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A 2D Front-Tracking Lagrangian Model for the Modeling of Anisotropic Grain Growth
Sebastian Florez1, Julien Fausty1, Karen Alvarado1
1CEMEF-Centre de Mise en Forme des Matériaux, Mines-ParisTech, PSL-Research University, CNRS UMR 7635, CS 10207 Rue Claude Daunesse, CEDEX, 06904 Sophia Antipolis, France.
This study introduces a front-tracking method for modeling anisotropic grain growth in polycrystalline materials. The new approach accurately simulates grain boundary motion and multiple junction behavior, outperforming existing methods.
Area of Science:
- Materials Science
- Computational Materials Science
- Physics
Background:
- Grain growth is a complex phenomenon in polycrystalline materials.
- Numerical modeling of grain growth is challenging due to anisotropy (grain orientation, boundary inclination).
- Existing models struggle to incorporate all anisotropy sources effectively.
Purpose of the Study:
- To apply a front-tracking methodology for simulating anisotropic grain boundary motion at the mesoscopic scale.
- To develop a new formulation for boundary migration that accounts for anisotropy at grain boundaries and multiple junctions (MJs).
- To propose an algorithm for decomposing high-order MJs based on local energy minimization.
Main Methods:
- Front-tracking methodology applied to mesoscopic scale.
- Formulation of anisotropic boundary migration.
- Algorithm for decomposition of high-order multiple junctions (MJs) using local energy minimization.
- Numerical simulations with heterogeneous configurations.
Main Results:
- The front-tracking method successfully models anisotropic grain boundary motion.
- The new formulation effectively incorporates anisotropy from grain boundaries and MJs.
- The proposed MJ decomposition algorithm is effective.
- Numerical tests show good performance, with comparisons to Finite-Element Level-Set (FE-LS) methods.
Conclusions:
- The front-tracking approach offers a robust framework for simulating anisotropic grain growth.
- The method provides accurate modeling of complex microstructural evolution.
- Computational performance is evaluated against isotropic models.
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