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DOE OSTI2025

Methods and Models for the Study of Aging Effects in Palladium Tritide

We have developed methods to expose milligram-scale samples of metals to tritium for studies of aging effects on material properties, primarily intended for metals that reversibly form hydrides. The tritium decays to helium within the solid, leading to profound changes in microstructural features and macroscopic properties. We present our combined experimental and modeling approach, and provide an example of a palladium foil aged 3 months.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Bright x-rays reveal shifting deformation states and effects of the microstructure on the plastic deformation of crystalline materials

The plastic deformation of crystalline materials is usually modeled as smoothly progressing in space and time, yet modern studies show intermittency in the deformation dynamics of single-crystals arising from avalanche behavior of dislocation ensembles under uniform applied loads. However, once the prism of the microstructure in polycrystalline materials disperses and redistributes the load on a grain-by-grain basis, additional length and time scales are involved. Thus, the question is open as to how deformation intermittency manifests for the nonuniform grain-scale internal driving forces interacting with the finer-scale dislocation ensemble behavior. In this work we track the evolution of elastic strain within individual grains of a creep-loaded titanium alloy, revealing widely varying internal strains that fluctuate over time. Here, the findings provide direct evidence of how flow intermittency proceeds for an aggregate of ~700 grains while showing the influences of multiscale ensemble interactions and opening new avenues for advancing plasticity modeling.

36 MATERIALS SCIENCE↗

DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗

NASA NTRS2003

Data and Modeling of Dendrites Subject to A Step Change in Pressure (TDSE)

There is considerable interest in dendritic solidification because of the influence dendrites have in the determination of microstructure, and thereby in the physical properties of cast metals and alloys. Current theories and models of dendritic growth generally couple diffusion effects in the melt with the physics of the interface. Data and subsequent analysis prior of the tip growth speed and radii of thermal succinonitrile dendrites in the near-convection free, on-orbit, free-fall environment demonstrate that these theories yield predictions that are reasonably in agreement with the results of experiment. However, data and analysis for assessing the interfacial physics component of theory are not sufficiently detailed or definitive. To study fundamental aspects of dendritic interface stability, we are measuring and modeling the kinetics and morphology of dendrites as they evolve from one well-defined steady state at a pre-set supercooling, through a transient stage, to a different well-defined steady state. More specifically, we subject succinonitrile dendrites, growing under steady-state conditions, to a rapid change in pressure. This leads to a rapid change in thermal driving force from the corresponding change in both the equilibrium melting temperature due to the Clapeyron effect, and a change in the far-field temperature due to adiabatic temperature changes in the bulk liquid and solid. Subsequently, we observe transformations from a well-characterized initial state into a new steady-state. Initial data reveal that the dendrite tip velocity changes almost as fast as the pressure charges, while the tip radius changes occur more slowly, taking from 10 60 seconds depending on the size of the step change and the final supercooling. Computer modeling of this process shows both agreements and disagreements with the experimental data. In making these observations and measurements, we are gaining new understandings of interfacial dynamics and state-selection physics.

Koss, Matthew B.↗

DOE OSTI2022

Quantitative assessment of particle dispersion in polymeric composites and its effect on mechanical properties

In this work, an automated image analysis tool is developed to establish quantitative correlations between the particle/cluster size distribution and the mechanical properties of particle reinforced polymer composites (PRPC).This automated image analysis tool is developed within python programming software to process and analyze the microstructural images of the polymerbased composite materials. The spent coffee bean powder (SCBP) reinforced poly-propylene carbonate (PPC) polymer composite with differing wt.% of the filler is selected for the analysis. Detailed statistical analysis of the microstructural images reveals that ‘clustering of clusters’ is also presented in addition to the most commonly reported ‘clustering of particles’, and the distribution of particle/clusters is bimodal. Based on these findings, an effective volume fraction for the filler material is proposed to mainly capture the agglomeration effect. With this effective volume fraction, the standard rule-of-mixture model correctly captures the experimentally measured tensile strength and modulus as a function of filler wt.%. Further, the applicability of this effective volume fraction for other theoretical models is also analyzed. The detailed statistical analysis of the microstructure and the proposed effective volume fraction helps to develop a deeper quantitative understanding of the PRPC than the conventional qualitative correlation of microstructural features with the properties and failure processes.

