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DOE OSTI2024

Dynamic response of additively manufactured Ti–5Al–5V–5Mo–3Cr as a function of heat treatment

Both shock and shockless compression experiments were performed on laser powder bed fusion (LPBF) Ti–5Al–5V–5Mo–3Cr (Ti-5553) to peak compressive stresses near 15 GPa. Experiments were performed on the as-built material, containing a purely β (body centered cubic) microstructure, and two differing heat treatments resulting in a dual phase α (hexagonal close packed) and β microstructure. The Hugoniot, Hugoniot elastic limit (HEL), and spallation strength were measured and compared to wrought Ti-6Al-4V (Ti-64). The results indicate the LPBF Ti-5553 Hugoniot response is similar between heat treatments and to Ti-64. The HEL stress observed in the LPBF Ti-5553 was considerably higher than Ti-64, with the as-built, fully β alloy exhibiting the largest values. The spallation strength of the LPBF Ti-5553 was also similar to Ti-64. Clear evidence of initial porosity serving as initiation sites for spallation damage was observed when comparing computed tomography measurements before and after loading. Post-mortem scanning electron microscopy images of the recovered spallation samples showed no evidence of retained phase changes near the spall plane. The spall plane was found to have kinks aligned with the loading direction near areas with large concentrations of twin-like, crystallographic defects in the as-built condition. For the heat-treated samples, the concentrations of twin-like, crystallographic defects were absent, and no preference for failure at the interface between the α and β phases was observed.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Determining the nanostructure of polymer foams using 3D ptycho-tomography for inertial fusion energy applications

Polymer foams play a critical role in contemporary inertial fusion energy (IFE) target designs by enhancing energy yield and optimizing implosion dynamics. However, the lack of high-resolution characterization of the nanostructure of these foams restricts progress in fusion science. In this work, we demonstrate the first high-resolution three-dimensional (3D) reconstruction of a low-density, Si-doped polymer foam fabricated via two-photon polymerization, using ptychographic x-ray computed tomography (PXCT) at an x-ray free electron laser (XFEL). This imaging method reconstructs two-dimensional (2D) attenuation and phase information at multiple sample angles that are combined into a 3D density map used to extract local mass density and determine structural dimensions. We achieve a 2D spatial resolution of 19 ± 3 nm on a high-contrast Ronchi pattern target and 78.7 ± 3 nm for low-contrast polymer foams, marking a significant advancement for XFEL-based ptychography of low-density materials. Furthermore, our experimental results reveal an average foam strut thickness of 1.17 ± 0.4 μm, consistent with fabrication expectations, and a reconstructed average mass density of 0.35 g/cc, aligning closely with the predicted density of 0.29 g/cc. These findings provide important insights for improving foam design and refining radiation hydrodynamics modeling in future IFE experiments. Our study establishes PXCT at an XFEL as a powerful tool for high-resolution characterization of fusion-relevant materials, paving the way for enhanced target performance in IFE research.

Hancock, Levi [Colorado State Univ., Fort Collins,↗

DOE OSTI2026

Practical Three-Dimensional Curvilinear Aperture Monopulse Synthetic Aperture Radar

When collected over a curved path, Synthetic Aperture Radar (SAR) contains information about the three-dimensional content of a scene that can aid interpretation of this difficult imaging modality. In a point scatterer simulation with amplitudes and heights derived from a real SAR image pair, we supplement confocal processing with a monopulse-based algorithm to compute heights of certain bright points, which we find automatically. In SAR, bright points often reflect man-made infrastructure central to scene interpretation.

Story, Mark [ORNL] (ORCID:0000000289148911)↗

DOE OSTI2022

O 2 based resonantly ionized photoemission thermometry analysis of supersonic flows

