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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 217 records · Page 12

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NASA NTRS1973

In situ spectroradiometric quantification of ERTS data

The author has identified the following significant results. A multispectral photo analysis of the Willcox Playa was also made using ERTS imagery on three consecutive overpasses. The results of the analysis showed that a time sequential multispectral color presentation relates color to environmental changes as a function of time, rather than spectral changes for any single date. Since the primary mission of ERTS is to detect changes in the environment, it is believed that this mode of data reduction would be employed further in conjunction with the conventional techniques.

Yost, E.↗

NASA NTRS2022

The hierarchical organization of autocatalytic reaction networks and its relevance to origin of life

Abiogenesis has long been suspected to require that chemical reaction networks contain multiple autocatalytic cores, but little is known about what features of these networks allow for the gradual accretion of complexity. To identify realistic scenarios for the emergence of life-like properties, we develop the concept of a seed-dependent autocatalytic system (SDAS), which is a subnetwork that can autocatalytically self-maintain given a flux of food, but cannot be initiated by food alone. Rather, initiation of SDASs requires the transient introduction of chemical “seeds”. We show that, depending on the topological relationship of SDASs in a chemical reaction network, a food-driven system can accrete complexity in a historically contingent manner, governed by rare seeding events. We develop new algorithms for detecting and analyzing SDASs in chemical reaction databases and describe parallels between multi-SDAS networks and biological ecosystems. Applying our algorithms to both an abiotic reaction network and a biochemical one, each driven by a set of simple food chemicals, we detect SDASs that are organized as trophic tiers, of which the higher tier can be seeded by relatively simple chemicals if the lower tier is already activated. This indicates that sequential activation of trophically organized SDASs by seed chemicals that are not much more complex than what already exist could be a mechanism of gradual complexification from relatively simple abiotic reactions to more complex life-like systems. Interestingly, in both reaction networks, higher-tier SDASs include chemicals that might alter emergent features of chemical systems and could serve as early targets of selection. Our analysis provides computational tools for analyzing very large chemical/biochemical reaction networks and suggests new approaches to studying abiogenesis in the lab.

Zhen Peng↗

DOE OSTI2020

Correlation of Advanced Accelerated Stress Testing with Polyamide-Based Photovoltaic Backsheet Field-Failures

Cracking of polyamide (PA)-based photovoltaic (PV) backsheet materials has been widely reported for field-aged modules. Failure was not detected by conventional accelerated stress tests (ASTs), which lacked the necessary combination of stress factors and/or factor sequences. PA-based AAA backsheet cracking has since been reproduced through combined and sequential stress testing. Planar- and cross-sectional-optical microscopy as well as Fourier-transform Infrared Spectroscopy (FTIR), have been used to elucidate the mechanical and chemical changes which lead to failure of the backsheet. Field-aged backsheet samples demonstrating failure in various climates (including locations in China and Italy) are also analyzed. Through the analysis, a comparison is made between the different stress testing protocols and the field-aged samples to validate relevance of the advanced stress tests. It is shown that the changes induced through combined-accelerated stress testing (C-AST) were most representative of changes induced by the field, supporting the relevance of C-AST and providing validity for the test protocol.

14 SOLAR ENERGY↗

DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1981

The Fabry-Perot Interferometer on Dynamics Explorer

The Fabry-Perot Interferometer (FPI) on Dynamics Explorer is a remote sensing instrument designed to measure the temperature, meridional wind and density of the metastable atoms O(1S) and O(1D), and the ion O(plus)(2P) in the thermosphere. The measurements are made with a stable, high resolution Fabry-Perot etalon, which performs a wavelength analysis on the light detected from the thermospheric emission features by spatially scanning the interference fringe plane with a multichannel array detector. The wavelength analysis characterizes the Doppler line profile of the emitting species. A sequential altitude scan, performed by a commandable horizon scan mirror, provides a cross-sectional view of the thermodynamic and dynamic state of the thermosphere below the orbit of the Dynamics Explorer B satellite.

