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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Visualization of Thermal and Strain Dynamics in X-ray Optics by Time-Resolved Rocking Curve Imaging

The absorption of intense X-ray pulses from free-electron lasers by X-ray crystal optics triggers rapid thermal and mechanical responses, including lattice compression, expan- sion, and strain-wave propagation, which dynamically modify diffraction conditions and wavefront properties. These effects pose significant challenges for next-generation high- repetition-rate X-ray sources, particularly for crystal monochromators used in XFEL beam- lines and self-seeding. Here, we demonstrate time-resolved rocking curve imaging (TrRCI) of X-ray Bragg optics with high spatial resolution (6.4 μm) and sub-1-μrad angular resolu- tion, enabling direct visualization of lattice dynamics from nanoseconds to microseconds. By combining a high-flux beamline at SPring-8, a scintillator-coupled sCMOS detector, and a synchronized femtosecond laser system, we capture lattice dynamics induced by tran- sient laser heating with a strain sensitivity on the order of 10−6 . Our results reveal transient lattice deformation and the propagation of laser-induced strain waves over millimeter-scale distances. This approach provides experimental benchmarks for understanding heat-load effects in high-repetition-rate X-ray optics.

Source record↗
DOE OSTI2022

Detecting damaged buildings using real-time crowdsourced images and transfer learning

After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~ 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision.

58 GEOSCIENCES↗
DOE OSTI2024

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

artificial intelligence↗
DOE OSTI2021

2D-to-3D image translation of complex nanoporous volumes using generative networks

Image-based characterization offers a powerful approach to studying geological porous media at the nanoscale and images are critical to understanding reactive transport mechanisms in reservoirs relevant to energy and sustainability technologies such as carbon sequestration, subsurface hydrogen storage, and natural gas recovery. Nanoimaging presents a trade off, however, between higher-contrast sample-destructive and lower-contrast sample-preserving imaging modalities. Furthermore, high-contrast imaging modalities often acquire only 2D images, while 3D volumes are needed to characterize fully a source rock sample. In this work, we present deep learning image translation models to predict high-contrast focused ion beam-scanning electron microscopy (FIB-SEM) image volumes from transmission X-ray microscopy (TXM) images when only 2D paired training data is available. We introduce a regularization method for improving 3D volume generation from 2D-to-2D deep learning image models and apply this approach to translate 3D TXM volumes to FIB-SEM fidelity. We then segment a predicted FIB-SEM volume into a flow simulation domain and calculate the sample apparent permeability using a lattice Boltzmann method (LBM) technique. Results show that our image translation approach produces simulation domains suitable for flow visualization and allows for accurate characterization of petrophysical properties from non-destructive imaging data.

58 GEOSCIENCES↗
DOE OSTI2024

Monitoring the impact of confinement on hyphal penetration and fungal behavior

Through their expansive mycelium network, soil fungi alter the physical arrangement and chemical composition of their local environment. This can significantly impact bacterial distribution and nutrient transport and can play a dramatic role in shaping the rhizosphere around a developing plant. However, direct observation and quantitation of such behaviors is extremely difficult due to the opacity and complex porosity of the soil microenvironment. In this study, we demonstrate the development and use of an engineered microhabitat to visualize fungal growth in response to varied levels of confinement. Microfluidics were fabricated using photolithography and conventional soft lithography, assembled onto glass slides, and prepared to accommodate fungal cultures. Selected fungal strains across three phyla (Ascomycota: Morchella sextalata , Fusarium falciforme ; Mucoromycota: Linnemannia elongata , Podila minutissima , Benniella ; Basidiomycota: Laccaria bicolor , and Serendipita sp.) were cultured within microhabitats and imaged using time-lapse microscopy to visualize development at the mycelial level. Fungal hyphae of each strain were imaged as they penetrated through microchannels with well-defined pore dimensions. The hyphal penetration rates through the microchannels were quantified via image analysis. Other behaviors, including differences in the degree of branching, peer movement, and tip strength were also recorded for each strain. Our results provide a repeatable and easy-to-use approach for culturing fungi within a microfluidics platform and for visualizing the impact of confinement on hyphal growth and other fungal behaviors pertinent to their remodeling of the underground environment.

Guo, Yi-Syuan (ORCID:0000000340142225)↗
DOE OSTI2024

Operando visualization of porous metal additive manufacturing with foaming agents through high-speed x-ray imaging

