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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

First 3D vector tracking of helium recoils for fast neutron measurements at Super$\mathrm{KEKB}$

Here we present results from the first deployment of novel, high definition, compact gas Time Projection Chambers (TPCs) with pixel chip readout as part of the BEAST II beam background measurement project at SuperKEKB. The TPCs provide detailed 3D imaging of ionization from neutron-induced nuclear recoils in a helium and carbon dioxide target gas mixture at standard temperature and pressure. We present the TPC performance and the neutron backgrounds observed during the initial stage of collider commissioning. We find excellent electron background rejection, leading to background-free nuclear recoil measurements above 50 keV ee , despite the extreme high-background environment. We measure an angular resolution better than 20° for recoil tracks longer than 1.7 mm, corresponding to an average ionization energy of approximately 100 keV ee . We also obtain the full 3D vector direction of helium recoils by utilizing charge profile measurements along the recoil axis, with a correct head/tail assignment efficiency of approximately 80%. With this performance, we present comparisons between measured and simulated event rates, recoil energy spectra, and directional distributions originating from beam–gas and Touschek beam losses at SuperKEKB. We utilize head/tail recognition to distinguish neutron components traveling with positive radial velocity in the Belle II coordinate system from those traveling in the opposite direction. Finally, we present a novel method of discriminating beam–gas interactions from Touschek beam losses that can eliminate the need for dedicated accelerator runs for background measurements. This method is still statistics-limited. However, future studies should be able to verify this method, which in turn could lead to neutron background analysis runs symbiotic with normal Belle II operation. The capabilities demonstrated here also suggest that high definition recoil imaging in gas TPCs is applicable to low energy, low-background experiments, such as directional dark matter searches.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

3D Tomography and Image Processing Using Fast Neutrons Final Report

This document is the final report for project OR16-ML-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons.” This project was motivated by the availability of three-dimensional (3D) tomographic neutron transmission and induced-reaction data following the development of associated-particle imaging (API) systems with two-dimensional (2D) or “area” neutron detectors such as the Advanced Portable Neutron Imaging System, which is capable of simultaneous measurement of transmission and induced-reaction neutrons.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

TorchBraid: High-Performance Layer-Parallel Training of Deep Neural Networks with MPI and GPU Acceleration

TorchBraid is a high-performance implementation of layer-parallel training for deep neural networks (DNNs) supporting MPI-based parallelism and GPU acceleration. Layer-parallel training has been developed to overcome the serialization inherent in forward and backward propagation of DNNs that limits utilization of computational resources in the strong scaling limit. To achieve this, TorchBraid integrates the PyTorch neural network framework with the state-of-the-art XBraid time-parallel library. Furthermore, this article presents the use and performance of TorchBraid, in addition to solutions for overcoming the algorithmic challenges inherent in combining automatic differentiation with layer-parallel. Results are presented with and without GPU acceleration for the Tiny ImageNet and MNIST image classification data sets, as well as recurrent neural networks. Overall, TorchBraid enables fast training of DNNs, both in a strong and weak scaling context. In addition to the TorchBraid software, several new advances in applying layer-parallel algorithms are detailed. Integration of layer-parallel with data-parallel algorithms is presented for the first time, showing the computational advantages of the combination. Standard deep learning techniques, like batch-normalization, are developed for layer-parallel training. Finally, a new approach combining layer-parallel with spatial coarsening in order to accelerate training for 3D image classification shows roughly a 10× speedup over serial execution.

Layer-parallel↗
DOE OSTI2025

Single-distance nano-holotomography with coded apertures

High-resolution phase-contrast 3D imaging using nano-holotomography typically requires collecting multiple tomograms at varying sample-to-detector distances, usually 3 to 4. This multi-distance approach limits temporal resolution, making it impractical for operando studies. Moreover, shifting the sample complicates reconstruction, requiring precise alignment, registration, and interpolation to correct for shift-dependent magnification on the detector. In response, we propose and validate through simulations a novel, to the best of our knowledge, single-distance approach that leverages coded apertures to structure beam illumination while the sample rotates. Finally, this approach relies on a joint reconstruction scheme, which integrates phase retrieval with 3D tomography, ensuring data consistency and achieving artifact-free reconstructions from a single distance, unlocking dynamic experiments.