Hays, P. B.↗

NASA NTRS1991

Solar system exploration in the 1990's and beyond

Exploration of Venus by Magellan and Pioneer Venus continues. Additionally, Galileo's Venus-earth-earth gravity assist trajectory to Jupiter has already provided targets of opportunity at Venus and at earth. Additional opportunities exist during the October 29, 1991, flyby of the asteroid Gaspra, the second earth gravity assist on December 8, 1992, and the flyby of the asteroid Ida on October 18, 1993. A fully successful mission through entry of the Jupiter atmospheric probe is anticipated in 1995/1996. The buildup of the Mars Observer spacecraft for a September 1992 launch is accelerating, and the development of the CRAF spacecraft and the orbiter for the NASA-ESA Cassini mission to Saturn is proceeding. Strategic plans to continue this ambitious program include early initiation of a phased program to detect other planetary systems; start of a program of low-cost, near-earth missions beginning with a near-earth asteroid rendezvous; continuation of moon-Mars exploration using sequentially launched single experiment Lunar Scouts and clusters of small landers to build up a Mars network of 16 stations; and finally, start of a major mission late in the century to explore Neptune and/or Pluto.

Huntress, Wesley T., Jr.↗

NASA NTRS1992

Inertial navigation sensor integrated obstacle detection system

A system that incorporates inertial sensor information into optical flow computations to detect obstacles and to provide alternative navigational paths free from obstacles. The system is a maximally passive obstacle detection system that makes selective use of an active sensor. The active detection typically utilizes a laser. Passive sensor suite includes binocular stereo, motion stereo and variable fields-of-view. Optical flow computations involve extraction, derotation and matching of interest points from sequential frames of imagery, for range interpolation of the sensed scene, which in turn provides obstacle information for purposes of safe navigation.

Bhanu, Bir↗

DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗

DOE OSTI2022

Learning high-dimensional parametric maps via reduced basis adaptive residual networks

We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_v$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.

42 ENGINEERING↗

DOE OSTI2024

Enhanced steam oxidation resistance of uranium nitride nuclear fuel pellets

Here, the steam oxidation resistance of UN and UN-(20 vol%)ZrN fuel pellets is evaluated to enhance understanding of steam corrosion mechanisms in advanced nuclear fuel materials. In situ neutron diffraction shows the modified UN fuel pellets form a (U 0.77 ,Zr 0.23 )N solid-solution and the sole crystalline oxidation product detected in bulk is (U 0.77 ,Zr 0.23 )O 2 . U 2 N 3 is not detected in significant quantities during the steam oxidation of UN or (U 0.77 ,Zr 0.23 )N and stable lattice parameters show that hydriding does not take place. Steam oxidation rates, obtained via sequential Rietveld refinement show how (U 0.77 ,Zr 0.23 )N has a higher activation energy (79 ± 1 kJmol -1 vs. 50 ± 5 kJmol -1 ), higher onset temperature (430 °C vs. 400 °C) and slower reaction rates for steam oxidation up to 616 °C, than pure UN. Throughout, both UN and (U 0.77 ,Zr 0.23 )N exhibit linear (non-protective) oxidation kinetics, signifying that degradation of the fuel pellets is caused by the evolution of gaseous products at the interface followed by oxide scale spallation. This quantitative and mechanistic understanding of material degradation enables better defined operating regimes and points towards (U,Zr)N solid solutions as a promising strategy for the design of advanced nuclear fuel materials with enhanced steam corrosion resistance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗

NASA NTRS1992

Two endogenous proteins that induce cell wall extension in plants

Plant cell enlargement is regulated by wall relaxation and yielding, which is thought to be catalyzed by elusive "wall-loosening" enzymes. By employing a reconstitution approach, we found that a crude protein extract from the cell walls of growing cucumber seedlings possessed the ability to induce the extension of isolated cell walls. This activity was restricted to the growing region of the stem and could induce the extension of isolated cell walls from various dicot stems and the leaves of amaryllidaceous monocots, but was less effective on grass coleoptile walls. Endogenous and reconstituted wall extension activities showed similar sensitivities to pH, metal ions, thiol reducing agents, proteases, and boiling in methanol or water. Sequential HPLC fractionation of the active wall extract revealed two proteins with molecular masses of 29 and 30 kD associated with the activity. Each protein, by itself, could induce wall extension without detectable hydrolytic breakdown of the wall. These proteins appear to mediate "acid growth" responses of isolated walls and may catalyze plant cell wall extension by a novel biochemical mechanism.

