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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A comparative analysis of YOLOv8 and U-Net image segmentation approaches for transmission electron micrographs of polycrystalline thin films

Metallic thin films offer a platform to experimentally study the dynamics of microstructural evolution, but the required transmission electron microscopy (TEM)-based imaging generates complex images that are challenging to segment and quantify. This work provides a comparative analysis of a new YOLOv8 model and an established U-Net model for bright-field TEM images of polycrystals, employing a framework leveraging physical observables to evaluate performance against two hand-traced benchmark datasets. This methodology obviates the comparison of large, diversely structured, and manually labeled datasets that are required to assess performance on a per-image/per-pixel basis. It is found that the YOLOv8 model, adapted for real-time instance segmentation, has up to 43× faster inferencing (NVIDIA GeForce RTX 4090) compared to U-Net and reconstructs hand-traced grain size distributions (GSDs) with excellent fidelity, finding mean diameter within 3% for grains near an optimal magnification; for grains that deviate from the optimal pixel-diameter, the size of small- (large)-diameter grains is systematically over- (under)-estimated. This is partially mitigated by including scale-aware augmentations during training. Moreover, when the bias is corrected post-inference by a rigid shift in distribution, the YOLOv8 model reproduces ground truth GSDs with exceptional fidelity, with statistical tests indicating <5% probability that the distributions are distinct. Based on ground truth data, calibration curves pertaining to this shift can be constructed for a given model. This issue is not present in the U-Net model’s results, indicating that for quantitative measurements where the true size of objects is of interest, special procedures must be implemented for YOLO-based models.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cold-Sprayed NMC622 Composite as a Cathode for Lithium-Ion Batteries

The growing demand for high-energy, low-cost lithium-ion batteries (LIBs) to power electric vehicles (EVs) necessitates advances in both materials and manufacturing processes. Conventional cathode fabrication methods, such as slurry casting and drying, are energy-intensive and pose challenges for scalability and environmental compliance. In this study, we propose cold-spray (CS) deposition as a solvent-free approach for fabricating LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) composite cathodes. Powder blends of NMC622, poly(vinylidene fluoride) (PVDF), and carbon black (CB) are directly deposited onto stainless steel and Inconel substrates under varied gas temperatures, pressures (and thus velocities), and standoff distances. The effects of temperature on the deposit morphology, coating density, and volume are systematically investigated. Computational fluid dynamics simulations reveal that increasing the gas temperature enhances the particle velocity, narrows the spray angle, and reduces the mass concentration radially at the nozzle outlet. CS deposition results in a dense cathode microstructure, accompanied by fracture in polycrystalline NMC622 particles. X-ray diffraction analysis further verifies that there are no phase changes during the deposition process. The electrochemical performance of the cold-sprayed cathodes reveals an initial capacity of approximately 96 mAh g –1 for single-crystal NMC622 and 167 mAh g –1 for polycrystalline NMC622. While these values are modest compared to state-of-the-art slurry-cast cathodes, which typically exhibit 180–200 mAh g –1 under optimized conditions. The results demonstrate a competitive performance given the solvent-free nature of the CS process and compare favorably with tape-cast samples made from identical feedstock. In conclusion, he CS process enables the formation of dense, binder-integrated cathode coatings without the need for solvent processing, offering a promising pathway for scalable, energy-efficient dry electrode manufacturing of next-generation LIBs.

Batteries↗
DOE OSTI2026

Evaluation of the interfacial bonding strength between hafnia and silica layers deposited by electron-beam evaporation

Factors contributing to the mechanical strength of multilayer optical coatings involving alternating layers of electron-beam evaporated hafnia and silica is investigated. It is observed that upon rupture resulting from nanosecond laser damage, fragments consisting of layer pairs are produced. These pairs are formed as a result of a strong asymmetry in adhesion strength between interfaces, leading to preferential delamination at interfaces where silica is deposited on hafnia, and not the reverse, despite the fact that both interface types consist of the same pair of materials. The observed disparity in adhesion strength was further studied using nanomechanical scratch tests, revealing loads at which fracture occurs, and fracture morphology, both of which are highly dependent on deposition order. Based on these measurements, along with the morphological analysis of the nanostructure of the coatings, we conclude that the surface microstructure in each coating layer affects its adhesion strength with the succeeding layer. The findings outlined here are particularly relevant to the production of large aperture, high-performance optics designed for use in next-generation lasers.

adhesion strength↗
DOE OSTI2025

Improving high temperature resilience of fiber sensor embedded smart components through laser shock peening

This study explores the use of laser shock peening (LSP) to enhance material properties and high-temperature performance of fiber-sensor-fused smart parts fabricated by additive manufacturing (AM) methods. Using embedded fiber sensors as distributed strain gauges, the study demonstrates that LSP can induce compressive strains of up to 130 µε on fiber embedded 1-mm below metal surfaces. The electron backscatter diffraction (EBSD) analysis shows that, with optimized LSP parameters, the metallic matrix undergoes substantial microstructural refinement, resulting in denser structures. Thermal cycling tests showed that the LSP process can increase fiber slippage temperatures by more than 50 °C. This work shows that the LSP process is an effective room-temperature process for enhancing both surface quality and increasing fiber slippage threshold under both thermal and mechanical stress.

