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Results for “microstructure clustering”

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

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Coupled cluster and dislocation dynamics modeling of microstructure evolution in irradiated materials

We develop here a coupled cluster and dislocation dynamics framework to study the microstructure evolution of irradiated materials. The framework not only accounts for the three dimensional diffusion of radiation-generated clusters, but also their interaction with dislocation networks and the resultant climb motion of discrete dislocations within finite crystals. The framework is solved with a superposition solution scheme, and is applied to investigate the evolution of the irradiation-induced dislocation loops in zirconium (Zr), considering the effects of various bias factors including the diffusion anisotropy difference (DAD) of interstitials and interstitial clusters, the dislocation bias of defects to discrete dislocation segments, and the production bias of defects from the radiation cascade. We find that the DAD is the most critical factor influencing the kinetics of the loop evolution in Zr, while the recombination/interaction of mobile defects can induce a strong spatial dependence of the loop evolution together with the DAD. Here, the method is also adopted to study the evolution of interstitial $\langle$a$\rangle$ and vacancy $\langle$c$\rangle$ dislocation loop ensembles consistent with the microstructure observed during irradiation-induced growth of Zr. Our findings not only reveal the spatial dependence of the size and ellipticity of the dislocation loops, but also suggest a limit on the anisotropy factor of interstitials to reproduce the co-growth of $\langle$a$\rangle$ and $\langle$c$\rangle$ loops in zirconium, in good agreement with experimental observations and other simulation results.

Bias factors↗
DOE OSTI2020

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low energy structures of Pd n (n = 1-21) clusters supported on CeO 2 (111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters and large cluster isomers share similarities in both shape and energy, and elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Defect cluster and nonequilibrium gas bubble associated growth in irradiated UMo fuels – a cluster dynamics and phase field model

Irradiation examination shows that gas bubble swelling kinetics is much faster after irradiation-induced recrystallization than that prior recrystallization in UMo fuels. It implies that gas bubbles in coarse grains and small recrystallized grains have different growth behavior. In this work, a phase-field model of gas bubble evolution integrating microstructure dependent cluster dynamics has been developed, for the first time, to study the gas bubble swelling behavior in the recrystallization zone of UMo fuels. Generation, diffusion, reaction, sink, emission and clustering of vacancies and interstitials are described by the cluster dynamics model while a phase-field model is used to describe the evolution of non-equilibrium gas bubbles including nucleation and growth. With the coupled model, the effect of defect generation rate, clustering rate, interstitial emission and sink rates on grain boundaries on the gas bubble evolution are systematically simulated. A set of model parameters (defect generation rate, clustering rate, interstitial emission and sink rates) is determined by comparing measured and simulated gas bubble swelling kinetics. The results demonstrate that interstitial clustering is one of the important physical mechanisms which results in a fast gas bubble swelling kinetics in the recrystallization zone. The developed model can also be extended to study the associated growth of defect and second phase precipitates often observed in irradiated materials.

Hu, Shenyang↗
DOE OSTI2024

Design of non-fluorinated proton exchange membranes from Poly(Terphenyl fluorenyl isatin) with fluorene-linked sulfonate groups and microblock structures

Proton exchange membranes (PEMs) are essential components in energy storage and conversion devices, such as fuel cells and electrolyzers. Here, in this study, we developed a series of non-fluorinated PEMs from poly(terphenyl fluorenyl isatin) with fluorene-pendent disulfonate groups. These polymers feature a microblock structure composed of hydrophobic blocks, hydrophilic blocks, and alternating blocks, arising from the differences in reactivity, concentration, and solubility between the hydrophobic p-terphenyl and hydrophilic disulfonated fluorene monomers. As a result, the sulfonic acid groups are unevenly distributed along the polymer chains, forming densely charged regions (IEC = 3.52 meq/g) with large ion clusters and lightly charged regions (IEC = 2.16 meq/g) with small ion clusters. This microstructure, combined with the degree of sulfonation, significantly influences the overall properties of the membranes, including robust mechanical strength (47.1–63.2 MPa), high thermal stability (up to 270 °C), low swelling ratio (18–25% at 80 °C), and high proton conductivity (136–169 mS/cm in deionized water at 80 °C). The PFLSH60 membrane demonstrated comparable fuel cell performance to Nafion 212. Its hydrogen crossover current density was more than two times lower (0.86 mA/cm 2 for PFLSH60 compared to 1.83 mA/cm 2 for Nafion 212) under testing conditions of 80 °C and 100% RH. This significantly reduced crossover improves fuel utilization in fuel cell stacks. This work offers valuable insights into the design of robust, high-performance PEMs by systematically analyzing the relationships between membrane structure, properties, and performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Quantitative microstructural investigation of 3D-printed and cast cement pastes using micro-computed tomography and image analysis

