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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

XFEL imaging techniques for high energy density and inertial fusion energy research at HED-HiBEF

The imaging platform developed at the High Energy Density-Helmholtz International Beamline for Extreme Fields (HED-HiBEF) instrument at the European X-ray Free Electron Laser (XFEL) and its applications to HED and fusion related research are presented. The platform combines the XFEL beam with the high-intensity short-pulse laser ReLaX and the high-energy nanosecond-pulse laser DiPOLE-100X. The spatial resolution is better than 500 nm and the temporal resolution of the order of 50 fs. The influence of the XFEL source in the x-ray imaging method is discussed. Free-propagation x-ray phase contrast imaging and Talbot-Lau imaging setups are shown. We show examples of blast waves and converging cylindrical shocks in aluminum, resonant absorption measurements of specific charged states in copper with ReLaX and planar shocks in polystyrene material generated by DiPOLE-100X. For the first time, we show the application of Talbot-Lau interferometry to convergent cylindrical shocks as well as resonant absorption processes. We also discuss the possibilities introduced by combining this imaging platform with a kJ-class laser.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Operando X-Ray Tomography Imaging of Solid-State Electrolyte Response to Li Evolution under Realistic Operating Conditions

Solid-state Li-ion conductors are a next-generation battery technology that are particularly promising for electric vehicles, offering the capacitive benefits of Li metal anodes with nonflammable electrolytes. Microstructural evolution in these solid-state batteries, especially the Li anode, requires deeper understanding of the conditions under which certain undesired behaviors are more likely to occur. This study utilizes operando X-ray computed tomography to visualize the behavior of lithium in response to device operation, considering variables that are relevant to a realistic battery: stack pressure, microscale defects such as pores, and temperature. This work demonstrates that experimental and operational conditions, especially temperature, affect the fundamental driving forces of realistic solid electrolyte systems and provides visual insight into possible mechanisms of Li migration. In particular, the effect of these variables on Li propagation from the anode through pre-existing defects and Li nucleation within the imperfectly electron-insulating electrolyte is highlighted.

battery↗
DOE OSTI2021

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities.

14 SOLAR ENERGY↗
DOE OSTI2023

Plasma flows during the ablation stage of an over-massed pulsed-power-driven exploding planar wire array

We characterize the plasma flows generated during the ablation stage of an over-massed exploding planar wire array, fielded on the COBRA pulsed-power facility (1 MA peak current, 250 ns rise time). The planar wire array is designed to provide a driving magnetic field (80–100 T) and current per wire distribution (about 60 kA), similar to that in a 10 MA cylindrical exploding wire array fielded on the Z machine. Over-massing the arrays enables continuous plasma ablation over the duration of the experiment without implosion. The requirement to over-mass on the Z machine necessitates wires with diameters of 75–100μm, which are thicker than wires usually fielded on wire array experiments. To test ablation with thicker wires, we perform a parametric study by varying the initial wire diameter between 33 and 100 μm. The largest wire diameter (100 μm) array exhibits early closure of the cathode-wire gap, while the gap remains open over the duration of the experiment for wire diameters between 33 and 75 μm. Laser plasma interferometry and time-gated extreme-ultraviolet (XUV) imaging are used to probe the plasma flows ablating from the wires. The plasma flows from the wires converge to generate a pinch, which appears as a fast-moving (V≈100kms−1) column of increased plasma density (n¯e≈2×1018cm−3) and strong XUV emission. Finally, we compare the results with three-dimensional resistive-magnetohydrodynamic (MHD) simulations performed using the code GORGON, the results of which reproduce the dynamics of the experiment reasonably well.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Mitigation of LEO Satellite Brightness and Trail Effects on the Rubin Observatory LSST

