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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

SeaDAS Tutorial: Applying Color Ramps to ACOLITE Turbidity Products

This tutorial provides step-by-step instructions for applying color ramps and legends to ACOLITE turbidity products derived from the Dogliotti turbidity equation. The user inputs their own image and the resulting product is a colorized visualization of turbidity. This visualization provides the user with a better understanding of turbidity in their image and can be used to support further analysis of turbidity patterns.

Kelly Young↗
DOE OSTI2022

PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.

Xu, Weihuang↗
DOE OSTI2021

Identification and correction of temporal and spatial distortions in scanning transmission electron microscopy

Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacements of atomic columns from high-symmetry positions can be used to map strain, polarization, octahedra tilts, and other physical and chemical order parameter fields. The latter can be used as inputs into mesoscopic and atomistic models, providing insight into the correlative relationships and generative physics of materials on the atomic level. However, these quantitative applications of STEM necessitate understanding the microscope induced image distortions and developing the pathways to compensate them both as part of a rapid calibration procedure for in situ imaging, and the post-experimental data analysis stage. Here, we explore the spatiotemporal structure of the microscopic distortions in STEM using multivariate analysis of the atomic trajectories in the image stacks. Based on the behavior of principal component analysis (PCA), we develop the Gaussian process (GP)-based regression method for quantification of the distortion function. The limitations of such an approach and possible strategies for implementation as a part of in-line data acquisition in STEM are discussed. Here, the analysis workflow is summarized in a Jupyter notebook that can be used to retrace the analysis and analyze the reader's data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Real-Time Diagnostics of 2D Crystal Transformations by Pulsed Laser Deposition: Controlled Synthesis of Janus WSSe Monolayers and Alloys

Energetic processing methods such as hyperthermal implantation hold special promise to achieve the precision synthesis of metastable two-dimensional (2D) materials such as Janus monolayers; however, they require precise control. Here, we report a feedback approach to reveal and control the transformation pathways in materials synthesis by pulsed laser deposition (PLD) and apply it to investigate the transformation kinetics of monolayer WS 2 crystals into Janus WSSe and WSe 2 by implantation of Se clusters with different maximum kinetic energies (<42 eV/Se-atom) generated by laser ablation of a Se target. Real-time Raman spectroscopy and photoluminescence are used to assess the structure, composition, and optoelectronic quality of the monolayer crystal as it is implanted with well-controlled fluxes of selenium for different kinetic energies that are regulated with in situ ICCD imaging, ion probe, and spectroscopy diagnostics. First-principles calculations, XPS, and atomic-resolution HAADF STEM imaging are used to understand the intermediate alloy compositions and their vibrational modes to identify transformation pathways. The real-time kinetics measurements reveal highly selective top-layer conversion as WS 2 transforms through WS 2(1–x) Se 2x alloys to WSe 2 and provide the means to adjust processing conditions to achieve fractional and complete Janus WSSe monolayers as metastable transition states. The general approach demonstrates a real-time feedback method to achieve Janus layers or other metastable alloys of the desired composition, and a general means to adjust the structure and quality of materials grown by PLD, addressing priority research directions for precision synthesis with real-time adaptive control.

2D materials↗
DOE OSTI2020

Chapter 6: Luminescence and Thermal Imaging of Thin-Film Photovoltaic Materials, Devices, and Modules

In this chapter, we describe luminescence and thermal imaging in the context of thin-film PV devices. First, we discuss both traditional and developing experimental designs, including dark LIT (DLIT), illuminated LIT, PL using different illumination configurations, traditional EL performed under forward -bias current injection, and contactless EL induced with sub -cell illumination patterns. Next, we provide some brief theoretical background to highlight relationships between fundamental materials properties and the resulting luminescence and thermal images. Finally, we give examples of thin-film PV PL, EL, and LIT images to demonstrate the utility of these imaging methods ranging from understanding the microstructure of active layers to managing the quality and reliability of full PV modules.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

Diagnostic development and needs for laser driven MeV x-ray radiography

Laser-driven MeV x-ray radiography of dynamic, dense objects demands a small, high flux source of energetic x-rays to generate an image with sufficient quality. Understanding the multi-MeV x-ray spectrum underscores the ability to extrapolate from the current laser sources to new future lasers that might deploy this radiography modality. Here, in this study, we present a small study of the existing x-ray diagnostics and techniques. We also present work from National Ignition Facility-Advanced Radiographic Capability, where we deploy three diagnostics to measure the x-ray spectrum up to 30 MeV. Finally, we also discuss the needs and developments of two new diagnostics: a single crystal scintillator spectrometer and a fast decay activation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

