SEARCH · Engineering Papers

Results for “Microstructures”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Evaluation of the Effect of Burnup Acceleration on UO 2 Microstructure Evolution

Accelerated fuel qualification (AFQ) is a methodology by which new nuclear fuels are developed in an accelerated time frame compared with historical fuel qualification approaches. AFQ generally relies on high-fidelity physics-based modeling and simulation tools to adequately describe fuel performance as well as on revolutionary methods to accelerate burnup accumulation and collect relevant data more quickly. This report summarizes the use of advanced fuel modeling and simulation tools to evaluate microstructures from commercially irradiated fuel and microstructures from proposed MiniFuel irradiations, in which burnup accumulation is accelerated while prototypic temperature conditions are maintained. In this milestone, we used the mesoscale fuel performance code MARMOT to model the evolution of irradiated UO 2 microstructures and their potential restructuring at high burnup. The simulation conditions were informed by BISON models of both commercially irradiated fuel and MiniFuel. A first set of simulations investigated the recrystallization behavior of fully dense microstructures and showed full recrystallization at burnups as low as 52 MWd/kgU at 950°C. However, these simulations did not account for the presence of fission gas bubbles (predicted by BISON). Therefore, a second set of simulations including fission gas bubbles was performed and indicated that at the lowest temperature considered (650°C), the porous UO 2 microstructures have the highest total Gibbs free energies and are likely to recrystallize earlier than higher temperature cases (800 and 950°C), which agrees with high-burnup fuel characterization data. The results also showed that at lower temperature (650°C), the total free energies of the PWR fuel and MiniFuel microstructures are not significantly different. However, at the highest temperature (950°C), MiniFuel microstructures have a lower free energy than that of the PWR fuel microstructure. The competing effects between the temperature-dependent grain nucleation rate and the reduction of the free energy of the microstructure at higher temperature as a result of diffusion indicated that restructuring may occur at even higher temperatures than those considered in this study. In addition to the microstructure evolution modeling efforts, the burnup gradient across a single fuel specimen was also considered. This evaluation was for the VXF-15 position of the High Flux Isotope Reactor (HFIR) using the code suite HFIRCON, which was developed to automate the workflow for evaluating targets and fuel as they are irradiated in HFIR. The burnup gradient evaluation showed a dependence on both the axial and radial locations within the specimen, with a maximum difference of 1.7 between the inner and outermost radial layers. This relationship was further supported by considering the fission product speciation with respect to location within the specimen, which showed a higher concentration of 239 Pu, 240 Pu, and 241 Pu on the outer radial locations of the specimen than the center. The findings of the burnup and speciation evaluation show that some amount of self-shielding is occurring in the specimen when irradiated in the high-flux environment of HFIR; however, this impact is more pronounced for natural uranium when compared to 6% enrichment due to the higher ratio of 238 U in the specimen. Further analyses are required to understand the sensitivity of this gradient to spatial mesh and enrichment of the specimen.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1994

Influence of convection on microstructure

The primary motivation for this research was to determine the cause for space processing altering the microstructure of some eutectics, especially the MnBi-Bi eutectic. Four primary hypotheses were to be tested under this current grant: (1) A fibrous microstructure is much more sensitive to convection than a lamellar microstructure, which was assumed in our prior theoretical treatment. (2) An interface with one phase projecting out into the melt is much more sensitive to convection than a planar interface, which was assumed in our prior theoretical treatment. (3) The Soret effect is much more important in the absence of convection and has a sufficiently large influence on microstructure that its action can explain the flight results. (4) The microstructure is much more sensitive to convection when the composition of the bulk melt is off eutectic. These hypotheses were tested. It was concluded that none of these can explain the Grumman flight results. Experiments also were performed on the influence of current pulses on MnBi-Bi microstructure. A thorough review was made of all experimental results on the influence of convection on the fiber spacing in rod eutectics, including results from solidification in space or at high gravity, and use of mechanical stirring or a magnetic field. Contradictory results were noted. The predictions of models for convective influences were compared with the experimental results. Vigorous mechanical stirring appears to coarsen the microstructure by altering the concentration field in front of the freezing interface. Gentle convection is believed to alter the microstructure of a fibrous eutectic only when it causes a fluctuating freezing rate with a system for which the kinetics of fiber branching differs from that for fiber termination. These fluctuations may cause the microstructure to coarsen or to become finer, depending on the relative kinetics of these processes. The microstructure of lamellar eutectics is less sensitive to freezing rate fluctuations and to gentle convection.

