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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

New perspectives on materials and device dynamics using time-resolved full-field diffraction X-ray imaging

Understanding how materials evolve during synthesis, processing, or device operation requires experimental access to structural dynamics across wide ranges of length and time scales, often in bulk samples or even within packaged devices. Time-resolved full-field diffraction X-ray microscopy has recently emerged as a powerful way to meet this need by combining the penetration and structural sensitivity of X-ray diffraction with objective-lens-based magnification, sensitive high-resolution X-ray imaging detectors, and pump-probe and real-time imaging strategies. Advances in full-field X-ray diffraction microscopy, often termed dark-field X-ray microscopy (DFXM), enable simultaneous imaging of extended fields of view while retaining crystallographic selectivity. Time-resolved DFXM promises to enable advances in materials processing and dynamics, electrochemical and photochemical processes, and the design of electronic devices. This Perspective summarizes the key instrumental concepts that define the performance of these methods, including the choice of imaging optics, detector considerations, and the impact of source time structure at synchrotron light sources and X-ray free-electron lasers (XFELs). We discuss the simultaneous use of complementary imaging modes that are increasingly used in practice. The early demonstrations of the potential of time-resolved DFXM include real-time defect and grain-boundary dynamics during metal annealing, stroboscopic imaging of functional devices with high strain sensitivity, and ultrafast pump-probe implementations at XFELs that directly visualize acoustic-wave propagation and energy dissipation in bulk crystals.

Dark-field X-ray microscopy↗
DOE OSTI2021

Processing of tungsten through electron beam melting

Additive manufacturing (AM) presents a new design paradigm for the manufacture of engineering materials through the layer-by-layer approach combined with welding theory. In the instance of difficult to process materials such as tungsten and other refractory metals, AM offers an opportunity for radical redesign of critical components for next-generation energy technologies including fusion. In this work, electron beam powder bed fusion (EB-PBF) is applied to process pure tungsten to study the influence of process parameters on the defect density of the material. Here, an in-situ image analysis algorithm is applied to pure tungsten for the first time, and is used to visualize the defect structure in AM tungsten. Finally, a cracking mechanism for AM tungsten is proposed, and suggestions for suppression of cracks in pure tungsten are offered.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Measurement of needle and armature dynamics in a gasoline direct injector by high-speed neutron imaging

In modern spark ignition engines, precise delivery of fuel with gasoline direct injection has become increasingly important in the effort to meet ever stricter efficiency and emissions regulations. Use of multiple small close-coupled injections has become more common in attempt to precisely control fuel distribution in the cylinder, but these strategies are hindered by nonlinear injection effects due to operation in the ballistic region of the solenoid-operated valve and due to armature bounce at the end of injection. Understanding the internal dynamics of the injector is crucial to minimizing and controlling non-linearity and shot-to-shot variation, but the experimental techniques available to date are capable only of tracking the position of either the top of the needle (via laser sensors or by monitoring current and voltage in the solenoid coil) or the bottom of the needle (via transparent nozzles or high-speed x-ray imaging). Furthermore, a complete picture of the axial and radial motion of valve needle has until now remained elusive. In this work, we present high-speed ensemble neutron transmission imaging of an entire 8-hole gasoline direct injector operating at 200 bar, allowing for both visualization and quantification of the actuation dynamics including lift and wobble of the valve ball, oscillation and bending of the valve needle, lift, rocking, and bounce of the armature, compression of the springs, and radial swelling of the solenoid during energization. Because neutrons offer high penetration through the metal injector while also being sensitive to the 1 H nuclei in fuel molecules, it is also possible to simultaneously see the fluid dynamics of the injection process, including filling of the sac volume, emanation of the spray through the nozzle holes and into the downstream gas, and the formation and evolution of fuel films on the tip of the injector and the walls of the spray container.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report

