In this study, we experimentally investigate the high stain rate and spall behavior of Cantor high-entropy alloy (HEA), CoCrFeMnNi. First, the Hugoniot equations of state (EOS) for the samples are determined using laser-driven CoCrFeMnNi flyers launched into known Lithium Fluoride (LiF) windows. Photon Doppler Velocimetry (PDV) recordings of the velocity profiles find the EOS coefficients using an impedance mismatch technique. Following this set of measurements, laser-driven aluminum flyer plates are accelerated to velocities of 0.5–1.0 km/s using a high-energy pulse laser. Upon impact with CoCrFeMnNi samples, the shock response is found through PDV measurements of the free surface velocities. From this second set of measurements, the spall strength of the alloy is found for pressures up to 5 GPa and strain rates in excess of 106 s-1. Further analysis of the failure mechanisms behind the spallation is conducted using fractography revealing the occurrence of ductile fracture at voids presumed to be caused by chromium oxide deposits created during the manufacturing process.
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.
Embrittling potency is a thermodynamic metric that assesses the influence of solute segregation to a grain boundary (GB) on intergranular fracture. Historically, authors of studies have reported embrittling potency as a single scalar value, assuming a single segregation site of importance at a GB and a particular cleavage plane. However, the topography of intergranular fracture surfaces is not generally known a priori. Accordingly, we, in this study, present a statistical ensemble approach to compute embrittling potency, where many free surface (FS) permutations are systematically considered to model fracture of a GB. The result is a statistical description of the thermodynamics of GB embrittlement. As a specific example, embrittling potency distributions are presented for Cr segregation to sites at two Ni $\langle 111 \rangle$ symmetric tilt GBs using atomistic simulations. We show that the average embrittling potency for a particular GB site, considering an ensemble of FS permutations, is not equal to the embrittling potency computed using the lowest energy pair of FSs. A mean GB embrittlement is proposed, considering both the likelihood of formation of a particular FS and the probability of solute occupancy at each GB site, to compare the relative embrittling behavior of two distinct GBs.
Elmslie, Timothy A.; Startt, Jacob K.; Soto-Medina, Sujeily S.; Fen, Keke F.; Zappala, Emma Z.; Frandsen, Benjamin A.; Meisel,
M.; Dingreville, Remi P.; Hamlin, james H.
Magnetic, specific heat, and structural properties of the equiatomic Cantor alloy system are reported for temperatures between 5 and 300 K, and up to fields of 70 kOe. Here magnetization measurements performed on as-cast, annealed, and cold-worked samples reveal a strong processing history dependence and that high-temperature annealing after cold working does not restore the alloy to a “pristine” state. Measurements on known precipitates show that the two transitions, detected at 43 and 85 K, are intrinsic to the Cantor alloy and not the result of an impurity phase. Experimental and ab initio density functional theory computational results suggest that these transitions are a weak ferrimagnetic transition and a spin-glass-like transition, respectively, and magnetic and specific heat measurements provide evidence of significant Stoner enhancement and electron-electron interactions within the material.
Radiation-induced segregation is a phenomenon commonly observed in many alloys which consists of the redistribution of elements (solute or interstitial impurities) under irradiation. The onset and development of radiation-induced segregation can only occur when a sufficient flux of defects is sustained and defect sinks are present. Irradiation dose, dose rate, and particle types all affect defect flux. In this work, we employ a phase-field model to examine the effects of dose, dose rate, and type of incident particles on radiation-induced segregation behavior in a model binary alloy. The phase-field model takes into account the formation and evolution of point defects as well as defect clusters, the diffusion and clustering of alloy species, the presence of additional extrinsic defect sinks in the form of dislocations, and two different methods of radiation-damage insertion, which are intended to simulate either light-ion/electron irradiation via Frenkel pairs or heavy-ion irradiation in the form of cascades. Our results show a dose-rate and particle-type dependence on the amount of solute segregation. We show that the material systems exposed to higher dose rates are less subjected to solute segregation at equivalent doses. We also show that such dose-rate-dependence behavior is due to a delay of the incubation dose at which radiation-induced segregation effectively starts. Particle type and the presence of dislocations can accentuate this behavior. Our model predictions correlate with many experimental observations made over the years on radiation-induced segregation providing credence to the simulation results. The methodology presented in this study allows for a first-order prediction of the dose rate at which proxy irradiation experiments could be performed to approximate radiation-induced segregation behaviors seen in targeted irradiation conditions.
