Numerical simulation of solid state sintering

Braginsky, Michael; Tikare, Veena; Olevsky, Eugene A.

doi:10.1016/j.ijsolstr.2004.06.022

Cited by 137 publications

(116 citation statements)

References 41 publications

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“…They are easy to code, readily extendable from two dimensions to three dimensions (2-D to 3-D), and can simulate the underlying physics of many materials evolution processes based on the statistical-mechanical nature of the model. These processes include curvature-driven grain growth [52,53], anisotropic grain growth [54], recrystallization [55], grain growth in the presence of a pinning phase [56,57], Ostwald ripening [58][59][60], and particle sintering [11,[61][62][63].…”

Section: Assessment Of the Thermodynamic Parametersmentioning

confidence: 99%

Mesoscale Benchmark Demonstration Problem 1: Mesoscale Simulations of Intra-granular Fission Gas Bubbles in UO2 under Post-irradiation Thermal Annealing

Li¹,

Hu²,

Montgomery³

et al. 2012

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SUMMARYA study was conducted to evaluate the capabilities of different numerical methods used to represent microstructure behavior at the mesoscale for irradiated material using an idealized benchmark problem. The purpose of the mesoscale benchmark problem was to provide a common basis for assessing several mesoscale methods to identify the strengths and areas of improvement in the predictive modeling of microstructure evolution. In this work, mesoscale models (phase-field, Potts, and kinetic Monte Carlo) developed by Pacific Northwest National Laboratory, Idaho National Laboratory, Sandia National Laboratory, and Oak Ridge National Laboratory were used to calculate the evolution kinetics of intragranular fission gas bubbles in UO 2 fuel under post-irradiation thermal annealing conditions. The benchmark problem was constructed to include important microstructural evolution kinetics of intragranular fission gas bubble behavior, such as the atomic diffusion of Xe atoms, U vacancies, and O vacancies; the effect of vacancy capture and emission from defects; and the elastic interaction on nonequilibrium gas bubbles. An idealized set of assumptions and a common set of thermodynamic and kinetic data were imposed on the benchmark problem to simplify the mechanisms considered. The modeling capabilities of different methods are compared against selected experimental and simulation results. These comparisons find that while the phase-field methods and Potts kinetic Monte Carlo methods are able to incorporate several of the mechanisms that influence intra-granular bubble growth and coarsening, the Potts model is challenged by the low solubility and long-range diffusion necessary to simulate this problem correctly. The statistical-mechanical nature of Potts kMC requires large ensembles with long simulation times to treat this problem. Future efforts are recommended to construct increasingly more complex mesoscale benchmark problems to further verify and validate the predictive capabilities of the mesoscale modeling methods used in this study.

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Section: Assessment Of the Thermodynamic Parametersmentioning

confidence: 99%

Mesoscale Benchmark Demonstration Problem 1: Mesoscale Simulations of Intra-granular Fission Gas Bubbles in UO2 under Post-irradiation Thermal Annealing

Li¹,

Hu²,

Montgomery³

et al. 2012

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“…Consequently, grain growth is simulated by converting a grain site into the state of a neighboring grain chosen at random, pore migration is simulated by exchanging a pore site with a grain site, this last assuming the state of the neighboring grain site that results in the minimum possible energy for the grain site, and densification is simulated by producing vacancies in the grain boundaries and annihilating them by moving the vacancy to the surface of the material but in a way that conserves mass globally and moves the centers of mass of the grains adjacent to the site being annihilated closer together [12]. In addition, note than since this densification algorithm requires the definition of the surface of the material, periodic boundary conditions cannot be used.…”

Section: Simulationmentioning

confidence: 99%

“…In this way, mass is globally conserved, the centers of mass of the adjacent grains are moved closer together and the compact shrinks. [12].…”

Section: Serial Vs Parallel Implementationmentioning

confidence: 99%

“…At the mesoscale level, sintering is the result of thermally activated adhesion processes which produce the bonding between particles and their coalescence [61]. The driving force for sintering is the reduction in surface free energy achieved by diffusional transport of material from the centers of the particles to the particle-particle neck [12].…”

Section: Introductionmentioning

confidence: 99%

“…As the physico-chemical and mechanical properties of the final piece are determined by the structure of the sintered body there is an interest in studying the microstructural evolution of the material. Furthermore, the microstructural evolution provides the driving force for the deformation observed at the macroscopic level [12]. In this sense, the simplicity and versatility of kinetic Monte Carlo method makes it a sensible choice to investigate the microstructural evolution during sintering [78].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Crossing the mesoscale no-man<U+2019>s land via parallel kinetic Monte Carlo.

Garcia-Cardona

Webb²,

Wagner³

et al. 2009

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The kinetic Monte Carlo method and its variants are powerful tools for modeling materials at the mesoscale, meaning at length and time scales in between the atomic and continuum. We have completed a 3 year LDRD project with the goal of developing a parallel kinetic Monte Carlo capability and applying it to materials modeling problems of interest to Sandia. In this report we give an overview of the methods and algorithms developed, and describe our new open-source code called SPPARKS, for Stochastic Parallel PARticle Kinetic Simulator. We also highlight the development of several Monte Carlo models in SPPARKS for specific materials modeling applications, including grain growth, bubble formation, diffusion in nanoporous materials, defect formation in erbium hydrides, and surface growth and evolution.3

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