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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 NTRS1982

High accuracy OMEGA timekeeping

The Smithsonian Astrophysical Observatory (SAO) operates a worldwide satellite tracking network which uses a combination of OMEGA as a frequency reference, dual timing channels, and portable clock comparisons to maintain accurate epoch time. Propagational charts from the U.S. Coast Guard OMEGA monitor program minimize diurnal and seasonal effects. Daily phase value publications of the U.S. Naval Observatory provide corrections to the field collected timing data to produce an averaged time line comprised of straight line segments called a time history file (station clock minus UTC). Depending upon clock location, reduced time data accuracies of between two and eight microseconds are typical.

Imbier, E. A.↗
NASA NTRS1994

Comparison of TOPEX sea surface heights and tide gauge sea levels

TOPEX sea surface height data from the first 300 days of the mission are compared to sea level data from 71 tide gauges. The initial comparison uses sea surface height data processed according to standard procedures as defined in the users handbook. It is found that the median correlations for island and for coastal tide gauges are 0.53 and 0.42, respectively. The analogous root mean square (RMS) differences between the two data sets are 7.9 and 10.4 cm. The comparisons improve significantly when a 60-day harmonic is fit to the differences and removed. This period captures aliased M(sub 2) and S(sub 2) tidal energy that is not removed by the tide model. Making this correction and smoothing the sea surface height data over 25-km along-track segments results in median correlations of 0.58 and 0.46 for the islands and coastal stations, and median RMS differences of 5.8 and 7.7 cm, respectively. Removing once per revolution signals from the sea surface heights results in degraded comparisons with the sea levels. It is also found that a number of stations have poor comparisons due to propagating signals that introduce temporal lags between the altimeter and tide gauge time series. A final comparison is made by eliminating stations where this propagation effect is large, discarding two stations that are suspected to have problems with the sea level data, smoothing over 10-day intervals, and restricting attention to islands gauges. This results in a set of 552 data pairs that have a correlation of 0.66 and a RMS difference of 4.3 cm. The conclusion is that on timescales longer than about 10 days the RMS sea surface height errors are less than or of the order of several centimeters.

Mitchum, Gary T.↗
NASA NTRS1989

Summary of 1987 and 1988 manatee aerial surveys at Kennedy Space Center

Aerial surveys of manatees conducted since 1977 at Kennedy Space Center (KSC) have provided a very useful and cost effective monitoring tool in the assessment of abundance and distribution of manatees in the northern Banana River. Data collected in the mid 1980's as part of the KSC Environmental Monitoring Program indicated that the numbers of manatees utilizing the northern Banana River had increased dramatically from earlier years and that the animals appeared to have changed their distribution patterns within the area as well (Provancha and Provancha 1988). United States Fish and Wildlife Service (USFWS) and Florida Department of Natural Resources (FLDNR) conducted bimonthly aerial surveys in 1986 for the entire Florida east coast. Their data clearly show that the Banana River has the highest concentration of manatees during the non-winter months when compared to all other segments of the east coast surveys (B. Wiegle/FLDNR, unpublished data). They further show that, in spring, an average of 71 percent of the manatees in Brevard county were located in the Banana River. During that period 85 percent of the animals were north of the NASA Causeway (State Road (SR) 402) in the KSC security zone. These data indicate the importance of the KSC waters to the Florida east coast manatee population. We reinitiated KSC surveys in 1987 to document distributions and numbers of manatees during the spring influx. Aerial censuses were continued throughout the year in 1988 and this report provides a summary of our findings for the two years.