In this work, we report studies on the mitigation of optical effects of bright low-Earth-orbit (LEO) satellites on Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST). These include options for pointing the telescope to avoid satellites, laboratory investigations of bright trails on the Rubin Observatory LSST camera sensors, algorithms for correcting image artifacts caused by bright trails, experiments on darkening SpaceX Starlink satellites, and ground-based follow-up observations. The original Starlink v0.9 satellites are g ~ 4.5 mag, and the initial experiment "DarkSat" is g ~ 6.1 mag. Future Starlink darkening plans may reach g ~ 7 mag, a brightness level that enables nonlinear image artifact correction to well below background noise. However, the satellite trails will still exist at a signal-to-noise ratio ~ 100, generating systematic errors that may impact data analysis and limit some science. For the Rubin Observatory 8.4 m mirror and a satellite at 550 km, the full width at half maximum of the trail is about 3'' as the result of an out-of-focus effect, which helps avoid saturation by decreasing the peak surface brightness of the trail. For 48,000 LEOsats of apparent magnitude 4.5, about 1% of pixels in LSST nautical twilight images would need to be masked.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Elucidating Cycling Rate-Dependent Electrochemical Strains in Sodium Iron Phosphate Cathodes for Na-ion Batteries

Battery electrodes materials undergo significant mechanical instabilities which affects their longevity and exert rate-limitations during the cycling process. In this study, we investigate the rate-dependent mechanical response of sodium iron phosphate (NaFePO4, NFP) cathodes during Na intercalation via galvanostatic cycling at different rates by employing digital image correlation, electrochemical methods, and mathematical model. The mechanical behaviour of the electrode shows strong dependance on the applied scan rate. At slower rates, electrode shows asymmetrical strain generation between anodic and cathodic cycles, which is attributed to the formation of cathode-electrolyte interface layers. The electrode undergoes smaller strain generation when cycled at slower rates when the same amount of Na ions is removed or inserted into the electrode. A mathematical model was developed to predict strain evolution in the composite electrode as well as the concentration profile of the Na ions in the electrode particles. Rate-dependent and time dependent factors on the strain generation in the electrode are attributed to the capacity-dependent intercalation strains, rate-dependent mismatch strains, and time-dependent irreversible strains. The combination of in situ strain measurements with the analytical model provided new insight into the electrochemically induced mechanical deformations in Na-ion cathode electrodes.

Ozdogru, Bertan↗
DOE OSTI2025

Geometric Interpretation of the Cluster Location Problem Part II: Application to the Pahala, Hawaii, Earthquake Sequence

In the companion “Theory” article, we presented a new framing of the seismic location problem in terms of differential geometry (Harris et al., 2025). From that viewpoint, we developed a “project and correct” approach for estimating the relative locations of earthquakes. Here, in this study, we use project and correct to estimate high-precision relative locations of events from an earthquake sequence beneath the town of Pahala, Hawaii, using high-precision correlation-derived picks. The sequence was active from 2020 through 2022 and produced many highly correlated signals at Hawaii Volcano Observatory (HVO) stations on the island of Hawaii. The data we inverted consisted of 2882 events with observations at 5 HVO stations. For comparison with the travel-time image, we also produced conventional hypocenter solutions using both the Bayesloc program (Myers et al., 2007, 2009) and a purpose-built double-difference code. There were obvious structural elements in the resulting image, the resolution of which we used to test the performance of the project and the correct algorithm. For the projection step, we first produced a 3D local basis using an singular value decomposition (SVD) of the 2882 groups of times. Projection of the travel-time vectors into this basis resulted in an image with structures similar to those produced by our conventional locators, but with distortion as predicted by theory. Removing the distortion requires an inverse operator generated from the metric tensor at the geometric centroid of the events. We compared two approaches to obtaining such an inverse operator. The first uses an estimate of the geographic centroid of the event cloud from the centroid of the travel-time data. The second approach uses the centroid of the conventionally produced locations. The first approach produces a corrected image very similar to the conventional results, but with a rotation. The corrected image produced using the conventionally derived centroid is a near-exact match to the conventional locations.