LATTE: open-source, high-performance traveltime computation, tomography and source location in acoustic and elastic media

Traveltime-based tomography and source location are fundamental approaches for imaging subsurface structures and understanding the spatiotemporal distribution of seismicity from local to global scales. We present an open-source, high-performance framework integrating eikonal equation solvers and adjoint-state theory for traveltime computation, velocity tomography, source location and joint tomography-location in 2-D/3-D acoustic and elastic media. We introduce novel regularization schemes based on total generalized p-variation, structural similarity and multitask machine learning to enhance the fidelity and interpretability of inverted models and source locations. Key features of our implementation also include the ability to leverage both absolute-difference and double-difference traveltime misfits for high-fidelity velocity tomography and source parameter estimation; support for traveltime computation and inversion in diverse 2-D/3-D scenarios with arbitrary source and receiver distributions; and a perturbation-based optimal step-size estimation method to reduce computational costs. In addition, our implementation employs shared-memory and distributed-memory parallelization to provide an efficient solution for traveltime computation, tomography, and source location. In conclusion, we validate the efficacy and accuracy of our approach through multiple synthetic data examples.

58 GEOSCIENCES↗
DOE OSTI2020

Sampling Subjective Polygons for Patch-Based Deep Learning Land-Use Classification in Satellite Images

Model generalization remains a key challenge in the analysis of large amounts of heterogeneous satellite image data. One major limiting factor in developing generalizable models, in the context of supervised learning, is the lack of high quality training datasets. A model's capacity to perform well on new data is often inhibited by imbalance and bias in the data that was used for training. This is especially a problem when using convolutional neural networks to classify urban land-use in satellite images. Notable dataset imbalance issues in this application include land-use type imbalance and image scene imbalance. To begin understanding these dataset imbalance problems in more detail, we develop and test a number of sampling methods for generating training image datasets from subjective training polygons for urban land-use classification. We investigate sampling at different point densities as a means to reduce content repetition and therefore content imbalance and bias in the training image dataset.

Arndt, Jacob↗
DOE OSTI2020

The Dark Energy Survey and Operations: Year 6 – The Finale

The Dark Energy Survey (DES) is an optical and near-infrared imaging survey aimed at understanding the accelerating expansion of the universe using four complementary methods: weak gravitational lensing, galaxy cluster counts, baryon acoustic oscillations, and Type Ia supernovae. To perform the 6-season 5000 sq-degree wide field and the 5-season 30 sq-degree supernova surveys, the DES Collaboration built the Dark Energy Camera (DECam), a 3 square-degree, 570-Megapixel CCD camera that was installed at the prime focus of the Blanco 4-meter telescope at the Cerro Tololo Inter-American Observatory (CTIO). DES started its observations in “Year 1” (Y1) on Aug. 31, 2013 and completed its sixth and final observing season on January 9, 2019. This paper describes DES Y6, the survey strategy, an outline of the survey operations procedures, the efficiency of operations and the causes of lost observing time. It provides details about the quality of the final season's data, and a summary of the Y1-Y6 observing. It contains a brief description of the last night of observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Report on High Energy Arcing Fault Experiments

This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena for medium voltage electrical switchgear containing aluminum conductors. This report covers full-scale laboratory experiments using representative nuclear power plant (NPP) three-phase electrical equipment. Electrical, thermal, and pressure data were recorded for each experiment and documented in this report. This report covers four of the fourteen planned medium voltage electrical enclosure experiments. Subsequent reports will document the additional experiments performed in the future. The experiments were performed at KEMA Labs located in Chalfont, Pennsylvania. The experimental design, setup, and execution were completed by staff from the United States Nuclear Regulatory Commission (NRC), the National Institute of Standards and Technology (NIST), Sandia National Laboratories (SNL) and KEMA. In addition, representatives from the Electric Power Research Institute (EPRI) observed some of the experimental setups and execution. The HEAF experiments were performed on four near-identical units of General Electric metal-clad medium voltage switchgear. The three-phase arcing fault was initiated on the primary cable connection bus. All four experiments used the same system voltage (6.9 kV) but varied the current and duration. Real-time electrical operating conditions, including voltage, current and frequency, were measured during the experiments. Heat fluxes and incident energies were measured with plate thermometers and slug calorimeters at various locations around the electrical enclosures. Internal enclosure pressures were measured during the experiments. The experiments were documented with normal and high-speed videography, infrared imaging, and photography. Insights from the experimental series included timing information related to enclosure breach, event progression, mass loss measurements for electrodes and steel enclosures, peak pressure rise, particle analysis, along with visual and thermal imaging data to better understand and characterize the hazard. These results will be used in subsequent efforts to advance the state of knowledge related to HEAF.

