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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Ice Growth Model and Lunar Tanker for Polar Water Extraction

In-Situ Resource Utilization (ISRU) is a major component of sustainable and cost-effective missions to the Moon and beyond. Ever since it has been identified in the Permanently Shadowed Regions (PSRs) of the Moon, water has become a resource of interest for it’s wide range of uses such as life support and hydrogen and oxygen rocket propellant. It is ice-bound in the regolith, so separation typically involves heating to sublimation temperatures and capturing at very low pressure. One option is to then store it in ice form and transport it out of the PSR and into a sunlit ridge. From here, processes such as electrolysis can be performed. An understanding of frost growth dynamics under rarefied conditions is necessary for successful water production, though. An analytical model was developed using a Diffusion-Limited Aggregation (DLA) approach to evaluate bulk thermal conductivity changes of frost under varying conditions. This model will be evaluated using a one-dimensional frost growth experiment on a horizontal, flat plate under vacuum and cryogenic conditions. The tests will measure frost growth rate and bulk density, and the thermal conductivity of the ice layer as a function of thickness for different plate temperatures. The test data will then be used to validate and improve the analytical model.

Lunar water↗
NASA NTRSDate not supplied

Wildfire Emergency Response Hazard Extraction and Analysis of Trends (HEAT) through Natural Language Processing and Time Series

Emerging wildfire operations aim to improve safety and performance through the integration of technologies including UAS and UTM. Recent advances in natural language processing (NLP) techniques, as well as the availability of wildfire incident reports, has made possible a large-scale analysis of wildfire hazards and trends. Identifying longitudinal trends will help us target risk mitigation and safety management activities. Note: This presentation does not include sound please disregard icon.

Sequoia R. Andrade↗
NASA NTRSDate not supplied

Information Extraction on an Earth Science Knowledge Graphs with Semantic Parsing

Knowledge graphs are an important tool, both for representing knowledge and for retrieving information. Fundamentally, they are semantic networks that represent entities and relationships in the form of nodes and edges. A large corpus of natural language text can bebroken down into discrete entities and relationships to form a useful knowledge graph. Existing research breaks down text into a subject, object, and verb relationship triple. Although this is a useful first step, it loses much of the original contextual information encoded within the text. Our process uses a novel 7-tuple approach, in which elements of sentences are programmatically parsed into seven categories: initiator, impacted, receiver, beneficiary, result, and context. In this presentation, we show a knowledge graph built using this 7-tupleprocessing of an Earth science corpus. We explain the techniques used to create the graph and analyze its information retrieval capability while assessing the accuracy and limitations of the results.

Carson Davis↗
NASA NTRSDate not supplied

Towards a program of record of inland water quality: Exploiting present and heritage multispectral sensors for maximum information extraction

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This research exploits recent advancements in bio-optical modeling, cloud computing, and machine learning to enhance our capacity to leverage present and heritage satellite data. Recent research suggests that sensors with low spectral resolution, such as Sentinel 2 and Landsat missions, contain enough hidden spectral variation which can be exploited using data-driven approaches. The availability of three decades of archival imagery will open doors to discover global trends of eutrophication and increased cyanobacteria dominance and provide valuable insight to the development of predictive methodologies. Preliminary efforts in synthetic emulation of global natural inland waters will be discussed and contextualized against satellite radiometric measurement uncertainty, satellite data product uncertainties and causal signal ambiguity over the visible wavelength range, supported by high quality field and image data for selected inland aquatic sites. Insights on water quality estimation via data-driven machine learning models versus matrix inversions will be discussed, and how we can exploit spectral-spatial relationships in high spatial resolution data. A cross-sensor synergistic approach with detailed uncertainty analysis based on optical water types, will allow for unprecedented global snapshots of fine scale ecological dynamics of inland waters.

Inland↗
NASA NTRS2021

A Century of Tidal Variability in the North Pacific Extracted from Hourly Geomagnetic Observatory Measurements at Honolulu

Previous analyses of hourly tide-gauge data taken at Honolulu since 1905 have shown that both sea level and the amplitude of the tides have increased synchronously over time, and a process has been proposed whereby the common cause is an increase in ocean temperature. Validation with independent data of this change in tides, as well as the proposed cause, has been lacking Ocean tides also generate magnetic fields that reach far outside the ocean, and tidal signals are clearly seen in the hourly data since 1905 from the Honolulu geomagnetic observatory. Here, it is found that the tidal amplitudes have increased synchronously in the tide-gauge and magnetic records, providing independent support for the previous results. Further information about the changing ocean is likely contained in the historical data from geomagnetic observatories as well as the global coverage of modern satellite magnetic surveys.

Robert H Tyler↗
NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

Automated Tracking of Shallow Maritime Clouds on Geostationary Imagery to Extract Lifecycle Characteristics

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Satellites have provided many statistics on shallow clouds, such as size, structure, and geographical coverage, from static views of recurring cloud fields. But determining why certain cloud features appear and persist for different periods requires a time-evolving view of their behaviors. Geostationary satellites provide a unique opportunity to follow the time evolution of individual convective features, given their enhanced spatial and temporal sampling. A cloud-tracking tool was developed to identify properties of cloud lifecycle from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2EX) field campaign of 2019. The mission conducted intensive sampling of shallow cumulus in the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus was segmented according to thresholds in 0.5-km visible reflectance and with blurring techniques. Despite being limited to daytime hours, the segmentations yielded the best resolution possible for capturing cloud initiation and decay. The tracking procedure is based on a computer vision package that includes Kalman filters for motion prediction, object overlap search, and the Hungarian (or Kuhn-Munkres) matching algorithm for track designation. AHI radiances available within the tracked cloud boundaries are assembled to form individual spectral histories. The resulting catalog provides thousands of cloud histories for domains measuring only a few degrees in latitude and longitude. We present an overview of the cloud-tracking tool, strategies to identify development stages from cloud tracks, and preliminary results that document cumulus lifecycle properties from satellite. The application of AHI 0.5-km reflectance has both strengths and limitations when attempting to track lifecycles of the smallest resolvable clouds. We show that by aggregating cloud tracks from a few case studies of CAMP2EX, we can discern differences in cloud lifetime and development according to ensembles selected from areas of interest. The results demonstrate an ability to quantify lifetimes and assess rates of change in cloud characteristics that are likely controlled by the surrounding environment and meteorology.

Cloud Tracking↗