Highlights: • Processing alters the microstructure in 3D-printed lamellar cubic element. • A unique patterned and connected pore microstructure network exists. • Micro-channels are connected through micro-pores present at interfaces regions. • Three distinct pore volume domains exists in hardened microstructure. • Volume and frequency of pore, hydrated, and unhydrated clusters are dissimilar. Microstructural phases and mechanical properties of lamellar 3D-printed and cast hardened cement paste (hcp) elements were investigated using a lab-based X-ray microscope at two levels of magnification (0.4× and 4×). K-means clustering was used for quantitative image analysis. The entire volume of intact 3-days-old 3D-printed and cast hcp elements was characterized at 0.4× magnification. Three microstructural features (macro-pores, micro-channels, and interfacial micro-pores) were found to reside in three distinct pore size domains. The largest pores of the 3D-printed element were larger than the largest pores of the reference cast hcp element. Moreover, the smallest pore sizes of the 3D-printed element were found to be smaller than those present in the cast counterparts. Micro-channels were found to be connected to one another through the micro-pores present at interfacial regions, indicating the presence of a uniquely patterned and interconnected pore network. The role of locally weak and porous interfaces on mechanical response and fracture properties is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantifying Microstructure Variability in Laser Powder Bed Fusion 316 L Stainless Steel Microstructures with Spatial Statistics

Here, we have explored data-driven methods for material microstructure quantification that improve sensitivity to microstructural changes compared to traditional approaches. The methods integrate multiple microstructural properties, including grain morphology, crystallographic orientation, and material phase information. The simpler method employs maps of the Euclidean distance transformation metric to evaluate the morphology of grain boundary networks. The more intensive approach employs generalized spherical harmonic mapping for crystallographic orientations, per-pixel phase information, and a variational auto-encoder for dimensionality reduction and results in a multidimensional clustering of by microstructure similarity. Applied to an experimental dataset of additively manufactured steel, both methods detected slight variations in samples produced under nominally identical processing conditions. Both methods were able to distinguish between samples from multiple (nominally identical) builds, while the generalized spherical harmonics-based method could additionally cluster data samples rotated at two orientations on the build plate. The improved sensitivity of the methods, demonstrated through comparison with traditional microstructure characterization techniques, offers advantages for microstructure quantification and comparisons in advanced manufacturing applications.

SS316L↗
DOE OSTI2024

Heterogeneous Cluster Energetics and Nonlinear Thermodynamic Response in Supercritical Fluids

Microstructural heterogeneities arising from molecular clusters directly affect the nonlinear thermodynamic properties of supercritical fluids. We present a physical model to elucidate the relation between energy exchange and heterogeneous cluster dynamics during the transition from liquidlike to gaslike conditions. By analyzing molecular-dynamics data and employing physical principles, the model considers contributions from three key processes, namely, changing cluster density, cluster separation, and transfer of molecules between clusters. We show that the proposed model is consistent with the energetics at subcritical conditions and can be used to explain the nonlinear behavior of thermodynamic response functions, including the peak in the isobaric heat capacity.

42 ENGINEERING↗
DOE OSTI2025

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

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

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of Sn microalloying on the nucleation of L1 2 Al 3 Zr precipitates in a dilute aluminum-zirconium alloy

While L1 2 -Al 3 Zr nanoprecipitates provide a balance between strengthening and good electrical conductivity, the precipitation of L1 2 -Al 3 Zr in aluminum requires aggressive heat treatments. An improved age-hardening response was observed during isochronal aging of an Al-0.24Zr (wt%) alloy when microalloyed with Sn. A new mechanism termed Low melting point Element-Assisted Nucleation (LEAN) is proposed to explain the lower temperature nucleation of L1 2 -Al 3 Zr precipitates observed in this alloy based on the addition of a low melting point element, such as Sn. Characterization verified the first-principles density functional theory prediction that Zr and Sn atoms cluster during homogenization owing to the favorable binding energy of Zr-Sn-vacancy triplets. Direct microstructural observations revealed these clusters form Sn nanoprecipitates that assist the nucleation of L1 2 -Al 3 Zr at 200°C, where L1 2 -Al 3 Zr precipitation is not expected due to the low diffusivity of Zr atoms in Al. At higher temperatures (≳350°C), the acceleration of L1 2 -Al 3 Zr precipitation is driven by faster Zr diffusion in Al with Sn microalloying and the nuclei formed via the LEAN mechanism. In conclusion, this combination of mechanisms explains the improvement in age hardening through L1 2 -Al 3 Zr precipitation with Sn microalloying.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-scale modeling of fuel fragmentation and microstructural evolution

An overview of NEAMS program activities to model fuel fragmentation and microstructual evolution to a group of industry attendees at a meeting hosted by EPRI. Topics include atomistic methods to determine grain boundary strength, phase-field fracture modeling to determine critical pressure at grain boundaries, and coupled phase-field-cluster dynamics to model microstructural evolution.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Atomistic Modeling of Irradiation-Induced Defects and Clusters in Additively-Manufactured Austenitic Stainless Steel

Understanding the progression of irradiation-induced defects in real materials and alloys requires insight into the interaction between point defects, defect clusters, and the existing microstructure. This is particularly true when it comes to additively manufactured (AM) materials with complicated microstructures and residual stresses. In this work, we employ molecular dynamics (MD) to simulate the creation of point defects via primary radiation damage modeling and point defect interactions with extended defects such as voids and dislocations using simplified but representative alloy compositions. We analyze the effect of alloying elements on the formation energies of the defect clusters and measure interactions such as sink strengths and binding energies. This study provides insight into the unique behaviors of irradiation damage in AM materials and information necessary to bridge the gap in length and time scales to connect the atomistic behavior of defects to longer length scale simulations such as cluster dynamics.