42 ENGINEERING↗
DOE OSTI2022

Deep Learning At Depth: Estimating subsurface parameters from geophysical monitoring data

Geophysical imaging techniques are a non-invasive way to image the subsurface and understand both subsurface solid (rock/soil) and fluid property distributions and their evolution in time. Inversions of the geophysical data, such as Electrical Resistance Tomography (ERT) data, are solved to estimate the subsurface property distributions, such as conductivity, and many inversion techniques smooth out sharp gradients in rock or fluid property distributions. Sharp gradients in subsurface properties tend to be present in situations with complex subsurface structures, which are common in many subsurface applications. We have successfully demonstrated that it is possible to inform, or constrain, inversions with neural networks trained on synthetic data with complex subsurface structures. Initial results suggest this process may be optimizable to yield property distributions that better represent the true property distributions than the same inversion process without the neural network constraint. Future work would optimize the neural network performance for this application and then apply the synthetic-data trained neural network to real data to understand the utility and performance of this technique for real data sets.

47 OTHER INSTRUMENTATION↗
NASA NTRS1993

Engine materials characterization and damage monitoring by using x ray technologies

X ray attenuation measurement systems that are capable of characterizing density variations in monolithic ceramics and damage due to processing and/or mechanical testing in ceramic and intermetallic matrix composites are developed and applied. Noninvasive monitoring of damage accumulation and failure sequences in ceramic matrix composites is used during room-temperature tensile testing. This work resulted in the development of a point-scan digital radiography system and an in situ x ray material testing system. The former is used to characterize silicon carbide and silicon nitride specimens, and the latter is used to image the failure behavior of silicon-carbide-fiber-reinforced, reaction-bonded silicon nitride matrix composites. State-of-the-art x ray computed tomography is investigated to determine its capabilities and limitations in characterizing density variations of subscale engine components (e.g., a silicon carbide rotor, a silicon nitride blade, and a silicon-carbide-fiber-reinforced beta titanium matrix rod, rotor, and ring). Microfocus radiography, conventional radiography, scanning acoustic microscopy, and metallography are used to substantiate the x ray computed tomography findings. Point-scan digital radiography is a viable technique for characterizing density variations in monolithic ceramic specimens. But it is very limited and time consuming in characterizing ceramic matrix composites. Precise x ray attenuation measurements, reflecting minute density variations, are achieved by photon counting and by using microcollimators at the source and the detector. X ray computed tomography is found to be a unique x ray attenuation measurement technique capable of providing cross-sectional spatial density information in monolithic ceramics and metal matrix composites. X ray computed tomography is proven to accelerate generic composite component development. Radiographic evaluation before, during, and after loading shows the effect of preexisting volume flaws on the fracture behavior of composites. Results from one-, three-, five-, and eight-ply ceramic composite specimens show that x ray film radiography can monitor damage accumulation during tensile loading. Matrix cracking, fiber-matrix debonding, fiber bridging, and fiber pullout are imaged throughout the tensile loading of the specimens. In situ film radiography is found to be a practical technique for estimating interfacial shear strength between the silicon carbide fibers and the reaction-bonded silicon nitride matrix. It is concluded that pretest, in situ, and post-test x ray imaging can provide greater understanding of ceramic matrix composite mechanical behavior.

Baaklini, George Y.↗
NASA NTRS2003

Grooved Terrain on Ganymede: A Galileo-based Synthesis

Swaths of bright "grooved terrain" (sulci) on Ganymede are 10s to 100s of kilometers wide and cross-cut the older dark terrain, forming an intricate patchwork across 2/3 of Ganymede's surface. The view of grooved terrain developed from Voyager images is that bright cells are broad graben infilled by extrusion of relatively clean (silicate-poor) liquid water, warm ice, or icy slush, and then extended and faulted. Galileo imaging has greatly improved understanding of the emplacement history and geological implications of grooved terrain, supporting a rift-like model for its formation.

