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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 Mars Exploration Rover mission plans to launch two spacecraft, each containing one rover, and to deliver them to the surface of Mars allowing the rovers to collect science data on the surface.
Pictographic data or imagery is commonly used in marine exploration. Pre-existing image processing techniques (software) similar to those used on imagery obtained from unmanned planetary exploration were used to improve marine photography and side-scan sonar imagery. Features and details not visible by conventional photo processing methods were enhanced by filtering and noise removal on selected deep-sea photographs. Information gained near the periphery of photographs allows improved interpretation and facilitates construction of bottom mosaics where overlapping frames are available. Similar processing techniques were applied to side-scan sonar imagery, including corrections for slant range distortion, and along-track scale changes. The use of digital data processing and storage techniques greatly extends the quantity of information that can be handled, stored, and processed.
MDSplus is a software tool designed for data acquisition, storage, and analysis of complex scientific experiments. Over the years, MDSplus has primarily been used for data management for fusion experiments. This paper demonstrates that MDSplus can be used for a much wider variety of systems and experiments. We present a step-by-step tutorial describing how to create a simple experiment, manage the data, and analyze it using MDSplus and Python. To this end, a custom example device was developed to be used as the data source. This device was built on an open-source electronic hardware platform, and it consists of a microcontroller and two sensors. Finally, we read data from these sensors, store it in MDSplus, and use JupyterLab to visualize and process it.
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© 1996-2018 Society of Exploration Geophysicists. All rights reserved. We explore the data collected by a 3-component induction coil magnetometer system with respect to motion of the instruments in earths static magnetic field. The sensitivtiy of the inductiuon coil magnetometer leads to unprecediented accuracy on tilt measurements. We model the signals observed during seismic events as being perturbations in coil orientation. In theory, these perturbations can include ground roll, ocean motion, nearby cultural seismicity, or any other field with a tilting effect. Using data from a magnetic observatory near Parkfield CA we invert several time series of coil data during different levels of seismic activity in an attempt to determine the magnitudes of rotation at which our model accurately describes the coil data. Finally, we explore the transfer function between the coils and nearby seismic instruments (accelerometers, tiltmeters, and velocity seismometers).
The main scientific contributions of this project are the following novel concepts for multidimensional arrays: shape-based similarity join (SIGMOD 2016), incremental view maintenance (SIGMOD 2017), user-defined stencil functions (HPDC 2017), and distributed caching for in-situ processing (SSDBM 2018). Building on our collaboration with the astrophysics group at LBNL, we applied these techniques to the data generated in the Palomar Transient Factory (PTF) astronomical survey. They played a pivotal role in the first-ever observation of a neutron star merger, which produces gravitational waves and turns out to be the origin of heavy elements, including gold. This has lead to a Science magazine article that has received extensive media coverage on ACM TechNews, Slashdot, FiveThirtyEight, and Quanta Magazine, among others. Additionally, two other articles detailing related aspects of the same discovery have been published in the Astrophysical Journal Letters journal. These publications have more than 3,000 citations according to Google Scholar (as of February 2022). This cross-disciplinary collaboration provided very good opportunities to apply database techniques to real-life scientific problems. The fact that they facilitated major discoveries in astrophysics proves the importance of our research. In addition to the work on multidimensional array databases, this project has also developed stochastic gradient descent (SGD) optimization algorithms for training large scale machine learning models, methods for querying in-situ data, and a database query optimizer based on sketch synopses.