36 MATERIALS SCIENCE↗

NASA NTRS2007

Characterization of Nonlinear Rate Dependent Response of Shape Memory Polymers

Shape Memory Polymers (SMPs) are a class of polymers, which can undergo deformation in a flexible state at elevated temperatures, and when cooled below the glass transition temperature, while retaining their deformed shape, will enter and remain in a rigid state. Upon heating above the glass transition temperature, the shape memory polymer will return to its original, unaltered shape. SMPs have been reported to recover strains of over 400%. It is important to understand the stress and strain recovery behavior of SMPs to better develop constitutive models which predict material behavior. Initial modeling efforts did not account for large deformations beyond 25% strain. However, a model under current development is capable of describing large deformations of the material. This model considers the coexisting active (rubber) and frozen (glass) phases of the polymer, as well as the transitions between the material phases. The constitutive equations at the continuum level are established with internal state variables to describe the microstructural changes associated with the phase transitions. For small deformations, the model reduces to a linear model that agrees with those reported in the literature. Thermomechanical characterization is necessary for the development, calibration, and validation of a constitutive model. The experimental data reported in this paper will assist in model development by providing a better understanding of the stress and strain recovery behavior of the material. This paper presents the testing techniques used to characterize the thermomechanical material properties of a shape memory polymer (SMP) and also presents the resulting data. An innovative visual-photographic apparatus, known as a Vision Image Correlation (VIC) system was used to measure the strain. The details of this technique will also be presented in this paper. A series of tensile tests were performed on specimens such that strain levels of 10, 25, 50, and 100% were applied to the material while it was above its glass transition temperature. After deforming the material to a specified applied strain, the material was then cooled to below the glass transition temperature (Tg) while retaining the deformed shape. Finally, the specimen was heated again to above the transition temperature, and the resulting shape recovery profile was measured. Results show that strain recovery occurs at a nonlinear rate with respect to time. Results also indicate that the ratio of recoverable strain/applied strain increases as the applied strain increases.

Volk, Brent↗

DOE OSTI2019

A review of computational modeling techniques in study and design of shape memory ceramics

Shape memory ceramics are a unique family of shape memory materials with a wide variety of applications, such as ultra-high energy dissipation and high-temperature actuation. Along with significant progress in the experimental study of zirconia-based shape memory ceramics in recent years, computational simulations have exhibited powerful capabilities in revealing nano/microstructure-dependent deformation and failure mechanisms in these materials. In this work, we review the recent progress in understanding the phase transformation behavior in shape memory ceramics, focusing on computational modeling of zirconia-based ceramics. Electronic structure calculations have provided new data on the phase stability and surface properties of shape memory ceramics. At the nanometer length scale, molecular dynamics simulations have captured fundamental information about martensitic phase transformation and the effects of grain boundaries and defects on mechanical response of bi- and polycrystalline zirconia-based ceramics. At the micrometer length scale, advanced phase-field models have shown the ability to predict morphological evolution of microstructures and the corresponding mechanical responses in good agreement with experimental observations. Despite the recent multiscale computational advancements, further developments are required to establish processing-structure-property-performance relations that will lead to reliable and practical strategies for designing zirconia-based (or other) ceramics with improved and sustained shape memory responses. This article critically reviews current computational modeling techniques and provides an outlook for the study and design of the next generation of shape memory ceramics.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Linking Initial Microstructure to ORR Related Property Degradation in SOFC Cathode: A Phase Field Simulation

