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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

Geology of selected lava tubes in the Bend Area, Oregon

Longitudinal profiles representing 5872.5 m of mapped lava tubes and a photogeologic map relating lava tubes to surface geology, regional structure and topography are presented. Three sets of lava tubes were examined: (1) Arnold Lava Tube System (7km long) composed of collapsed and uncollapsed tube segments and lava ponds, (2) Horse Lava Tube System (11 km long) composed of parallel and anastomosing lava tube segments, and (3) miscellaneous lava tubes. Results of this study tend to confirm the layered lava hypothesis of Ollier and Brown (1965) for lava tube formation; however, there are probably several modes of formation for lava tubes in general. Arnold System is a single series of tubes apparently formed in a single basalt flow on a relatively steep gradient. The advancing flow in which the tubes formed was apparently temporarily halted, resulting in the formation of lava ponds which were inflated and later drained by the lava tube system. Horse System probably formed in multiple, interconnected flows. Pre-flow gradient appears to have been less than for Arnold System, and resulted in meandrous, multiple tube networks.

Greely, R.↗
NASA NTRS1997

Extension of a Kolmogorov Atmospheric Turbulence Model for Time-Based Simulation Implementation

The development of any super/hypersonic aircraft requires the interaction of a wide variety of technical disciplines to maximize vehicle performance. For flight and engine control system design and development on this class of vehicle, realistic mathematical simulation models of atmospheric turbulence, including winds and the varying thermodynamic properties of the atmosphere, are needed. A model which has been tentatively selected by a government/industry group of flight and engine/inlet controls representatives working on the High Speed Civil Transport is one based on the Kolmogorov spectrum function. This report compares the Dryden and Kolmogorov turbulence forms, and describes enhancements that add functionality to the selected Kolmogorov model. These added features are: an altitude variation of the eddy dissipation rate based on Dryden data, the mapping of the eddy dissipation rate database onto a regular latitude and longitude grid, a method to account for flight at large vehicle attitude angles, and a procedure for transitioning smoothly across turbulence segments.

McMinn, John D.↗
NASA NTRS2011

The Lightning Nitrogen Oxides Model (LNOM): Status and Recent Applications

Improvements to the NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) are discussed. Recent results from an August 2006 run of the Community Multiscale Air Quality (CMAQ) modeling system that employs LNOM lightning NOx (= NO + NO2) estimates are provided. The LNOM analyzes Lightning Mapping Array (LMA) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning NOx. The latest LNOM estimates of (a) lightning channel length distributions, (b) lightning 1-m segment altitude distributions, and (c) the vertical profile of NOx are presented. The impact of including LNOM-estimates of lightning NOx on CMAQ output is discussed.

Koshak, William↗
NASA NTRS2017

Orbit Determination Adaptations for the Cassini Grand Finale

satellite encounters. Over this period, there have been several papers describing the orbit determination process and performance up through 2016 [1-5]. In April of 2017, Cassini will enter its Grand Finale mission phase when it will traverse the gap between the D-ring and the Saturn atmosphere twenty-two times before plunging deep into the atmosphere to end the mission. The lack of targeted satellite encounters during this period necessitates updates to the nominal Cassini Orbit Determination (OD) process. This paper describes these planned adaptations for the operation of the Grand Finale. During the Equinox and Solstice Mission Phase (2008-2016), navigation analysis has been divided into segments focused on two particular targeted satellite encounters, called an “arc”. Maneuvers in an arc were usually targeted to encounter B-plane position and time, so the OD state and covariance were mapped forward to the B-plane of the encounter within the arc. Trajectory dispersions during the Grand Finale need instead to be mapped to equator crossings and targeted Cartesian positions. In addition, trajectory arcs have typically covered a few orbital revolutions (~2-8 weeks), in order to span the time between two encounters. However, the Grand Finale will encompass five months of time without an encounter which necessitates an adjusted arc strategy. A modified arc strategy was developed based on OD behavior during long multi-rev periods between encounters in the year leading up to the Grand Finale. The OD covariance study conducted for the Grand Finale mission phase will also be examined.