36 MATERIALS SCIENCE↗
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↗
DOE OSTI2023

Coarsening dynamics of ternary polymer solutions with mobility and viscosity contrasts

Using phase-field simulations, we investigate the bulk coarsening dynamics of ternary polymer solutions undergoing a glass transition for two models of phase separation: diffusion only and with hydrodynamics. The glass transition is incorporated in both models by imposing mobility and viscosity contrasts between the polymer-rich and polymer-poor phases of the evolving microstructure. For microstructures composed of polymer-poor clusters in a polymer-rich matrix, the mobility and viscosity contrasts significantly hinder coarsening, effectively leading to structural arrest. For microstructures composed of polymer-rich clusters in a polymer-poor matrix, the mobility and viscosity contrasts do not impede domain growth; rather, they change the transient concentration of the polymer-rich phase, altering the shape of the discrete domains. This effect introduces several complexities to the coarsening process, including percolation inversion of the polymer-rich and polymer-poor phases—a phenomenon normally attributed to viscoelastic phase separation.

Chemistry↗
DOE OSTI2025

Temperature-dependent mechanism on hardening mitigation in thermomechanically processed 800H alloys under neutron irradiation

Irradiation induced hardening is commonly observed in structural materials, and is often associated with the degraded ductility. This study explores the role of thermomechanical processing (TMP) in mitigating irradiation-induced hardening in Incoloy 800H. The radiation response of solution-annealed and TMP-treated samples was systematically investigated to elucidate the underlying mechanisms of hardening reduction. Neutron irradiation was performed on solution-annealed and TMP-treated samples at 359, 431, and 580 °C, revealing consistently lower hardening in TMP samples. Microstructural characterization compared dislocation loops, cavities, clusters, and precipitates to assess their contributions to hardening. The strengthening contributions from individual microstructural features were estimated, and the microstructure-derived yield strength is consistent with the hardness-converted yield strength. The microstructure–properties correlation revealed the temperature-dependent primary hardening mechanism: dislocation loops dominated at lower temperature, whereas Ni3(Al,Ti) γ′ precipitates were predominant at higher temperature. TMP reduced radiation-induced hardening through two mechanisms: (1) increased sink strength from dislocations and Ti(C,N) precipitates limited dislocation loop formation at lower temperatures, and (2) reduced Ti solute within the matrix in TMP samples suppressed γ′ precipitate formation at higher temperatures. These findings identify the mechanisms governing radiation-induced hardening across different temperatures, which lays the foundation for TMP optimization for the development of advanced radiation-tolerant materials.

Zhong, Weicheng [ORNL] (ORCID:0000000231589271)↗
DOE OSTI2020

Connecting particle interactions to agglomerate morphology and rheology of boehmite nanocrystal suspensions

Rheology imposes significant challenges on processing of complex suspensions such as nuclear waste slurries at the Hanford and Savannah River sites. Understanding rheology connecting to microstructures and underlying particle interactions in complex slurries is therefore important for both fundamental knowledge and practical applications. Here, we use suspensions of aluminum oxyhydroxide minerals in the form of boehmite as an analog of the radioactive waste slurry to gain physical insights on the correlation between particle interactions, microstructures, and slurry rheology. Specifically, we use a combination of Couette rheometry and small-angle scattering techniques (independently and simultaneously) to understand how the slurry microstructure changes under flow and how these structural changes manifest themselves in the bulk rheology of the suspensions. Our experiments show that the boehmite slurries are thixotropic, with the rheology and structure of the suspensions changing with increasing exposure to flow. In the slurries, particle aggregates begin as loose, system-spanning clusters, but exposure to moderate shear rates causes the aggregates to irreversibly consolidate into denser clusters of finite size. The microstructural changes directly influence the rheological properties of the slurries such as viscosity and viscoelasticity. More importantly, our study shows that solution pH affects the amount of structural rearrangement and the kinetics of the rearrangement process, with an increase in pH leading to faster and more dramatic changes in the bulk rheology. Such dynamic microstructural changes and resultant rheology are understood via correlations between particle interactions and strength of particle network, coupled with surface chemistry and anisotropic nature of particle interactions. Nearly identical structural changes are also observed in Poiseuille flow geometries, implying that the observed changes are relevant in the pipe flow conditions present during waste processing.

Weston, Javen S.↗
DOE OSTI2020

Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification

Here, we present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.

36 MATERIALS SCIENCE↗