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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A new database website for nuclear level densities

We introduce a new open-access, web-based database (http://nld.ascsn.net), Current Archive of Nuclear Density of Levels (CANDL), that hosts experimental nuclear level density (NLD) datasets from a variety of techniques and energy ranges. Built using the Dash framework in Python, the database is designed to be interactive and user-friendly, allowing researchers to search, visualize, fit, and export NLD data with minimal effort. This resource includes data extracted from evaporation spectra, Oslo method variants, and other experimental techniques that cover excitation energies beyond the neutron resonance region. The database supports on-the-fly fitting with two widely-used phenomenological models—the Constant Temperature (CT) model and the Back-Shifted Fermi Gas (BSFG) model—selected for their simplicity and computational efficiency. Future versions aim to include additional datasets and model types, as well as easy-to-use interfaces to data science techniques. Here, this platform offers a vital tool for the nuclear physics, astrophysics, medicine, and reactor design communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Analysis of time-dependent strain heterogeneity in shales using X-ray microscopy and digital volume correlation

In this paper, we report the results of a 4-dimensional (3 spatial dimensions plus time) in-situ creep experiment on a milli-core shale plug from Barnett formation in Texas. An outstanding challenge in shale mechanics is connecting observable characteristics to predictable mechanical and hydraulic behavior. We employ novel imaging and analysis techniques to statistically link sample mineralogy and structure to observed deformation. In particular, we focus on mechanisms of porosity reduction during transient response to step changes in stress, analogous to the aseismic deformation that occurs after an episode of fracturing. Our main goal in this study is to take the first steps to visualize and capture the small-scale deformation mechanisms involved in creep of shales, using imaging techniques. To do so, we conducted a uniaxial creep experiment inside an Xradia Versa 520 micro-CT system for 12 h at room temperature under a constant uniaxial stress of 25 MPa. We employed a Digital Volume Correlation (DVC) method to measure micro-scale deforspmation of the sample by comparing the resulting volumetric images with a high-resolution scan of the sample obtained prior to the creep experiment. The results indicate that the time-dependent deformation in the compliant clay plus kerogen matrix constituents facilitates rigid grain rotation and compaction of intergranular porosity at the boundary between rigid grains and matrix.

58 GEOSCIENCES↗
DOE OSTI2020

Characterizing Dislocation Defects in Dark Field X-ray Microscopy Images of Bulk Aluminum

Material defects play a large role in material response under shock loading, yet the process of how these defects initiate, propagate, and annihilate is not well understood. Using a newly developed diagnostic, dark field X-ray microscopy (DFXM), we can now visualize the behavior of dislocation defects in materials at the mesoscale under varying conditions. Using data from the European Synchrotron Radiation Facility, we apply a variety of image processing techniques to capture relevant features to locate and characterize size and orientation of dislocation defects in bulk aluminum DFXM images. Beyond simple visualization, this analysis drives statistical characterization of the defects and their dynamic response to further improve the relevant physics models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Potential annual daylighting performance of a high-efficiency daylight redirecting slat system

While the primary role of window attachments is often to moderate glare and solar heat gains, they are also able to provide additional daylight to interior spaces. For this purpose, a variety of daylight-redirecting window systems have been developed over the past 150 years. Fixed reflective systems (slats/light shelves) or prismatic systems that rely on total internal reflection work well under specific solar conditions, but generally sacrifice performance over a much wider range of incident solar angles and sky conditions. Dynamic systems - typically reflective slats - are more responsive to sun angles but have not been able to achieve optimal performance for glare and daylight redirection efficiency. A previous investigation into an adjustable, reflective blind concept first conceived of in the late 1970s showed promise but was not reduced to practice due to lack of adequate simulation and analysis tools. In this paper, this concept is further developed and its energy and visual comfort performance evaluated for four mid-latitude, temperate climates using ray-tracing simulation techniques. Overall, results indicate significant potential lighting energy savings when compared with conventional automated reflective blinds (2.1–4.9 kWh/(m 2 ·a), or 14%–42%, depending on climate and orientation) or, especially, manually-operated matte white venetian blinds (1.4–7.9 kWh/(m 2 ·a), or 9%–54%, depending on climate and orientation), while maintaining acceptable or better visual comfort conditions throughout the interior space.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Direct Fabrication of Atomically Defined Pores in MXenes Using Feedback-Driven STEM

