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Connect microstructure evolution to property degradation with validated simulation
Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500$^o$C to 1500$^o$C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 $\frac{D}{m^2s}$. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.
Coupling Microstructural Evolution Simulations to Material Property Degradation Predictions for Plasma-Facing Materials
Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500 C to 1500 C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 D/m^2-s. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.
Design of a separate effects MiniFuel irradiation experiment investigating microstructure evolution in high burnup UO 2
The microstructural evolution of UO 2 fuel pellets during commercial operation in light water reactors (LWRs) is known to vary significantly across the pellet radius due to spatial variations in local temperature and burnup. The primary obstacle to extending LWR refueling cycles to 24-month intervals is the susceptibility of certain high burnup fuel microstructures to fuel fragmentation, relocation, and dispersal (FFRD) during a loss of coolant accident (LOCA). Although FFRD of the high burnup structure in the rim region of a pellet is well studied, the fine fragmentation that has been observed in a second region, near the midradius of the pellet (termed the “dark zone”) following mock LOCA testing of high burnup commercial fuel rods is less understood. This paper describes the design, analysis, and execution of a separate effects MiniFuel irradiation experiment that aims to identify the specific temperature and burnup regimes under which FFRD-susceptible dark zone microstructures form. The small disc specimens (3 mm diameter by ∼0.3 mm thick) enable more precise control of the relatively uniform temperature and burnup conditions. A total of 42 specimens were fabricated with typical LWR fuel densities (∼96%–98% of theoretical density) and grain sizes (∼12 μm) and are being irradiated over a range of temperatures (600°C–1000°C) and discharge burnups (50–72 MWd/kg-U) that bound the midradius region of high burnup LWR fuel. Fuel specimens with identical 235 U enrichments were inserted in two irradiation locations in the High Flux Isotope Reactor and are currently undergoing irradiation to further evaluate the impact of rate effects (fission rate, time at temperature) on the microstructural evolution. The fuel fabrication and the thermal and neutronic simulations used for designing the experiment are detailed in this paper. A secondary objective of the experiment is to observe fission gas release (FGR) under the various irradiation conditions, and this work provides first-order predictions of FGR from all fuel specimens. The insights gained from these experiments will inform future high burnup core designs that could minimize the formation of susceptible microstructures and ultimately enable 24-month refueling cycles while minimizing the fraction of the fuel susceptible to FFRD.
Phase field simulation of anode microstructure evolution of solid oxide fuel cell through Ni(OH) 2 diffusion
Microstructure evolution in a solid oxide fuel cell anode is strongly affected by operating condition. The detailed mesoscale evolution mechanism under operating condition is still under debate. Here we develop a phase-field model to simulate microstructure evolution through formation and diffusion of gaseous Ni(OH)2. We studied the coarsening kinetics and redistribution of Ni under different steam distributions and compare it to that under pure hydrogen condition. Overall, the results suggest that although the presence of a steady gradient of steam leads to redistribution of Ni, Ni(OH) 2 formation and diffusion do not significantly change the Ni coarsening rate under strictly humid conditions, contrary to commonly reported hypotheses. It is concluded that competing mechanisms other than Ni(OH) 2 diffusion must be responsible for the experimentally observed Ni redistribution and enhanced coarsening under humid conditions.
Alloying effects on deformation induced microstructure evolution in copper
In this work, we investigated the effects of alloying elements on plastic deformation and microstructure evolution in polycrystalline copper (Cu) and Cu alloyed with 1 wt. % lead (Cu-1 %Pb). These materials were selected due to the size mismatch between Cu and Pb, with the latter forming precipitates at grain boundaries. Multi-modal characterization techniques, including neutron diffraction, electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM), along with finite element simulations were employed to study the deformation behavior across multiple length scales. While both Cu and Cu-1%Pb exhibited similar macroscale response and final deformation textures, both dislocation line profile analysis and TEM revealed increased dislocation density in deformed Cu-1%Pb specimens. The presence of lead precipitates also significantly affected local plastic deformation during compression, with their influence diminishing with increasing strain. These results demonstrate the complex relationships between alloying elements, plastic deformation, microstructural evolution, and material behavior under load. The insights gained from this multi-scale and multi-technique approach contribute to the fundamental understanding of microstructural evolution in immiscible alloys and are valuable for tailoring the properties of structural materials for specific engineering applications.
Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials
Understanding the relationship between the evolution of microstructures of irradiated LiAlO2pellets and tritium diffusion, retention and release could improve predictions of tritium performance. Given expert-labeled segmented images of irradiated and unirradiated pellets, we trained Deep Convolutional Neural Networks to segment images into defect, grain, and boundary classes. Qualitative microstructural information was calculated from these segmented images to facilitate the comparison of unirradiated and irradiated pellets. We tested modifications to improve the sensitivity of the model, including incorporating meta-data into the model and utilizing uncertainty quantification. The predicted segmentation was similar to the expert-labeled segmentation for most methods of microstructural qualification, including pixel proportion, defect area, and defect density. Overall, the high performance metrics for the best models for both irradiated and unirradiated images shows that utilizing neural network models is a viable alternative to expert-labeled images.
Python codes for the paper - "Trade-offs in the latent representation of microstructure evolution"
SAND2024-00946O Python codes used in "Trade-offs in the latent representation of microstructure evolution," a manuscript accepted for publication in Acta Materialia, are part of a repository. The code was developed to perform analysis of microstructure evolution. The repository consists of two main directories: models, which train and test models such as autoencoders and diffusion maps, and analysis, which analyzes microstructures. Source code is used to perform dimensionality reduction of microstructure data for analysis of its evolution in time. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
A High-Speed Rotational Diamond Anvil Cell for In Situ Analysis of Hierarchical Microstructural Evolution of Metallic Alloys during Extreme Shear Deformation
High speed shear deformation is ubiquitous in engineering applications, ranging from material processing methods such as friction stir processing/extrusion and in tribological contacts. However, analyzing the microstructural evolution of materials while they are undergoing high speed shear deformation have been a long-standing challenge. This led to predominant reliance on ex situ microscopy before and after shear deformation. But ex situ microscopy lacks the ability to analyze dynamic and transient hierarchical microstructural evolution mechanisms that could occur during shear deformation of materials. Therefore, to better understand the dynamic mechanisms of mass and energy transfer in materials under shear deformation, we developed a first of its kind high-speed rotational diamond anvil cell (HS-RDAC) for synchrotron-based in situ high-energy x-ray diffraction (XRD). We studied the time resolved lattice strain evolution, XRD peak broadening and changes in spatial variation of shear deformation induced alloying in pure metal and metal alloy sheets and powder mixture using the HS-RDAC. These in situ results were combined with detailed ex situ microstructural characterization before and after the shear deformation using transmission electron microscopy and atom probe tomography, which revealed the different stages of evolution of a shear deformation induced hierarchical nanostructure. Multiscale computational simulations including computational fluid dynamics, crystal plasticity, molecular dynamic simulation and density functional theory uncovered the mechanisms behind morphological changes, evolution of defect structures and changes in driving force for shear deformation induced intermixing. In conclusion, this in situ HS-RDAC capability, in combination with ex situ microstructural characterization and computational simulations, can provide new insights into the hierarchical microstructural evolution pathway during shear deformation.
MEUMAPPS (Microstructure Evolution Using Massively Parallel Phase-field Simulations)
The software “MEUMAPPS” is a h high-performance computing code used to simulate microstructure evolution associated with diffusional solid-state transformations in structural alloys. Understanding microstructure evolution during thermo-mechanical processing of structural alloys is the first step towards designing and processing alloys for specific applications by meeting property requirements demanded by the application. In this instance, the code is an important component in predicting processing-structure linkages during additive manufacturing of structural alloys as a function of processing parameters and alloy composition used in an additive manufacturing process. The code provides a detailed three-dimensional distribution of different types of phases / constituents and their morphologies, as well as the compositions of these phases that make up the microstructure. The microstructure is simulated by solving the governing partial differential equations using a Fourier Spectral Method.