Zhong, Shuda [University of Pittsburgh, PA (United↗
DOE OSTI2025

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

In Situ Prediction of Microstructure and Mechanical Properties in Laser-Remelted Al-Si Alloys: Towards Enhanced Additive Manufacturing

Laser surface remelting of aluminum alloys has emerged as a promising technique to enhance mechanical properties through refined microstructures. This process involves rapid cooling rates ranging from 10 3 to 10 8 °C/s, which increase solid solubility within aluminum alloys, shifting their eutectic composition to a larger value of silicon content. Consequently, the resulting microstructure combines a strengthened aluminum matrix with silicon fibers. This study focuses on the laser scanning of Al-Si aluminum alloy to reduce the size of aluminum matrix spacings and transform fibrous silicon particles from micrometer to nanometer dimensions. Analysis revealed that the eutectic structure contained 17.55% silicon by weight, surpassing the equilibrium eutectic composition of 12.6% silicon. Microstructure dimensions within the molten zones, termed ‘melt pools’, were extensively examined using Scanning Electron Microscopy (SEM) at intervals of approximately 20 μm from the surface. A notable increase in hardness, exceeding 50% compared to the base plate, was observed in the melt pool regions. Thus, it is exemplified that laser surface remelting introduces a novel strengthening mechanism in the alloy. Moreover, this study develops an in situ method for predicting melt pool properties and dimensions. A predictive model is proposed, correlating energy density and spectral signals emitted during laser remelting with mechanical properties and melt pool dimensions. This method significantly reduces characterization time from days to seconds, offering a streamlined approach for future studies in additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Stress localization investigation of additively manufactured GRCop-42 thin-wall structure

A full-field crystal plasticity (CP) framework is presented for the GRCop-42 alloy to study microscopic mechanical behavior and local stress heterogeneities. The microstructures of additively manufactured (AM) materials are often unique relative to conventionally processed materials, and the local thermal histories drive these differences during the build process. These thermal histories depend on the process parameters (laser power, scan speed, and scan strategy) and the part geometry. Prior research has shown that the mechanical properties of thin-walled structures can vary significantly with wall thickness due to changes in the thermal boundary conditions during manufacturing. It is, therefore, desirable to perform CP simulations based on the phenomenological constitutive model to predict the local mechanical responses induced by microstructural heterogeneities. This work generates representative microstructures based on experimentally collected grain information (i.e., texture) for grain scale stress analysis, and the material constitutive parameters are calibrated using the experimental mechanical testing data. Here, we specifically investigated the effect of crystallographic texture and grain morphologies on the size-dependent mechanical properties of AM GRCop-42. The selection of appropriate material properties for implementing an effective free surface boundary condition and the influence of adjacent buffer layers are also discussed. Analysis of local field results reveals a strong correlation between stress localization and the initial grain orientation. However, no significant relationship between the misorientation of the individual adjacent grains and the average misorientation is observed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Desmearing two-dimensional small-angle neutron scattering data by central moment expansions

Resolution smearing is a critical challenge in the quantitative analysis of two-dimensional small-angle neutron scattering (SANS) data, particularly in studies of soft-matter flow and deformation using SANS. Here, we present a central moment expansion technique to address smearing in anisotropic scattering spectra, offering a model-free desmearing methodology. By accounting for directional variations in resolution smearing and enhancing computational efficiency, this approach reconstructs desmeared intensity distributions from smeared experimental data. Computational benchmarks using interacting hard-sphere fluids and Gaussian chain models validate the accuracy of the method, while simulated noise analyses confirm its robustness under experimental conditions. Experimental validation using rheological SANS data from shear-induced micellar structures demonstrates the practicality and effectiveness of the proposed algorithm. The desmearing technique provides a powerful tool for advancing the quantitative analysis of anisotropic scattering patterns, enabling precise insights into the interplay between material microstructure and macroscopic flow behavior.

anisotropic scattering spectra↗
DOE OSTI2024

Embedded High-Temperature Sensors: Enhancing Thermoelectrical Performance with Refractory Composites Gradient Layers