Wilcox, William R.↗

NASA NTRS2022

Analysis of Base Metal Microstructures and Mechanical Properties of a Single-Piece, Spin-Formed Forward Pressure Vessel Bulkhead

In 2012, a pathfinder forward pressure vessel bulkhead (FPVBH) for the Orion Multi-Purpose Crew Vehicle (MPCV) was fabricated using aluminum (Al) alloy 2219. The demonstration article was offered as a replacement for the baseline configuration, which at that time was a multi-piece welded construction using aluminum-lithium (Al-Li) alloy 2195. In 2014, the NASA Engineering and Safety Center (NESC) funded a Phase I effort to explore spin-forming as a manufacturing method to produce a complex-shaped, single-piece FPVBH for the crew module (CM) [1]. The primary focus of the NESC effort was to expand spin-forming technology to the fabrication of an Al-Li 2195 FPVBH. As a result, limited testing and analysis was performed on the original Al 2219 article. In this study, a sufficiently large plate of Al 2219 was not available for the fabrication of a FPVBH. Therefore, the forming blank comprised two plates (from different lots) butt welded together using a single friction stir weld. The welded forming blank was then spin formed into the FPVBH configuration and heat treated to the T62 temper. Mechanical property testing of the spin-formed 2219-T62 FPVBH revealed that the tensile properties were comparable to the Metallic Material Properties Development and Standardization (MMPDS) A-basis allowable for Al 2219-T6 wrought products. However, post-spin forming application of the standard heat treatment produced large, recrystallized grains in the material. The response varied within the two plates; one plate lot exhibited nearly complete recrystallization, while the other lot contained bands of recrystallization. Although no reduction in tensile properties was observed due to these microstructural variations, a more detailed investigation was recommended to better understand the effect of spin forming on the recrystallization mechanism and the impact on other material properties. The current study examined the microstructure and mechanical properties of the fully processed Al 2219-T62 FPVBH with emphasis on comparing differences between the two plates. Testing was limited to the cone region of the FPVBH. The two specific tasks in this study were: a) Characterization of the microstructure and mechanical properties in the two plates used in the FPVBH. b) Isolation of the impact of each step in the thermo-mechanical processing (TMP) sequence of the FPVBH on recrystallization and grain growth. Mechanical property testing sampled regions containing both large, recrystallized and small, unrecrystallized grains. Tensile strengths and elongations were equivalent in both regions and exceeded MMPDS A-basis allowables. Fracture toughness was equivalent in both regions and fracture morphology was typical of aluminum alloys. The fatigue precrack region of the fracture toughness specimens appeared more faceted in the recrystallized plate. This suggests that property testing that is more sensitive to grain size, such as fatigue crack growth rate and stress corrosion cracking, may be warranted. Alcoa reviewed the production records for the two plate lots and confirmed that composition of both plates was within the allowable range for Al 2219 and that plate processing steps were all performed within production limits. However, the Fe content was notably higher in the fully recrystallized plate, resulting in a greater volume fraction of large Fe-bearing inclusions and Al-Cu constituent particles. Alcoa did not provide details regarding plate processing variables for the two plate lots. It was surmised that deformation by hot rolling was the most accurate TMP simulation of commercial spin forming within a laboratory setting. Both hot rolling and spin forming comprise combinations of shear and compressive deformation. The through-thickness strain caused by hot rolling is symmetrical about the mid-plane, as the rollers contact both sides of the material. Shear strains are maximum at the inner and outer surfaces and trend toward zero at the mid-thickness. In contrast, spin forming utilizes a single roller on the outer surface, producing a gradient in shear stress through the thickness. Shear strains in spin-formed material are highest at the outer surface due to direct contact with the roller, and trend toward zero at the inner surface. Consequently, there will undoubtedly be some discrepancies in the mechanical response of hot-rolled and spin-formed products in the through-thickness direction. Interrupted TMP simulations were performed on remnant plate from the FPVBH forming blank in order to isolate the effects of a post-weld anneal, spin-forming deformation/thermal cycling, and solution heat treatment (SHT) on development of the recrystallized microstructures. Analysis confirmed that large, recrystallized grains formed exclusively during SHT and only after a certain deformation level was exceeded. Remnant plate from both lots that received thermal processing only did not exhibit recrystallization but exhibited fully recrystallized microstructures when processed by hot rolling. This indicates that (1) the level of deformation exceeded the critical level for recrystallization in both plates and (2) that the level of deformation during hot rolling was greater than that which accumulated during spin forming of the FPVBH. Comparison of the hot-rolled plate with the spin-formed material showed that the recrystallized grain size was larger in the FPVBH, providing further evidence that deformation levels were lower in the FPVBH. The most plausible explanation for the differing degrees of recrystallization in the two plates is particle stimulated nucleation (PSN) at the Fe-bearing inclusions and large Al-Cu constituent particles. In the plate with higher solute content, the greater volume fraction of these particles reduces the deformation level required to promote recrystallization. During spin forming the critical deformation level was likely exceeded for the higher Fe content plate resulting in a fully recrystallized microstructure. The lower Fe content plate developed a microstructure that exhibited bands of recrystallization due to the combination of a non-uniform distribution of particles and deformation. The critical deformation required for recrystallization was likely only exceeded during spin forming in regions of higher particle content. While there were differences in Fe content between these lots of Al 2219 plate, the composition of both was within defined alloy limits and Alcoa reported no anomalies during plate production. The observed differences in recrystallization after spin forming may reflect the inherent variability between these two lots of plate. The spin-forming vendor, Spincraft, reported that similar variations in recrystallized microstructures have been observed during examination of multiple spin-forming trials of a different 2xxx series aluminum alloy. Hence, tighter chemical specification limits on impurity levels in Al 2219 plate may help ensure uniform, predictable microstructures. Finally, the tensile and fracture toughness properties were not affected by the microstructural variations noted.