Due to their recent increases in performance, machine learning and deep learning models are being increasingly adopted across many domains for visual processing tasks. One such domain is international nuclear safeguards, which seeks to verify the peaceful use of commercial nuclear energy across the globe. Despite recent impressive performance results from machine learning and deep learning algorithms, there is always at least some small level of error. Given the significant consequences of international nuclear safeguards conclusions, we sought to characterize how incorrect responses from a machine or deep learning-assisted visual search task would cognitively impact users. We found that not only do some types of model errors have larger negative impacts on human performance than other errors, the scale of those impacts change depending on the accuracy of the model with which they are presented and they persist in scenarios of evenly distributed errors and single-error presentations. Further, we found that experiments conducted using a common visual search dataset from the psychology community has similar implications to a safeguards- relevant dataset of images containing hyperboloid cooling towers when the cooling tower images are presented to expert participants. While novice performance was considerably different (and worse) on the cooling tower task, we saw increased novice reliance on the most challenging cooling tower images compared to experts. These findings are relevant not just to the cognitive science community, but also for developers of machine and deep learning that will be implemented in multiple domains. For safeguards, this research provides key insights into how machine and deep learning projects should be implemented considering their special requirements that information not be missed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Quantification of silicon carbide grain structure in TRISO fuel by BSE image analysis

The grain structure of the silicon carbide (SiC) layer in tristructural-isotropic (TRISO) fuels has commonly been qualified in fuel specifications based on a qualitative comparison to visual standards to ensure undesirable microstructure are not produced. This approach is inherently dependent on subjective judgement and provides limited data for analysis or comparison. An alternative method to rapidly quantify (i.e., provide a numeric measure of) the grain structure of the SiC layer in TRISO fuels has been developed. This method relies on the identification of silicon carbide grain boundaries based on brightness variation in backscattered electron (BSE) images through the application of automated image processing algorithms. The speed with which the requisite BSE imaging and analysis may be completed enables the practical use of this method on an industrial scale as a part of qualifying TRISO fuel to meet a given specification on silicon carbide grain structure, which is an important parameter for TRISO fuel performance. The method is benchmarked by comparison to prior Electron-Backscattered Diffraction (EBSD) results for TRISO particles from the Advanced Gas Reactor (AGR) program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Discovering strongly lensed quasar candidates with catalogue-based methods from DESI Legacy Surveys

The Hubble tension, revealed by a ~5σ discrepancy between measurements of the Hubble-Lemaitre constant among observations of the early and local Universe, is one of the most significant problems in modern cosmology. In order to better understand the origin of this mismatch, independent techniques to measure H 0 , such as strong lensing time delays, are required. Notably, the sample size of such systems is key to minimising the statistical uncertainties and cosmic variance, which can be improved by exploring the datasets of large-scale sky surveys such as Dark Energy Spectroscopic Instrument (DESI). We identify possible strong lensing time-delay systems within DESI by selecting candidate multiply imaged lensed quasars from a catalogue of 24 440 816 candidate QSOs contained in the ninth data release of the DESI Legacy Imaging Surveys (DESI-LS). Using a friend-of-friends-like algorithm on spatial co-ordinates, our method generates an initial list of compact quasar groups. This list is subsequently filtered using a measure of the similarity of colours among a group’s members and the likelihood that they are quasars. A visual inspection finally selects candidate strong lensing systems based on the spatial configuration of the group members. We identified 620 new candidate multiply imaged lensed quasars (101 grade-A, 214 grade-B, 305 grade-C). This number excludes 53 known spectroscopically confirmed systems and existing candidate systems identified in other similar catalogues. When available, these new candidates will be further checked by combining the spectroscopic and photometric data from DESI.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Dynamic in situ imaging of methane hydrate formation in coal media

Fast 3D X-ray imaging has proved to provide crucial insights into multi-phase dynamic processes in various geomaterials. In particular, many in situ imaging experiments have been made to study gas-hydrate formation in porous sandy samples. Such imaging is challenging for the methane gas-hydrate formation in coal samples because of the coal micro-porosity structure and lower X-ray contrast. Here we present results of the first dynamic in situ micro-computed tomography experiment of methane-hydrate formation in coal samples. Synchrotron phase-contrast tomography techniques allowed to achieve necessary contrast levels to separate all the materials of interest (gas, water, coal, and gas hydrate) in reconstructed images with high spatial and temporal resolution. The imaging results are compared to the ones from a similar tomographic experiment with sand samples. Methane-hydrate formation is accompanied by the water movements caused by cryogenic water suction that happens in sequences of short fast movements with longer equilibrium states in between. Only one type of the hydrate formation was observed in coal (growth as shells on grain boundaries) as opposed to three types in sand (shells, growth into gas pockets, and inside water volumes). In particular, this leads to a slower hydrateformation speed in coal. For the coal sample, we also observed water extraction from grains, and interpreted it as competitive sorption of methane. Lastly, we visualized the dynamic behavior of this water extraction via microchannels inside the coal grains, and performed nano-tomography imaging of these channels for a better understanding of this phenomenon.