Metals subjected to irradiation environments undergo microstructural evolution and concomitant degradation, yet the nanoscale mechanisms for such evolution remain elusive. Here, we combine in situ heavy ion irradiation, atomic resolution microscopy, and atomistic simulation to elucidate how radiation damage and interfacial defects interplay to control grain boundary (GB) motion. While classical notions of boundary evolution under irradiation rest on simple ideas of curvature-driven motion, the reality is far more complex. Focusing on an ion-irradiated Pt Σ3 GB, we show how this boundary evolves by the motion of 120° facet junctions separating nanoscale {112} facets. Our analysis considers the short- and mid-range ion interactions, which roughen the facets and induce local motion, and longer-range interactions associated with interfacial disconnections, which accommodate the intergranular misorientation. We suggest how climb of these disconnections could drive coordinated facet junction motion. These findings emphasize that both local and longer-range, collective interactions are important to understanding irradiation-induced interfacial evolution.
In this work, we present a combination of machine-learned models that predicts the surface elastic properties of general free surfaces in face-centered cubic (FCC) metals. These models are built by combining a semi-analytical method based on atomistic simulations to calculate surface properties with the artificial neural network (ANN) method or the boosted regression tree (BRT) method. The latter is also used to link bulk properties and surface orientation to surface properties. The surface elastic properties are represented by their invariants considering plane elasticity within a polar method. The resulting models are shown to accurately predict the surface elastic properties of seven pure FCC metals (Cu, Ni, Ag, Au, Al, Pd, Pt). The BRT model reveals the correlations between bulk and corresponding surface properties in terms of invariants, which can be used to guide the design of complex nano-sized particles, wires and films. Finally, by expressing the surface excess energy density as a function of surface elastic invariants, fast predictions of surface energy as a function of in-plane deformations can be made from these model constructs.
Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data.
Garnet-type, solid electrolytes, such as Li7La3Zr2O12 (LLZO), are a promising alternative to liquid electrolytes for lithium-metal batteries. However, such solid-electrolyte materials frequently exhibit undesirable lithium (Li) metal plating and fracture along grain boundaries. In this study, we employ atomistic simulations to investigate the mechanisms and key fracture properties associated with intergranular fracture along one such boundary. Our results show that, in the case of a Σ5(310) grain boundary, this boundary exhibits brittle fracture behavior, i.e. the absence of dislocation activity ahead of the propagating crack tip, accompanied with a decrease in work of separation, peak stress, and maximum stress intensity factor as the temperature increases from 300 K to 1500 K. As the crack propagates, we predict two temperature-dependent Li clustering regimes. For temperatures at or below 900 K, Li tends to cluster in the bulk region away from the crack plane driven by a void-coalescence mechanism concomitant a simultaneous cubic-to-tetragonal phase transition. The tetragonalization of LLZO in this temperature regime acts as an emerging toughening mechanism. At higher temperatures, this phase transition mechanism is suppressed leading to a more uniform distribution of Li throughout the grain-boundary system and lower fracture properties as compared to lower temperatures.
Structural alloys may experience corrosion when exposed to molten chloride salts due to selective dissolution of active alloying elements. One way to prevent this is to make the molten salt reducing. For the KCl + MgCl2 eutectic salt mixture, pure Mg can be added to achieve this. However, Mg can form intermetallic compounds with nickel at high temperatures, which may cause alloy embrittlement. This study shows that an optimum level of excess Mg could be added to the molten salt which will prevent corrosion of alloys like 316 H, while not forming any detectable Ni-Mg intermetallic phases on Ni-rich alloy surfaces.
Echeverria, Marco J.; Galitskiy, Sergey; Mishra, Avanish; Dingreville, Remi P.; Dongare, Avinash M.