Porous metals find extensive applications in soundproofing, filtration, catalysis, and energy-absorbing structures, thanks to their unique internal pore structure and high specific strength. In recent years, there has been an increasing interest in fabricating porous metals using additive manufacturing (AM), leveraging its unique advantages, including improved design freedom, spatial material control, and cost-effective small-batch production. In this study, we conducted pioneering operando visualization of AM porous metal using a laser powder bed fusion (L-PBF) setup combined with a high-speed synchrotron x-ray imaging system. Single track printing experiments using Ti6Al4V (Ti64) combined with titanium hydride (TiH 2 ) and sodium carbonate (Na 2 CO 3 ) as foaming agents, with varying mixing ratios were performed under different processing conditions. Here. the results elucidate the dynamic development of porosity formation. The average pore size is significantly influenced by the particle size of foaming agents when pore coalescence is absent. For all foaming agent content tested in the current study, the number of pores is found to be more sensitive to changes in laser power than in laser scanning speed. Increasing linear energy density (increasing laser power or reducing laser scanning speed) promotes the foaming agent activation thereby porosity formation. However, high linear energy density skews pore distribution towards the surface despite forming deeper melt pools. In addition, the impact of additional factors including foaming agent's laser absorptivity and decomposition kinetics with respect to AM time scales should be carefully considered to avoid ineffective activation of foaming agents during the AM of porous metals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The effect of transient needle lift on the internal flow and near-nozzle spray characteristics for modern GDI systems investigated by high-speed X-ray imaging

The development of the injector nozzle is a dynamic area in regard of several technical aspects. At first, the internal flow influences the near-field spray characteristics via various phenomena such as cavitation and turbulence. However, these phenomena are not fully understood due to their extremely fast, complex and multiscale nature. Furthermore, it governs the spray targeting inside the combustion chamber. High-speed X-ray imaging of GDI injector nozzles is performed in this study. The experimental results presented are related to the internal flow and primary breakup of discharged liquid jets. The injectors used are equipped with nozzles made of aluminum which have been specially developed for these investigations to enhance optical accessibility. The visualization of the needle motion, in-nozzle flow and the primary breakup region provides several exciting observations. First, the needle lift tracking exhibits short overshooting right before the steady-state of the injection phase. Additionally, this event leads to a short-term, however, significant change in the associated performance of the breakup. This phenomenon is found to be a consequence of the transient behavior of the in-nozzle flow. It is shown that under some circumstances hydraulic flip may occur during this overshooting period. The primary jet breakup region is visualized and evaluated by means of image processing. Thus, the transient behavior of liquid jet expansion is quantified in the vicinity of the nozzle. It is observed that the liquid jet direction deviates from the hole axis already at the nozzle outlet, which is caused by internal flow characteristics.

42 ENGINEERING↗
DOE OSTI2022

Montage electron tomography of vitrified specimens

Cryo-electron tomography provides detailed views of macromolecules in situ. However, imaging a large field of view to provide more cellular context requires reducing magnification during data collection, which in turn restricts the resolution. To circumvent this trade-off between field of view and resolution, we have developed a montage data collection scheme that uniformly distributes the dose throughout the specimen. In this approach, sets of slightly overlapping circular tiles are collected at high magnification and stitched to form a composite projection image at each tilt angle. These montage tilt-series are then reconstructed into massive tomograms with a small pixel size but a large field of view. For proof-of-principle, we applied this method to the thin edge of HeLa cells. Thon rings to better than 10 Å were detected in the montaged tilt-series, and diverse cellular features were observed in the resulting tomograms. These results indicate that the additional dose required by this technique is not prohibitive to performing structural analysis to intermediate resolution across a large field of view. We anticipate that montage tomography will prove particularly useful for lamellae, increase the likelihood of imaging rare cellular events, and facilitate visual proteomics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Shock Response of Polymethyl Methacrylate (PMMA) Under Explosive Loading

Abstract The dynamic response of polymethyl methacrylate (PMMA) is well understood for one-dimensional planar impact shocks, but limited research has been performed on the response of PMMA under spherical shock loading. In this work, the shock decay of an explosively-driven shock wave into PMMA was experimentally measured. PMMA cubes of various geometries were explosively loaded with an RP-80 detonator to produce the explosive shock wave. High-speed schlieren imaging was implemented to measure the explosively-driven shock wave velocity throughout the PMMA cubes. Photon Doppler velocimetry (PDV) was used to measure the particle velocity imparted by the shock wave at the surface of the cubes. The material shock response was studied at distances from 21.91 to 133.3 mm from the explosive source. The particle velocity history measured by PDV was compared to the wave profile visualized in the high-speed images. The shock wave pulse amplitude decreased with increased distance from the source. The conducted experiments extend the PMMA shock Hugoniot relating to the lower shock and particle velocity regime.

Torres, S. M. (ORCID:0009000287177206)↗
DOE OSTI2025

Observation of Persistent Zero Modes and Superconducting Vortex Doublets in UTe 2

Superconducting vortices can reveal electron pairing details and nucleate topologically protected states. Yet, vortices of bulk spin-triplet superconductors have never been visualized at the atomic scale. Recently, UTe 2 has emerged as a prime spin-triplet superconductor, but its superconducting order parameter is elusive, and whether time-reversal symmetry (TRS) is broken remains unsettled. Here, in this work, we visualize vortices on the (011) surface of ultraclean UTe 2 single crystals (T c = 2.1 K) using scanning tunneling microscopy (STM). We introduce $\frac{d^{2}𝐼}{d𝑉^{2}}$ imaging as an effective technique for vortex visualization in superconductors with substantial residual zero-energy density of states (DOS), as in UTe 2 . Anisotropic single-flux-quantum vortices, with coherence lengths of ∼12 nm (4 nm) parallel (perpendicular) to the a -axis, form a triangular vortex lattice (VL) under a small out-of-plane magnetic field. The invariance of vortex structures and VL under changes of field polarity and cooling history strongly supports time-reversal invariant superconductivity under zero field. At vortex cores (VCs), nonsplit, spectrally sharp zero-bias conductance peaks (ZBPs) persist up 8 T that are consistent with symmetry-protected Majorana zero modes (MZMs) in a topological vortex line. Close examination of vortex structures reveals a mirror-asymmetric doublet─one with ZBPs and another with an enhanced apparent gap, possibly originating from a field-induced multicomponent order parameter.