Microstructure evolution driven by thermal coarsening is an important factor for the loss of oxygen reduction reaction rates in SOFC cathode. In this work, the effect of an initial microstructure on the microstructure evolution in SOFC cathode is investigated using a recently developed phase field model. Specifically, we tune the phase fraction, the average grain size, the standard deviation of the grain size and the grain shape in the initial microstructure, and explore their effect on the evolution of the grain size, the density of triple phase boundary, the specific surface area and the effective conductivity in LSM-YSZ cathodes. It is found that the degradation rate of TPB density and SSA of LSM is lower with less LSM phase fraction (with constant porosity assumed) and greater average grain size, while the degradation rate of effective conductivity can also be tuned by adjusting the standard deviation of grain size distribution and grain aspect ratio. The implication of this study on the designing of an optimal initial microstructure of SOFC cathodes is discussed.

25 ENERGY STORAGE↗

DOE OSTI2023

Solidification modes during additive manufacturing of steel revealed by high-speed X-ray diffraction

Solidification during fusion-based additive manufacturing (AM) is characterized by high solidification velocities and large thermal gradients, two factors that control the solidification mode of metals and alloys. Using two synchrotron-based, in situ setups, we perform high-speed X-ray diffraction measurements to investigate the impact of the solidification velocities and thermal gradients on the solidification mode of a hot-work tool steel over a wide range of thermal conditions of relevance to AM of metals. The solidification mode of primary δ-ferrite is observed at a cooling rate of 2.12x10 4 K/s, and at a higher cooling rate of 1.5x10 6 K/s, δ-ferrite is sup-pressed, and primary austenite is observed. The experimental thermal conditions are evaluated and linked to a Kurz-Giovanola-Trivedi (KGT) based solidification model. The modelling results show that the predictions from the multicomponent KGT model agree with the experimental observations. This work highlights the role of in situ XRD measurements for a fundamental understanding of the microstructure evolution during AM and for vali-dation of computational thermodynamics and kinetics models, facilitating parameter and alloy development for AM processes.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Microstructure Clones

Microstructure drives component behavior. Contemporary crystal plasticity studies compare strain measurements of polycrystal specimens to models. Because each specimen is unique, it is impossible to know which differences are significant. In this project, we invented microstructure clones and explored their use in understanding crystal plasticity. Microstructure clones are specimens with nearly identical microstructures, which allows for multiple destructive tests of a microstructure, insight into how a specimen will deform, variability quantification, and the ability to measure the effects of microstructural changes. Several sets of microstructure clones, pure nickel tensile bars, were tested. The techniques of digital image correlation, crystal plasticity finite element analysis, high resolution electron backscatter diffraction, transmission electron microscopy, and dislocation dynamics were used to understand the structural behavior of these microstructures. This work reshapes the fields of crystal plasticity and structure-property relationships by providing a technique to control for specific variables, quantify microstructural stochasticity, and replicate experiments.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Updates to the Predictive Materials Modeling Software Tools

Updates on NASA's efforts to build predictive material models from the micro-scale to the macro-scale are presented. To complement the mission design cycle process and reduce the need for extensive testing, NASA is developing modeling and simulation tools that enable characterizing material properties and response to hot plasma experienced during atmospheric entry. The PuMA (microstructure analysis), PATO (macroscale material response), SPARTA (direct simulation Monte Carlo) and ARCHeS (arc heater modeling) codes are described. A range of applications, encompassing calculating effective material properties, study of high shear boundary layer flow over woven materials, new model of silicone-based coatings, new model of mechanical erosion, as well as approach to loose multi-physics coupling, are presented.

material modeling↗

DOE OSTI2017

Temperature dependence of the atomic structure and electrical activity of defects in ZnSb thermoelectric lightly doped with copper