Dodge, Douglas A. [Lawrence Livermore National Lab↗
DOE OSTI2025

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
DOE OSTI2024

Multi-frame x-ray radiography and image tracking for quantification of expansion in laser-driven tin ejecta microjets

One regime of experimental particle-laden flow study involves ejecta microjets—often defined as a stream of micrometer-scale particles generated through shock interaction with a non-uniform surface and generally travel above 1 km/s. In order to capture the change in characteristics as a function of propagation time, we apply a multi-frame x-ray radiography platform to observe and track the jet transport dynamics. A synchrotron x-ray source allows us to perform quantitative analyses and comparisons between the eight images captured by the imaging system. Observation of a single jet through time allows the use of a cross correlation algorithm to independently track various regions within the jet and quantify the jet expansion over time using normalized area and normalized areal density values. Through a comparison with the calculated values of ballistic transport, these findings show less expansion than expected for ballistically transporting particles. In conclusion, this work combines multi-frame synchrotron radiography with image tracking to establish a foundation for future studies on jet transport and particle interaction dynamics.

Sun, Yuchen [Lawrence Livermore National Laborator↗
DOE OSTI2021

Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification

Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity model, which suppresses noise in the gradients and mitigates local minima. Furthermore, the velocity model generated by neural networks is input to the same partial differential equation (PDE) solvers used in conventional FWI. The gradients of the neural networks and PDEs are calculated using automatic differentiation, which back propagates gradients through the acoustic PDEs and neural network layers to update the weights of the generative neural network. Experiments on 1D velocity models, the Marmousi model, and the 2004 BP model determine that NNFWI can mitigate local minima, especially for imaging high-contrast features such as salt bodies, and it significantly improves the inversion in the presence of noise. Adding dropout layers to the neural network model also allows analyzing the uncertainty of the inversion results through Monte Carlo dropout. NNFWI opens a new pathway to combine deep learning and FWI for exploiting the characteristics of deep neural networks and the high accuracy of PDE solvers. Because NNFWI does not require extra training data and optimization loops, it provides an attractive and straightforward alternative to conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2020

Roadmap on quantum light spectroscopy

Conventional spectroscopy uses classical light to detect matter properties through the variation of its response with frequencies or time delays. Quantum light opens up new avenues for spectroscopy by utilizing parameters of the quantum state of light as novel control knobs and through the variation of photon statistics by coupling to matter. This Roadmap article focuses on using quantum light as a powerful sensing and spectroscopic tool to reveal novel information about complex molecules that is not accessible by classical light. It aims at bridging the quantum optics and spectroscopy communities which normally have opposite goals: manipulating complex light states with simple matter e.g. qubits versus studying complex molecules with simple classical light, respectively. Here, articles cover advances in the generation and manipulation of state-of-the-art quantum light sources along with applications to sensing, spectroscopy, imaging and interferometry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Elementary Particle Interactions (Final Technical Report)

Neutrinos provide a promising window to probe a wide range of fundamental physics. The past two decades have brought several remarkable neutrino-related discoveries indicating that one may answer the most sought after question of our matter dominated universe from within the neutrino sector. The global neutrino physics program is currently focused on studying neutrino oscillations, and to understand the implications of this phenomena on the evolution of the universe. Addressing all of these open questions and completing the story of neutrino oscillations requires technological advances in neutrino detection. Liquid argon time projection chambers (LArTPCs) are imaging detectors that offer fine-grained tracking, calorimetry and scalability, and are increasingly favored for the next generation of large neutrino detectors. Through leading technical and scientific roles on MicroBooNE, Short-Baseline Near Detector (SBND) and the Deep Underground Neutrino Experiment (DUNE) LArTPC neutrino experiments, the research program supported through this funding has significantly advanced the understanding of neutrino properties along with technical development of LArTPCs for both short- and long-baseline neutrino oscillations physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

