A coupled finite element-cellular automaton model for the prediction of dendritic grain structures in solidification processes

Gandin, Charles‐André; Rappaz, M.

doi:10.1016/0956-7151(94)90302-6

Cited by 611 publications

(259 citation statements)

References 26 publications

Supporting

Mentioning

252

Contrasting

Unclassified

Order By: Relevance

“…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%

“…It is widely accepted that dendrites whose h100i preferential growth direction is almost parallel to the heat flow direction can continue to grow by stopping the growth of dendrites having a h100i crystallographic orientation that deviates from the heat flow direction. 16,93,94 On the other hand, unusual dendrite selections, in which inclined dendrites overgrow dendrites growing in the heat flow direction, have recently been observed in the unidirectional solidification of a bicrystal sample. 95,96 To clarify the mechanism of this unusual overgrowth, two-dimensional phase-field simulations have been performed.…”

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%

See 2 more Smart Citations

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Shibuta

Ohno

Takaki

2015

JOM

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 99%

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

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Shibuta

Ohno

Takaki

2015

JOM

View full text Add to dashboard Cite

show abstract

“…Fras et al paper [7], related with Newton (NTA) and Fourier (FTA) analyses indicates the differences in approximations of zero curves, assigned to solidifying casting particular points. FTA analysis requires that the nucleation and growth phenomena must be based on an empirical model [9,10,11,12] (e.g. the Oldfield model or similar e.g.…”

Section: Introductionmentioning

confidence: 99%

Methodology of Comparative Validation of Selected Foundry Simulation Codes

Ignaszak¹,

Popielarski²,

Hajkowski³

et al. 2015

Archives of Foundry Engineering

View full text Add to dashboard Cite

The validation of each simulation code used in foundry domain requires individual approach due to its specificity. This validation can by elaborated on the basis of experimental results or in particular cases by comparison the simulation results from different codes. The article concerns the influence of grey cast iron density curve and different forms of solid fraction curve Fs=f(T) on the formation of shrinkage discontinuities. Solid fraction curves applying Newtonian Thermal Analysis (NTA) were estimated. The experimental and numerical simulation tests were performed on the castings, which were made with Derivative Thermal Analysis (DerTA) standard cups. The numerical tests were realized using NovaFlow&Solid (NF&S), ProCast and Vulcan codes. In this work, the coupled influence of both curves on the dynamics of the shrinkage-expansion phenomena and on shrinkage defects prognosis in grey cast iron castings has been revealed. The final evaluation of the simulation systems usefulness should be based on validation experiment, preceded by comparing the simulation results of available systems which are proposed in given technology.

show abstract

“…The CA technique has been used in different areas of materials science. Some important applications are the mathematical modeling of the microstructure evolution during recrystallization 2 , sintering 3 , and solidification 4 . Further applications are given by Raabe 1 .…”

Section: Introductionmentioning

confidence: 99%

Micro-macroscopic coupling in the cellular automaton model of solidification

Biscuola

Martorano

2010

Mat. Res.

View full text Add to dashboard Cite

A cellular automaton (CA) model to predict the formation of grain macrostructure during solidification has been implemented and the coupling between the microscopic and the macroscopic submodels has been investigated. The microscopic submodel simulates the nucleation and growth of grains, whereas the macroscopic solves the heat conduction equation. The directional solidification of an Al-7 wt. (%) Si alloy was simulated, enabling the calculation of the temperature and solid fraction profiles. The calculated temperature was used to obtain the solid fraction profile by an application of Scheil equation. This solid fraction disagrees with that calculated in the micro-macro coupling of the model, although this coupling is completely based on Scheil equation. Careful examination of the discrepancies shows that it is a result of the undercoolings for nucleation and growth of grains and also of the interpolations of enthalpy change and temperature from the finite volume mesh to the CA cell mesh.

show abstract

A coupled finite element-cellular automaton model for the prediction of dendritic grain structures in solidification processes

Cited by 611 publications

References 26 publications

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Methodology of Comparative Validation of Selected Foundry Simulation Codes

Micro-macroscopic coupling in the cellular automaton model of solidification

Contact Info

Product

Resources

About