A model for describing the temperature dependence of the defect microstructure in high-efficiency ZnSb thermoelectric with a copper content of 0.1 at % is chosen. The temperature dependences of the chargecarrier density and mobility for thermal cycle I (300–700–300 K) are analyzed taking into account the features of the crystal structure and covalent chemical bond in ZnSb. The basic defect structure (at temperatures of T = 560–605 K) is the state when all Cu atoms are equally distributed between sites of both sublattices and behave as acceptors, and the number of intrinsic donor and acceptor defects is much smaller. The effect of the latter becomes noticeable when the temperature goes beyond the above-mentioned range. At T > 605 K, extra acceptors (antisite Zn{sub Sb}) occur; upon cooling below 560 K, Cu{sub 2} dimers arise and the electrical activity of the impurity lowers. Dimer decay upon heating leads to growth in the concentration with temperature up to saturation in the above-mentioned range. Additional thermal cycles II–VIII are performed; the observed changes in the temperature dependences of the hole concentration and mobility are discussed in the context of the investigated model.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Laser propagation in a subcritical foam: Ion and electron heating

We develop a model for laser propagation and heating in a subcritical foam (homogeneous electron density as a fraction of critical n e,0 /n c <1). Our model describes the partition of energy between ions and electrons in an expanding foam element irradiated by a laser, and we solve for the plasma conditions generated by burning down the foam microstructure. We find that a substantial fraction of laser energy goes into breaking down and homogenizing the foam microstructure, which slows down the laser heat front. We also find that the ion temperature in a plasma generated by burning down a foam can exceed the electron temperature. This is because laser energy is deposited into the expansion kinetic energy of ions as the foam microstructure burns down explosively. The higher ion temperature increases ion-acoustic wave damping which reduces stimulated Brillouin scattering (SBS). We test our model against data from an experiment that shot a subcritical foam with a high intensity laser pulse on the Janus laser facility at LLNL. We find that by modeling the effect of the foam microstructure, we can explain both the experimentally measured laser propagation velocity and the backscattered SBS power.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Dealloying, Microstructure and the Corrosion/Protection of Cast Magnesium Alloys

The purpose of this project was to develop a greater understanding of micro-galvanic corrosion effects in cast magnesium alloys using both experimental and computational methods. Experimental accomplishments have been made in the following areas of interest: characterization, aqueous free-corrosion, atmospheric corrosion, ionic liquid dissolution, rate kinetics of oxide dissolution, and coating investigation. Commercial alloys (AZ91D, AM60, and AZ31B), binary-phase alloys (αMg-2at.%Al, αMg-5at.%Al, and Mg-8at.%Al), and component phases (Mg, Al, β-Mg, β-1%Zn, MnAl3) were obtained and characterized using energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), and scanning electron microscopy (SEM). Full immersion in aqueous chloride was used to characterize the corrosion behavior of alloys. Rotating disc electrodes (RDEs) were used to observe accelerated long-term corrosion behavior. Al surface redistribution for freely corroded samples was analyzed using SEM, EDS, and lithium underpotential deposition (Li UPD). Atmospheric corrosion was observed using contact angle evolution, overnight pH monitoring, and surface pH evolution studies. Ionic liquid corrosion characterization was performed using linear sweep voltammetry and potentiostatic dissolution in 150° choline chloride-urea (cc-urea). Two surface coatings were investigated: (1) Li-carbonate and (2) cc-urea. Li-carbonate coatings were characterized using X-ray photoelectron spectroscopy (XPS), SEM, and aqueous free corrosion potential monitoring. Hydrophobic cc-urea coatings were characterized using contact angle measurements and electrochemical impedance spectroscopy. Oxide dissolution rate kinetics were studied using inductively coupled plasma mass spectroscopy (ICP-MS). Computational accomplishments have been made through the development of Kinetic Monte Carlo (KMC) simulations which model time- and composition-dependent effects on the microstructure due to spatial redistribution of alloying elements during corrosion.