Nikitin, Viktor [Argonne National Laboratory (ANL)↗
DOE OSTI2023

Three-dimensional topological magnetic monopoles and their interactions in a ferromagnetic meta-lattice

Topological magnetic monopoles (TMMs), also known as hedgehogs or Bloch points, are three-dimensional (3D) nonlocal spin textures that are robust to thermal and quantum fluctuations due to the topology protection. Although TMMs have been observed in skyrmion lattices, spinor Bose–Einstein condensates, chiral magnets, vortex rings and vortex cores, it has been difficult to directly measure the 3D magnetization vector field of TMMs and probe their interactions at the nanoscale. Here, we report the creation of 138 stable TMMs at the specific sites of a ferromagnetic meta-lattice at room temperature. We further develop soft x-ray vector ptycho-tomography to determine the magnetization vector and emergent magnetic field of the TMMs with a 3D spatial resolution of 10 nm. This spatial resolution is comparable to the magnetic exchange length of transition metals, enabling us to probe monopole-monopole interactions. We find that the TMM and anti-TMM pairs are separated by 18.3±1.6 nm, while the TMM and TMM, anti-TMM and anti-TMM pairs are stabilized at comparatively longer distances of 36.1±2.4 nm and 43.1±2.0 nm, respectively. We also observe virtual TMMs created by magnetic voids in the meta-lattice. This work demonstrates that ferromagnetic meta lattices could be used as a platform to create and investigate the interactions and dynamics of TMMs. Furthermore, we expect that soft x-ray vector ptycho-tomography can be broadly applied to quantitatively image 3D vector fields in magnetic and anisotropic materials at the nanoscale.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Optical recognition of constructs using hyperspectral imaging and detection (ORCHID)

Abstract Challenges to deep sample imaging have necessitated the development of special techniques such as spatially offset optical spectroscopy to collect signals that have travelled through several layers of tissue. However, these techniques provide only spectral information in one dimension (i.e., depth). Here, we describe a general and practical method, referred to as Optical Recognition of Constructs Using Hyperspectral Imaging and Detection (ORCHID). The sensing strategy integrates (1) the spatial offset detection concept by computationally binning 2D optical data associated with digital offsets based on selected radial pixel distances from the excitation source; (2) hyperspectral imaging using tunable filter; and (3) digital image binding and collation. ORCHID is a versatile modality that is designed to collect optical signals deep inside samples across three spatial (X, Y, Z) as well as spectral dimensions. The ORCHID method is applicable to various optical techniques that exhibit narrow-band structures, from Raman scattering to quantum dot luminescence. Samples containing surface-enhanced Raman scattering (SERS)-active gold nanostar probes and quantum dots embedded in gel were used to show a proof of principle for the ORCHID concept. The resulting hyperspectral data cube is shown to spatially locate target emitting nanoparticle volumes and provide spectral information for in-depth 3D imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Real-time imaging of acoustic waves in bulk materials with X-ray microscopy

The dynamics of lattice vibrations govern many material processes, such as acoustic wave propagation, displacive phase transitions, and ballistic thermal transport. The maximum velocity of these processes and their effects is determined by the speed of sound, which therefore defines the temporal resolution (picoseconds) needed to resolve these phenomena on their characteristic length scales (nanometers). Here, we present an X-ray microscope capable of imaging acoustic waves with subpicosecond resolution within mm-sized crystals. We directly visualize the generation, propagation, branching, and energy dissipation of longitudinal and transverse acoustic waves in diamond, demonstrating how mechanical energy thermalizes from picosecond to microsecond timescales. Bulk characterization techniques capable of resolving this level of structural detail have previously been available on millisecond time scales—orders of magnitude too slow to capture these fundamental phenomena in solid-state physics and geoscience. As such, the reported results provide broad insights into the interaction of acoustic waves with the structure of materials, and the availability of ultrafast time-resolved dark-field X-ray microscopy opens a vista of new opportunities for 3D imaging of materials dynamics on their intrinsic submicrosecond time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scaling deep learning for material imaging with a pseudo 3D model for domain transfer

The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (10 11 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.

25 ENERGY STORAGE↗
DOE OSTI2026

Combining ToF‐SIMS and Multivariate Analysis to Resolve Active Sites on Ni‐Based HER Catalysts

Unambiguous identification of active sites in heterogeneous catalysis remains a major challenge, particularly for materials with ultrathin, chemically mixed surface layers. Here, we demonstrate a generalizable approach that combines time-of-flight secondary ion mass spectrometry (ToF-SIMS) with multivariate statistical analysis (principal component analysis [PCA] and multivariate curve resolution [MCR]) to resolve catalytically relevant motifs at the nanoscale. Using Ni electrodes as a model system, PCA distinguished hydroxide-enriched domains from oxide- and metal-rich regions, while MCR decomposed depth profiles and 3D images into hydroxide, oxide, and metallic layers with nanometer resolution. A unique secondary-ion fragment, NiO 3 H 3 − (m/z 108.94), emerged as a marker of hydroxide-rich environments and correlated with hydrogen evolution reaction (HER) activity across a series of Ni electrodes. Complementary density functional theory (DFT) calculations revealed that Ni(OH) 2 clusters adjacent to metallic Ni offer the most favorable water dissociation energetics, establishing the structural origin of the marker. While illustrated here for Ni-based HER, this workflow provides a broadly applicable framework to isolate and rank near-surface patterns that govern catalytic activity, thereby extending ToF-SIMS from a qualitative probe to a predictive tool for active site identification.