58 GEOSCIENCES↗
DOE OSTI2020

Experimental investigation of internal two-phase flow structures and dynamics of quasi-stable sheet cavitation by fast synchrotron x-ray imaging

The quasi-stable sheet cavitation produced in a small Venturi channel is investigated using a fast synchrotron X-ray imaging technique aided with conventional high speed photography. The use of X-rays instead of visible light solves cavitation opacity related issues, and X-ray phase contrast-based edge enhancement enables high-definition visualization of the internal two-phase morphology. The simultaneous acquisition of time-resolved velocity and void fraction fields through post-processing the recorded X-ray images reveals, for the first time, the complex diphasic flow structures inside the sheet cavity, which is essentially divided into 6 characteristic parts. Distinct from the current mainstream view, the globallysteady sheet cavitation is found to be characterized by a weak but constantly-existing re-entrant flow that can penetrate the entire cavity. The turbulent velocity fluctuations inside the sheet cavity are also investigated. The turbulence level in the reverse flow region is observed to be as low as in the outer main flow demonstrating the relatively steady status of the reentrant flow. Unlike the streamwise and cross-stream fluctuations, the shear stress appears to be weakly correlated with the velocity gradient. The collapse of vapor phase and the vaporization at the upstream cavity interface are found to be the primary causes of shear stress intensification.

42 ENGINEERING↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2024

Nonlinear encoding in diffractive information processing using linear optical materials

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

42 ENGINEERING↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
DOE OSTI2022

Vision-based inspection of prefabricated components using camera poses: Addressing inherent limitations of image-based 3D reconstruction

Modular construction can lead to additional cost overruns and delays when a defect is found on the construction site and is not easily repairable. Researchers have developed various methods that use image-based 3D reconstruction for quality assessment, but they have inherent limitations, such as inconsistency and dealing with surfaces with reflectivity and limited visual features. Therefore, this paper presents a vision-based quality assessment method using cameras for prefabricated components by addressing these limitations. Specifically, this paper proposes a novel quality inspection method with sub-millimeter accuracy using cameras focused on leveraging camera poses (as opposed to 3D point clouds that are often not consistent in quality) from the image-based 3D reconstruction. The 3D point estimation by computing triangulation was used for achieving accurate measurement. The proposed method is validated using six different variances and two case studies – an aluminum pipe with a reflective surface and a fabricated concrete column. Furthermore, the results demonstrate the accuracy and effectiveness of the proposed method.

42 ENGINEERING↗
DOE OSTI2022

XRIPL

XRIPL is a library of tools for processing x-ray radiographs and extracting contours of interest within the image using computer vision techniques. Features include spatial calibration, denoising, background and attenuation correction through pseudo-flatfielding, watershed segmentation, contour processing, and visualization. The details of the XRIPL analysis pipeline are published in the Proceedings of the 23rd Topical Conference on High-Temperature Plasma Diagnostics Proceedings (LA-UR-20-30133) [1]. [1] P. M. Kozlowski, Y. Kim, B. M. Haines, H. F. Robey, T. J. Murphy, H. M. Johns, and T. S. Perry. Use of Computer Vision for analysis of image data sets from high temperature plasma experiments. Review of Scientific Instruments 92, 033532 (2021) https://doi.org/10.1063/5.0040285

Kozlowski, Pawel↗
DOE OSTI2024

Stress field measurements using quantitative schlieren

Quantitative schlieren analysis is extended here to optically transparent solids in quasi-static and dynamic experiments to measure stress distributions. The quasi-static experiments in polymethyl methacrylate (PMMA) compared refraction angles and stress gradients calculated from schlieren images to the analytical Flamant solution of a line load on a half-space. The quantitative schlieren measurements of the stress field in the thin sample with a load compared well to the analytical solution. The analysis method was then extended to explosive induced shock waves in PMMA. The explosive induced response of PMMA was experimentally studied using high-speed schlieren to visualize the shock propagation in conjunction with Photon Doppler Velocimetry (PDV) to record surface velocity histories. The stress state estimated from the schlieren images was compared to the stress calculated from the PDV measurements. High-speed imaging limitations caused the shock wave to not be fully resolved in the images, but was resolved in the PDV measurement. The stress state behind the shock calculated from the high-speed images followed a similar trend to the stress calculated from the PDV measurements.