In Situ and Post-Mortem Characterizations of Ultrasonic Spot Welded AZ31B and Coated Dual Phase 590 Steel Joints

Ultrasonic spot welding using different welding conditions was applied to join dissimilar metals of galvanized DP590 steel and AZ31B magnesium sheets. In situ high-speed imaging, digital image correlation, and infrared thermography were utilized to quantitatively study the interfacial relative motion, surface indentation, and heat generation across the joint faying interface and the sheet/sonotrode interfaces under the welding condition of moderate welding power and short welding time. For welds made with high power and long welding time, lap shear tensile tests as well as fatigue tests were carried out. Different fracture modes were observed after the lap shear tensile tests and fatigue tests performed under different peak loads. Post-weld cross-sectional analysis with scanning electron microscopy coupled with energy dispersive X-Ray spectroscopy revealed the variation of morphology and chemical composition at the joint interface for welds made with different welding conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Digital encoding algorithm for pixelated detectors

A detector for imaging and efficiently digitizing a spatial distribution of photon flux includes pixel circuits that compressively encode pixel values generated by integrated analog to digital converters (ADCs). On-pixel digital compression circuits (DCCs) implement compression to increase continuous frame rate by reducing the number of bits per pixel while keeping quantization error below Poisson noise. Several mapping algorithms for photon-counting and charge-integrating detectors and compact digital logic implementations are presented.

Hammer, Michael P.↗
DOE OSTI2023

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗
DOE OSTI2025

Optical Imaging of Laser-Driven Fast Electron Weibel-like Filamentation in Overcritical Density Plasma

We report on the measurement of filamented transport of laser-generated fast electron beams in near-critical density plasma. A relativistic intensity long-wave-infrared laser irradiated a hydrodynamically shaped helium gas flow at an electron density n e ≃ 10 25 m − 3 , generating a large flux of fast electrons that propagated beyond the critical surface. The beam-to-background electron density ratio was sufficiently high to drive growth of Weibel-like filamentation, which was measured by optical probing to extend up to 800 μ m with radii ∼ 10 μ m . Particle-in-cell simulations reproduce the main features of the filamentation generation, suggesting that collisionless processes are dominant in these interactions. Expansion of the filaments after formation infers a fast electron heated plasma temperature ∼ 400 eV in the overcritical density plasma. Published by the American Physical Society 2025

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Micro-CT Imaging and Fluid Flow Simulations of Fractures in MSEEL Shale

micro-CT scans of a naturally fractured MSEEL shale sample as a shear fracture is generated and displaced in the center, intact region of the sample. Steady State laminar fluid flow simulations were then performed on each individual fracture during each experimental segment as effective stress and shear displacement changed. Released under LA-UR-21-24335.

Fractures,Fracturing Initiation,Marcellus Shale↗
DOE OSTI2023

Quantitative Insight to Fission Gas Bubble Distribution in Irradiated Annular U-10Zr Metallic Fuel Using Deep Learning

U-10Zr Metal fuel is a promising nuclear fuel candidate for next-generation sodium-cooled fast spectrum reactors. Porosity is one of the key facts to impact the performance of metallic fuel. Additionally, a mechanical understanding of fission gas bubbles evolution behavior is a prerequisite for fuel development and qualification. Previous study of fission gas bubbles relied on a simple threshold method working on low resolution optical microscopy images, which has challenges in recognizing bubble boundaries, and caused inaccurate statistics of bubble properties. In this paper, a pre-trained deep learning model on Scanning Electron Microscopy (SEM) images from an annular U-10Zr fuel (AF1), was applied to another U-10Zr annular fuel (AF2). More accurate fission gas bubble segmentation results were generated, which leads to more precise qualitative analysis on the morphology, size, density, and orientation of bubbles. Furthermore, we investigated the lanthanide movement along the radial temperature gradient and obtained conclusive findings.

36 MATERIALS SCIENCE↗