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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

INR-TEM: Robust cavity detection in multifocus TEM images via implicit neural representations

When characterizing materials using transmission electron microscopy (TEM) images, detecting and quantifying small features in microstructures, such as cavities, pose significant challenges. Off-the-shelf object detection models, including YOLOv8, show considerable performance degradation, particularly when images vary in resolution and the objects of interest possess a low percentage of the total image region of interest. In this study, we introduce a novel detection pipeline that incorporates an implicit neural representation (INR)-based detection method, INR-TEM, and two-modality imaging (e.g., under-focused and over-focused images typically acquired during materials characterization) to improve object detection performance. The INR-TEM method incorporates a pixel-wise prediction principle inspired by pixel-wise centerness weighting. INR-TEM demonstrates superior robustness to resolution variability, maintaining high detection accuracy even at low image resolutions compared to YOLOv8. To leverage INR-TEM effectively in real-world two-modality characterization applications, we further integrate a two-stage motion correction pipeline designed explicitly for aligning multifocus TEM images. The alignment process, comprising keypoint (based on scale-invariant feature transform, SIFT) and intensity matching, significantly mitigates the adverse effects of perceived motion-induced image degradation during through-focal TEM imaging, directly enhancing INR-TEM’s detection capability over conventional single-focus images. Our integrated INR-TEM cavity detection framework notably improves performance across various cavity sizes, outperforming off-the-shelf YOLOv8 detections that rely on a single image modality.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Neural Phase Simulation

The Neural Phase Simulation (NPS) is a package of codes for simulating microstructure evolution and accelerated molecular dynamics with deep neural-networks based surrogate models. NPS is designed to offer quantitatively accurate and computationally efficient simulation capabilities by leveraging modern machine-learning techniques. The primary intended use cases of NPS are training neural network surrogate models, though performing simulations on a single node is also supported. The NPS surrogate models can be trained from ground truth simulation methods, which are supposed to be accurate but expensive, such as molecular dynamics, phase field methods, kinetic Monte Carlo and discrete dislocation dynamics.

Zhou, Fei↗
DOE OSTI2025

Characterization and Quantification of Radiation-Induced Clusters/Precipitates in RPV Steels Using STEM-EDS and Machine Learning

Over the operational lifespan of a nuclear reactor, reactor pressure vessel (RPV) steels are subjected to significant neutron irradiation, resulting in complex microstructural changes and the consequent degradation of mechanical properties. Various physically motivated correlation models have been developed to predict neutron irradiation-induced embrittlement of RPVs under different irradiation conditions. However, the efficient and accurate characterizations and quantification of radiation-induced clusters in RPVs are still challenging, which will affect the precision of the predictive models for embrittlement of RPV components. In the DOE Visiting Faculty Program (VFP) research work at Oak Ridge National Lab (ORNL), I integrate machine learning to aid Scanning Transmission Electron Microscopy – Energy Dispersive X-ray Spectroscopy (STEM-EDS) analyses, which improve the characterization and quantification of radiation-induced clusters in RPV steels, thereby enabling more accurate predictions of material behavior under irradiation. The surveillance base- and welded- RPV steels were annealed at various temperatures of 340 °C, 450 °C and 500 °C for up to 168 hours, respectively. Afterwards, I have characterized radiation-induced clusters using advanced STEM-EDS techniques and subsequently applying machine learning algorithms to analyze and refine STEM-EDS datasets, enhancing the quantification of clusters compositions and distributions. In the end, an efficient workflow for integrating STEM-EDS data analysis with machine learning to address challenges including noise reduction has been developed. The completion of this VFP work will support bridge critical gaps in the accurate quantification of radiation-induced clusters in RPV steels using STEM-EDS and support the development of more precise models for predicting RPV embrittlement in the Light Water Reactor Sustainability program supported by Department of Energy and enhancing the collaboration between ORNL and Alred University. The outcome of the VFP project will leverage a few research papers submission to peer-reviewed journals in the relevant scientific field and a few oral presentations at national and international conferences.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

Uranium dioxide (UO 2 ), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO 2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO 2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Colloidal Oxide Properties and Kinetic Studies

Silica based oxide particles prepared by various methods are compared in terms of their gellation kinetics, densification kinetics, and microable structural evolutions begin at very low temperature. Additional work reported in literature further suggested that the colloidal behaviors and the dried gel structures of the same oxide from different preparation techniques differ fundamentally. These differences significantly affect the densification kinetics and the product microstructure. Gravitational segregation of particles of different sizes in a gravitational field is modelled as a diffusion/segregation problem. A structural free volume model was used to describe the activation process of jump statistics. The sizes of the particles, the interstices, and their packing topology are accounted for in the estimation of the free volume. An attempt frequency is ascribed to particles to account for the vibrational rearrangement during powder packing.