Provancha, Jane A.↗
DOE OSTI2024

The Number and Pattern of Viral Genomic Reassortments are not Necessarily Identifiable from Segment Trees

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Since large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the “first-infection tree,” a potentially counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our observation of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment, but also the four additional situations that can complicate the inference of reassortment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗
NASA NTRS2010

Image Segmentation Analysis for NASA Earth Science Applications

NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

Tilton, James C.↗
NASA NTRS1972

Crop identification and acreage measurement utilizing ERTS imagery

There are no author-identified significant results in this report. The microdensitometer will be used to analyze data acquired by ERTS-1 imagery. The classification programs and software packages have been acquired and are being prepared for use with the information as it is received. Photo and digital tapes have been acquired for coverage of virtually 100 percent of the test site areas. These areas are located in South Dakota, Idaho, Missouri, and Kansas. Hass 70mm color infrared, infrared, black and white high altitude aerial photography of the test sites is available. Collection of ground truth for updating the data base has been completed and a computer program written to count the number of fields and give total acres by size group for the segments in each test site. Results are given of data analysis performed on digitized data from densitometer measurements of fields of corn, sugar, beets, and alfalfa in Kansas.

Vonsteen, D. H.↗
DOE OSTI2022

Statistical characterization of experimental magnetized liner inertial fusion stagnation images using deep-learning-based fuel–background segmentation

Significant variety is observed in spherical crystal x-ray imager (SCXI) data for the stagnated fuel–liner system created in Magnetized Liner Inertial Fusion (MagLIF) experiments conducted at the Sandia National Laboratories Z-facility. As a result, image analysis tasks involving, e.g., region-of-interest selection (i.e. segmentation), background subtraction and image registration have generally required tedious manual treatment leading to increased risk of irreproducibility, lack of uncertainty quantification and smaller-scale studies using only a fraction of available data. We present a convolutional neural network (CNN)-based pipeline to automate much of the image processing workflow. This tool enabled batch preprocessing of an ensemble of N scans = 139 SCXI images across N exp = 67 different experiments for subsequent study. The pipeline begins by segmenting images into the stagnated fuel and background using a CNN trained on synthetic images generated from a geometric model of a physical three-dimensional plasma. The resulting segmentation allows for a rules-based registration. Our approach flexibly handles rarely occurring artifacts through minimal user input and avoids the need for extensive hand labelling and augmentation of our experimental dataset that would be needed to train an end-to-end pipeline. Here we also fit background pixels using low-degree polynomials, and perform a statistical assessment of the background and noise properties over the entire image database. Our results provide a guide for choices made in statistical inference models using stagnation image data and can be applied in the generation of synthetic datasets with realistic choices of noise statistics and background models used for machine learning tasks in MagLIF data analysis. We anticipate that the method may be readily extended to automate other MagLIF stagnation imaging applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1979

Preliminary results of the mission profile life test of a 30 cm Hg bombardment thruster

The paper deals with some preliminary results of the Mission Profile Life Test planned to conduct a program of long-term test segments of 30-cm diameter thrusters and power processing units under computer control. Thruster performance data and other operational characteristics taken at various times during a test segment are compared and the results are evaluated in light of the life-timing mechanisms. Thruster control algorithms are also presented. The first test segment completed 2700 hr of a planned 4000 hr test with a J-series 30-cm thruster. The last 1600 hr used a functional model power processing unit (PPU) operated in vacuum. The thruster-PPU was controlled by a computer with software developed to control start-ups, throttling, and variety of off-normal conditions.

Bechtel, R. T.↗
NASA NTRS1985

The National Aeronautics and Space Administration (NASA) Tracking and Data Relay Satellite System (TDRSS) program Economic and programmatic, considerations

The Tracking and Data Relay Satellite System (TDRSS) represents the principal element of a new space-based tracking and communication network which will support NASA spaceflight missions in low earth orbit. In its complete configuration, the TDRSS network will include a space segment consisting of three highly specialized communication satellites in geosynchronous orbit, a ground segment consisting of an earth terminal, and associated data handling and control facilities. The TDRSS network has the objective to provide communication and data relay services between the earth-orbiting spacecraft and their ground-based mission control and data handling centers. The first TDRSS spacecraft has been now in service for two years. The present paper is concerned with the TDRSS experience from the perspective of the various programmatic and economic considerations which relate to the program.