To monitor the stability of various energy and manufacturing systems, sensors capable of operating at temperatures exceeding 1000 °C in diverse environments for extended durations are essential. However, under harsh conditions, degradation of sensing materials is a concern that can be controlled by embedding the sensors into refractory oxides. Doped-LaCrO3 based composites are excellent candidates for high-temperature sensing applications due to their good thermoelectrical properties. However, chemical reactivity between the conductive phase and the refractory oxides can decrease the performance of the sensors. In this work, it was devised gradient-type protective layers safeguarding conductive phases within embedded thermocouples, which were fabricated and characterized by X-Ray Diffraction for phase development analysis, Scanning Electron Microscopy and Energy-dispersive X-ray spectroscopy for microstructure and cationic interdiffusion kinetics, long-term thermoelectric testing was completed up to 1400 °C. The enhanced performance of these novel sensors addresses a critical limitation, rendering them viable for long-term high-temperature applications.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Comprehensive new insights on the potential use of SiC as plasma-facing materials in future fusion reactors

Abstract The performance of silicon carbide as an alternative plasma facing material (PFM) was studied at various irradiation conditions relevant to ion energies and fluxes of a fusion reactor. This analysis involves detailed modeling of subsurface plasma/material interactions, sputtered particle transport above the surface and redeposition, and related changes in material composition and microstructure induced by steady-state and Edge Localized Mode ion fluxes. Transition of a crystalline SiC surface to semi-crystalline and amorphous phases was analyzed based on advanced modeling of DIII-D tokamak experiments where SiC was irradiated in single- and multiple- L-mode and H-mode discharges. This analysis shows that displacement damage, particle deposition/redeposition, and D accumulation on the SiC divertor surface can lead to significant microstructural changes that result in enhanced sputtering erosion in comparison with the original crystalline material. However, the resulting total net erosion rate for a full-coverage, advanced tokamak, SiC coated divertor may well be acceptably low. Moreover, the C sputtering yield from the evolved SiC surface can be seven times lower than from a pure graphite surface; this would imply significantly reduced tritium co-deposition rates in a D-T tokamak reactor, compared with a pure carbon surface. It was also determined that chemical sputtering of both C and Si should not result in any noticeable effect on the net erosion, for attached plasma regimes. Our results thus show encouraging results overall for use of SiC as a PFM in tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructural origin of high‐strength Tyranno SA4 SiC fiber studied by comparative Raman spectroscopy #

Tyranno SA4 grade SiC fiber is an emerging continuous fiber with excellent mechanical properties. The origin of the fiber's high-performance was studied by comparative Raman spectroscopy of four different grades of SiC fibers and reference SiC monolith. Analysis of the Raman spectra of fiber cross-sections, surface and homogeneity, and fibers under stress, revealed how differences in microstructure, such as crystalline order, size, and the presence of critical flaws, dictate mechanical properties. In conclusion, this information can help optimize SiC fiber manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Physicochemical evolution of uranium nitride kernel microstructure with varying carbon distribution for advanced TRISO fuel forms

Uranium nitride (UN) has emerged as a fuel candidate for advanced nuclear reactor concepts due to its superior uranium density, thermal conductivity, and high melting temperature. However, the fabrication route for converting UO 2 to UN is complex and difficult to standardize. Although the chemistry of this conversion process is well-studied, more insight into the physicochemical dynamics of this conversion using advanced characterization techniques can help further our understanding of this material system. This work leveraged thermogravimetric analysis (TGA), X-ray diffraction (XRD), and nondestructive 3D X-ray computed tomography (XCT) to characterize dynamic microstructural changes in the UO 2 → UCO → UN fabrication pathway for two kernels with a varying carbon distribution in the starting composition. TGA and XRD were used to quantify changes in the mass, density, and chemical composition of the two kernels, while three-dimensional image processing and segmentation of XCT data were used to quantify the volume, surface area, and spatial distribution of features within each kernel for multiple steps along the fabrication pathway. The analysis indicates distinct differences between the two kernels that are correlated to downstream conversion efficiency. In conclusion, this work is among the first to perform 3D quantification of physicochemical evolution during UN conversion, providing quantitative correlation between processing, properties, and expected fuel performance.

