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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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NASA NTRS2018

Summary of Pressure Gain Combustion Research at NASA

NASA has undertaken a systematic exploration of many different facets of pressure gain combustion over the last 25 years in an effort to exploit the inherent thermodynamic advantage of pressure gain combustion over the constant pressure combustion process used in most aerospace propulsion systems. Applications as varied as small-scale UAV's, rotorcraft, subsonic transports, hypersonics and launch vehicles have been considered. In addition to studying pressure gain combustor concepts such as wave rotors, pulse detonation engines, pulsejets, and rotating detonation engines, NASA has studied inlets, nozzles, ejectors and turbines which must also process unsteady flow in an integrated propulsion system. Other design considerations such as acoustic signature, combustor material life and heat transfer that are unique to pressure gain combustors have also been addressed in NASA research projects. In addition to a wide range of experimental studies, a number of computer codes, from 0-D up through 3-D, have been developed or modified to specifically address the analysis of unsteady flow fields. Loss models have also been developed and incorporated into these codes that improve the accuracy of performance predictions and decrease computational time. These codes have been validated numerous times across a broad range of operating conditions, and it has been found that once validated for one particular pressure gain combustion configuration, these codes are readily adaptable to the others. All in all, the documentation of this work has encompassed approximately 170 NASA technical reports, conference papers and journal articles to date. These publications are very briefly summarized herein, providing a single point of reference for all of NASA's pressure gain combustion research efforts. This documentation does not include the significant contributions made by NASA research staff to the programs of other agencies, universities, industrial partners and professional society committees through serving as technical advisors, technical reviewers and research consultants.

Perkins, H. Douglas↗

DOE OSTI2016

Technical Advisor to the American Indian Research and Education Initiatives (AIREI).

The American Indian Research & Education Initiative (AIREI) is a pilot program that started in 2011 and is funded by the US Department of Energy (DOE) Economic Impact & Diversity and National Nuclear Security Administration in partnership with the American Indian Higher Education Consortium (AIHEC) and the American Indian Science and Engineering Society. AIREI brings science, technology, engineering, and mathematics (STEM) research and education funding to Tribal Colleges and Universities (TCU) and other US universities. AIREI has funded eight schools, including four pairs of tribal colleges and mainstream universities, in order for student and faculty research teams to bring energy projects to tribal lands. The research team from Southwest Indian Polytechnic Institute (SIPI) and Northern Arizona University (NAU) has performed a student-centric research and analysis feasibility study of a potential utility-scale solar power plant on the Jemez Pueblo reservation trust land. The research team from Navajo Technical University (NTU) and Arizona State University (ASU) has assessed the effectiveness of solar photovoltaic (PV) system designs in meeting the electricity demands of Navajo Tribal homes and public buildings in addition to the development of a solar technology curriculum that incorporates the outcomes of this study, helping to advance PV system design and installations on local Tribal lands. The Little Big Horn College (LBHC) and Montana State University-Bozeman (MSUB) team has developed fast growing strains of nitrogen-fixing cyanobacteria to help advance carbon capture and sequestration (CCS) technologies. The research supported the Crow Nation reservation as it evaluates opportunities for coal-to-liquid fuel and CCS projects. The Sinte Gleska University (SGU) and South Dakota School of Mines (SDSM) team developed computer modeling and simulation technologies to evaluate the feasibility of oil and gas development from the Niobrara Formation on the Rosebud Sioux reservation. Through this project, the students developed skills in applied energy-related research involving computer simulation, chemistry, geology, and petroleum engineering. AIREI supports collaboration between these universities and connects the teams with the technological expertise and mentorship opportunities provided through Sandia National Laboratories (Sandia). AIHEC consists of 37 American Indian tribally controlled colleges around the nation and provides technical assistance through professional development workshops, strategic planning meetings, and information sharing strategies.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2011

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

Leptoukh, Gregory↗

NASA NTRSDate not supplied

Repurposing Drilling Control Diagnostics for Subsurface Edge Detection and Boundary Advisement During Planetary Drilling

Informed decision-making during lunar drilling and sampling missions will require data monitoring tools and specialized ground data systems. Accurate and updated situational awareness, with ongoing data monitoring, is critical for timely responses by to incoming science data. Traverse plans and scheduled activities may need to be flexibly changed in order to react to unexpected data or situations. Unlike (for example) Mars missions, the relative lightspeed closeness of the Moon allows for near-real-time ground processing of incoming mission and instrument data. An Apollo-class lunar regolith drill will in a sense “travel” a meter or two vertically at a given subsurface characterization site. As the drill penetrates into lunar regolith, it is likely to encounter a range of material densities, orientations, fracture toughness, and (perhaps) ice percentages. Lunar drill telemetry can provide science teams with a valuable first look into the subsurface structure, the regolith bulk properties, and constituents at each drilled site. Real-time AI-based recognition and reaction to downhole situations has been developed for automated deeper drilling on Mars and beyond. We can leverage the same knowledge bases and pattern-matching as areal-time interpreter of the subsurface, a situational awareness tool during drilling operations. We recently (Sept. 2019) demonstrated this AI drilling monitoring and analysis capability, in control of in-situ drilling and sampling operations, mounted on a KREX-2 rover in Chile’s Atacama Desert. Terrestrial automated drilling log analyses in oil exploration have used similar machine learning techniques in classifying and identifying features in drilling logs –but these typically are designed assuming a drilling fluid influencing downhole measurements and data (permeability, resistivity). Drilling models and existing AI software designed to detect and respond to drilling faults and hard materials can be repurposed, for near-real-time (ground-based) interpretation of drilling telemetry –a potentially valuable advisory tool for strata boundaries and changes in drilling parameters. On the Moon, this approach could be used to study the structure and to some extent the composition of lunar regolith vs. borehole depth, based on recognizable variations in fracture hardness, drilling energy and penetration rates while actively drilling. Since the early 2000s, a series of increasingly-capable real-time drilling telemetry interpretation and characterization software tools have been developed. These subsurface models and software tools have monitored the real-time drilling data received, and automatically identified changes in drill behavior (e.g., encountering a harder target layer, bit inclusions, drill choking due to infall downhole, and others) correlating these with subsurface structures and features. We discuss the mappings between drill borehole parameters, faults or events detected, and modeled changes in rock layer boundaries, in examples drawn from field testing at analog sites in an Arctic impact crater, Rio Tinto, and Chile’s Atacama Desert. These demonstrate how subsurface structural boundaries led to fault detections and responses by the software.

drilling advisor↗