Chen, I. W.↗
NASA NTRS1987

Computer models of the growth of monolayers

Preliminary results from a novel reversible computer model of monolayer nucleation and growth are compared with extant irreversible computer models of monolayer growth's early process stages. Attention is given to the microstructures resulting from direct impingement, the cluster density attributable to direct impingement, microstructures resulting from impingement combined with surface migration, and the cluster density attributable to impingement in combination with surface migration.

Salik, Joshua↗
NASA NTRS1997

Meteorites, Microfossils and Exobiology

The discovery of evidence for biogenic activity and possible microfossils in a Martian meteorite may have initiated a paradigm shift regarding the existence of extraterrestrial microbial life. Terrestrial extremophiles that live in deep granite and hydrothermal vents and nanofossils in volcanic tuffs have altered the premise that microbial life and microfossils are inconsistent with volcanic activity and igneous rocks. Evidence for biogenic activity and microfossils in meteorites can no longer be dismissed solely because the meteoritic rock matrix is not sedimentary. Meteorite impact-ejection and comets provide mechanisms for planetary cross-contamination of biogenic chemicals, microfossils, and living microorganisms. Hence, previously dismissed evidence for complex indigenous biochemicals and possible microfossils in carbonaceous chondrites must be re-examined. Many similar, unidentifiable, biological-like microstructures have been found in different carbonaceous chondrites and the prevailing terrestrial contaminant model is considered suspect. This paper reports the discovery of microfossils indigenous to the Murchison meteorite. These forms were found in-situ in freshly broken, interior surfaces of the meteorite. Environmental Scanning Electron Microscope (ESEM) and optical microscopy images indicate that a population of different biological-like forms are represented. Energy Dispersive Spectroscopy reveals these forms have high carbon content overlaying an elemental distribution similar to the matrix. Efforts at identification with terrestrial microfossils and microorganisms were negative. Some forms strongly resemble bodies previously isolated in the Orgueil meteorite and considered microfossils by prior researchers. The Murchison forms are interpreted to represent an indigenous population of the preserved and altered carbonized remains (microfossils) of microorganisms that lived in the parent body of this meteorite at diverse times during the past 4.5 billion years (Gy).

Hoover, Richard B.↗
NASA NTRS2001

Dendritic Alloy Solidification Experiment (DASE)

A space experiment, and supporting ground-based research, is proposed to study the microstructural evolution in free dendritic growth from a supercooled melt of the transparent model alloy succinonitrile-acetone (SCN-ACE). The research is relevant to equiaxed solidification of metal alloy castings. The microgravity experiment will establish a benchmark for testing of equiaxed dendritic growth theories, scaling laws, and models in the presence of purely diffusive, coupled heat and solute transport, without the complicating influences of melt convection. The specific objectives are to: determine the selection of the dendrite tip operating state, i.e. the growth velocity and tip radius, for free dendritic growth of succinonitrile-acetone alloys; determine the growth morphology and sidebranching behavior for freely grown alloy dendrites; determine the effects of the thermal/solutal interactions in the growth of an assemblage of equiaxed alloy crystals; determine the effects of melt convection on the free growth of alloy dendrites; measure the surface tension anisotropy strength of succinon itrile -acetone alloys establish a theoretical and modeling framework for the experiments. Microgravity experiments on equiaxed dendritic growth of alloy dendrites have not been performed in the past. The proposed experiment builds on the Isothermal Dendritic Growth Experiment (IDGE) of Glicksman and coworkers, which focused on the steady growth of a single crystal from pure supercooled melts (succinonitrile and pivalic acid). It also extends the Equiaxed Dendritic Solidification Experiment (EDSE) of the present investigators, which is concerned with the interactions and transients arising in the growth of an assemblage of equiaxed crystals (succinonitrile). However, these experiments with pure substances are not able to address the issues related to coupled heat and solute transport in growth of alloy dendrites.

Beckermann, C.↗
NASA NTRS2017

Tortuosity Computations of Porous Materials using the Direct Simulation Monte Carlo