single-piece↗

DOE OSTI2023

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗

DOE OSTI2019

Three-Dimensional Additively Manufactured Microstructures and Their Mechanical Properties

Metal additive manufacturing (AM) allows for the freeform creation of complex parts. However, AM microstructures are highly sensitive to the process parameters used. Resulting microstructures vary significantly from typical metal alloys in grain morphology distributions, defect populations and crystallographic texture. AM microstructures are often anisotropic and possess three-dimensional features. These microstructural features determine the mechanical properties of AM parts. Here, we reproduce three “canonical” AM microstructures from the literature and investigate their mechanical responses. Stochastic volume elements are generated with a kinetic Monte Carlo process simulation. A crystal plasticity-finite element model is then used to simulate plastic deformation of the AM microstructures and a reference equiaxed microstructure. Results demonstrate that AM microstructures possess significant variability in strength and plastic anisotropy compared with conventional equiaxed microstructures.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Designing Ti-6Al-4V microstructure for strain delocalization using neural networks

Abstract The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental observations reveal that intense slip bands in Ti-6Al-4V form at strains well below the macroscopic yield strain and may serially propagate across grain boundaries, resulting in long-range localization that percolates through the microstructure. These connected, localized slip bands serve as potential sites for crack initiation. Although slip localization in Ti-6Al-4V is known to be influenced by various factors, an investigation of optimal microstructures that limit localization remains lacking. In this work, we develop a novel strategy that integrates an explicit slip band crystal plasticity technique, graph networks, and neural network models to identify Ti-6Al-4V microstructures that reduce the propensity for strain localization. Simulations are conducted on a dataset of 3D polycrystals, each represented as a graph to account for grain neighborhood and connectivity. The results are then used to train neural network surrogate models that accurately predict localization-based properties of a polycrystal, given its microstructure. These properties include the ratio of slip accumulated in the band to that in the matrix, fraction of total applied strain accommodated by slip bands, and spatial connectivity of slip bands throughout the microstructure. The initial dataset is enriched by synthetic data generated by the surrogate models, and a grid search optimization is subsequently performed to find optimal microstructures. Describing a 3D polycrystal with only a few features and a combination of graph and neural network models offer robustness compared to the alternative approaches without compromising accuracy. We show that while each material property is optimized through a unique microstructure solution, elongated grain shape emerges as a recurring feature among all optimal microstructures. This finding suggests that designing microstructures with elongated grains could potentially mitigate strain localization without compromising strength.