Characterization of the thermal gradients within supersonic and hypersonic flows is essential for understanding transition, turbulence, and aerodynamic heating. Developments in novel, impactful non-intrusive techniques are key for enabling flow characterizations of sufficient detail that provide experimental validation datasets for computational simulations. In this work, Resonantly Ionized Photoemission Thermometry (RIPT) signals are directly imaged using an ICCD camera to realize the techniques 1D measurement capability for the first time. The direct imaging scheme presented for oxygen-based RIPT (O 2 RIPT) uses the previously established calibration data to direct excite various resonant rotational peaks within the S-branch of the C 3 Π, ( v = 2) ← X 3 Σ( v ′ = 0) absorption band of O 2 . The efficient ionization of O 2 liberates electrons that induce electron avalanche ionization of local N 2 molecules generating N 2 + , which primarily deexcites via photoemissions of the first negative band of N 2 + ( B 2 Σ <#comment/> u + − <#comment/> X 2 Σ <#comment/> g + ) . When sufficient lasing energy is used, the ionization region and subsequent photoemission signal is achieved along a 1D line thus, if directly imaged can allow for gas temperature assignments along said line; demonstrated here of up to five centimeters in length. The temperature gradients present within the ensuing shock train of a supersonic under expanded free jet serves as a basis of characterization for this new RIPT imaging scheme. The O 2 RIPT results are extensively compared and validated against well-known and established techniques (i.e., CARS and CFD). The direct imaging capability fully realizes the technique’s fundamental potential and is expected to be the standard of implementation going forward. The direct imaging capability can play instrumental roles in future scientific studies that rely upon acute characterization of thermal gradients within a medium that cannot be easily resolved by a point. Furthermore, the removal of the spectrometer greatly reduces the cost, complexity, and optical alignment associated with prior RIPT measurements.

42 ENGINEERING↗

DOE OSTI2022

Probing the solidification of quasicrystals via joint experiment and simulation (Final Report)

Quasicrystals (QCs) possess long-range positional order but non-crystallographic orientational order. Their classically ‘forbidden’ symmetry has long attracted the interest of scientists worldwide. Despite their frequent observation in both metal alloys and soft matter structures in the 35 years since their discovery, little is known about how QCs evolve from a liquid, amorphous, or crystalline precursor. In this project, we sought to resolve the enigma of QC self-assembly through a combined experimental and computational program. The Shahani group employed in situ electron and synchrotron X ray imaging to peer into the growth dynamics of QCs in a liquid, covering a broad range of length scales and solidification pathways. Glotzer's team developed new simulation models incorporating phasonic defects — which are unique to quasicrystals — and used this model to investigate two grains growing together, closely mimicking the experimental conditions. While QCs remain exceptional structures, most compounds in the realm of intermetallics adopt non-trivial geometries. In fact, only around 6% of phases are comprised of the simplest sphere packings that researchers so readily associate with metals. State-of-the-art knowledge of phase transformations at the outset of this collaborative project did not encompass the remaining 94% of intermetallic compounds that possess complex and aperiodic structure types. Therefore, we expect that outcomes from this project will have immediate and profound impact on synthesis and processing science: our efforts will be used to not only explain the growth dynamics of QCs but also complex intermetallics more broadly.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Measuring Multicomponent Adsorption of Tracer Gases on Natural Zeolites

A natural clinoptilolite sample near the Nevada National Security Site was obtained to study adsorption and retardation on gas transport. Of interest is understanding the competition for adsorption sites that may reduce tracer gas adsorption relative to single-component measurements, which may be affected by the multi-scale pore structure of clinoptilolite. Clinoptilolite has three distinct domains of pore size distributions ranging from nanometers to micrometers: micropores with 0.4–0.7 nm diameters, measured on powders by CO2 adsorption at 273 K, representing the zeolite cages; mesopores with 4–200 nm diameters, observed using liquid nitrogen adsorption at 77 K; and macropores with 300–1000 nm diameters, measured by mercury injection on rock chips (~ 100 mesh), likely representing the microfractures. These pore size distributions are consistent with X-ray computed tomography (CT) and focused ion beam scanning electron microscope (FIB-SEM) images, which are used to construct the three-dimensional (3D) pore network to be used in future gas transport modeling. To quantify tracer gas adsorption in this multi-scale pore structure and multicomponent gas species environment, natural zeolite samples initially in equilibrium in air were exposed to a mixture of tracer gases. As the tracer gases diffuse and adsorb in the sample, the remaining tracer gases outside the sample fractionate. Using a quadrupole mass spectrometer to quantify this fractionation, the degree of adsorption of tracer gases in the multicomponent gas environment and multi-scale pore structure is assessed. The major finding is that Kr reaches equilibrium much faster than Xe in the presence of ambient air, which leads to more Kr uptake than Xe over limited exposure periods. When the clinoptilolite chips were exposed to humid air, the adsorption capability decreases significantly for both Xe and Kr with relative humidity (RH) as low as 3%. Both Xe and Kr reaches equilibrium faster at higher RH. The different, unexpected, adsorption behavior for Xe and Kr is due to their kinetic diameters similar to the micropores in clinoptilolite which makes it harder for Xe to access compared to Kr.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Microstructure of Neutron-Irradiated Al 3 Hf-Al Thermal Neutron Absorber Materials