36 MATERIALS SCIENCE↗

DOE OSTI2019

A Monte Carlo Model of Irradiation-Induced Recrystallization in Polycrystalline UMo Fuels

Experiments show that irradiation-induced recrystallization speeds up the swelling kinetics in U10wt%Mo fuels. However, recrystallization mechanisms and the effect of initial grain microstructures on recrystallization kinetics are still unclear. In this work a Monte Carlo model coupling the rate theory of defect evolution has been developed to study the irradiation-induced recrystallization. The rate theory is used to describe the spatial evolution of gas bubbles, interstitials and interstitial loops; First-Passage Kinetic Monte Carlo (FPKMC) approach is used to describe the fast and strongly anisotropic migration of interstitials, and a Cellular Automata method is used to model the formation of recrystallized grains. With the assumption that recrystallization may occur when the local interstitial loop density is larger than a given critical value, simulation results reveal that 1) recrystallized grains first nucleate on grain boundaries and the recrystallization zone front moves to the center of original coarse grains in the UMo matrix, and 2) recrystallization starts earlier in coarse polycrystalline structures, while the overall recrystallization kinetics decreases with increasing grain size. These results agree with experimental observations. The comparison of recrystallization kinetics obtained from experiments and modeling suggests that the interstitial loop accumulation leads to the recrystallization and the interstitial loop growth is suppressed inside coarse grains due to the over-pressured intra-granular gas bubbles.

Frazier, William E.↗

DOE OSTI2020

Mesoscale simulations of High Burnup Fuel Fragmentation: Applying phase-field modeling to understand high burnup fuel fragmentation during loss of coolant accident conditions

During a loss-of-coolant accident (LOCA) in a nuclear power plant, a rapid increase in temperature is experienced in the fuel. In commercial light water reactors (LWRs), the high burnup structure (HBS) forms in regions of the UO 2 fuel where local burnup is high, characterized by smaller grain sizes and large, overpressurized bubbles. During a LOCA transient, fuel in the HBS region is susceptible to fine fragmentation, where the fuel breaks up into micron-size fragments (sometimes referred to as pulverization). To better understand the mechanisms behind this phenomenon, mesoscale computational modeling using the Idaho National Laboratory code Marmot has been employed to simulate the process of HBS formation and response to a LOCA transient. The process of complete and partial HBS formation was demonstrated in prototypical LWR conditions. The simulated HBS microstructures from these simulations were passed to a phase-field model of fracture. To provide a more direct comparison with experiments, a set of experiments conducted by Studsvik on fine fragmentation/pulverization during LOCA conditions was simulated using the BISON fuel performance code. The temperature profiles from the BISON simulations were passed to a phase-field model to determine the gas bubble pressure as a function of time. These pressure histories were used in a phase-field model of fracture to determine under what conditions and in which types of bubbles fine fragmentation was likely to occur.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

TEM Approaches for Microstructure-Informed Prediction of Mechanical Properties in Structural Alloys

