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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach with Kernel Parameterization

We report N\non-blind deblurring (NBD) is a modeling method of the image deblurring problem in computer vision, where the blurring kernel is known or can be externally estimated. In this paper, we attempt to solve a parametric NBD problem, inspired by the simultaneous acquisition of ptychography and fluorescent imaging (FI). Ptychography is an imaging method that favors larger probes, i.e. convolutional kernels, while FI relies on a small probe for high resolution. Also, the kernel can be solved during ptychographic reconstruction. With Ptycho-FI using the same larger kernel, we can perform NBD on the blurred fluorescent images to achieve high-resolution FI, and thus speed up the experiments. To this end, we design a deep latent space deformation network that is directly parameterized by the kernel. The network consists of three components: encoder, deformer, and decoder, where the deformer is specifically meant to rectify the latent space representations of blurred images to a standard latent space, regardless of the kernel. The deformation network is trained with a two-stage training scheme. We conduct extensive experiments to confirm that our parametric model can adapt to drastically different blurring kernels and perform robust deblurring.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

42 ENGINEERING↗
DOE OSTI2026

Flight Arrival Data for Each Airport

This dataset contains flight arrival data for each airport, which serves as the input for generating load profiles and estimating energy needs and fleet and charger requirements. ![image](distribution-flight-arrivals.png) Distribution of annual flight arrivals across different airport categories.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluating Neural Radiance Fields for Commercial Satellite Video

We evaluate neural radiance fields (NeRFs) as a method for reconstructing 3D volumetric scenes from low Earth orbit satellite imagery. We leverage commercial satellite data to reconstruct a scene using existing software tools. In doing so, we identify difficulties in these mapping datasets for NeRF generation. We propose potential applications in geospatial intelligence for context and improved image interpretation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Elastic Stochastic Full Waveform Inversion (eSFWI)

This collaboration between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Chevron USA Inc., acting through its Chevron Technical Center division, aimed at developing next-generation computational methods for the Elastic Stochastic Full Waveform Inversion (eSFWI). Seismic imaging is heavily used in the oil and gas industry for identifying and operating subsurface reservoirs. Improved seismic imaging methods can improve productivity, lower costs, and improve operational and environmental safety. This CRADA demonstrated that new high-performance computing (HPC) architectures being rolled out over the next five years can enable unprecedented seismic imaging resolution when using eSFWI techniques to process active seismic data. An open-source computational mini-application was developed, capable of demonstrating near-peak performance for eSFWI algorithms on CPU and GPU enabled HPC platforms. Performance was demonstrated on LLNL HPC systems such as Lassen, as well as on Chevron systems. This project benefited Chevron USA Inc. by demonstrating the potential computational efficiency of their full waveform inversion capabilities used to characterize oil/gas reservoirs, which in turn benefits the public through potential increases in capabilities to perform analysis of leasing sites.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2020

A global DSCOVR/EPIC-based hourly/daily shortwave radiation/PAR dataset

Accurate characterization of the global downward shortwave (SW) and photosynthetically active radiation (PAR) is fundamental for Earth system modeling and global change research. Combined with a machine-learning method, we used the Earth Polychromatic Imaging Camera (EPIC) data onboard the Deep Space Climate Observatory (DSCOVR) to generate the global SW/PAR dataset at both hourly and daily temporal scales and 0.1degree×0.1degree spatial resolution. Covering a period from June, 2015 to June (about 4 years), 2019, the dataset consists of total SW/PAR and their direct and diffuse components. Our dataset will be valuable for understanding the diurnal cycles and seasonal patterns of ecosystem processes, as well as the solar energy utilizations

14 SOLAR ENERGY↗
DOE OSTI2020

Sensors and methods for monitoring flying objects

Described herein are sensing methods, sensor systems, and non-transitory, computer-readable, storage media having programs for long-duration, continuous monitoring of flying objects during the day or the night and regardless of weather conditions. The methods and systems are computationally efficient and can provide compact, three-dimensional representations of motion from the observed object. A 3D track of the flying object can be generated from a point-matched pair of stereo composite motion track images and not directly from the videos, wherein each composite motion track image is based on a composite of a plurality of video frames composited in part according to video frame numbers.