Criddle, Kevin↗
DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗
DOE OSTI2022

Transmission of Human Influenza A Virus in Pigs Selects for Adaptive Mutations on the HA Gene

Influenza A viruses (FLUAV) cause respiratory diseases in many host species, including humans and pigs. The spillover of FLUAV between swine and humans has been a concern for both public health and the swine industry. With the emergence of the triple reassortant internal gene (TRIG) constellation, establishment of human-origin FLUAVs in pigs has become more common, leading to increased viral diversity. However, little is known about the adaptation processes that are needed for a human-origin FLUAV to transmit and become established in pigs. Here, we generated a reassortant FLUAV (VIC11pTRIG) containing surface gene segments from a human FLUAV strain and internal gene segments from the 2009 pandemic and TRIG FLUAV lineages and demonstrated that it can replicate and transmit in pigs. Sequencing and variant analysis identified three mutants that emerged during replication in pigs, which were mapped near the receptor binding site of the hemagglutinin (HA). The variants replicated more efficiently in differentiated swine tracheal cells compared to the virus containing the wildtype human-origin HA, and one of them was present in all contact pigs. These results show that variants are selected quickly after replication of human-origin HA in pigs, leading to improved fitness in the swine host, likely contributing to transmission.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1990

Geology of the Venus equatorial region from Pioneer Venus radar imaging

The data collected by Pioneer Venus have been used to map the broad radar characteristics of the equatorial region on the basis of radar brightness and texture. Seven units are defined and are used to assess the geologic character of the equatorial region. The correspondence between features in the 15 deg region of overlap between Pioneer Venus ans Venera 15/16 images is examined and used to extend units mapped in the northern high latitudes into the equatorial region. Using the properties of rms slope, reflectivity, the scattering behavior of the surface, and topographic signature, seven physiographic units are mapped on the basis of the distribution of the radar units in the equatorial region and are identified by increasing complexity as plains, dark halo plains, upland rises, upland plateaus, interhighland tectonic zones, tectonically segmented linear highlands, and tectonic junctions. The distribution of larger circular structures interpreted as coronae is also examined.

Senske, D. A.↗
NASA NTRS2005

Data-Analysis System for Entry, Descent, and Landing

A report describes the Entry Descent Landing Data Analysis (EDA), which is a system of signal-processing software and computer hardware for acquiring status data conveyed by multiple-frequency-shift-keying tone signals transmitted by a spacecraft during descent to the surface of a remote planet. The design of the EDA meets the challenge of processing weak, fluctuating signals that are Doppler-shifted by amounts that are only partly predictable. The software supports both real-time and post processing. The software performs fast-Fourier-transform integration, parallel frequency tracking with prediction, and mapping of detected tones to specific events. The use of backtrack and refinement parallel-processing threads helps to minimize data gaps. The design affords flexibility to enable division of a descent track into segments, within each of which the EDA is configured optimally for processing in the face of signal conditions and uncertainties. A dynamic-lock-state feature enables the detection of signals using minimum required computing power less when signals are steadily detected, more when signals fluctuate. At present, the hardware comprises eight dual-processor personal-computer modules and a server. The hardware is modular, making it possible to increase computing power by adding computers.

Pham, Timothy↗
NASA NTRS2016

The Asteroid Redirect Mission (ARM): Exploration of a Former Binary NEA?