A thermal neutron-absorbing metal matrix composite (MMC) comprised of Al 3 Hf particles in an aluminum matrix was developed to filter out thermal neutrons and create a fast flux environment for material testing in a mixed-spectrum nuclear reactor. Intermetallic Al 3 Hf particles capture thermal neutrons and are embedded in a highly conductive aluminum matrix that provides conductive cooling of the heat generated due to thermal neutron capture by the hafnium. These Al 3 Hf-Al MMCs were fabricated using powder metallurgy via hot pressing. The specimens were neutron-irradiated to between 1.12 and 5.38 dpa and temperatures ranging from 286 °C to 400 °C. The post-irradiation examination included microstructure characterization using transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy. This study reports the microstructural observations of four irradiated samples and one unirradiated control sample. All the samples showed the presence of oxide at the particle–matrix interface. The irradiated specimens revealed needle-like structures that extended from the surface of the Al 3 Hf particles into the Al matrix. An automated segmentation tool was implemented based on a YOLO11 computer vision-based approach to identify dislocation lines and loops in TEM images of the irradiated Al-Al 3 Hf MMCs. This work provides insight into the microstructural stability of Al 3 Hf-Al MMCs under irradiation, supporting their consideration as a novel neutron absorber that enables advanced spectral tailoring.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Passive method to measure strength of turbulence

Disclosed is a method to passively measure and calculate the strength of turbulence via the index of refraction structure constant Cn2 from video imagery gathered by an imaging device, such as a video camera. Processing may occur with any type computing device utilizing a processor executing machine executable code stored on memory. This method significantly simplifies instrumentation requirements, reduces cost, and provides rapid data output. This method combines an angle of arrival methodology, which provides scale factors, with a new spatial/temporal frequency domain method. As part of the development process, video imagery from high speed cameras was collected and analyzed. The data was decimated to video rates such that statistics could be computed and used to confirm that this passive method accurately characterizes the atmospheric turbulence. Cn2 accuracy from this method compared well with scintillometer data through two full orders of magnitude and more capability is expected beyond this verification.

O'Neill, Mary Morabito↗

DOE OSTI2021

High-energy X-ray source and detector for wellbore inspection

A system, method, and apparatus for wellbore inspection comprise an electron accelerator to generate X-rays, a rotating collimator assembly configured to produce a cone of X-rays, and at least one detector assembly configured to collect backscattered X-rays. A position assembly can be provided to move the electron accelerator, rotating collimator assembly, and detector through a wellbore. A computer system is configured to receive data from the detector and generate an image of the wellbore.

Kroc, Thomas K.↗

DOE OSTI2023

Inspection system of wellbores and surrounding rock using penetrating X-rays

Kroc, Thomas K.↗

DOE OSTI2024

Poster for 12th World Conference for Neutron Radiography (WCNR-12)

Complimentary Relationships Between Neutron Computed Radiography and Digital X-ray Radiography of Radioactive Specimens The Transient Reactor Test (TREAT) Facility is a 19,000 MW reactor located at the Idaho National Laboratory which is used for transient testing of materials and experiments. A neutron radiography facility is installed which allows for low resolution neutron imaging with a capability of items up to 20 cm wide and 400 cm long. Along with the neutron radiography facility, a 370 kV digital X-ray system is located onsite for when X-ray imaging is more practical or when rapid imaging is requested. The X-ray facility can accommodate specimens up to 61 cm wide and 1000 cm long. This poster discusses the complimentary relationship between transfer-method Neutron Computed Radiography (nCR) and Digital X-ray Radiography (DR) imaging of radioactive specimens along with practical applications for nuclear fuels and materials testing programs. Both nCR and DR images were acquired of inert gas and water filled apparatus containing differing materials at varying radiation levels. Highly radioactive items have no effect on nCR image quality whereas radioactivity has varying degrees of effect in DR. Radioactive items as high as 20 mSv/h have been successfully imaged using X-ray DR methods.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

High spatial resolution neutron imaging of lithium-ion batteries: Correlating microstructure and lithium transport