Low-density carbon fiber preforms, used as thermal protection systems (TPS) materials for planetary entry systems, have permeable, highly porous microstructures consisting of interlaced fibers. Internal gas transport in TPS is important in modeling the penetration of hot boundary-layer gases and the in-depth transport of pyrolysis and ablation products. The gas effective diffusion coefficient of a porous material must be known before the gas transport can be modeled in material response solvers; however, there are very little available data for rigid fibrous insulators used in heritage TPS.The tortuosity factor, which reflects the efficiency of the percolation paths, can be computed from the effective diffusion coefficient of a gas inside a porous material and is based on the micro-structure of the material. It is well known, that the tortuosity factor is a strong function of the Knudsen number. Due to the small characteristic scales of porous media used in TPS applications (typical pore size of the order of 50 micron), the transport of gases can occur in the rarefied and transitional regimes, at Knudsen numbers above 1. A proper way to model the gas dynamics at these conditions consists in solving the Boltzmann equation using particle-based methods that account for movement and collisions of atoms and molecules.In this work we adopt, for the first time, the Direct Simulation Monte Carlo (DSMC) method to compute the tortuosity factor of fibrous media in the rarefied regime. To enable realistic simulations of the actual transport of gases in the porous medium, digitized computational grids are obtained from X-ray micro-tomography imaging of real TPS materials. The SPARTA DSMC solver is used for simulations. Effective diffusion coefficients and tortuosity factors are obtained by computing the mean-square displacement of diffusing particles.We first apply the method to compute the tortuosity factors as a function of the Knudsen number for computationally designed materials such as random cylindrical fibers and packed bed of spheres with prescribed porosity. Results are compared to literature values obtained using random walk methods in the rarefied and transitional regime and a finite-volume method for the continuum regime. We then compute tortuosity factors for a real carbon fiber material with a transverse isotropic structure (FiberForm), quantifying differences between through-thickness and in-plain tortuosities at various Knudsen regimes.

Tortuosity↗
DOE OSTI2020

In-Situ Visualization of Long-Range Defect Interactions at the Edge of Melting

Connecting a bulk material’s microscopic defects to its macroscopic properties is an age-old problem in materials science. Long-range interactions between dislocations (line defects) are known to play a key role in how materials deform or melt, but we lack the tools to connect these dynamics to the macroscopic properties. We introduce time-resolved dark-field X-ray microscopy to directly visualize how dislocations move and interact over hundreds of micrometers, deep inside bulk aluminum. With real-time movies, we reveal the thermally activated motion and interactions of dislocations that comprise a boundary, and show how weakened binding forces inhomogeneously destabilize the structure at 99% of the melting temperature. Connecting dynamics of the microstructure to its stability, we provide important opportunities to guide and validate multiscale models that are yet untested.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Multiscale modeling of twinning and detwinning behavior of HCP polycrystals

A multiscale scheme is presented here to capture the twinning and detwinning mechanisms during cyclic loading of HCP polycrystals. In the current framework, the crystal plasticity finite element (CPFE) method is employed at the microstructural scale. The response of twinned regions at a material point is calculated using a novel homogenization scheme via a Taylor-type model. The homogenized behavior is then used in the CPFE to obtain the response of a polycrystal. An efficient rate-independent crystal plasticity model that has been incorporated into the open source PRISMS-Plasticity software is introduced here to capture the response of the twinned region. Accordingly, the corresponding algorithmic tangent modulus is derived. Finally, this multiscale scheme is validated against data available for the cyclic response of Mg alloy ZK60A. The results show that the multiscale model can capture the key elements of the sample response observed during the experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structural complexity of γ-Al 2 O 3 : The nature of vacancy ordering and the structure of complex antiphase boundaries

The structure of γ-Al2O3 remains largely undetermined despite decades of research. This is due to the high degree of disorder, which poses significant challenges for structural analysis using conventional crystallographic approaches. Herein, we study the structure of γ-Al2O3 with Scanning Transmission Electron Microscopy (STEM) and ab-initio calculations to provide a complete structural description. We show that the microstructure can be understood in terms of two key structural features of nanoscale spinel domains and finite thickness segments termed as complex antiphase boundaries (cAPB) that provide the domain interconnectivity. The spinel domains have a distinctive preference for vacancy ordering, which can be rationalized in terms of a structure with a stacking disorder. Tetragonal P4 1 2 1 2 or monoclinic P2 1 models, all based on the identical motif, can be considered as representative ordered forms. Individual spinel domains are interconnected via cAPBs, which adopt a distinct non-spinel bonding environment of δ-Al 2 O 3 . The most common cAPB consists of a single delta motif with thickness of just 0.6 nm on (001), with the resulting displacement a/4 [101]. Remarkably, the cAPBs are shown to energetically stabilize the spinel domains of γ-Al 2 O 3 explaining their high abundance. We demonstrate how the tetragonal distortions naturally arise in this intricate microstructure and place the proposed model in the context of phase transformations to high temperature transition aluminas.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Effects of strain rate on mechanical properties and deformation behavior of an austenitic Fe-25Mn-3Al-3Si TWIP-TRIP steel