A hybrid atomic-scale and continuum-modeling framework is used to study the microstructural evolution during the laser-induced shock deformation and failure (spallation) of copper microstructures. A continuum two-temperature model (TTM) is used to account for the interaction of Cu atoms with a laser in molecular dynamics (MD) simulations. The MD-TTM simulations study the effect of laser-loading conditions (laser fluence) on the microstructure (defects) evolution during various stages of shock wave propagation, reflection, and interaction in single-crystal (sc) Cu systems. In addition, the role of the microstructure is investigated by comparing the defect evolution and spall response of sc-Cu and nanocrystalline Cu systems. The defect (stacking faults and twin faults) evolution behavior in the metal at various times is further characterized using virtual in situ selected area electron diffraction and x-ray diffraction during various stages of evolution of microstructure. The simulations elucidate the uncertain relation between spall strength and strain-rate and the much stronger relation between the spall strength and the temperatures generated due to laser shock loading for the small Cu sample dimensions considered here.
Metamaterials are artificial structures that can manipulate and control sound waves in ways not possible with conventional materials. While much effort has been undertaken to widen the bandgaps produced by these materials through design of heterogeneities within unit cells, comparatively little work has considered the effect of engineering heterogeneities at the structural scale by combining different types of unit cells. In this paper, we use the relaxed micromorphic model to study wave propagation in heterogeneous metastructures composed of different unit cells. We first establish the efficacy of the relaxed micromorphic model for capturing the salient characteristics of dispersive wave propagation through comparisons with direct numerical simulations for two classes of metamaterial unit cells: namely phononic crystals and locally resonant metamaterials. We then use this model to demonstrate how spatially arranging multiple unit cells into metastructures can lead to tailored and unique properties such as spatially-dependent broadband wave attenuation, rainbow trapping, and pulse shaping. In the case of the broadband wave attenuation application, we show that by building layered metastructures from different metamaterial unit cells, we can slow down or stop wave packets in an enlarged frequency range, while letting other frequencies through. In the case of the rainbow-trapping application, we show that spatial arrangements of different unit cells can be designed to progressively slow down and eventually stop waves with different frequencies at different spatial locations. Finally, in the case of the pulse-shaping application, our results show that heterogeneous metastructures can be designed to tailor the spatial profile of a propagating wave packet. Collectively, these results show the versatility of the relaxed micromorphic model for effectively and accurately simulating wave propagation in heterogeneous metastructures, and how this model can be used to design heterogeneous metastructures with tailored wave propagation functionalities.
Designing next generation thin films, tailor-made for specific applications, relies on the availability of robust processing-structure-property relationships. Traditional structure zone diagrams are limited to low-dimensional mappings, with machine-learning methods only recently attempting to relate multiple processing parameters to the final microstructure. Despite this progress, structure-processing relationships are unknown for processing conditions that vary during thin-film deposition, limiting the range of microstructures and properties achievable. In this project, we employed a phase-field computational model combined with a genetic algorithm (GA) to identify and design time-dependent processing protocols that achieve tailor-made microstructures. We simulate the physical vapor deposition of a binary-alloy thin film by employing a phase-field model, where deposition rates and diffusivities are controlled via the genetic algorithm. Our GA-guided protocols achieve targeted microstructures with lateral and vertical concentration modulations, as well as more complex, hierarchical microstructures previously not described in simple structure zone diagrams. Our algorithm provides insight to experimentalists looking for additional avenues to design novel thin-film microstructures.
With the rapid proliferation of additive manufacturing and 3D printing technologies, architected cellular solids including truss-like 3D lattice topologies offer the opportunity to program the effective material response through topological design at the mesoscale. The present report summarizes several of the key findings from a 3-year Laboratory Directed Research and Development Program. The program set out to explore novel lattice topologies that can be designed to control, redirect, or dissipate energy from one or multiple insult environments relevant to Sandia missions, including crush, shock/impact, vibration, thermal, etc. In the first 4 sections, we document four novel lattice topologies stemming from this study: coulombic lattices, multi-morphology lattices, interpenetrating lattices, and pore-modified gyroid cellular solids, each with unique properties that had not been achieved by existing cellular/lattice metamaterials. The fifth section explores how unintentional lattice imperfections stemming from the manufacturing process, primarily sur face roughness in the case of laser powder bed fusion, serve to cause stochastic response but that in some cases such as elastic response the stochastic behavior is homogenized through the adoption of lattices. In the sixth section we explore a novel neural network screening process that allows such stocastic variability to be predicted. In the last three sections, we explore considerations of computational design of lattices. Specifically, in section 7 using a novel generative optimization scheme to design novel pareto-optimal lattices for multi-objective environments. In section 8, we use computational design to optimize a metallic lattice structure to absorb impact energy for a 1000 ft/s impact. And in section 9, we develop a modified micromorphic continuum model to solve wave propagation problems in lattices efficiently.