Thick electrodes for lithium-ion batteries can increase the overall energy density, but increasing the electrode thickness introduces charge transport limitations. These limitations may be mitigated through proper electrode structuring. Here, high spatial resolution neutron imaging was used to understand the correlation between microstructure and lithium transport in lithium-ion anodes. Batteries with distinct graphite anode microstructures were produced and studied with high spatial resolution in operando neutron radiography to observe the effects of structure on transport. High spatial resolution neutron computed tomography was performed following in operando neutron radiography. X-ray computed tomography and scanning electron microscopy were used to observe the finer scale anode structure to complement neutron imaging. Solvent-free anodes containing a tightly-packed layered structure confined lithium movement close to the separator. This structure limited capacity, but supported better rate capability. Conversely, a more open pore structure in the wet cast anodes yielded higher capacity with reduced rate capability. Together, these results show that lithium distributions can be controlled by the macroscopic structure of the electrodes, the microstructural pore network, and the microscale active areas that support electrochemical reactions. Furthermore, multimodal imaging applying the complementary strengths of neutron and X-ray methods is shown as a tool for advancing battery design.

25 ENERGY STORAGE↗

DOE OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗

DOE OSTI2021

SU(n) and Quantum SU(n) Symmetries in Physical Systems [Slides]

Presence of SU(n) or other Lie group symmetry in a physical system is its powerful, usually underutilized property. In many cases it allows for finding analytical solutions to nonlinear differential equations describing this system. Power of the method is presented on diversified examples from mathematical physics: Lie-group symmetries in finding solutions of generalized, multidimensional theory of gravity; analytical Dirac–equation solutions for description of conducting polymers; stability of qubit states in quantum computers; spatial defects in condensed matter; reconstruction of 3D object from its 2D tomographic image; significant improvement of numerical solutions stability for Euler equations. The next question after obtaining such Lie group symmetric solution is: does a generalized solution with appropriate quantum group symmetry exists for the given physical system, and if yes what is the physical meaning of the deformation parameter q introduced by such solution. In many cases it can be identified. Any SU(n) solution is by its nature singular, assuming a perfect symmetry of the physical system discussed. Such solution gives a powerful insight to theoretical physics, yet the assumption may be too demanding for experimental applications. Deformation parameter q from a quantum group symmetry allows for a continuum of solutions, more applicable to experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗

DOE OSTI2023

NWChem: Recent and Ongoing Developments

In this paper we summarize developments in the NWChem computational chemistry suite since the last major release (NWChem 7.0). Specifically, we focus on functionalities, along with input blocks, that are currently accessible in the current stable release (NWChem 7.2) and master branches, interfaces to quantum computing simulators, interfaces to external libraries, the NWChem GitHub repository, and containerization of NWChem executable images. In conclusion, some of the ongoing developments that will be available in the near future are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

SpaceNet 9—Cross-Sensor Alignment of Optical and SAR Imagery

Precise registration of high-resolution synthetic aperture radar (SAR) and optical imagery is necessary for realizing the full potential and benefits of multimodal image analysis. However, two significant challenges presently exist. First, there is a lack of annotated datasets and benchmarks available for high-resolution SAR–optical image registration. Second, an assessment of efficient and reliable image registration methods that can precisely align these modalities is lacking. Here, we present a holistic description of the SpaceNet 9 Challenge and its results. We present a description of the dataset and baseline algorithm along with the results of the challenge, including a description of the winning algorithms. We release the SpaceNet 9 dataset along with open-sourcing the winning algorithms and baseline. The objective of SpaceNet 9 was to compute a dense displacement map that indicates the shift needed to align pixels in an optical image to the pixels in a SAR image. The challenge launched in April 2025 and was active for approximately two months. The top five solutions reduced image alignment error from approximately 34 m to under 13 m for public and private test data, with the best results obtaining a registration error of only 8.5 and 6.7 m on the public testing and private testing dataset, respectively. Usage of pretrained image matching models, robust outlier rejection with RANSAC, and estimating local displacement were common among the top solutions. The results of this challenge provide insight into high-resolution SAR–optical image registration and offer opportunities for future benchmarking in this domain. The baseline algorithm, winning solutions, and datasets are available at https://spacenet.ai/sn9-challenge/.

benchmark datasets↗

DOE OSTI2022

Layer Time Control for Large Scale Additive Manufacturing Using High Performance Computing

This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 30% build time reduction for simple geometries. This proposed work would use high-performance computing (HPC) to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost. We have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale AM via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreements with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation. In addition to the development of a layer time optimization method, we have developed a numerical framework for AM simulation with element activations in sync with toolpath, based on an open source finite element framework, DEAL.II. A major portion of this work was presented at SAMPE 2022 Conference and Exhibition on May 2022, and published in Proceedings of SAMPE 2022.

42 ENGINEERING↗