Ahmadikia, Behnam↗

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 NTRS1999

Use of Microgravity to Control the Microstructure of Eutectics

This grant began in June of 1996. Its long term goal is to be able to control the microstructure of directionally solidified eutectic alloys, through an improved understanding of the influence of convection. The primary objective of the projects in the present grant is to test hypotheses for the reported influence of microgravity on the microstructure of eutectics. The prior experimental results on the influence of microgravity on the microstructure of eutectics have been contradictory. With lamellar eutectics, microgravity had a negligible effect on the microstructure. Microgravity experiments with fibrous eutectics sometimes showed a finer microstructure and sometimes a coarser microstructure. Most research has been done on the MnBi/Bi rod-like eutectic. Larson and Pirich obtained a two-fold finer microstructure both from microgravity and by use of a magnetic field to quench buoyancy-driven convection. Smith, on the other hand, observed no change in microgravity. Prior theoretical work at Clarkson University showed that buoyancy-driven convection in the vertical Bridgman configuration is not vigorous enough to alter the concentration field in front of a growing eutectic sufficiently to cause a measurable change in microstructure. We assumed that the bulk melt was at the eutectic composition and that freezing occurred at the extremum, i.e. with minimum total undercooling at the freezing interface. There have been four hypotheses attempting to explain the observed changes in microstructure of fibrous eutectics caused by convection: I .A fluctuating freezing rate, combined with unequal kinetics for fiber termination and branching. 2. Off-eutectic composition, either in the bulk melt due to an off-eutectic feed or at the freezing interface because of departure from the extremum condition. 3. Presence of a strong habit modifying impurity whose concentration at the freezing interface would be altered by convection. At the beginning of the present grant, we favored the first of these hypotheses and set out to test it both experimentally and theoretically. We planned the following approaches: I .Pass electric current pulses through the MnBi/Bi eutectic during directional solidification in order to produce an oscillatory freezing rate. 2. Directionally solidify the MnBi/Bi eutectic on Mir using the QUELD II gradient freeze furnace developed by Professor Smith at Queen's University. 3. Select another fibrous eutectic system for investigation using the Accelerated Crucible Rotation Technique to introduce convection. 4. Develop theoretical models for eutectic solidification with an oscillatory freezing rate. Because of the problems with Mir, we substituted ground-based experiments at Queen's University with QUELD II vertical and horizontal, with and without vibration of the furnace. The Al-Si system was chosen for the ACRT experiments. Three related approaches were used to model eutectic solidification with an oscillatory freezing rate. A sharp interface model was used to calculate composition oscillations at the freezing interface in response to imposed freezing rate oscillations.

Wilcox. William R.↗

DOE OSTI2026

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.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

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.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Process control-enabled mitigation of microstructural and plastic heterogeneities in additively manufactured Grade 91 steel