We present simultaneous ground-based radial velocity (RV) measurements and space-based photometric measurements of the young and active K dwarf Epsilon Eridani. These measurements provide a data set for exploring methods of identifying and ultimately distinguishing stellar photospheric velocities from Keplerian motion. We compare three methods we have used in exploring this data set: Dalmatian, an MCMC spot modeling code that fits photometric and RV measurements simultaneously; the FF′ method, which uses photometric measurements to predict the stellar activity signal in simultaneous RV measurements; and H α analysis. We show that our H α measurements are strongly correlated with the Microvariability and Oscillations of STars telescope ( MOST ) photometry, which led to a promising new method based solely on the spectroscopic observations. This new method, which we refer to as the HH′ method, uses H α measurements as input into the FF′ model. While the Dalmatian spot modeling analysis and the FF′ method with MOST space-based photometry are currently more robust, the HH′ method only makes use of one of the thousands of stellar lines in the visible spectrum. By leveraging additional spectral activity indicators, we believe the HH′ method may prove quite useful in disentangling stellar signals.
NREL completed a temporal and geospatial analysis of telematics data to estimate the fraction of platoonable miles traveled by class 8 tractor trailers currently in operation. This paper discusses the value and limitations of very large but low time-resolution data sets, and the fuel consumption reduction opportunities from large scale adoption of platooning technology for class 8 highway vehicles in the US based on telematics data. The telematics data set consist of about 57,000 unique vehicles traveling over 210 million miles combined during a two-week period. 75% of the total fuel consumption result from vehicles operating in top gear, suggesting heavy highway utilization. The data is at a one-hour resolution, resulting in a significant fraction of data be uncategorizable, yet significant value can still be extracted from the remaining data. Multiple analysis methods to estimate platoonable miles are discussed. Results indicate that 63% of total miles driven at known hourly-average speeds happens at speeds amenable to platooning. When also considering availability of nearby partner vehicles, results indicate 55.7% of all classifiable miles driven were platoonable. Analysis also address the availability of numerous partners enabling platoons greater than 2 trucks and the percentage of trucks that would be required to be equipped with platooning equipment to realize more than 50% of the possible savings.
MDSplus is a software tool designed for data acquisition, storage, and analysis of complex scientific experiments. Over the years, MDSplus has primarily been used for data management for fusion experiments. This paper demonstrates that MDSplus can be used for a much wider variety of systems and experiments. We present a step-by-step tutorial describing how to create a simple experiment, manage the data, and analyze it using MDSplus and Python. To this end, a custom example device was developed to be used as the data source. This device was built on an opensource electronic hardware platform, and it consists of a microcontroller and two sensors. We read data from these sensors, store it in MDSplus, and use JupyterLab to visualize and process it. This project and code demo are available on the GitHub site at this URL: https://github.com/santorofer/MDSplusAndCustomeDevices
There are no author-identified significant results in this report.
The primary objectives of the investigation are to determine fully the utility of Shuttle Imaging Radar-B (SIR-B) images for providing valuable surface indicators for ground-water prospecting in the Arabian shield and to identify and assess defining characteristics of sand sheets, sand streaks, and sand dunes in the fringe areas of An Nafud and Al Jafurah. Specific objectives include the determination of the incremental contribution of incidence angle to the total information that can be extracted from SIR-B standard and digitally-enhanced images in the AL Jafurah fringe area; the determination of the incremental contribution of digitally-registered multisensor images; and the development of a groundwater exploration plan for the Ha'il test area in the Arabian Shield.
No abstract available
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Abstract not provided.
Validation of Innovative Exploration Technologies for Newberry Volcano: Seismic data - raw taken by Apex Hipoint for 1st test 2012 (data in .ff format)
Fluid dynamic response characteristics of Explorer ionization gage data for orbital analysis
The NGDS released version 1 of the system on April 30, 2014 using the US Geoscience Information Network (USGIN) as its data integration platform. NGDS supports the 2013 Open Data Policy, and as such, the launch was featured at the 2014 Energy Datapalooza. Currently, the NGDS features a comprehensive user interface for searching and accessing nearly 41,000 documents and more than 9 million data points shared by scores of data providers across the U.S. The NGDS supports distributed data sharing, permitting the data owners to maintain the raw data that is made available to the consumer. Researchers and industry have been utilizing the NGDS as a mechanism for promoting geothermal development across the country, from hydrothermal to ground source heat pump applications. Case studies in geothermal research and exploration from across the country are highlighted.