The National Aeronautics and Space Administration (NASA) is developing the Asteroid Redirect Mission (ARM) as a capability demonstration for future human exploration, including use of high-power solar electric propulsion, which allows for the efficient movement of large masses through deep space. The ARM will also demonstrate the capability to conduct proximity operations with natural space objects and crewed operations beyond the security of quick Earth return. The Asteroid Redirect Robotic Mission (ARRM), currently in formulation, will visit a large near-Earth asteroid (NEA), collect a multi-ton boulder from its surface, conduct a demonstration of a slow push planetary defense technique, and redirect the multi-ton boulder into a stable orbit around the Moon. Once returned to cislunar space in the mid-2020s, astronauts aboard an Orion spacecraft will dock with the robotic vehicle to explore the boulder and return samples to Earth. The ARM is part of NASA's plan to advance technologies, capabilities, and spaceflight experience needed for a human mission to the Martian system in the 2030s. The ARM and subsequent availability of the asteroidal material in cis-lunar space, provide significant opportunities to advance our knowledge of small bodies in the synergistic areas of science, planetary defense, and in-situ resource utilization (ISRU). The current reference target for the ARM is NEA (341843) 2008 EV5, which may have been the primary body of a former binary system (Busch et al., 2011; Tardivel et al., 2016). The ARRM will perform several close proximity operations to investigate the NEA and map its surface. A detailed investigation of this object may allow a better understanding of binary NEA physical characteristics and the possible outcomes for their evolution. An overview of the ARM robotic and crewed segments, including mission operations, and a discussion of potential opportunities for participation with the ARM will be provided in this presentation.

Abell, P. A.↗
DOE OSTI2022

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Comparative Study of Deep Learning Models for Fracture and Pore Space Segmentation in Synthetic Fractured Digital Rocks

This study focuses on the comparative study of deep learning (DL) models for pore space and discrete fracture networks (DFNs) segmentation in synthetic fractured digital rocks, specifically targeting low-permeability rock formations, such as shale and tight sandstones. Accurate characterization of pore space and DFNs is critical for subsequent property analysis and fluid flow modeling. Four DL models, SegNet, U-Net, U-Net-wide, and nested U-Net (i.e., U-Net++), were trained, validated, and tested using synthetic datasets, including input and label image pairs with varying properties. The model performance was assessed regarding pixel-wise metrics, including the F1 score and pixel-wise difference maps. In addition, the physics-based metrics were considered for further analysis, including sample porosity and absolute permeability. Particularly, We first simulated the permeability of porous media containing only pore space and then simulated the permeability of porous media with DFNs added. The difference between these two values is used to quantify the connectivity of segmented DFNs, which is an important parameter for low-permeability rocks. The pixel-wise metrics showed that the nested U-Net model outperformed the rest of the DL models in pore space and DFNs segmentation, with the SegNet model exhibiting the second-best performance. Particularly, nested U-Net enhanced segmentation accuracy for challenging boundary pixels affected by partial volume effects. The U-Net-wide model achieved improved accuracy compared to the U-Net model, which indicated the influence of parameter numbers. Similarly, nested U-Net has the closest match to the ground truth of physics-based metrics, including the porosity of pore space and DFNs, and the permeability difference quantifying the connectivity of DFNs. The findings highlight the effectiveness of DL models, especially the U-Net++ model with nested architecture and redesigned skip connections, in accurately segmenting pore spaces and DFNs, which are crucial for pore-scale fluid flow and transport simulation in low-permeability rocks.

Wang, Hongsheng↗
DOE OSTI2022

Geometrically necessary dislocation fingerprints of dislocation loop absorption at grain boundaries

Here we present a numerical methodology to compute the Nye-tensor fingerprints of dislocation loop absorption at grain boundaries (GBs) for comparison with TEM observations of irradiated polycrystals. Our approach links atomistic simulations of self-interstitial atom (SIA) prismatic loops gliding toward and interacting with GBs in body-centered cubic iron with experimentally extracted geometrically necessary dislocation (GND) maps to facilitate the interpretation of damage processes. The Nye-tensor analysis is strongly mesh-size dependent—corresponding to resolution-dependent TEM observations. The method computes GND fingerprints from discretized dislocation line segments extracted from molecular dynamics simulations of dislocation loops being absorbed at a GB. Specifically, we perform MD simulation of prismatic loops of two diameters and monitor the three stages of the absorption process: loop glide, the partial, and full absorption of the loops at a [1 0 0] symmetric tilt GB. These methods provide a framework for future investigations of the nature of defect absorption by grain boundaries under irradiation conditions.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Evaluation of ERTS imagery for spectral geological mapping in diverse terranes of New York State

