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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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At least 883 records · Page 49
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NASA NTRS2023

Aggregating Confidence: A Problem and A Solution

This paper has two main purposes: to articulate a concern about assessing arguments based on confidence; and to provide an assessment procedure which takes this concern into account. The concern is that confidence is a tricky notion for non-deductive Arguments: doubts inherent in Reasoning coalesces with doubts inherent in Premises. And, within a Compound Argument, this may happen many times over, thus accumulating in unpredictable ways. We explain two procedures we have developed to help address the concern in a methodical way.

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NASA NTRSDate not supplied

Multidimensional Data Aggregation in the Cloud with Application to Geostationary Satellite-based Air Quality Monitoring

Scientists use satellite data for studying Earth's systems, and the remote sensing data that these satellites collect are typically separated into files of a size small enough for efficient network transfer and storage. However, researchers usually prefer to analyze the data based on real-world dimensions like time, space, or elevation. To help with this, NASA's Atmospheric Science Data Center (ASDC) developed a new cloud-based tool that combines these smaller data chunks into larger, more useful datasets. The tool works on Network Common Data Form (netCDF4) and some HDF5 formatted files, and it is available as a service in NASA's Earthdata Cloud. In this presentation, we showcase this service using data from the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument. By combining TEMPO's continuous observations over time, we create longer and more informative analysis-ready time series to facilitate the study of air quality patterns. Insights gained will provide a more comprehensive understanding of pollution sources, transport patterns, and their effects on the environment and human health.

Daniel Kaufman↗
NASA NTRS2024

Constraining the Surface Exposure Age of Asteroid Bennu Using Cosmogenic Radionuclides in an OSIRIS-REx Aggregate Sample and Individual Particles

Observations by NASA’s OSIRIS-REx spacecraft of asteroid Bennu revealed evidence that this small rubble-pile asteroid experiences several active surface processes that likely control its recent surface exposure history. In October 2020, the spacecraft collected a surface sample from a relatively young ~20 m diameter crater (Hokioi) on asteroid Bennu, and the sample was successfully delivered to Earth on September 24, 2023. Based on the population of craters >50 m diameter, Bennu’s surface is 0.1–1 Ga old, but the old surface is affected by recent processes including smaller meteoroid impacts, mass movement, and particle ejections. The sampling location within Hokioi crater was selected based on its relatively flat surface devoid of large boulders, but the crater floor is also believed to be one of the youngest surfaces on asteroid Bennu, with an age of <100 kyr. As the crater is ~2 m deep, the samples that were collected by OSIRIS-REx may have been relatively shielded from cosmic rays during most of its recent history. This scenario is different from the samples that were collected by Hayabusa2 from asteroid Ryugu, where cosmogenic noble gas and radionuclides indicate cosmic-ray exposure (CRE) ages of ~5 Myr near the surface. Our goal is to understand the recent exposure history of surface samples of asteroid Bennu utilizing radionuclides ( 10 Be, 26 Al, 36 Cl, 41 Ca) produced by both galactic (GCR) and solar (SCR) cosmic rays. With half- lives ranging from 0.1 to 1.36 million years (Myr), the concentrations of these cosmogenic nuclides provide information on the duration and exposure conditions (irradiation depth) of these samples on a timescale of a few Myr. This work will test one of the OSIRIS-REx mission’s driving hypotheses about asteroid Bennu’s recent evolution: that Hokioi crater is part of a population of small (25 m) spectrally red craters on Bennu that are less than 0.1 Myr old.

K. C. Welten↗
DOE OSTI2017

Building thermal model development of typical house in U.S. for virtual storage control of aggregated building loads based on limited available information

A building thermal model is an essential component for achieving optimal control of a building’s heating, ventilation, and air conditioning (HVAC). A self-learning grey-box model has been developed by implementing data-driven techniques and utilizing limited available information about the building. In previous research, detailed foreknown knowledge/information, e.g. physical and thermal properties of building materials, as well as sufficient number of observation points, e.g. indoor temperature sensors in different zones, are available for making the decision of model structure and parameters searching range easier. The availability of measured data and a narrowed searching range of model parameters, e.g. resistance and capacity for each wall, made the adopted algorithms quickly achieve near optimal values, which closely approximate the actual heat transfer of a building. Meanwhile, the details of pre-processing of the key inputs were seldom explained. Compared to the previous research, in this study, the available information is assumed to be limited which can fill the requirement for large scale virtual storage control of real residential community in near future, where it may be impractically or over time-consuming to acquire the detailed information for each single house. The developed model is validated by comparing the results to measured data in a recently-built home typical of the southeastern United States.

Cui, Borui↗