This project focused on providing a fundamental physico-chemical understanding of the coupling mechanisms of corrosion- and radiation-induced degradation at material-salt interfaces in Ni-based alloys operating in emulated Molten Salt Reactor(MSR) environments through the use of a unique suite of aging experiments, in-situ nanoscale characterization experiments on these materials, and multi-physics computational models. The technical basis and capabilities described in this report bring us a step closer to accelerate the deployment of MSRs by closing knowledge gaps related to materials degradation in harsh environments.
Twin boundaries play an important role in the thermodynamics, stability, and mechanical properties of nanocrystalline metals. Understanding their structure and chemistry at the atomic scale is key to guide strategies for fabricating nanocrystalline materials with improved properties. In this work, we report an unusual segregation phenomenon at gold-doped platinum twin boundaries, which is arbitrated by the presence of disconnections, a type of interfacial line defect. By using atomistic simulations, we show that disconnections containing a stacking fault can induce an unexpected transition in the interfacial-segregation structure at the atomic scale, from a bilayer, alternating-segregation structure to a trilayer, segregation-only structure. This behavior is found for faulted disconnections of varying step heights and dislocation characters. Supported by a structural analysis and the classical Langmuir–McLean segregation model, we reveal that this phenomenon is driven by a structurally induced drop of the local pressure across the faulted disconnection accompanied by an increase in the segregation volume.
During the various stages of shock loading, many transient modes of deformation can activate and deactivate to affect the final state of a material. In order to fundamentally understand and optimize a shock response, researchers seek the ability to probe these modes in real-time and measure the microstructural evolutions with nanoscale resolution. Neither post-mortem analysis on recovered samples nor continuum-based methods during shock testing meet both requirements. High-speed diffraction offers a solution, but the interpretation of diffractograms suffers numerous debates and uncertainties. By atomistically simulating the shock, X-ray diffraction, and electron diffraction of three representative BCC and FCC metallic systems, we systematically isolated the characteristic fingerprints of salient deformation modes, such as dislocation slip (stacking faults), deformation twinning, and phase transformation as observed in experimental diffractograms. This study demonstrates how to use simulated diffractograms to connect the contributions from concurrent deformation modes to the evolutions of both 1D line profiles and 2D patterns for diffractograms from single crystals. Harnessing these fingerprints alongside information on local pressures and plasticity contributions facilitate the interpretation of shock experiments with cutting-edge resolution in both space and time.
AbstractThe phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.
Cellini, Filippo C.; Lavini, Francesco L.; Chen, Elton Y.; Bongiorno, Angelo B.; Popovic, Filip P.; Hartman, Ryan H.; Dingreville, Remi P.; Riedo, Elisa R.
Understanding phase transformations in 2D materials can unlock unprecedented developments in nanotechnology, since their unique properties can be dramatically modified by external fields that control the phase change. Here, experiments and simulations are used to investigate the mechanical properties of a 2D diamond boron nitride (BN) phase induced by applying local pressure on atomically thin h-BN on a SiO2 substrate, at room temperature, and without chemical functionalization. Molecular dynamics (MD) simulations show a metastable local rearrangement of the h-BN atoms into diamond crystal clusters when increasing the indentation pressure. Raman spectroscopy experiments confirm the presence of a pressure-induced cubic BN phase, and its metastability upon release of pressure. Å-indentation experiments and simulations show that at pressures of 2–4 GPa, the indentation stiffness of monolayer h-BN on SiO2 is the same of bare SiO2, whereas for two- and three-layer-thick h-BN on SiO2 the stiffness increases of up to 50% compared to bare SiO2, and then it decreases when increasing the number of layers. Up to 4 GPa, the reduced strain in the layers closer to the substrate decreases the probability of the sp2-to-sp3 phase transition, explaining the lower stiffness observed in thicker h-BN.