Microstructural evolution in shear-punch tests: A comparative study of pure Cu and Cu-Cr alloy
Understanding the mechanisms behind microstructural evolution during shear deformation has been a long-standing area of interest. However, establishing a connection between microstructure, mechanical properties, and extent of shear deformation is challenging and requires refined experimental approaches. Shear-punch testing (SPT) provides a controlled method to introduce shear into small volumes of material that later can be subjected to detailed microstructural characterization. In this study, we utilize an SPT device to induce shear deformation to pure copper and a binary copper-chromium alloy. Electron backscatter diffraction and transmission electron microscopy were used to study the mechanisms of plastic deformation after SPT. Our results indicate that shear deformation of pure Cu produces a dense network of intercepting microshear bands upon sustained deformation. Twin boundaries undergo degradation into high angle grain boundaries due to simultaneous deviation from the axis-angle pair condition of 60° misorientation on [111] direction. The presence of 50% volume Cr particles in the soft Cu matrix fundamentally altered the shear deformation mechanism. Preferential deformation of the Cu matrix led to accelerated shear-induced formation of low and high angle grain boundaries, and subsequent grain refinement. Comparatively, no grain refinement occurred in pure Cu at a strain ~10 times larger (ϵ = 4.73) than that of the copper-chromium case (ϵ = 0.42). Overall, our study sheds light on the microstructural evolution during shear deformation and highlights the significant influence of particle reinforcements on the shear deformation mechanisms of metals.
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Abstract Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. After DeepONet is trained in the latent space, it can be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks.
Understanding the microstructural evolution of hypersaline cemented paste backfill with low-field NMR relaxation
Highlights: • Microstructural evolution of CPB, prepared with tap and hypersaline water, is monitored using low-field NMR relaxometry. • T{sub 1} and T{sub 2} NMR relaxation measurements compared with mechanical strength via UCS tests • Hypersaline mixing water results in slower structural evolution and lower mechanical strength. Cemented paste backfill (CPB) comprising mineral tailings, binders and mixing water is an important potential support material in the mining industry. As the mechanical properties of CPB are significantly influenced by its microstructural characteristics the development of measurement tools to better understand its pore structure evolution is important for its increased utilisation. This study reports the application of low-field nuclear magnetic resonance (NMR) relaxation time measurements to characterise the microstructural evolution of CPB materials over 56 days of hydration, contrasting common tap water and hypersaline water (~22 wt% salt) as mixing water. Distinct NMR relaxation time populations were evidenced within each CPB sample, revealing the presence of both capillary (T{sub 1,2} ≈ 10 ms) and gel pore water (T{sub 1,2} ≈ 300–500 μs), with time-dependent relaxation measurements facilitating characterisation of capillary pore structure evolution over the hydration period assessed. Hypersaline samples demonstrated a time-lag in this measured capillary pore evolution, relative to those hydrated with tap water, while pore structure evolution rates were observed to increase with increased CPB binder content. Further, both T{sub 1} and T{sub 2} NMR relaxation times were found to correlate with the uniaxial compressive strength of the CPB materials investigated, facilitating the formulation of a predictive correlation function between NMR relaxation characteristics and mechanical properties.
Microstructural evolution of NARloy-Z at elevated temperatures
Microstructural evolution was studied in samples of wrought and vacuum plasma sprayed (VPS) NARloy-Z exposed to temperatures up to 970 C (1,780 F) for up to 60 h. Samples were heated in a vacuum furnace, followed by rapid quenching in helium (He) gas at a cooling rate of 166 C (300 F) per second. Microstructural analyses were conducted using optical microscopy, scanning electron microscopy (SEM), and electron probe microanalysis (EPMA). In both the wrought and VPS conditions, precipitates rich in silver (Ag) and zirconium (Zr) were present in the matrix and at the grain boundaries even after long exposure to elevated temperatures. Islands rich in oxygen (O2) and Zr were also observed, as well as incipient melting at the grain boundary triple points. Results indicated that the alloy cannot be homogenized by heat treatment at elevated temperatures.
