensive RRAMS database will support the performance of future NASA and aerospace industry risk and reliability studies. One of the primary goals has been to acquire unprocessed information relating to the reliability and availability of launch vehicles and the subsystems and components thereof from the 45th Space Wing (formerly Eastern Space and Missile Command -ESMC) at Patrick Air Force Base. After evaluating and analyzing this information, it was encoded in terms of parameters pertinent to ascertaining reliability and availability statistics, and then assembled into an appropriate database structure.
In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.
The Integrated Data Collection Analysis (IDCA) program conducted a final program review at the Department of Homeland Security on September 12, 2012. The review was focused on the results of the program over the complete performance period. A summary presentation delineating the accomplished tasks started the meeting, followed by technical presentations on various issues that arose during the performance period. The presentations were completed with a statistical evaluation of the testing results from all the participants in the IDCA Proficiency Test study. The meeting closed with a discussion of potential sources of funding for continuing work to resolve some of these technical issues. This effort, funded by the Department of Homeland Security (DHS), put the issues of safe handling of these materials in perspective with standard military explosives. The study added Small-Scale Safety and Thermal (SSST) testing results for a broad suite of different HMEs to the literature, and suggested new guidelines and methods to develop safe handling practices for HMEs. Each participating testing laboratory used identical test materials and preparation methods wherever possible. Note, however, the test procedures differ among the laboratories. The results were compared among the laboratories and then compared to historical data from various sources. The testing performers involved were Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory (LANL), Naval Surface Warfare Center, Indian Head Division (NSWC IHD), Sandia National Laboratories (SNL), and Air Force Research Laboratory, Tyndall AFB (AFRL/RXQL). These tests were conducted as a proficiency study in order to establish some consistency in test protocols, procedures, and experiments and to compare results when these testing variables cannot be made consistent.
The results of the analysis of aircraft-acquired thematic mapper simulator (TMS) data, collected to investigate the utility of thematic mapper data in crop area and land cover estimates, are discussed. Results of the analysis indicate that the seven-channel TMS data are capable of delineating the 13 crop types included in the study to an overall pixel classification accuracy of 80.97% correct, with relative efficiencies for four crop types examined between 1.62 and 26.61. Both supervised and unsupervised spectral signature development techniques were evaluated. The unsupervised methods proved to be inferior (based on analysis of variance) for the majority of crop types considered. Given the ground truth data set used for spectral signature development as well as evaluation of performance, it is possible to demonstrate which signature development technique would produce the highest percent correct classification for each crop type.
ZAPP shot series summary of collected data to be transferred to UNR and WVU.
The Integrated Data Collection Analysis (IDCA) program is conducting a proficiency study for Small-Scale Safety and Thermal (SSST) testing of homemade explosives (HMEs). Described here are the results for impact, friction, electrostatic discharge, and scanning calorimetry analysis of a mixture of KClO 3 sized through a 100-mesh sieve mixed with icing sugar, also sized through a 100-mesh sieve—KClO 3 /icing sugar (-100) mixture. This material was selected because of the challenge of performing SSST testing of a mixture of two solid materials. The mixture was found to be: 1) more sensitive to impact than RDX, with sensitivity similar to PETN, 2) the same or more sensitive to friction than PETN, and 3) less sensitive to spark than RDX. The analysis showed that the mixture has thermally stability similar to RDX and is perhaps more energetic upon decomposition but variable results indicate sampling issues.
The MODIS Cloud Optical and Microphysical Product (MOD_PR060D) for Data Collection 6 has entered full scale production. Aqua reprocessing is almost completed and Terra reprocessing will begin shortly. Unlike previous collections, the CHIMAERA code base allows for simultaneous processing for multiple sensors and the operational CHIMAERA 6.0.76 stream is also available for VIIRS and SEVIRI sensors and for our E-MAS airborne platform.
Subjects covered at the meeting concerned results on the overall data collection system including sensors, interface hardware, power supplies, environmental enclosures, data transmission, processing and distribution, maintenance and integration in resources management systems.
Progress is reviewed on the reduction and analysis of tornado data collected on analog tape. The strip chart recording of 7 tracks from all available analog data for quick look analysis is emphasized.
Many diverse types of data were collected as part of the SECARB Early Test. Very large amounts of data were collected and have been archived at BEG as reported in 9.1.1-h. This report overviews the main data collection, backup, distribution, and archival systems used.
The Global Oscillation Network Group (GONG) Project is planning to place a set of instruments around the world to observe solar oscillations as continuously as possible for at least three years. The Project has now chosen the sites that will comprise the network. This paper describes the methods of data collection and analysis that were used to make this decision. Solar irradiance data were collected with a one-minute cadence at fifteen sites around the world and analyzed to produce statistics of cloud cover, atmospheric extinction, and transparency power spectra at the individual sites. Nearly 200 reasonable six-site networks were assembled from the individual stations, and a set of statistical measures of the performance of the networks was analyzed using a principal component analysis. An accompanying paper presents the results of the survey.
The National Aeronautics and Space Administration (NASA) has developed an airborne data acquisition and communication system for the National Oceanic and Atmospheric Administration (NOAA). This system known as ASDAR, the Aircraft to Satellite Data Relay, consists of a microprocessor based controller, time clock, transmitter and antenna. Together they acquire meteorological and position information from existing aircraft systems on B-747 aircraft, convert and format these, and transmit them to the ground via the GOES meteorological satellite series. The development and application of the ASDAR system is described with emphasis on unique features. Performance to date is exceptional, providing horizon-to-horizon coverage of aircraft flights. The data collected is of high quality and is considered a valuable addition to the data base from which NOAA generates its weather forecasts.
The Combined Release and Radiation Effects Satellite (CRRES) program experiments data collection, analysis, and publication activities are described. These activities were associated with both the satellite chemical release and a planned Puerto Rico sounding rocket campaign. To coordinate these activities, a working group meeting was organized and conducted.
This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.
The key issues in all areas of Differential Absorption Lidar (DIAL) data collection and analysis techniques were examined. This included consideration of the practical and theoretical limitations of DIAL and the range of possible DIAL measurements.
Gamma-ray spectral data were collected from sensors mounted to traffic signals around Northern Virginia. The data were collected over a span of approximately fifteen months. A subset of the data were analyzed manually and subsequently used to train machine-learning models to facilitate the evaluation of the remaining 50k anomalous events identified in the dataset. We describe the analysis approach used here and discuss the results in terms of radioisotope classes and frequency patterns over day-of-week and time-of-day spans. Data from this work has been archived and is available for future and ongoing research applications.