Controlled fabrication of nanopores in 2D materials offer the means to create robust membranes needed for ion transport and nanofiltration. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation; however, aberration-corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a sub-Å sized electron beam for atomic manipulation along with atomic resolution imaging. Here, for this work, a method for automated nanopore fabrication is utilized with real-time atomic visualization to enhance the mechanistic understanding of beam-induced transformations. Additionally, an electron beam simulation technique, Electron-Beam Simulator (E-BeamSim) is developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the MXene Ti 3 C 2 T x , the influence of temperature on nanopore fabrication is explored by tracking atomic transformations and find that at room temperature the electron beam irradiation induces random displacement and results in titanium pileups at the nanopore edge, which is confirmed by E-BeamSim. At elevated temperatures, after removal of the surface functional groups and with the increased mobility of atoms results in atomic transformations that lead to the selective removal of atoms layer by layer. This work can lead to the development of defect engineering techniques within functionalized MXene layers and other 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
DOE OSTI2026

MAGIC: M arching Cubes Isosurface Uncertainty Visualization for G auss i an Uncertain Data With Spatial C orrelation

Here, in this paper, we study the propagation of data uncertainty through the marching cubes algorithm for isosurface visualization for correlated uncertain data. Consideration of correlation has been shown paramount for avoiding errors in uncertainty quantification and visualization in multiple prior studies. Although the problem of isosurface uncertainty with spatial data correlation has been previously addressed, there are two major limitations to prior treatments. First, there are no analytical formulations for uncertainty quantification of isosurfaces when the data uncertainty is characterized by a Gaussian distribution with spatial correlation. Second, as a consequence of the lack of analytical formulations,existing techniques resort to a Monte Carlo sampling approach, which is expensive and difficult to integrate into visualization tools. To address these limitations, we present a closed-form framework to efficiently derive uncertainty in marching cubes level-sets for Gaussian uncertain data with spatial correlation (MAGIC). To derive closed-form solutions, we leverage the Hinkley's derivation on the ratio of Gaussian distributions. With our analytical framework, we achieve a significant speed-up and enhanced accuracy of uncertainty quantification over classical Monte Carlo methods. We further accelerate our analytical solutions using many-core processors to achieve speed-ups up to 585× and integrability with production visualization tools for broader impact. We demonstrate the effectiveness of our correlation-aware uncertainty framework through experiments on meteorology, urban flow, and astrophysics simulation datasets.

Gaussian↗
DOE OSTI2026

Novel thermal energy storage component: Development, performance, and phase transition diagnosis

Thermal energy storage (TES) using phase change materials (PCMs) is a promising technology for capturing and storing excess thermal energy for later use. However, challenges such as poor heat transfer efficiency and a lack of modular, scalable designs have limited widespread adoption of TES in real-world applications. This study developed and evaluated modular brick-type and blade-type TES prototypes featuring an aluminum housing, an embedded serpentine coil for active or passive thermal exchange, and a cost-effective metal mesh to enhance PCM thermal conductivity. The blade-type TES achieved notable geometric efficiency, with a thickness-to-length ratio of 0.03 and a thickness-to-width ratio of 0.08, enabling highly compact and modular thermal storage suitable for space-constrained applications. The paper presents a detailed evaluation of the TES prototypes’ performance. The comparative analysis indicated that the TES prototypes provide a highly cost-effective, thermally optimized alternative for compact energy storage and load shifting. A novel diagnostic technique was also introduced: using a portable endoscope to capture real-time visualizations of PCM phase transitions inside the TES. This method provides critical insights into internal heat transfer mechanisms, identifies potential issues, and offers valuable support for optimizing the TES design and developing the control algorithm. Overall, the modular brick-type and blade-type TES designs demonstrated in this work provide a scalable, efficient, and economically viable solution for advancing TES across residential, commercial, and industrial sectors. The designs’ compact structure, enhanced thermal performance, and integrated diagnostic capabilities make them strong candidates for future deployment in energy-efficient systems.