Pappalardo, Robert T.↗
NASA NTRS2012

Algorithm for Compressing Time-Series Data

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Hawkins, S. Edward, III↗
NASA NTRS2016

Spiral Structure and Differential Dust Size Distribution in the LkH(alpha) 330 Disk

Dust trapping accelerates the coagulation of dust particles, and, thus, it represents an initial step toward the formation of planetesimals. We report H-band (1.6 microns) linear polarimetric observations and 0.87 mm interferometric continuum observations toward a transitional disk around LkH(alpha) 330. As a result, a pair of spiral arms were detected in the H-band emission, and an asymmetric (potentially arm-like) structure was detected in the 0.87 mm continuum emission. We discuss the origin of the spiral arm and the asymmetric structure and suggest that a massive unseen planet is the most plausible explanation. The possibility of dust trapping and grain growth causing the asymmetric structure was also investigated through the opacity index (beta) by plotting the observed spectral energy distribution slope between 0.87 mm from our Submillimeter Array observation and1.3 mm from literature. The results imply that grains are indistinguishable from interstellar medium-like dust in the east side (beta = 2.0 +/- 0.5) but are much smaller in the west side beta = 0.7+0.5 -0.4, indicating differential dust size distribution between the two sides of the disk. Combining the results of near-infrared and submillimeter observations, we conjecture that the spiral arms exist at the upper surface and an asymmetric structure resides in the disk interior. Future observations at centimeter wavelengths and differential polarization imaging in other bands (Y-K) with extreme AO imagers are required to understand how large dust grains form and to further explore the dust distribution in the disk.

polarimetric observations↗
NASA NTRS1990

Volumetric imaging of space debris using sun microsystem's TAAC-1 application accelerator board

Analysis of space debris requires the researcher to have a three-dimensional spatial framework and understanding. By using computers to image and display the space debris volumes, the researcher is provided with an added visual analysis tool. This paper describes volumetric visualization of space debris. Two imaging approaches are used. The first method visualizes the cube faces of the volumetric image. The second method uses a ray-tracing technique to display surfaces within the volume. Three examples of space debris volumes are described.

Luetkemeyer, Kelly G.↗
NASA NTRS1984

The investigation of selected oceanographic applications of spaceborne synthetic-aperture radar

Synthetic aperture radar images obtained from Seasat and SIR-A showed that a number of oceanographic features were imaged in considerable detail, like internal waves, large ocean waves, bathymetric features, eddies, and slicks. the imaging mechanisms however, are not well understood, and for both SEASAT and SIR-A there are few supporting sea surface measurements to assist in the study of these imaging mechanisms. The SIR-B will conduct three separate experiments to provide a better understanding of the use of spaceborne SAR for imaging: (1) internal waves; (2) ocean surface waves, and (3) shallow water bathymetry. These experiments are chosen because they lead to possible applications for microwave remote sensing of the ocean surface and give a better understanding of the microwave/sea surface imaging mechanism.

Keyte, G. E.↗
NASA NTRSDate not supplied

Study of Coronal Heating in Solar Active Regions Using Wide-Field Imaging Spectroscopy: Hinode EIS Slot Observations

Understanding the frequency of heating events that keep the coronal plasma at several million Kelvin above the photospheric temperature of~ 6000K, is one of the most important problems in solar astrophysics. Spectroscopic observations of the Sun in the extreme ultraviolet (EUV) indicate that the coronal plasma reaches temperatures from 1 to 5 MK in active regions. It is also established that temperature in active regions can vary strongly with time and, moreover, contain sub-regions that evolve and develop separately. Tracking the spatio-temporal evolution of temperature requires continuous observation of the entire active region via imaging and spectroscopy. Traditional slit imaging spectroscopy probes plasma heating in solar active regions through observations of diagnostic emission lines and the resulting data are spectrally pure. Here, imaging is performed through rastering process, which severely limits co-temporal observations and often can be slow to miss events that evolve at other portions of the active region. In contrast, wide-field imaging spectroscopy offer simultaneous coverage of a large field of view as well as obtain spectral information in the same direction. This data suffers from spatial-spectral confusion, and are called spectroheliograms. Using the state-of-the-art inversion techniques that are developed recently, now spectroheliogram data can be unfolded to yield spectrally pure maps of large fields over long duration of observations. We use wide slit data, usually referred as ‘slot’, from the EUV Imaging Spectrometer (EIS) onboard Hinode satellite, focusing on active region observations. Here, we present our study of coronal heating in an active region using a long duration Hinode EIS slot observation.

Active region heating↗