Non-NASA Center↗

NASA NTRS2015

Effect of DM Actuator Errors on the WFIRST/AFTA Coronagraph Contrast Performance

The WFIRST/AFTA 2.4 m space telescope currently under study includes a stellar coronagraph for the imaging and the spectral characterization of extrasolar planets. The coronagraph employs two sequential deformable mirrors (DMs) to compensate for phase and amplitude errors in creating dark holes. DMs are critical elements in high contrast coronagraphs, requiring precision and stability measured in picometers to enable detection of Earth-like exoplanets. Working with a low-order wavefront-sensor the DM that is conjugate to a pupil can also be used to correct low-order wavefront drift during a scientific observation. However, not all actuators in a DM have the same gain. When using such a DM in low-order wavefront sensing and control subsystem, the actuator gain errors introduce high-spatial frequency errors to the DM surface and thus worsen the contrast performance of the coronagraph. We have investigated the effects of actuator gain errors and the actuator command digitization errors on the contrast performance of the coronagraph through modeling and simulations, and will present our results in this paper.

adaptive optics↗

NASA NTRS1980

Test vectors development and optimization for a microprocessor

This paper describes a method for generating and optimizing test vectors for a microprocessor, with the aid of a fault simulator implemented entirely by hardware. The development and optimization of test vectors has been done on a tester, with the fault simulator plugged directly into the test head. The fault simulator is capable of automatically injecting over a thousand single or multiple stuck faults in the sequential and combinatorial parts of the microprocessor. The test vectors developed by a programmer working interactively with the tester were applied through the tester to the fault simulator, and the percent of faults detected was measured. The vectors were developed and optimized for the 1802 microprocessor, with the objective of detecting 100% of the single stuck faults with a minimum set of vectors. Experimental results show that 99.7% of the single stuck faults are being detected with approximately 14,000 vectors.

Timoc, C. C.↗

NASA NTRS2016

A Collision Avoidance Strategy for a Potential Natural Satellite around the Asteroid Bennu for the OSIRIS-REx Mission

The cadence of proximity operations for the OSIRIS-REx mission may have an extra induced challenge given the potential of the detection of a natural satellite orbiting the asteroid Bennu. Current ground radar observations for object detection orbiting Bennu show no found objects within bounds of specific size and rotation rates. If a natural satellite is detected during approach, a different proximity operation cadence will need to be implemented as well as a collision avoidance strategy for mission success. A collision avoidance strategy will be analyzed using the Wald Sequential Probability Ratio Test.

satellite↗

NASA NTRS2016

A Collision Avoidance Strategy for a Potential Natural Satellite Around the Asteroid Bennu for the OSIRIS-REx Mission

bennu↗

NASA NTRS2022

Biological Oxidation of Fe(II)-Bearing Smectite by Microaerophilic Iron Oxidizer Sideroxydans lithotrophicus Using Dual Mto and Cyc2 Iron Oxidation Pathways

Fe(II) clays are common across many environments, making them a potentially significant microbial substrate, yet clays are not well established as an electron donor. Therefore, we explored whether Fe(II)-smectite supports the growth of Sideroxydans lithotrophicus ES-1, a microaerophilic Fe(II)-oxidizing bacterium (FeOB), using synthesized trioctahedral Fe(II)-smectite and 2% oxygen. S. lithotrophicus grew substantially and can oxidize Fe(II)-smectite to a higher extent than abiotic oxidation, based on X-ray near-edge spectroscopy (XANES). Sequential extraction showed that edge-Fe(II) is oxidized before interior-Fe(II) in both biotic and abiotic experiments. The resulting Fe(III) remains in smectite, as secondary minerals were not detected in biotic and abiotic oxidation products by XANES and Mössbauer spectroscopy. To determine the genes involved, we compared S. lithotrophicus grown on smectite versus Fe(II)-citrate using reverse-transcription quantitative PCR and found that cyc2 genes were highly expressed on both substrates, while mtoA was upregulated on smectite. Proteomics confirmed that Mto proteins were only expressed on smectite, indicating that ES-1 uses the Mto pathway to access solid Fe(II). We integrate our results into a biochemical and mineralogical model of microbial smectite oxidation. This work increases the known substrates for FeOB growth and expands the mechanisms of Fe(II)-smectite alteration in the environment.

smectite↗

NASA NTRS1974

Boundary and object detection in real world images

A solution to the problem of automatic location of objects in digital pictures by computer is presented. A self-scaling local edge detector which can be applied in parallel on a picture is described. Clustering algorithms and boundary following algorithms which are sequential in nature process the edge data to locate images of objects.

Yakimovsky, Y.↗