The effects of quasi-static and low-dynamic strain rate (ε = 10 –4 /s to ε = 10 2 /s) on tensile properties and deformation mechanisms were studied in a Fe-25Mn-3Al-3Si (wt%) twinning and transformation-induced plasticity [TWIP-TRIP] steel. The fully austenitic microstructure deforms primarily by dislocation glide but due to the room temperature stacking fault energy [SFE] of 21 ± 3 mJ/m 2 for this alloy, secondary deformation mechanisms such as mechanical twinning (TWIP) and epsilon martensite formation (TRIP) also play an important role in the deformation behavior. The mechanical twins and epsilon-martensite platelets act as planar obstacles to subsequent dislocation motion on non-coplanar glide planes and reduce the dislocation mean free path. A high-speed thermal camera was used to measure the increase in specimen temperature as a function of strain, which enabled the use of a thermodynamic model to predict the increase in SFE. The influence of strain rate and strain on microstructural parameters such as the thickness and spacing of mechanical twins and epsilon-martensite laths was quantified using dark field transmission electron microscopy, electron channeling contrast imaging, and electron backscattered diffraction. The effect of sheet thickness on mechanical properties was also investigated. Increasing the tensile specimen thickness increased the product of ultimate tensile strength and total elongation, but had no significant effect on uniform elongation or yield strength. The yield strength exhibited a significant increase with increasing strain rate, indicating that dislocation glide becomes more difficult with increasing strain rate due to thermally-activated short-range barriers. Finally, a modest increase in ultimate tensile strength and minimal decrease in uniform elongation were noted at higher strain rates, suggesting adiabatic heating, slight changes in strain-hardening rate and observed strain localizations as root causes, rather than a significant change in the underlying TWIP-TRIP mechanisms at low values of strain.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A robust process-structure model for predicting the joint interface structure in impact welding

The impact welding is gaining popularity in joining of dissimilar metals as any intermetallics are often very thin or discontinous with little to no heat affected zone resulting from the process. This work focuses on a recently developed impact welding technique, known as the Vaporizing Foil Actuator Welding (VFAW), which uses the pressure created from the electrically driven rapid vaporization of a thin metallic conductor to drive the flyer. A typical characteristic of an impact-welded joint is the wavy weld interface which is attributed to the complex interfacial kinematics resulting from the high-speed oblique impact of the metal plates. A numerical simulation framework based on the finite element method (FEM) with Eulerian formalism is developed to model the high-speed impact between the metal plates. The model accounts for the thermomechanical interactions in the process and captures the complex interfacial deformations. A thorough validation of the model is achieved by comparing the wave characteristics obtained from numerical simulations with the experimental results from vaporizing foil actuator welding. The amplitude and wavelength of the interfacial waves and the resulting joint microstructure is shown to be strongly dependent on the process conditions and the specific material system, which is well captured by the model.

42 ENGINEERING↗
DOE OSTI2026

Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation

The performance of rechargeable batteries is fundamentally influenced by the physicochemical properties and microstructural features of their key material components. Recent experimental advancements have highlighted the potential of single-crystal (SC) morphologies to address inherent limitations of polycrystalline (PC) electrodes and solid-state electrolytes, offering tunable charge transport kinetics and improved cell cycling performance. Here, this review examines how state-of-the-art computational modeling, from atomistic and mesoscale to continuum-level approaches, including machine learning methodologies, has been utilized to investigate the critical factors governing the electrochemical behavior of SC battery materials. We explore how predictive modeling can elucidate the processing–structure–property–performance relationships of SC cathodes, anodes, and solid-state electrolytes, with a focus on unique SC characteristics such as crystallographic anisotropy, size effects, and facet-dependent properties. Additionally, we identify limitations in commonly used modeling techniques and discuss strategies to address these challenges. By integrating high-fidelity simulations with experimental insights, this review aims to outline a clear path for the rational design and optimization of SC battery components, paving the way for accelerated advancements in energy storage technologies.

Materials science↗
DOE OSTI2020

Learning grain boundary segregation energy spectra in polycrystals

The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lattice misorientation evolution and grain refinement in Al-Si alloys under high-strain shear deformation

The starting microstructure of an alloy can be tailored to achieve varying degrees of microstructural refinement and enhance mechanical properties through severe plastic shear deformation during solid-phase processing. Crystal plasticity-based grain misorientation modeling, coupled with systematic pin-on-disk tribometry-based subsurface shear deformation experiments on as-cast Al-xSi alloys (x = 0, 1, 4 at %), was conducted. The post-deformation microstructural analysis, through a combined computational and experimental approach, conclusively shows that the initial volume fraction of the hard Si phase enhances the evolution of local lattice misorientation, leading to efficient grain refinement during severe plastic shear deformation. The shear–deformation-induced nanostructure resulted in more than double the nanoindentation hardness in the processed alloy.

Gwalani, Bharat↗