The self-interstitial atom (SIA) is one of two fundamental point defects in bulk Si. Isolated Si SIAs are extremely difficult to observe experimentally. Even at very low temperatures, they anneal before typical experiments can be performed. Given the challenges associated with experimental characterization, accurate theoretical calculations provide valuable information necessary to elucidate the properties of these defects. Previous studies have applied Kohn-Sham density functional theory (DFT) to the Si SIA, using either the local density approximation or the generalized gradient approximation to the exchange-correlation (XC) energy. The consensus of these studies indicates that a Si SIA may exist in five charge states ranging from -2 to +2 with the defect structure being dependent on the charge state. This study aims to re-examine the existence of these charge states in light of recently derived "approximate bounds"on the defect levels obtained from finite-size supercell calculations and new DFT calculations using both semi-local and hybrid XC approximations. We conclude that only the neutral and +2 charge states are directly supported by DFT as localized charge states of the Si SIA. Within the current accuracy of DFT, our results indicate that the +1 charge state likely consists of an electron in a conduction-band-like state that is coulombically bound to a +2 SIA. Furthermore, the -1 and -2 charge states likely consist of a neutral SIA with one and two additional electrons in the conduction band, respectively.
This report details the current benchmark results to verify, validate and demonstrate the capabilities of the in-house multi-physics phase-field modeling framework Mesoscale Multiphysics Phase Field Simulator (MEMPHIS) developed at the Center for Integrated Nanotechnologies (CINT). MEMPHIS is a general phase-field capability to model various nanoscience and materials science phenomena related to microstructure evolution. MEMPHIS has been benchmarked against a suite of reported 'classical' phase-field benchmark problems to verify and validate the correctness, accuracy and precision of the models and numerical methods currently implemented into the code.
Engineers performing safety analyses throughout the U.S. Department of Energy (DOE) complex rely heavily on the information provided in the DOE Handbook, DOE-HDBK-3010, Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities, to determine radionuclide source terms from postulated accident scenarios. In calculating source terms, analysts tend to use the DOE Handbook's bounding values on airborne release fractions (ARFs) and respirable fractions (RFs) for various categories of insults (representing potential accident release categories). This is typically due to both time constraints and the avoidance of regulatory critique. Limited experimental data on fragmentation of solids, such as ceramic pellets (i.e., PuO2), and container breach due to mechanical insults (i.e., explosion-induced fragmentation, drop and forklift impact), can be supplemented by modeling and simulation using high fidelity computational tools. This paper presents the use of Sandia National Laboratories' SIERRA Solid Mechanics (SIERRA/SM) finite element code to investigate the behavior of two widely utilized waste containers (Standard Waste Box and 7A Drum) subject to a range of free fall impact and puncture scenarios. The resulting behavior of the containers is assessed, and a methodology is presented for calculating bounding airborne release fractions from calculated breach areas for the various accident conditions considered. The paper also describes a novel multi-scale constitutive model recently implemented in SIERRA/SM that can simulate the fracture of brittle materials such as PuO2 and determining the amount of hazardous respirable particles generated during the fracture process. Comparisons are made between model predictions and simple bench-top experiments.
Safety basis analysts throughout the U.S. Department of Energy (DOE) complex rely heavily on the information provided in the DOE Handbook, DOE-HDBK-3010,Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities, to determine radionuclide source terms from postulated accident scenarios. In calculating source terms, analysts tend to use the DOE Handbook's bounding values on airborne release fractions (ARFs) and respirable fractions (RFs) for various categories of insults (representing potential accident release categories). This is typically due to both time constraints and the avoidance of regulatory critique. Unfortunately, these bounding ARFs/RFs represent extremely conservative values. Moreover, they were derived from very limited small-scale bench/laboratory experiments and/or from engineered judgment.Thus, the basis for the data may not be representative of the actual unique accident conditions and configurations being evaluated.
For long-term storage, spent nuclear fuel (SNF) is placed in dry storage systems, commonly consisting of welded stainless steel canisters enclosed in ventilated overpacks. Choride-induced stress corrosion cracking (CISCC) of these canisters may occur due to the deliquescence of sea-salt aerosols as the canisters cool. Current experimental and modeling efforts to evaluate canister CISCC assume that the deliquescent brines, once formed, persist on the metal surface, without changing chemical or physical properties. Here we present data that show that magnesium chloride rich-brines, which form first as the canisters cool and sea-salts deliquesce, are not stable at elevated temperatures, degassing HCl and converting to solid carbonates and hydroxychloride phases, thus limiting conditions for corrosion. Moreover, once pitting corrosion begins on the metal surface, oxygen reduction in the cathode region surrounding the pits produces hydroxide ions, increasing the pH under some experimental conditions, leads to precipitation of magnesium hydroxychloride hydrates. Because magnesium carbonates and hydroxychloride hydrates are less deliquescent than magnesium chloride, precipitation of these compounds causes a reduction in the brine volume on the metal surface, potentially limiting the extent of corrosion. If taken to completion, such reactions may lead to brine dry-out, and cessation of corrosion.