Matzner, Shari↗
DOE OSTI2025

Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks

Abstract Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in LiNi0.8Mn0.1Co0.1O2 (NMC811), and their inner grain architecture with electrode performance is challenging, particularly, due to the significant length-scale difference between grain and particle sizes. Experimentally, it is not feasible to image such a high number of particles with full granular detail. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. Here, we present a stereological generative adversarial network-based model fitting approach to tackle this, that generates representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model can rapidly generate virtual cathode particles that are statistically similar to experimental data, and thus is suitable for virtual characterization and materials testing through numerical simulations. A large dataset of simulated particles with inner grain architecture has been made publicly available.

25 ENERGY STORAGE↗
DOE OSTI2021

Full-field imaging learning machine (FILM)

A method of determining dynamic properties of a structure (linear or nonlinear) includes receiving spatio-temporal inputs, generating mode shapes and modal components corresponding to the spatio-temporal inputs using a trained deep complexity coding artificial neural network, and subsequently generating the dynamic properties by analyzing each modal component using a trained learning machine. A computing system for non-contact determination of dynamic properties of a structure includes a camera, a processor, and a memory including computer-executable instructions. When the instructions are executed, the system is caused to receive spatio-temporal image data, decompose the spatio-temporal image data into constituent manifold components using an autoencoder, and analyze the constituent manifold components using a trained learning machine to determine the dynamic properties.

Yang, Yongchao↗
DOE OSTI2023

Irradiation of Mo-100 Targets and Testing Fe(ll)CI 2 Precipitation for Tc-99 Removal

The production of molybdenum-99 (Mo-99) is a critical step in the generation of technetium-99m (Tc-99m), a radioisotope widely used in medical imaging. NorthStar Medical Radioisotopes, LLC is planning to produce the important medical radioisotope Mo-99 through a photonuclear reaction on molybdenum-100 (Mo-100). Accelerator production of Mo-99 using enriched Mo-100 targets yields undesirable isotopes, such as niobium-95 (Nb-95) and zirconium-95 (Zr-95). Previous studies reported that Nb and Zr isotopes can be effectively removed from Mo-99 products through coprecipitation with Fe(III). To enable the potential utilization of the first milking of Tc-99m from a Mo-99/Tc-99m generator, it is desirable to remove long-lived Tc-99. This can be accomplished by substituting Fe(III) with Fe(II) during the precipitation step, without affecting the removal of Zr and Nb isotopes. Previous experiments demonstrated up to 99+% removal of Tc using this method. It was also observed that the presence of hydrogen peroxide significantly impacts Tc removal. This was evident in the processing of enriched Mo-100 targets, where only 25% of Tc was removed. To further investigate this, another irradiation of enriched Mo-100 disks was conducted, followed by Tc-99 removal using Fe(II). Prior to irradiation, experimental conditions for Tc removal were optimized through several test runs using Mo-100 disks spiked with Tc-99. The results of these tests are discussed in this report.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

In-situ L-TEM observations of dynamics of nanometric skyrmions and antiskyrmions

Nanometer-scale magnetic skyrmions and antiskyrmions exhibit unique dynamical behaviors in response to external stimuli, which are critical for their applications in low-power-consumption spintronic devices. This review discusses recent advancements in in-situ Lorentz transmission electron microscopy (L-TEM) observations of skyrmion and antiskyrmion dynamics, and demonstrates the manipulation and evolution of these textures in various magnetic materials under electric, magnetic, and thermal stimuli. Specifically, the motion tracking of single skyrmions and their clusters, and the deformation and transformation of skyrmions has been demonstrated in chiral helimagnets FeGe, Co 9 Zn 9 Mn 2 , and Co 10 Zn 10 with precise application of electric currents. Skyrmions can undergo dynamic transitions in current-driven skyrmion motions, from pinned states to linear flows, and even exhibit deformation into elliptical shapes, underscoring their topological robustness and dynamic flexibility. In addition, the manipulation of single antiskyrmions and antiskyrmion-lattice phases in (Fe 0.63 Ni 0.3 Pd 0.07 ) 3 P with S 4 symmetry is discussed, highlighting their high mobility and unique sliding capabilities along stripe domains at room temperature, facilitated by nanosecond pulsed electric currents. Finally, the temperature gradient-driven motion and topological transformation of elliptical skyrmions and antiskyrmions in this same material are investigated. In conclusion, the comprehensive insights gained from the L-TEM imaging technique are pivotal in advancing the design and functionality of next-generation skyrmion/antiskyrmion-based spintronic devices.