Nuclear fuel↗
DOE OSTI2026

Spectro-Microscopic Analysis of Soot Particle Composition and Source Attribution

Ambient soot particles significantly impact Earth’s radiative balance, human health, and atmospheric visibility. Their microstructural properties depend on formation and aging mechanisms, which vary by emission source and atmospheric processes. Hence, accurately identifying sources of soot enhances our understanding of their physicochemical properties and atmospheric implications. This study used a multi-modal approach to characterize and attribute sources of submicron soot particles collected in Israel during new particle formation events, biomass burning episodes, and background atmospheric conditions. Synchrotron-based X-ray microscopy was used to map soot (elemental carbon), organic carbon, and inorganic species. Implemented atomic force microscopy showed highly diverse phase states, with soot consistently exhibiting a solid-like phase. Automated µ-Raman analysis was subsequently performed on ~690 particles, identifying three soot classes based on spectral features corresponding to the "Defect" (D) and "Graphite" (G) bands of soot. We applied two-peak and five-peak fitting approaches to deconvolute the “Defect” peaks (D1, D2, D3, and D4) and G band from average Raman, revealing varying degrees of graphitic order. The degree of graphitic order was determined from metrics such as the D3 peak area, often observed when soot was internally mixed with organic material. Raman spectral features, along with temporal variations in particle classes contributions, suggest that Particle Type 1 corresponds to traffic related soot and Particle Type 2 to less graphitic soot from biomass burning, while Particle Type 3 is associated with more heterogeneous particulate representative of soot-OC mixtures emitted during new particle formation and biomass burning episodes.

Rivera-Adorno, Felipe (ORCID:0000000273557999)↗
DOE OSTI2026

Dynamic strength of iron under pressure-temperature conditions of Earth’s inner core

Iron (Fe) is a primary constituent of terrestrial planetary cores, yet its rheological properties under extreme conditions remain uncertain. Here we present direct measurements of Fe strength at 310-430 GPa pressures and 3700-5800 K temperatures, obtained using Rayleigh-Taylor (RT) instability experiments at the National Ignition Facility. Single-crystal α-Fe samples with [001] and [111] orientations are shock-ramp compressed past the α-ε transition along paths approaching Earth’s inner core conditions. We find that ε-Fe derived from [001] α-Fe is consistently stronger (11-20 GPa) than that from [111] α-Fe (8-18 GPa), contrary to the trend at ambient conditions. Large-scale molecular dynamics simulations reproduce this atypical strength anisotropy and attribute it to microstructural evolution during the phase transition and subsequent ε-phase plasticity. Ripple growth analysis further constrains viscosities of 100-170 Pa·s under the driven conditions. Our results provide experimental benchmarks for Fe rheology at inner-core conditions, with implications for seismic anisotropy and the geodynamo.

Condensed-matter physics↗
DOE OSTI2026

High Temperature High Vacuum Mechanical Property Assessment of Zirconium Nuclear Fuel Cladding

This report presents the mechanical characterization of a specific Zry-4 cladding batch serving as the foundation for a diverse range of fuel performance research at Oak Ridge National Laboratory (ORNL). This effort supports research needs for the U.S. Department of Energy (DOE), particularly regarding evaluating accident tolerant fuel (ATF) cladding coating concepts, expanding understanding of cladding response to loss-of-coolant accidents (LOCA) transients, refining post-critical heat flux (CHF) limits (t@T), and upcoming irradiation campaigns. The central objective was to define the baseline performance of the substrate Zircaloy-4 (Zry-4) material leveraged across ORNL Advanced Fuel Campaign (AFC) efforts through controlled high-temperature vacuum tensile testing. This work begins to address gaps in existing models where implementation based on nominal heat-treatment labels, such as stress relief annealed (SRA), often fail to capture the interplay of recovery, recrystallization, and grain growth. To quantify this, data was benchmarked against the Pacific Northwest National Laboratory (PNNL) stress strain model to determine where this material falls in comparison to assumed values for materials in the same heat treatment regime. Analysis of the tensile data revealed that this specific SRA batch exhibits a transitional microstructural state best described by an effective cold-work (CW) parameter of 0.09, diverging from the previous estimation of 0.5 for SRA materials. Additionally, comparative testing of Cr coated specimens demonstrated no distinct difference in axial strength relative to the bare substrate. This suggests that the strengthening benefits of Cr coatings observed in burst scenarios are driven by residual stress mechanisms acting solely in the hoop direction.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Examining Constituent Redistribution in U-19Pu-10Zr Fuel as it Evolves with Local Burnup

While constituent redistribution is a known irradiation behavior in U-Pu-Zr fuel, new data have shown it is more complex than our current understanding and predictive capabilities. The size and composition of redistributed rings evolve as a function of pin composition, burnup, geometry, and irradiation temperature. In this work, we extract microstructural information from optical microscopy conducted on U-19Pu-10Zr pins (irradiated between 1.9 at. % and 11.6 at. % peak burnup). Both manual image analysis techniques and machine learning-assisted segmentation are used to quantify the thicknesses of the cladding, fuel-cladding interaction layers, and rings of fuel constituent redistribution in addition to pore distribution. These microstructural features and individual redistributed regions affect local thermomechanical properties, and identifying the relationship between burnup and constituent redistribution will improve accurate prediction of advanced reactor fuel performance.

Constituent Redistribution↗