HER active sites↗
DOE OSTI2024

Development and cross‐validation of a circumference‐based predictive equation to estimate body fat in an active population

Abstract Objective The U.S. Army uses sex‐specific circumference‐based prediction equations to estimate percent body fat (%BF) to evaluate adherence to body composition standards. The equations are periodically evaluated to ensure that they continue to accurately assess %BF in a diverse population. The objective of this study was to develop and validate alternative field expedient equations that may improve upon the current Army Regulation (AR) body fat (%BF) equations. Methods Body size and composition were evaluated in a representatively sampled cohort of 1904 active‐duty Soldiers (1261 Males, 643 Females), using dual‐energy X‐ray absorptiometry (%BF DXA ), and circumferences obtained with 3D imaging and manual measurements. Sex stratified linear prediction equations for %BF were constructed using internal cross validation with %BF DXA as the criterion measure. Prediction equations were evaluated for accuracy and precision using root mean squared error, bias, and intraclass correlations. Equations were externally validated in a convenient sample of 1073 Soldiers. Results Three new equations were developed using one to three circumference sites. The predictive values of waist, abdomen, hip circumference, weight and height were evaluated. Changing from a 3‐site model to a 1‐site model had minimal impact on measurements of model accuracy and performance. Male‐specific equations demonstrated larger gains in accuracy, whereas female‐specific equations resulted in minor improvements in accuracy compared to existing AR equations. Equations performed similarly in the second external validation cohort. Conclusions The equations developed improved upon the current AR equation while demonstrating robust and consistent results within an external population. The 1‐site waist circumference‐based equation utilized the abdominal measurement, which aligns with associated obesity related health outcomes. This could be used to identify individuals at risk for negative health outcomes for earlier intervention.

Taylor, Kathryn M.↗
DOE OSTI2021

Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution

Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of ~ 10 27 yr, requiring suppressing backgrounds to < 1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of ~ 5 when reconstructing electron-positron pairs in the 208Tl 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterráneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of ~ 10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV e - e + pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Plastic shrinkage of mortars cured with a paraffin-based compound – Bimodal neutron/X-ray tomography study

Early-age drying (immediately after casting) of mortars and the corresponding plastic shrinkage were studied using bimodal neutron/X-ray computed tomography. This novel, correlative 3D imaging mode enabled studying simultaneously and without any source of spurious perturbation the water migration and loss processes together with the corresponding deformations due to plastic shrinkage. Bimodal imaging opens up new possibilities for studying dynamic processes of coupled water transport and deformations in porous solids. The measurements were carried out on model systems (cylindrical mortar specimens with height of 19 mm). The study focused on the effect of a paraffin-based curing compound. Our results confirm that when the curing compound was applied directly onto the drying surface in a sufficient amount, both the evaporation rate and the rate of vertical displacement (settlement) were substantially reduced. The results shed a new light on the mechanisms of plastic shrinkage and the action of curing compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Topological analysis of X-ray CT data for the recognition and trending of subtle changes in microstructure under material aging

X-ray computed tomography (CT) is an established non-destructive tool for 3D imaging of multiphasic composites. Numerous applications of X-ray CT in medical diagnosis and materials characterization have been reported, many involving field-specific innovations in the imaging technology itself. Yet, quantitative summarization to link image features to properties of interest has been rare. Here we address this issue by employing state-of-the-art technics in scalar field topology for the summarization of X-ray CT images of an example biphasic system. By varying processing-parameters we create different microstructures, evolve them through accelerated thermal aging, CT-image them pre- and post-aged, and demonstrate the ability of our image summarization method to systematically track process- and age-related changes, which can often be very subtle. A novel aspect of the algorithm involves recognition over multiple resolution levels, which provides deeper insight into the pattern relationship between grain-like features and their neighbors. The method is general, adaptable to diverse image reconstruction methods and materials systems, and particularly useful in applications where practical constraints on the sample-size limits the reliable use of more complex models, e.g., convolutional neural networks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Influence of gradation on shear behavior and pore water response of sand

