Cellular automata simulation of grain growth in three dimensions based on the lowest-energy principle

Ding, Hanlin; He, Yi; Liu, L.F.; Ding, Wenjiang

doi:10.1016/j.jcrysgro.2006.05.060

Cited by 56 publications

(29 citation statements)

References 37 publications

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“…[4][5][6][7][8][9][10][11][12][13][14][15] The reported results generally agree in that the grain growth kinetics in the steady-state regime, where the normalized grain size distribution is time invariant, 16 obeys the parabolic law. 17,18 However, the values calculated for the kinetic coefficient of the above law vary from study to study.…”

Section: Introductionsupporting

confidence: 69%

Ultra-large-scale phase-field simulation study of ideal grain growth

et al. 2017

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Grain growth, a competitive growth of crystal grains accompanied by curvature-driven boundary migration, is one of the most fundamental phenomena in the context of metallurgy and other scientific disciplines. However, the true picture of grain growth is still controversial, even for the simplest (or 'ideal') case. This problem can be addressed only by large-scale numerical simulation. Here, we analyze ideal grain growth via ultra-large-scale phase-field simulations on a supercomputer for elucidating the corresponding authentic statistical behaviors. The performed simulations are more than ten times larger in time and space than the ones previously considered as the largest; this computational scale gives a strong indication of the achievement of true steady-state growth with statistically sufficient number of grains. Moreover, we provide a comprehensive theoretical description of ideal grain growth behaviors correctly quantified by the present simulations. Our findings provide conclusive knowledge on ideal grain growth, establishing a platform for studying more realistic growth processes.

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Section: Introductionsupporting

confidence: 69%

Ultra-large-scale phase-field simulation study of ideal grain growth

et al. 2017

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“…The CA method has been used in modeling not only recrystallization (14,15,17,27) and grain growth (29)(30)(31), but also surface mass transport (32,33). However, so far the transition rules used for modelling microstructure evolution appear to be rather inconsistent either in their format or in physical meaning.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast Ding et al (31) and He et al (30) used energy based probabilistic transition rules in modeling grain growth. Generally speaking the transition rules used in the literature fall into two categories, based on either cell counting or free energy.…”

Section: Introductionmentioning

confidence: 99%

Modeling Microstructure Evolution of Ni Cermet Using a Cellular Automaton Approach

Wang

Atkinson

2014

J. Electrochem. Soc.

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The degradation of SOFC electrodes is often determined by their microstructure changes. It is desirable that the microstructure evolution of electrodes in service can be simulated in 3-D for real materials so that the changes in their electrochemical performance can be predicted.This involves changes in important parameters such as: electronic and ionic conductivities; TPB (triple phase boundary) density and connectivity; and tortuosity of condensed phases and porosity. In this contribution a new cellular automaton (CA) approach to simulating complex microstructure evolution is presented, which is based on free energy reduction by using an interface imbalanced interaction model. The approach is validated first in 2-D examples to reproduce wetting between two solids and grain boundary grooving, which shows the new approach is fully consistent with classical theories. The approach is then applied to simulation of the evolution of a real 3 dimensional microstructure of a Ni-YSZ cermet fuel cell anode. The modelling results show that the microstructure evolution is sensitive to the wettability of Ni on YSZ and that a good wettability is helpful in maintaining a slower coarsening rate and smaller reduction of electrode performance.

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“…He, Ding and coauthors studied the morphology and kinetics of grain growth in two 15 and three dimensions 16 by computer simulation, using a model of cellular automata based on the principle of minimum energy. Through a balance between the system's thermodynamic energy and the energy associated to grain boundaries, the algorithm they developed promotes the transition of a cell of the automaton, ensuring the reduction of the system's power.…”

mentioning

confidence: 99%

Simulation of CP-Ti Recrystallization and Grain Growth by a Cellular Automata Algorithm: Simulated Versus Experimental Results

2017

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The application of cellular automata in materials science requires the conversion of the automata's rules and abstract general properties to rules and properties associated with the material and phenomena under study. In this paper we propose a model which uses cellular automata to simulate recrystallization and grain growth during isothermal and non-isothermal treatments of cold worked polycrystalline materials. The algorithm's spatial and temporal scaling is based on known experimental results for recrystallization and grain growth in highly cold-worked commercially pure titanium grade 2. In the recrystallization, the best agreement between experimental and computational results in terms of the process kinetics and the average diameter of recrystallized grains is obtained from a nucleation model that considers the temperature-dependent nuclei formation rate. In the simulation of grain growth after primary recrystallization, the results indicate the normal growth of an equiaxed grain structure whose kinetics and dimensions are comparable to those observed experimentally.

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Cellular automata simulation of grain growth in three dimensions based on the lowest-energy principle

Cited by 56 publications

References 37 publications

Ultra-large-scale phase-field simulation study of ideal grain growth

Ultra-large-scale phase-field simulation study of ideal grain growth

Modeling Microstructure Evolution of Ni Cermet Using a Cellular Automaton Approach

Simulation of CP-Ti Recrystallization and Grain Growth by a Cellular Automata Algorithm: Simulated Versus Experimental Results

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