Physics↗
DOE OSTI2022

Fluorescence-coupled micropipette aspiration assay to examine calcium mobilization caused by red blood cell mechanosensing

Abstract Mechanical stimuli such as tension, compression, and shear stress play critical roles in the physiological functions of red blood cells (RBCs) and their homeostasis, ATP release, and rheological properties. Intracellular calcium (Ca 2+ ) mobilization reflects RBC mechanosensing as they transverse the complex vasculature. Emerging studies have demonstrated the presence of mechanosensitive Ca 2+ permeable ion channels and their function has been implicated in the regulation of RBC volume and deformability. However, how these mechanoreceptors trigger Ca 2+ influx and subsequent cellular responses are still unclear. Here, we introduce a fluorescence-coupled micropipette aspiration assay to examine RBC mechanosensing at the single-cell level. To achieve a wide range of cell aspirations, we implemented and compared two negative pressure adjusting apparatuses: a homemade water manometer (− 2.94 to 0 mmH 2 O) and a pneumatic high-speed pressure clamp (− 25 to 0 mmHg). To visualize Ca 2+ influx, RBCs were pre-loaded with an intensiometric probe Cal-520 AM, then imaged under a confocal microscope with concurrent bright-field and fluorescent imaging at acquisition rates of 10 frames per second. Remarkably, we observed the related changes in intracellular Ca 2+ levels immediately after aspirating individual RBCs in a pressure-dependent manner. The RBC aspirated by the water manometer only displayed 1.1-fold increase in fluorescence intensity, whereas the RBC aspirated by the pneumatic clamp showed up to threefold increase. These results demonstrated the water manometer as a gentle tool for cell manipulation with minimal pre-activation, while the high-speed pneumatic clamp as a much stronger pressure actuator to examine cell mechanosensing directly. Together, this multimodal platform enables us to precisely control aspiration and membrane tension, and subsequently correlate this with intracellular calcium concentration dynamics in a robust and reproducible manner.

Wang, Haoqing↗
DOE OSTI2023

Solar water splitting Pt-nanoparticle photosystem I thylakoid systems: Catalyst identification, location and oligomeric structure

In this study, photosynthetic conversion of light energy into chemical energy occurs in sheet-like membrane-bound compartments called thylakoids and is mediated by large integral membrane protein-pigment complexes called reaction centers (RCs). Oxygenic photosynthesis of higher plants, cyanobacteria and algae requires the symbiotic linking of two RCs, photosystem II (PSII) and photosystem I (PSI), to split water and assimilate carbon dioxide. Worldwide there is a large research investment in developing RC-based hybrids that utilize the highly evolved solar energy conversion capabilities of RCs to power catalytic reactions for solar fuel generation. Of particular interest is the solar-powered production of H 2 , a clean and renewable energy source that can replace carbonbased fossil fuels and help provide for ever-increasing global energy demands. Recently, we developed thylakoid membrane hybrids with abiotic catalysts and demonstrated that photosynthetic Z-scheme electron flow from the light-driven water oxidation at PSII can drive H 2 production from PSI. One of these hybrid systems was created by self-assembling Pt-nanoparticles (PtNPs) with the stromal subunits of PSI that extend beyond the membrane plane in both spinach and cyanobacterial thylakoids. Using PtNPs as site-specific probe molecules, we report the electron microscopic (EM) imaging of oligomeric structure, location and organization of PSI in thylakoid membranes and provide the first direct visualization of photosynthetic Z-scheme solar water-splitting biohybrids for clean H 2 production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy

Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this paper a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue is introduced. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with current deep learning-based 3D instance segmentation methods.

3D instance segmentation↗
DOE OSTI2023

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments

‘Cell Painting’ is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ~100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

60 APPLIED LIFE SCIENCES↗