Here, this paper examines the influence of particle size distribution (PSD) on the shearing behavior of sand using axisymmetric triaxial compression (ATC) testing in conjunction with 3D imaging using synchrotron micro-computed tomography (SMT). Angular sand was sieved to obtain a narrow gradation (uniform) and a wide gradation (non-uniform) with uniformity coefficients (C u ) of 1.2 and 3.0, respectively. Experiments were conducted under drained conditions and a constant effective cell pressure (σ$^{'}_3$) of 50 kPa at low and high back pressures (BP). Results of paired ATC-SMT were analyzed in terms of global constitutive behavior, strain localization, and localized flow characteristics. A new particle tracking algorithm is developed to expose strain localization. Relative particle translation gradient (RPTG) results revealed that gradation affects how early the shear band develops and a tendency for narrow gradation to exhibit varying degrees of diffused shear band at high axial strain (ε 1 ) even though a clear single shear band was observed at the peak strength. Geometric tortuosity (τ) is used as a quantitative parameter to assess flow changes and the efficiency of the flow path. Representative elementary volumes (REVs) inside and outside the shear band were analyzed and the results showed a variation in flow path characteristics where wide gradation specimens had preferential flow paths.

Computed tomography↗
DOE OSTI2024

Understanding a novel form of intergranular corrosion of stainless steel 316L exposed to molten LiCl-Li 2 O-Li

We report stainless steel 316 L exposed to LiCl-Li2O molten salt containing Li metal for 500 and 1000 h experiences deep intergranular attack and bulk void formation. This degradation is due to a synergistic corrosion mechanism, different than other forms of molten salt corrosion, where Cr and Mn oxides and carbides form along grain boundaries and are dissolved by Li metal. Synchrotron transmission X-ray microscopy 3D imaging is used to show corrosion morphology and Cr enrichment and depletion in a novel format that has not previously been used for bulk corrosion samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Microstructure of irradiated AGR TRISO particle buffer layers as measured by X-ray computed tomography

Shrinkage of the initially low-density buffer layer in tristructural isotropic (TRISO) coated fuel particles during irradiation is a well-known phenomenon with potential implications for fission product and actinide transport, as well as potential pyrocarbon fracture that in some cases has been observed to impact particle performance. During post-irradiation examination, the buffer layer's structure is commonly determined using 2D microscopy of a particle cross section or a series of particle cross sections at staggered depths. Although these methods provide a general idea of the irradiated buffer microstructure, they do not provide a full picture of the TRISO coating microstructure. By contrast, x-ray computed tomography (XCT) provides full 3D imaging of the TRISO particle. Particles from various compacts irradiated for the Advanced Gas Reactor Fuel Development and Qualification Program were imaged using XCT. Some of these particles were selected because of abnormal fission product inventories (e.g., low 137 Cs), whereas others were randomly selected from the center of the fission product inventory distribution. Qualitative and quantitative analysis techniques were applied to the randomly selected particles as representatives of typical TRISO behavior to study the post-irradiation structure of the buffer layer. These results showed that, while separation of the buffer and inner pyrolytic carbon layers was a common behavior during the radiation-induced shrinkage of the buffer, a portion of the original buffer/inner pyrolytic carbon interface remained intact throughout irradiation in nearly all cases. Furthermore, these results also indicated clear trends in the degree of buffer densification with irradiation temperature and fluence.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Energy resolution and gain measurements in Argon-based gas mixtures: Exploring Ar:CF 4 for low energy measurements with TPCs

Time Projection Chambers (TPCs) are among the most advanced charged-particle detectors. Gas-filled TPCs have tracking capabilities that provide 3D-imaging of charged particles with a good energy resolution for spectroscopy. Different gas mixtures have different properties that determine the energy resolution as well as the spatial resolution. Therefore, optimization of operating conditions is required to simultaneously obtain adequate gain, energy resolution, spatial/track resolution, as well as higher drift velocities for high counting rates applications. Ar:CF 4 gas mixture has higher electron drift velocities and lower electron diffusion, which makes it an attractive candidate for TPC filling gas for low energy nuclear physics applications as compared to commonly used Ar:CH 4 and Ar:CO 2 gas mixtures, namely when tracking information is needed. However, other properties, including energy resolution and gain, remain largely unexplored in Ar:CF 4 especially at pressures and other operating conditions relevant for low-energy nuclear physics applications. Here, in this paper we report on gain and energy resolution measurements, using Gas Electron Multipliers (GEMs), in the less explored Ar:CF 4 mixture (Alfonsi et al., 2006), as well as in the more commonly used gas mixtures Ar:CH 4 and Ar:CO 2 . In addition to obtaining energy resolution and gain, we provide results from Garfield++ simulations for gain fluctuations, and their impact on energy resolution is discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