(Anti)skyrmion↗
DOE OSTI2020

Modular repeat protein sculpting using rigid helical junctions

The ability to precisely design large proteins with diverse shapes would enable applications ranging from the design of protein binders that wrap around their target to the positioning of multiple functional sites in specified orientations. We describe a protein backbone design method for generating a wide range of rigid fusions between helix-containing proteins and use it to design 75,000 structurally unique junctions between monomeric and homo-oligomeric de novo designed and ankyrin repeat proteins (RPs). Of the junction designs that were experimentally characterized, 82% have circular dichroism and solution small-angle X-ray scattering profiles consistent with the design models and are stable at 95 °C. Crystal structures of four designed junctions were in close agreement with the design models with rmsds ranging from 0.9 to 1.6 Å. Electron microscopic images of extended tetrameric structures and ~10-nm-diameter “L” and “V” shapes generated using the junctions are close to the design models, demonstrating the control the rigid junctions provide for protein shape sculpting over multiple nanometer length scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Peculiarities of planar shockwave interaction with air–water interface and solid target

We present the results of the research on underwater electrical explosion of planar copper wire arrays, accompanied by the generation of a planar shock, using the synchrotron-based phase-contrast radiography imaging capabilities of the ID19 beamline at the European Synchrotron Radiation Facility. It is shown that the interaction of a strong shock with an air–water interface leads to additional acceleration of the wire expansion due to a rarefaction wave along with the emergence of micro-jets. In the case of a target placed above the array, we observed the formation of a cavity between the array and the target due to the interaction of two rarefaction waves. Furthermore, the results of two-dimensional hydrodynamic simulations of the wire explosion and the interaction of the generated shock with the water–air and water-target interfaces showed good agreement with experimental results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

AutoSourceID-Classifier: Star-galaxy classification using a convolutional neural network with spatial information

Aims.Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods.The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results.We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.

Astronomy & Astrophysics↗
DOE OSTI2026

Longitudinal Phase Space Tomography for the Booster Synchrotron

Efforts in the study of the longitudinal behavior of charged particles in the Fermilab Booster can be catalyzed with an image of the two-dimensional phase space distribution. In the past, tomography had been employed in the reconstruction of the phase space in accelerators such as the Recycler at Fermilab and the Proton Synchrotron Booster at CERN. However, such a capability had yet to realize for the Fermilab Booster synchrotron. In this work, the first successful tomographic phase space reconstruction of a low-energy Booster bunch is presented along with validation metrics. A numerical turn-by-turn model of the longitudinal particle dynamics in the Booster has been implemented, which utilizes a fast, map-based particle transport algorithm. Using a sinogram generated from the Wall Current Monitor signal, the iterative reconstruction algorithm recovers a discretized image of the original phase space distribution at variable resolution. The reconstruction result shows low root-mean-square error and a rapid convergence toward the solution, providing strong evidence of accuracy. Future and ongoing work includes modeling high-energy bunches above transition and using tomography to infer certain machine parameters such as synchronous phase, peak gap voltage, and synchronous energy in addition to the phase space distribution.

Ebeid, Safi [Unlisted; Fermilab]↗
DOE OSTI2022

Few-femtosecond resolved imaging of laser-driven nanoplasma expansion

Abstract The free expansion of a planar plasma surface is a fundamental non-equilibrium process relevant for various fields but as-yet experimentally still difficult to capture. The significance of the associated spatiotemporal plasma motion ranges from astrophysics and controlled fusion to laser machining, surface high-harmonic generation, plasma mirrors, and laser-driven particle acceleration. Here, we show that x-ray coherent diffractive imaging can surpass existing approaches and enables the quantitative real-time analysis of the sudden free expansion of laser-heated nanoplasmas. For laser-ionized SiO 2 nanospheres, we resolve the formation of the emerging nearly self-similar plasma profile evolution and expose the so far inaccessible shell-wise expansion dynamics including the associated startup delay and rarefaction front velocity. Our results establish time-resolved diffractive imaging as an accurate quantitative diagnostic platform for tracing and characterizing plasma expansion and indicate the possibility to resolve various laser-driven processes including shock formation and wave-breaking phenomena with unprecedented resolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Recent advances and applications of deep learning methods in materials science

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

36 MATERIALS SCIENCE↗