Synergizing wire arc-directed energy deposition (WA-DED) additive manufacturing (AM) with particle-strengthened creep strength-enhanced ferritic (CSEF) steels enables fabrication and repair of critical power-plant components. Investigations focused on fusion-welded particle-strengthened CSEF steels, such as Grade 91 steel, have linked microstructurally heterogeneous regions—forming due to heat affected zones (HAZ)—with premature failure during elevated temperature service. Fusion-based AM, including WA-DED, likewise generates microstructurally and plastically heterogeneous regions due to spatiotemporally varying thermokinetics during deposition. However, works investigating such microstructural heterogeneities, their implications for mechanical behavior, and strategies to mitigate their formation remain scarce. This work identifies microstructurally and plastically heterogeneous regions within the WA-DED-processed Grade 91 steel. Spatial microhardness variations in the as-fabricated specimen correlate with the variation in the attributes of grain, martensitic microstructure, and precipitates across the fusion zone and HAZ. Digital image correlation-enabled tensile tests performed at 500 °C revealed pronounced deformation localization and a wave-like strain distribution, with wavelength close to the melt pool depth, indicating susceptibility of the as-fabricated components to premature creep failure. Such heterogeneity in microstructural and mechanical behavior was attributed to recurring solid-state phase transformations. Subsequently, an interlayer temperature control strategy was implemented, wherein maintaining interlayer temperature above the martensitic start temperature mitigated the heterogeneous microstructural and plastic response in the as-fabricated condition. Findings open pathways to achieving deformation-localization- and creep-resistant microstructures in WA-DED fabricated particle-strengthened CSEF steel components, reducing reliance on post-welding heat treatments—conventionally required to enhance creep resistance—and enabling on-demand, short lead-time fabrication of next-generation power-plant components.

Heat affected zones↗

DOE OSTI2026

Microstructure‐Informed Analysis Framework for Lattice Structures: Guiding Topology‐Material Synergy in Titanium Alloys

In this work we discuss a microstructure‐informed analysis framework for lattice structures that maps the material's microstructural response to guide topology selection and mechanical performance optimization. By coupling geometrical topology with intrinsic material behavior, we demonstrate how anisotropic microstructural response can inform the design of optimized lattice structures. To illustrate this concept, we focus on two distinct classes of titanium alloys: Ti5553 (Ti‐5Al‐5Mo‐5V‐3Cr wt%), which exhibits a predominantly ‐phase microstructure, and Ti64 (Ti‐6Al‐4V wt%), which features a dual‐phase structure. These alloys exhibit markedly different mechanical responses under multiaxial loading in “fully dense” solid form. The strut‐level stress analysis of these alloys reveals how specific microstructural characteristics can guide the selection of appropriate lattice topologies. Two representative lattice configurations, one stretching‐dominated and one bending‐dominated, are evaluated under identical loading conditions to explore how microstructure‐driven design can lead to topology choices that are better suited to accommodate shear or other critical local stress states, thereby enhancing mechanical performance. A strut‐level mechanics‐based analysis is performed to evaluate shear stress distribution and highlight the role of topology‐microstructure synergy and compatibility in determining overall lattice behavior. The findings emphasize the importance of designing structures that are both load‐aware and microstructure‐responsive, enabling more effective material utilization in advanced engineering applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning

Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

25 ENERGY STORAGE↗

DOE OSTI2022

Effect of microstructure on fatigue crack propagation in additive manufactured nickel-based superalloy Haynes 282: an experiment and crystal plasticity study

Haynes 282 is a γ' precipitation-strengthened nickel-based superalloy known for its exceptional high-temperature creep resistance and excellent fabricability. Recent advancements in powder-bed fusion-based additive manufacturing (PBF-AM) have enabled the fabrication of Haynes 282 with accurate, site-specific control of the grain orientation and morphology at the microscale. This ability opens up new avenues for microstructure design, to improve the material’s fatigue crack resistance and service life. This paper investigates the fatigue crack growth behavior of hybrid microstructure Haynes 282 fabricated via PBF-AM. Previous experiments revealed a higher crack propagation rate in the coarse columnar-grained microstructural regions when compared against fine-grained areas. Here, a strain gradient crystal plasticity model was adapted to study the fracture-related mechanical fields at the crack tip in the two microstructures. The simulation results showed a consistent influence of grain structure and texture on crack propagation, as was seen in the experiment. The model analysis revealed higher crack propagation driving force along crack direction in the coarse-grained sharply textured microstructure and higher driving force for crack kinking in fine-grained more diffusely textured microstructure, which is ascribed to the combined effect of yield stress, hardening rate, texture and grain morphology. The presented modeling approach will facilitate the development of the AM-based accurate microstructure design by deepening the fundamental study in AM-specific microstructure-properties relations.

36 MATERIALS SCIENCE↗