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Efficient Simulation Schemes for Large-Scale Phase-Field Modelling of Polycrystalline Growth During Alloy Solidification

  • Yun ChenEmail author
  • Dianzhong Li
  • Tongzhao Gong
  • Hongri Hao
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 130)

Abstract

Ameliorating the computing efficiency is always of importance in phase-field simulations of material microstructure formation and evolution. Borrowing from the nonlinear preconditioning treatment of diffuse interface models [1], the usual quantitative phase-field model for a binary alloy [2] has been transformed to make it easier to compute accurately (see Fig. 1 the transform procedure). The transformation yields a new variable whose value changes linearly across the interface. The dependences of simulated results of the nonlinearly preconditioned phase-field formula on the interface grid size and the discretization time step have been examined in detail through numerical experiments, including the growth velocity, the radius and the solute concentration of a steady tip. The results show that the new evolution equations are able to be solved on a computational mesh with interface grids 2–4 times coarser than those used in the conventional method, as show in Fig. 2. In combination with the front-tracking method to capture the crystallographic orientation of each crystal, the orientation gradient energy is incorporated into the nonlinearly preconditioned phase-field model, which enables simulations of grain boundary behaviors. The algorithm of the distributed parallel finite element method on an adaptive mesh is applied to further raise the computing efficiency. Simulations of multi-dendrites growth of Al-4 wt.% Cu alloy in undercooled melt cooling down continuously are performed. The results demonstrate that the proposed fast simulation approaches allow quantitative simulations of a large number of dendrites growth on the scale of centimeters or millimeters, respectively in two (Fig. 3) or three dimensions (Fig. 4), just using an ordinary workstation instead of clusters or supercomputers [3]. The proposed fast simulation schemes make it possible to perform full-scale simulations of the in situ and real-time observation experiments [4, 5, 6, 7]. In addition, the nonlinear preconditioning transformation can be adopted to other phase-field models used for simulations of the solid-state grains growth, the precipitation of a second phase, the crack propagation, etc.
Fig. 1.

The transformation of the quantitative phase-field model for dilute binary alloy solidification proposed by Karma [2] using a nonlinear hyperbolic tangent function \( \varphi (\varvec{r},t) = \tanh [\psi (\varvec{r},t)/\sqrt 2 ] \).

Fig. 2.

Variation of the steady-state tip velocity and number of grid size with the interface (minimum) grid size for a free dendrite growth from undercooled melt in two (a, b) and three (c, d) dimensions, which demonstrates the available large grid size that can be employed at the interface to obtain the converged results. The statistical histogram show that as the interface grid size decreases, the computing amounts can be reduced orders of magnitude.

Fig. 3.

The 2D phase-field simulated thousands of dendritic crystals growth in a centimeter-scale domain during continuously cooling-down of Al-Cu alloy melt. (a) The solute concentration, (b) the magnified local view of the boxed dendrites in (a) and the corresponding mesh. The cooling rate is 0.8 K/s. The domain size is of 1.058 × 1.058 cm2 (10000W0 × 10000W0). The grain number with different orientations is 2000. The simulations are implemented in a tower workstation with 2 Intel® Xeon E5-2697 v3 (2.6 GHz) CPUs and 256 GB of memory. 48 cores are employed for parallel computing and 5 days are taken to finish the simulation.

Fig. 4.

The evolution of dendrite morphology during solidification in 3-D simulation with cooling rate R = 0.8 K/s. The different colors represent different crystallographic orientations. The domain size is of 1.058 × 1.058 × 1.058 mm3 and the grain number is 125. The configuration of computation is identical to the 2-D simulation, but takes 30 h to finish.

Keywords

Phase-field Large-scale simulation Alloy solidification Crystal growth 

References

  1. 1.
    Glasner, K.: Nonlinear preconditioning for diffuse interfaces. J. Comput. Phys. 174, 695 (2001)CrossRefGoogle Scholar
  2. 2.
    Karma, A.: Phase-field formulation for quantitative modeling of alloy solidification. Phys. Rev. Lett. 87, 115701 (2001)CrossRefGoogle Scholar
  3. 3.
    Gong, T.Z., Chen, Y., Cao, Y.F., Kang, X.H., Li, D.Z.: Fast simulations of a large number of crystals growth in centimeter-scale during alloy solidification via nonlinearly preconditioned quantitative phase-field formula. Comput. Mater. Sci. 147, 338 (2018)CrossRefGoogle Scholar
  4. 4.
    Liu, C., Shen, J., Yang, X.: Decoupled energy stable schemes for a phase-field model of two-phase incompressible flows with variable density. J. Sci. Comput. 62, 601 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chen, Y., Li, D.Z., Billia, B., Nguyen-Thi, H., Qi, X.B., Xiao, N.M.: Quantitative phase-field simulation of dendritic equiaxed growth and comparison with in situ observation on Al-4 wt.% Cu alloy by means of synchrotron X-ray radiography. ISIJ Int. 54, 445 (2014)CrossRefGoogle Scholar
  6. 6.
    Chen, Y., Bogno, A.-A., Xiao, N.M., Billia, B., Kang, X.H., Nguyen-Thi, H., Luo, X.H., Li, D.Z.: Quantitatively comparing phase-field modeling with direct real time observation by synchrotron X-ray radiography of the initial transient during directional solidification of an Al–Cu alloy. Acta Mater. 60, 199 (2012)CrossRefGoogle Scholar
  7. 7.
    Bogno, A., Nguyen-Thi, H., Buffet, A., Reinhart, G., Billia, B., Mangelinck-Noël, N., Bergeon, N., Baruchel, J., Schenk, T.: Analysis by synchrotron X-ray radiography of convection effects on the dynamic evolution of the solid–liquid interface and on solute distribution during the initial transient of solidification. Acta Mater. 59, 4356 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yun Chen
    • 1
    Email author
  • Dianzhong Li
    • 1
  • Tongzhao Gong
    • 1
    • 2
  • Hongri Hao
    • 1
  1. 1.Institute of Metal ResearchChinese Academy of SciencesShenyangPeople’s Republic of China
  2. 2.School of Materials Science and EngineeringUniversity of Science and Technology of ChinaHefeiPeople’s Republic of China

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