Aller, R. O.↗
NASA NTRS1993

Apollo 16 core 60013/14 as a product of path 1 and path 2 regolith evolution processes

We have made a petrographic study of 12 samples along the 62 cm profile of Apollo 16 core 60013/14 taken at Station 10 near the lunar module. The basic core description and maturity variations have been discussed earlier. This core is important because it enables a stratigraphic comparison with nearby cores 60001-7 and 60009/10. These cores form a triangle with sides 35-40 m. FMR data show a maturity-match between 60009/10 and 60013/14. Unfortunately, segments of the core 60001-7 are too disturbed for any cm scale comparison. We present new data on the grain size distribution and the modal petrography for six samples from six levels of 60013 using the same approach and rationale used for 60014. We have combined the new data with our previous data on 60014 to plot the variations of grain size distributions and grain type abundances, in the 500-1000 m and 90-150 micrometers size fractions, with depth. Agglutinate abundances match the I sub s/FeO profile and both show a significant break at a depth of about 45 cm, with lower maturity material below that point. Grain size distributions show that for all analyzed samples, the mean grain size is below 90 micrometers. The two lowest samples are somewhat more coarse-grained than the rest of the core. The break at about 45 cm is the most prominent feature of the core and was described during the initial dissection as a pronounced change in color from dark to light. It now seems clear that this break is primarily one of maturity rather than of bulk chemical composition.

Basu, A.↗
DOE OSTI2022

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2002

[High Pressure Gas Tanks]

Four high-pressure gas tanks, the basis of this study, were especially made by a private contractor and tested before being delivered to NASA Kennedy Space Center. In order to insure 100% reliability of each individual tank the staff at KSC decided to again submit the four tanks under more rigorous tests. These tests were conducted during a period from April 10 through May 8 at KSC. This application further validates the predictive safety model for accident prevention and system failure in the testing of four high-pressure gas tanks at Kennedy Space Center, called Continuous Hazard Tracking and Failure Prediction Methodology (CHTFPM). It is apparent from the variety of barriers available for a hazard control that some barriers will be more successful than others in providing protection. In order to complete the Barrier Analysis of the system, a Task Analysis and a Biomechanical Study were performed to establish the relationship between the degree of biomechanical non-conformities and the anomalies found within the system on particular joints of the body. This relationship was possible to obtain by conducting a Regression Analysis to the previously generated data. From the information derived the body segment with the lowest percentage of non-conformities was the neck flexion with 46.7%. Intense analysis of the system was conducted including Preliminary Hazard Analysis (PHA), Failure Mode and Effect Analysis (FMEA), and Barrier Analysis. These analyses resulted in the identification of occurrences of conditions, which may be becoming hazardous in the given system. These conditions, known as dendritics, may become hazards and could result in an accident, system malfunction, or unacceptable risk conditions. A total of 56 possible dendritics were identified. Work sampling was performed to observe the occurrence each dendritic. The out of control points generated from a Weighted c control chart along with a Pareto analysis indicate that the dendritics "Personnel not Wearing Proper Protective and Hose/tubing located in high-traffic area" which account for 59.18% of total dendritic frequency need to be addressed to reduce the chance of a hazard from occurring. However, the occurrences of some dendritics are more important than others. As a result immediate, from a Weighted c perspective, corrective action should be taken to ameliorate the cause of the Class A dendritic "Personnel located under suspended or moving loads" rather than just the most commonly occurring dendritics. In any case the vast majority of data obtained indicates that testing operations possess a relatively high degree of safety.