Gao, Zhiming [ORNL] (ORCID:0000000271397995)↗
DOE OSTI2021

Nonlinear nanoelectrodynamics of a Weyl metal

Chiral Weyl fermions with linear energy-momentum dispersion in the bulk accompanied by Fermi-arc states on the surfaces prompt a host of enticing optical effects. While new Weyl semimetal materials keep emerging, the available optical probes are limited. In particular, isolating bulk and surface electrodynamics in Weyl conductors remains a challenge. We devised an approach to the problem based on near-field photocurrent imaging at the nanoscale and applied this technique to a prototypical Weyl semimetal TaIrTe 4 . As a first step, we visualized nano-photocurrent patterns in real space and demonstrated their connection to bulk nonlinear conductivity tensors through extensive modeling augmented with density functional theory calculations. Notably, our nanoscale probe gives access to not only the in-plane but also the out-of-plane electric fields so that it is feasible to interrogate all allowed nonlinear tensors including those that remained dormant in conventional far-field optics. Surface- and bulk-related nonlinear contributions are distinguished through their “symmetry fingerprints” in the photocurrent maps. Robust photocurrents also appear at mirror-symmetry breaking edges of TaIrTe 4 single crystals that we assign to nonlinear conductivity tensors forbidden in the bulk. Here, nano-photocurrent spectroscopy at the boundary reveals a strong resonance structure absent in the interior of the sample, providing evidence for elusive surface states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Project Fringe, Testing and Validation on Metal Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Additive Monitoring Systems (AMS) to demonstrate the real-time inspection of additive manufacturing (AM) binder jet fusion 3D printing (BJF). Test coupons on the ExOne Innovent were printed and monitored using AMS’s Project Fringe structured light system. Anomalies were detected automatically and classified post build as recoater powder layer non-uniformities, binder powder interaction, depressions, and sub optimal print area. These anomalies were not apparent in the visual spectrum, which suggests that the structured light system has advanced the monitoring techniques state-of-the-art for BJF.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chiral population analysis: a real space visualization of X-ray circular dichroism

The microscopic understanding of probing and controlling molecular chirality is of considerable interest. Numerous spectroscopic techniques are capable of monitoring molecular asymmetry and its consequences, ranging from the infrared to the X-ray regime. Resonant X-rays have long been used to investigate local atomic sites within molecules thanks to the localized nature of core electronic transitions. These techniques can be used to determine the extent to which chirality is a local versus a delocalized property. However, how to systematically partition dichroic contributions from the point of view of electronic structure simulations remains an open question. Here, we introduce the concept of chiral population analysis that connects chirality to the atomic orbital picture. In analogy with Mulliken population analysis, which assigns charges to atomic orbitals, chiral populations allow the dichroic response to be distributed among the participating atomic orbitals. This decomposition can be further visualized in real space by representing it in terms of isosurface plots, providing an intuitive way to connect the dichroic response to its origins. Thus chiral population analysis can be particularly useful to assess the extent to which a given electronic transition is sensitive to chirality as a local or global feature of the molecular geometry.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probing polarization dynamics at specific domain configurations: Computer-vision based automated experiment in piezoresponse force microscopy

Topological defects in ferroelectric materials have attracted much attention due to the emergence of conductive, ferroic, and magnetic functionalities. However, many topological configurations dynamically evolve during the switching processes, making them a challenge to characterize via traditional techniques. Here, we implement an automated experimentation approach for the exploration of functional properties in BiFeO 3 thin films. Specifically, we visualize the ferroelectric domain structures via single frequency piezoresponse force microscopy and implement a computer vision-based algorithm to discover features of interest at which spectroscopic measurements are taken. Subsequently, we employ dimensionality reduction techniques to reveal characteristic polarization behaviors at these features. This approach can be extended to other spectroscopies and modalities to probe only specific features of interest, ultimately enabling dynamical processes in ferroelectrics to be studied.

36 MATERIALS SCIENCE↗
DOE OSTI2022

In situ visualization of multicomponents coevolution in a battery pouch cell

Lithium-ion battery (LIB) is a broadly adopted technology for energy storage. With increasing demands to improve the rate capability, cyclability, energy density, safety, and cost efficiency, it is crucial to establish an in-depth understanding of the detailed structural evolution and cell-degradation mechanisms during battery operation. Here, we present a laboratory-based high-resolution and high-throughput X-ray micro–computed laminography approach, which is capable of in situ visualizing of an industry-relevant lithium-ion (Li-ion) pouch cell with superior detection fidelity, resolution, and reliability. This technique enables imaging of the pouch cell at a spatial resolution of 0.5 μm in a laboratory system and permits the identification of submicron features within cathode and anode electrodes. We also demonstrate direct visualization of the lithium plating in the imaged pouch cell, which is an important phenomenon relevant to battery fast charging and low-temperature cycling. Our development presents an avenue toward a thorough understanding of the correlation among multiscale structures, chemomechanical degradation, and electrochemical behavior of industry-scale battery pouch cells.