Anisotropic physics-regularized interpretable machine learning of microstructure evolution
Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its neighboring sites to model normal, isotropic, grain growth behavior. This work aims to extend this method by incorporating grain boundary misorientation-based grain growth behavior. APRIMME is trained on anisotropic simulations created using the Monte Carlo-Potts (MCP) model. Furthermore, the results of this work are compared statistically using grain radius, number of sides per grain, mean neighborhood misorientations, and the standard deviation of triple junction dihedral angles, and are found to match in most cases. The exceptions are small and seem to be related to two causes: (1) the deterministic model of APRIMME is learning from the stochastic simulations of MCP, which seems to accentuate triple junction behaviors; and, (2) a bias against very small grains is made evident in a quicker decrease in grains than expected at the beginning of an APRIMME simulation. APRIMME is also evaluated for its general ability to capture anisotropic grain growth behavior by first investigating different test case initial conditions, including a circle grain, three grain, and hexagonal grain microstructures.
Phase-field modeling of aging-induced microstructure evolution in pentaerythritol tetranitrate thin films and ramifications for shock initiation
Aging of energetic materials may change performance and affect their safety and reliability, but the relationship between microstructure changes induced by aging and consequent performance changes has not been fully established. This work presents results of phase-field method simulations used to model microstructure evolution of vapor-deposited pentaerythritol tetranitrate (PETN) thin films. Simulated aging is shown to induce grain coarsening and substantial changes of the configuration of porosity in the film: Specifically, we show that porosity tends to concentrate in large pores to a greater degree in aged films, a state that is arrived at by closure or consolidation of small pores. To evaluate the performance of the as-deposited and aged films, we perform two-dimensional hydrocode flyer-film impact simulations that incorporate the phase-field output microstructures directly, permitting us to connect features therein to changes in reactivity, a key metric of energy output for shock initiation. The results demonstrate that declining sensitivity obtained for the simulated aged films can be correlated with the loss of fine-structured pores relatively early in the aging process, while long-term microstructure evolution that gradually alters the shape of large, branching pores is less impactful. Finally, we discuss commonalities and discrepancies between our simulation results and high-throughput initiation experiments on shock initiation of aged PETN thin films.
A Monte Carlo-finite element model for strain energy controlled microstructural evolution - 'Rafting' in superalloys
This paper presents a specialized microstructural lattice model, MCFET (Monte Carlo finite element technique), which simulates microstructural evolution in materials in which strain energy has an important role in determining morphology. The model is capable of accounting for externally applied stress, surface tension, misfit, elastic inhomogeneity, elastic anisotropy, and arbitrary temperatures. The MCFET analysis was found to compare well with the results of analytical calculations of the equilibrium morphologies of isolated particles in an infinite matrix.
Predicting material microstructure evolution via data-driven machine learning
The recent rapid rise in data-driven practices in the materials science domain has led to the development of efficient, generalizable, and accurate approaches for several applications, including: material property prediction, mining (micro)structure- property and (micro)structure-processing relationships, and characterization of material microstructures. Central to the materials science domain is linking microstructure to properties and performance, and critical to building such linkages is understanding how microstructures evolve as a function of environmental exposure or processing conditions (e.g., time, temperature, applied stress or strain, irradiation). This preview article summarizes recent work on the development of recurrent neural networks for predicting spatio-temporal microstructure evolution detailed by Yang et al in their manuscript entitled 'Self-supervised Learning and Prediction of Microstructure Evolution with Recurrent Neural Networks'.