This project focused on providing a fundamental mechanistic understanding of the complex degra- dation mechanisms associated with Pellet/Clad Debonding (PCD) through the use of a unique suite of novel synthesis of surrogate spent nuclear fuel, in-situ nanoscale experiments on surrogate interfaces, multi-modeling, and characterization of decommissioned commercial spent fuel. The understanding of a broad class of metal/ceramic interfaces degradation studied within this project provided the technical basis related to the safety of high burn-up fuel, a problem of interest to the DOE.
The structures and properties of Ce1–xZrxO2 (x = 0–1) solid solutions, selected Ce1–xZrxO2 surfaces, and Ce1–xZrxO2/CeO2 interfaces were computed within the framework of density functional theory corrected for strong electron correlation (DFT+U). The calculated Debye temperature increases steadily with Zr content in (Ce, Zr)O2 phases, indicating a significant rise in microhardness from CeO2 to ZrO2, without appreciable loss in ductility as the interfacial stoichiometry changes. Surface energy calculations for the low-index CeO2(111) and (110) surfaces show limited sensitivity to strong 4f-electron correlation. The fracture energy of Ce1–xZrxO2(111)/CeO2(111) increases markedly with Zr content, with a significant decrease in energy for thicker Ce1–xZrxO2 films. These findings suggest the crucial role of Zr acting as a binder at the Ce1–xZrxO2/CeO2 interfaces, due to the more covalent character of Zr–O bonds compared to Ce–O. Finally, the impact of surface relaxation upon interface cracking was assessed and found to reach a maximum for Ce0.25Zr0.75O2/CeO2 interfaces.
The high density of interfaces in nanostructured materials are hypothesized to improve radiation tolerance compared to coarse-grained materials. In order to investigate the roles of vacancies, self-interstitials, and helium, both room temperature in situ TEM He+ implantation and annealing, as well as high temperature He+ implantation was performed on nanocrystalline iron. Dislocation loops are formed by the accumulation of mobile point defects rather than by displacement cascades at intermediate temperatures. Around 600 °C, loops disappeared through gradual shrinking, which is hypothesized to correspond to the annihilation of self-interstitial atoms by mobile vacancies that also resulted in cavity formation. The room temperature implantation resulted in cavities evenly distributed throughout the grain after annealing, whereas cavities were predominately observed at grain boundaries for the elevated temperature implantation. This difference is associated with the formation of stable helium-vacancy complexes in the grains during room temperature implantation, which is not present during high temperature implantation.
Safety basis analysts throughout the U.S. Department of Energy (DOE) complex rely heavily on the information provided in the DOE Handbook, DOE - HDBK - 3010, Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities, to determine radionuclide source terms. In calculating source terms, analysts tend to use the DOE Handbook's bounding values on airborne release fractions (ARFs) and respirable fractions (RFs) for various categories of insults (representing potential accident release categories). This is typically due to both time constraints and the avoidance of regulatory critique. Unfortunately, these bounding ARFs/RFs represent extremely conservative values. Moreover, they were derived from very limited small-scale bench/laboratory experiments and/or from engineered judgment. Thus, the basis for the data may not be representative of the actual unique accident conditions and configurations being evaluated. The goal of this research is to develop a more accurate and defensible method to determine bounding values for the DOE Handbook using state-of-art multi-physics-based computer codes. This enables us to better understand the fundamental physics and phenomena associated with the types of accidents in the handbook. In this year, this research included improvements of the high-fidelity codes to model particle resuspension and multi-component evaporation for fire scenarios. We also began to model ceramic fragmentation experiments, and to reanalyze the liquid fire and powder release experiments that were done last year. The results show that the added physics better describes the fragmentation phenomena. Thus, this work provides a low-cost method to establish physics-justified safety bounds by taking into account specific geometries and conditions that may not have been previously measured and/or are too costly to perform.