Predicting the mechanical performance of structural alloys from their evolving microstructure remains a major challenge in materials science, particularly for nuclear structural materials, where irradiation-induced defects span multiple types and length scales and interact through complex mechanisms. The dispersed barrier hardening (DBH) [1] and Friedel–Kroupa–Hirsch (FKH) [2,3] models have been widely used to evaluate the hardening contributions of individual obstacles and to estimate tensile strength from quantified microstructures; however, when multiple size-dependent obstacles coexist and evolve, predicting temperature-dependent tensile strength becomes significantly more complex, and a fully consistent hardening model is still lacking. Transmission electron microscopy (TEM) plays a central role in refining hardening models and enabling predictive assessments of tensile strength evolution by providing quantitative characterization of dislocations, irradiation-induced defects (e.g., dislocation loops and cavities), precipitates, and grain structure (Fig. 1.). These experimentally measured defect densities are incorporated into physically based hardening models with size- and shape- dependent obstacle strengths [4], using root-sum-square superposition for obstacles of comparable strength and linear superposition for dissimilar ones [5]. In addition, recent advances in TEM [6-8], including high-resolution imaging, 4D-STEM strain mapping, EDS/EELS elemental analysis, and flash-polishing-based TEM specimen preparation and extraction-replica methods (Fig. 2), further improve the accuracy of microstructural quantification. By comparison with prior studies as well as our own results, we show that when TEM-derived microstructural information is carefully integrated with physically grounded hardening models, yield strength (or irradiation-induced hardening) measured at room temperature can be predicted with good quantitative agreement across multiple alloy classes. In-situ TEM combined with high-temperature mechanical testing represents an important next step for refining hardening models by directly probing dislocation–obstacle interactions across varying irradiation doses and temperatures [9]. Because the barrier strength factor (α) depends on both temperature and obstacle size, it should not be treated as a constant fitting parameter; rather, it must be explicitly evaluated to achieve physically meaningful predictions of mechanical behaviour at operating temperatures. This presentation therefore discusses why all strengthening contributions (e.g., Peierls stress, solid-solution strengthening, voids, bubbles, dislocation loops, dislocation lines, and grain boundaries) must be considered collectively, why appropriate superposition methods are essential when obstacles possess different barrier strength factors, how hardness measurements can be meaningfully related to tensile properties, and how TEM-derived microstructural information can be systematically incorporated into hardening models. More broadly, it outlines a pathway toward microstructure-informed prediction of mechanical properties and supports the goal of establishing science-based tools for evaluating structural materials in extreme environments [10].

Lin, Yan-Ru [ORNL] (ORCID:0000000339991473)↗

NASA NTRSDate not supplied

Computationally Guided Development of Components for High Energy Density Solid-State Lithium-Sulfur Batteries

All electric vertical take-off and landing vehicles (eVTOL) for urban air mobility (UAM) concepts face numerous challenging technical barriers before their introduction into the consumer marketplace. The most challenging of these technical barriers to overcome is developing an energy storage system capable of meeting the rigorous aerospace safety and performance criteria1. The performance metrics for eVTOL craft, such as specific energy, specific power, and safety, exceed those of electric automobiles by a factor of two to four. Current state-of-the-art (SOA) lithium-ion batteries are incapable of meeting the key performance criteria of energy and safety for eVTOL. Therefore, next generation advanced chemistries and designs must be developed to meet required performance metrics for electric aviation2. Beyond lithium-ion chemistries, such as lithium-sulfur, show promise in their high energy, while limitations exist in their power and cyclability due to low electrical conductivity and high intermediate solubility in organic liquid electrolytes. Several strategies to overcome the low electrical conductivity involve the use of selenium as a dopant in the active sulfur material, along with the incorporation of 2-dimensional electron-conducting holey-graphene to improve the composite cathodes electronic conductivity. Furthermore, combining this chemistry with a solid-electrolyte avoids the components’ dissolution issues3. However, the development of composite solid-state cathodes is non-trivial as several components must be intimately mixed so that the active component has sufficient access to both electrons and lithium ions to undergo full electrochemical conversion. Mathematical modeling of battery components can assist experimental design through a robust and rigorous combination of computational modeling techniques covering multiple length scales. The objective is to leverage modern computational materials methods combined with battery multiphysics tools to develop radically advanced compatible cathode and electrolyte materials, build and test solid state lithium-sulfur cells and packs. A NASA-based cross-organizational team of high-powered experts combined integrated computational predictive modeling, fundamental chemistry analysis, advanced material science, and battery cell development to tackle this very challenging, multidisciplinary problem. This presentation will show a multiscale computational modeling approach that has produced a novel particle dynamics method called Solid Electrolyte Sphere Approximation Model (SESAM). SESAM modeling targets the 1-10 µm scale structures and provides electromechanical and grain interactions for predictive design guidelines for the manufacturing of solid-state components. Parameters such as particle size and volume fraction of the constituent materials were modeled and experimentally fabricated to optimize electrochemical performance through improved microstructure design. Experimental feedback was provided through ionic and electronic conductivity assessment and structural analysis of developed materials and cell components.

battery↗