Quintana, Rolando↗
NASA NTRS1982

Advanced technology for earth observation - Data processing

The Experimental Land Observing System (ELOS) is to demonstrate major advances in space-based land observation. Program success will depend upon successful demonstration of both space-segment performance and ground-system capabilities. Increases in data-rate, volume, and processing requirements present substantial challenges to the ground data processing system. ELOS is based on the utilization of the multispectral linear array (MLA) sensor, a solid-state, electronically scanned instrument. Current MLA instrument designs offer a number of capabilities beyond those provided in the Landsat imaging devices. These capabilities include greater resolution, inherent registration, onboard calibration, off-nadir pointing capability, and fore/aft pointing capability. The proposed MLA flight mission scenario will be one of continued growth and will include flights on the Space Transportation System. Attention is also given to ground-to-ground data transport, data capture, and the use of high speed computers.

Heffner, P.↗
DOE OSTI2024

STRUCTURAL MODELING TO SUPPORT POST-YIELD ACCEPTANCE CRITERIA FOR SPENT NUCLEAR FUEL CLADDING

Spent nuclear fuel (SNF) is evaluated for structural failure during storage and transportation scenarios. The U.S. Department of Energy’s Spent Fuel and Waste Science and Technology (SFWST) program has sponsored significant research in quantifying mechanical loads on SNF during storage and transportation scenarios using experimental and modeling methods. The SFWST program has also performed significant research on measuring the mechanical behavior of irradiated SNF as defueled cladding segments and cladding with fuel pellets to measure composite behavior. This paper considers some of the key material data from the Sibling Pin testing and uses structural modeling and analysis methods that have been informed by testing to consider post-yield acceptance criteria for SNF cladding structural analysis. Test data published by Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory (PNNL) are the foundation for informing the material behavior of the models developed in this study. In particular, four-point bend (4PB) tests of fueled and defueled cladding segments provide significant information about the bending failure mode of SNF. ORNL’s 4PB test data is on fueled cladding segments, so the composite behavior of SNF is demonstrated. This paper describes PNNL’s coincident beam model that was developed to approximate the composite behavior of SNF. This paper also presents PNNL’s structural dynamic finite element models of a cask tip-over scenario, which is predicted to cause the strongest mechanical loads on SNF of all postulated storage and transportation scenarios. SNF bending loads predicted in the cask tip-over scenario and cladding acceptance criteria beyond yield are considered, with justification based on the Sibling Pin test data. ASME Boiler and Pressure Vessel code stress intensity limits are also considered. The ultimate goal of this work is to aid in the justification of structural acceptance criteria for SNF cladding beyond the cladding’s irradiated yield strength for use in structural analysis of all storage and transportation scenarios.

Klymyshyn, Nicholas A.↗
NASA NTRS1994

Graphic Server: A real time system for displaying and monitoring telemetry data of several satellites

Known as a Graphic Server, the system presented was designed for the control ground segment of the Telecom 2 satellites. It is a tool used to dynamically display telemetry data within graphic pages, also known as views. The views are created off-line through various utilities and then, on the operator's request, displayed and animated in real time as data is received. The system was designed as an independent component, and is installed in different Telecom 2 operational control centers. It enables operators to monitor changes in the platform and satellite payloads in real time. It has been in operation since December 1991.

Douard, Stephane↗
NASA NTRS1993

Security aspects of space operations data

This paper deals with data security. It identifies security threats to European Space Agency's (ESA) In Orbit Infrastructure Ground Segment (IOI GS) and proposes a method of dealing with its complex data structures from the security point of view. It is part of the 'Analysis of Failure Modes, Effects Hazards and Risks of the IOI GS for Operations, including Backup Facilities and Functions' carried out on behalf of the European Space Operations Center (ESOC). The security part of this analysis has been prepared with the following aspects in mind: ESA's large decentralized ground facilities for operations, the multiple organizations/users involved in the operations and the developments of ground data systems, and the large heterogeneous network structure enabling access to (sensitive) data which does involve crossing organizational boundaries. An IOI GS data objects classification is introduced to determine the extent of the necessary protection mechanisms. The proposal of security countermeasures is oriented towards the European 'Information Technology Security Evaluation Criteria (ITSEC)' whose hierarchically organized requirements can be directly mapped to the security sensitivity classification.

Schmitz, Stefan↗