25 ENERGY STORAGE↗
DOE OSTI2025

Resolving three-dimensional nanoscale heterogeneities in lithium metal batteries with cryoelectron tomography

Current direct observation of sensitive battery materials and interfaces primarily relies on two-dimensional (2D) imaging, leaving out their three-dimensional (3D) relationship. Here, in this study, we used cryoelectron tomography (cryo-ET) to visualize the lithium metal anode in 3D at nanometer resolution and cryoelectron microscopy (cryo-EM) to reveal atomic details in local regions. We imaged both freshly prepared and calendar-aged Li metal anodes to reveal the development of LiH in Li dendrites and the Li-LiH interface, as well as the development of the solid-electrolyte interphase (SEI). Using a convolutional neural network-based technique, the 3D arrangement of Li metal, along with nanoscale LiH and Cu heterogeneities in dendrites, was visualized and annotated. In longer-term calendar aging, we observed more substantial LiH growth accompanied by extended SEI growth. Our results show that the growth of LiH and the extended SEI during battery calendar aging are temporally and spatially separate processes.

LiH↗
DOE OSTI2025

Biogeochemical Controls on Wood Degradation as a Source of Bioavailable Carbon in Denitrifying Bioreactors

Woodchip bioreactors (WBRs) are important tools for the removal of nitrate in agricultural drainage, but their effectiveness is often limited by the slow degradation of lignocellulosic wood residues into bioavailable forms of carbon that fuel denitrifying microorganisms. Here, we examine biogeochemical factors regulating wood degradation in saturated woodchip beds, with a focus on the effects of dissolved oxygen (DO), iron (Fe), and manganese (Mn) in generating oxidative activity that can enhance wood decomposition. Woodchips from a 10-year-old WBR were characterized with bulk techniques and a novel combination of μX-ray scattering, μXRF, and μXANES to visualize the depletion of crystalline cellulose as a proxy for wood degradation. Woodchips from upstream portions of the reactor exhibited the greatest degradation, probably due to greater DO exposure, and degradation was localized to a 100 to 200 μm thick surface layer that was also associated with higher concentrations of Fe and Mn. Greater degradation was associated with faster nitrate removal. μXANES analysis of Fe and Mn in the surface layer indicated the presence of a microenvironment in which oxygenation reactions of Fe(II) and Mn(II) could contribute to the formation of reactive oxidants involved in the degradation of lignocellulose. In conclusion, our results provide new insight into biogeochemical properties that influence wood decomposition in WBRs at both micro- and macroscales and how these wood degradation processes are coupled with denitrification.

36 MATERIALS SCIENCE↗
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 OSTI2023

Enabling quantitative analysis of complex polymer blends by infrared nanospectroscopy and isotopic deuteration

Atomic-force microscopy coupled with infrared spectroscopy (AFM-IR) deciphers surface morphology of thin-film polymer blends and composites by simultaneously mapping physical topography and chemical composition. However, acquiring quantitative phase and composition information from multi-component blends can be challenging using AFM-IR due to the possible overlapping infrared absorption bands between different species. Isotope labeling one of the blend components introduces a new type of bond (carbon-deuterium vibration) that can be targeted using AFM-IR and responds at wavelengths sufficiently shifted toward unoccupied regions (around 2200 cm –1 ). In this project, AFM-IR was used to probe the surface morphology and chemical composition of three polymer blends containing deuterated polystyrene; each blend is expected to exhibit various degrees of miscibility. AFM-IR results successfully demonstrated that deuterium labeling prevents infrared spectral overlap and enables the visualization of blend phases that could not normally be distinguished by other scanning probe techniques. The nanoscale domain composition was resolved by fast infrared spectrum analysis. Overall, we presented isotope labeling as a robust approach for circumventing obstacles preventing the quantitative analysis of multiphase systems by AFM-IR.

36 MATERIALS SCIENCE↗
DOE OSTI2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ${\approx}2.3$ the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