Probabilistic modelling of microstructure formation in solidification processes

Rappaz, M.; Gandin, Charles‐André

doi:10.1016/0956-7151(93)90065-z

Cited by 888 publications

(546 citation statements)

References 27 publications

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“…Actually, many solidification structures are formed through the interactions during the competitive growth of dendrite assemblages. 1,2 Although the cellular automaton method has been widely used for polycrystal solidification simulations, 15,16,82 and has been employed for large-scale solidification simulations, 83,84 multiple-dendrite-growth simulation by the phase-field method is crucial for accurate prediction of the solidification microstructure. An adaptive mesh refinement technique, in which fine meshes are used only around the interface, [85][86][87][88][89] can reduce the computational cost.…”

Section: High-performance Computation For the Phase-field Methodsmentioning

confidence: 99%

“…In the early stage of research, Monte Carlo simulations with the Pott model 8 were often performed to study the kinetics of grain growth, 9,10 and this became a popular method for the study of solidification, recrystallization and other phenomena. 11,12 The front tracking method 13,14 and cellular automata 15,16 have also been widely employed for simulations of dendritic growth. In 1993, Kobayashi succeeded in reproducing a complicated dendrite structure using the phase-field model.…”

Section: Introductionmentioning

confidence: 99%

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Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Shibuta

Ohno

Takaki

2015

JOM

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Thanks to the recent progress in high-performance computational environments, the range of applications of computational metallurgy is expanding rapidly. In this paper, cutting-edge simulations of solidification from atomic to microstructural levels performed on a graphics processing unit (GPU) architecture are introduced with a brief introduction to advances in computational studies on solidification. In particular, million-atom molecular dynamics simulations captured the spontaneous evolution of anisotropy in a solid nucleus in an undercooled melt and homogeneous nucleation without any inducing factor, which is followed by grain growth. At the microstructural level, the quantitative phase-field model has been gaining importance as a powerful tool for predicting solidification microstructures. In this paper, the convergence behavior of simulation results obtained with this model is discussed, in detail. Such convergence ensures the reliability of results of phase-field simulations. Using the quantitative phase-field model, the competitive growth of dendrite assemblages during the directional solidification of a binary alloy bicrystal at the millimeter scale is examined by performing two-and threedimensional large-scale simulations by multi-GPU computation on the supercomputer, TSUBAME2.5. This cutting-edge approach using a GPU supercomputer is opening a new phase in computational metallurgy.

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Section: High-performance Computation For the Phase-field Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Shibuta

Ohno

Takaki

2015

JOM

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“…In the framework of the approach under consideration, original grain structure of a polycrystalline substrate is simulated. For this purpose, use is made of a 2D cellular automata (CA) finite difference (FD) model based on the approach put forward by Rappaz and Gandin in [2] and implemented for the 2D case in [3]. The simulation of the substrate microstructure assumes grain nucleation and growth in a uniform temperature field.…”

Section: Description Of the Modelmentioning

confidence: 99%

On the numerical simulation of the microstructural evolution induced by laser additive manufacturing of steel products

Zinoviev

Zinovieva

Ploshikhin

et al. 2016

AIP Conference Proceedings

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Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges Applied Physics Reviews 2, 041304 (2015) Abstract. A numerical model is developed to simulate the microstructural evolution during a laser additive manufacturing process. The model is based on original algorithms designed with the use of a modified cellular automata method proposed by Rappaz and Gandin to describe the formation of grain structure during solidification, together with the classical numerical finite difference solution of a heat transfer equation. A double ellipsoid heat source model is employed to calculate the heat input during laser additive manufacturing. The model is validated using experimental data.

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“…The Cellular Automaton (CA) technique [1] has successfully applied to generate realistic-looking microstructures because it based on the consideration of physical mechanisms of nucleation, growth kinetics and crystallographic orientation competition. However, the original CA is only related to the local temperature in the solidifying area for a given alloy composition.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Dendrite Growth during Solidification

Yao¹,

He²,

Wang³

et al. 2006

International Journal of Nonlinear Sciences and Numerical Simulation

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A comprehensive probabilistic model for simulating dendrite morphology and investigating dendritic growth kinetics during solidification has been developed, based on a modified Cellular Automaton (mCA) for microscopic modeling of nucleation, growth of crystals and solute diffusion. The mCA model numerically calculated solute redistribution both in the solid and liquid phases, the curvature of dendrite tips and the growth anisotropy. This modeling takes account of thermal, curvature and solute diffusion effects. Therefore, it can simulate microstructure formation both on the scale of the dendrite tip length. This model was then applied for simulating dendritic solidification of an Al-7%Si alloy. Both directional and equiaxed dendritic growth has been performed to investigate the growth anisotropy and cooling rate on dendrite morphology. Furthermore, the competitive growth and selection of dendritic crystals have also investigated.

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Probabilistic modelling of microstructure formation in solidification processes

Cited by 888 publications

References 27 publications

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

On the numerical simulation of the microstructural evolution induced by laser additive manufacturing of steel products

Numerical Simulation of Dendrite Growth during Solidification

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