Linear anomalies dominate the new geological information derived from ERTS-1 imagery, total lengths now exceeding 6000 km. Experimentation with a variety of viewing techniques suggests that conventional photogeologic analyses of band 7 results in the location of more than 97 percent of all linears found. The maxima on rose diagrams for ERTS-1 anomalies correspond well with those for mapped faults and topographic lineaments, despite a difference in relative magnitudes of maxima thought due to solar illumination direction. A multiscale analysis of linears showed that single topographic linears at 1:2,500,000 became segmented at 1:1,000,000, aligned zones of shorter parallel, en echelon, or conjugate linears at 1:500,000, and still shorter linears lacking obvious alignment at 1:250,000. Visible glacial features include individual drumlins, best seen in winter imagery, drumlinoids, eskers, ice-marginal drainage channels, glacial lake shorelines and sand plains, and end moraines.

Isachsen, Y. W.↗
NASA NTRS1975

Maximum likelihood signature estimation

Maximum-likelihood estimates are discussed which are based on an unlabeled sample of observations, of unknown parameters in a mixture of normal distributions. Several successive approximation procedures for obtaining such maximum-likelihood estimates are described. These procedures, which are theoretically justified by the local contractibility of certain maps, are designed to take advantage of good initial estimates of the unknown parameters. They can be applied to the signature extension problem, in which good initial estimates of the unknown parameters are obtained from segments which are geographically near the segments from which the unlabeled samples are taken. Additional problems to which these methods are applicable include: estimation of proportions and adaptive classification (estimation of mean signatures and covariances).

Walker, H. F.↗
NASA NTRS2003

Fault Creep along the Southern San Andreas from Interferometric Synthetic Aperture Radar, Permanent Scatterers, and Stacking

Interferometric synthetic aperture radar (InSAR) provides a practical means of mapping creep along major strike-slip faults. The small amplitude of the creep signal (less than 10 mm/yr), combined with its short wavelength, makes it difficult to extract from long time span interferograms, especially in agricultural or heavily vegetated areas. We utilize two approaches to extract the fault creep signal from 37 ERS SAR images along the southem San Andreas Fault. First, amplitude stacking is utilized to identify permanent scatterers, which are then used to weight the interferogram prior to spatial filtering. This weighting improves correlation and also provides a mask for poorly correlated areas. Second, the unwrapped phase is stacked to reduce tropospheric and other short-wavelength noise. This combined processing enables us to recover the near-field (approximately 200 m) slip signal across the fault due to shallow creep. Displacement maps fiom 60 interferograms reveal a diffuse secular strain buildup, punctuated by localized interseismic creep of 4-6 mm/yr line of sight (LOS, 12-18 mm/yr horizontal). With the exception of Durmid Hill, this entire segment of the southern San Andreas experienced right-lateral triggered slip of up to 10 cm during the 3.5-year period spanning the 1992 Landers earthquake. The deformation change following the 1999 Hector Mine earthquake was much smaller (4 cm) and broader than for the Landers event. Profiles across the fault during the interseismic phase show peak-to-trough amplitude ranging from 15 to 25 mm/yr (horizontal component) and the minimum misfit models show a range of creeping/locking depth values that fit the data.

Lyons, Suzanne↗
NASA NTRS2016

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗
NASA NTRS2015

Automated Segmentation and Classification of Coral Using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the worlds coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗
DOE OSTI2021

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Abstract Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO 3 /Ge, (2) La 0.8 Sr 0.2 FeO 3 thin films, and (3) MoO 3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.

36 MATERIALS SCIENCE↗