UTe2↗
DOE OSTI2022

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Valleti, Sai Mani Prudhvi↗
DOE OSTI2025

Implementation of compound refractive lenses for large field-of-view x-ray phase-contrast imaging during hypervelocity impact experiments

Synchrotron x-ray phase-contrast imaging (XPCI) offers time-resolved visualization of dynamic compression phenomena, but its intrinsically small field-of-view (FOV) limits the time that key features remain in frame. A novel approach to enlarge the FOV is achieved by positioning a two-dimensional parabolic compound refractive lens (CRL) upstream of the sample to deliberately defocus the white beam. Ray-tracing simulations and XPCI measurements show that this CRL configuration can expand the beam by ∼50% vertically and ∼15% horizontally based on the full width at half-maximum of the beam. Implementing the CRL, however, attenuates the photon flux and lowers signal-to-noise ratio (SNR). Task-based analysis using a calibration grid (30 μm dots) showed that both setups fail to consistently meet the Rose criterion (SNR ≥ 5) for features of this size in single-bunch imaging. Extrapolating the measured SNR Rose values suggests that the minimum consistently detectable feature lies closer to 30–40 μm for the standard XPCI setup and above 40 μm for CRL-XPCI. Despite this limitation, the CRL configuration nearly doubles the illuminated area, enabling simultaneous tracking of front and rear observations of boron carbide targets subjected to rod and sphere impacts at 1.0–2.6 km/s. Image tracking algorithms and photonic Doppler velocimetry were used to measure penetration and rear-surface velocity histories. Together, these measurements capture crack fronts, penetration, and material breakout, offering new benchmark data for validating high-strain-rate constitutive models of ceramic materials.

Ceramic materials↗
DOE OSTI2021

A Review of Doses for Dental Imaging in 2010–2020 and Development of a Web Dose Calculator

Dental imaging is one of the most common types of diagnostic radiological procedures in modern medicine. We introduce a comprehensive table of organ doses received by patients in dental imaging procedures extracted from literature and a new web application to visualize the summarized dose information. We analyzed articles, published after 2010, from PubMed on organ and effective doses delivered by dental imaging procedures, including intraoral radiography, panoramic radiography, and cone-beam computed tomography (CBCT), and summarized doses by dosimetry method, machine model, patient age, and technical parameters. Mean effective doses delivered by intraoral, 1.32 (0.60–2.56) μSv, and panoramic, 17.93 (3.47–75.00) μSv, procedures were found to be about1% and 15% of that delivered by CBCT, 121.09 (17.10–392.20) μSv, respectively. In CBCT imaging, child phantoms received about 29% more effective dose than the adult phantoms received. The effective dose of a large field of view (FOV) (>150 cm2) was about 1.6 times greater than that of a small FOV (<50 cm2). The maximum CBCT effective dose with a large FOV for children, 392.2 μSv, was about 13% of theeffective dose that a person receives on average every year from natural radiation, 3110 μSv. Monte Carlo simulations of representative cases of the three dental imaging procedures were then conducted to estimate and visualize the dose distribution within the head. The user-friendly interactive web application (available at http://dentaldose.org) receives user input, such as the number of intraoral radiographs taken, and displays total organ and effective doses, dose distribution maps, and a comparison with other medical and natural sources of radiation. The web dose calculator provides a practical resource for patients interested in understanding the radiation doses delivered by dental imaging procedures.

Lee, Hawon↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗
DOE OSTI2021

A Visual Designer of Layer‐wise Relevance Propagation Models

Abstract Layer‐wise Relevance Propagation (LRP) is an emerging and widely‐used method for interpreting the prediction results of convolutional neural networks (CNN). LRP developers often select and employ different relevance backpropagation rules and parameters, to compute relevance scores on input images. However, there exists no obvious solution to define a “best” LRP model. A satisfied model is highly reliant on pertinent images and designers' goals. We develop a visual model designer, named as VisLRPDesigner, to overcome the challenges in the design and use of LRP models. Various LRP rules are unified into an integrated framework with an intuitive workflow of parameter setup. VisLRPDesigner thus allows users to interactively configure and compare LRP models. It also facilitates relevance‐based visual analysis with two important functions: relevance‐based pixel flipping and neuron ablation. Several use cases illustrate the benefits of VisLRPDesigner. The usability and limitation of the visual designer is evaluated by LRP users.

Huang, Xinyi↗
DOE OSTI2025

Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. Here, we propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback.

image segmentation↗