In order to accurately describe defect accumulation in heterogeneous microstructures and under complex irradiation conditions, simulation methods are needed that can explicitly account for the effect of nonhomogeneous microstructures on damage accumulation. In this project, an advanced simulation tool called spatially resolved stochastic cluster dynamics (SRSCD) is developed for this purpose. The proposed approach relies on solving spatially resolved coupled rate equations of standard cluster dynamics methods in a kinetic Monte Carlo scheme. Large-scale simulations of radiation damage in polycrystalline materials are enabled through several improvements made to this method, including a pseudo-adaptive meshing scheme for cascade implantation and implementation of this method in a synchronous parallel kinetic Monte Carlo framework.
This letter report presents a probabilistic performance assessment model to evaluate the probability of canister failure (through-wall penetration) by SCC. The model first assesses whether environmental conditions for SCC – the presence of an aqueous film – are present at canister weld locations (where tensile stresses are likely to occur) on the canister surface. Geometry-specific storage system thermal models and weather data sets representative of U.S. spent nuclear fuel (SNF) storage sites are implemented to evaluate location-specific canister surface temperature and relative humidity (RH). As the canister cools and aqueous conditions become possible, the occurrence of corrosion is evaluated. Corrosion is modeled as a two-step process: first, pitting is initiated, and the extent and depth of pitting is a function of the chloride surface load and the environmental conditions (temperature and RH). Second, as corrosion penetration increases, the pit eventually transitions to a SCC crack, with crack initiation becoming more likely with increasing pit depth. Once pits convert to cracks, a crack growth model is implemented. The SCC growth model includes rate dependencies on both temperature and crack tip stress intensity factor, and crack growth only occurs in time steps when aqueous conditions are predicted. The model suggests that SCC is likely to occur over potential SNF interim storage intervals; however, this result is based on many modeling assumptions. Sensitivity analyses provide information on the model assumptions and parameter values that have the greatest impact on predicted storage canister performance, and provide guidance for further research to reduce uncertainties.
Enhanced radiation tolerance of nanostructured metals is attributed to the high density of interfaces that can absorb radiationinduced defects. Here, cavity evolution mechanisms during cascade damage, helium implantation, and annealing of nanocrystalline nickel are characterized via in situ transmission electron microscopy (TEM). Films subjected to self-ion irradiation followed by helium implantation developed evenly distributed cavity structures, whereas films exposed in the reversed order developed cavities preferentially distributed along grain boundaries. Post-irradiation annealing and orientation mapping demonstrated uniform cavity growth in the nanocrystalline structure, and cavities spanning multiple grains. These mechanisms suggest limited ability to reduce swelling, despite the stability of the nanostructure.
The transient degradation of semiconductor device performance under irradiation has long been an issue of concern. A single high-energy charged particle can degrade or permanently destroy the microelectronic component, potentially altering the course or function of the systems. Disruption of the the crystalline structure through the introduction of quasi-stable defect structures can change properties from semiconductor to conductor. Typically, the initial defect formation phase is followed by a recovery phase in which defect-defect or defect-dopant interactions modify the characteristics of the damaged structure. In this LDRD Express, in-situ ion irradiation transmission microscopy (TEM) in-situ TEM experiments combined with atomistic simulations have been conducted to determine the feasibility of imaging and characterizing the defect structure resulting from a single cascade in silicon. In-situ TEM experiments have been conducted to demonstrate that a single ion strike can be observed in Si thin films with nanometer resolution in real time using the in-situ ion irradiation transmission electron microscope (I3TEM). Parallel to this experimental effort, ion implantation has been numerically simulated using Molecular Dynamics (MD). This numerical framework provides detailed predictions of the damage and follow the evolution of the damage during the first nanoseconds. The experimental results demonstrate that single ion strike can be observed in prototypical semiconductors.
Material response to dynamic loading is often dominated by microstructure (grain structure, porosity, inclusions, defects). An example critically important to Sandia's mission is dynamic strength of polycrystalline metals where heterogeneities lead to localization of deformation and loss of shear strength. Microstructural effects are of broad importance to the scientific community and several institutions within DoD and DOE; however, current models rely on inaccurate assumptions about mechanisms at the sub-continuum or mesoscale. Consequently, there is a critical need for accurate and robust methods for modeling heterogeneous material response at this lower length scale. This report summarizes work performed as part of an LDRD effort (FY11 to FY13; project number 151364) to meet these needs.