A new finite element strategy to simulate microstructural evolutions

Abstract The Level-set (LS) method has been shown to be a powerful approach to model dynamic interfaces in the context of large deformations. The LS method has been applied to the simulation of microstructural evolutions as Grain Growth (GG) and Recrystallization (ReX) at the mesoscale Maire et al. (2017). Interfaces between grains are implicitly described in an Eulerian framework, as the zero-isovalue of the LS fields and their evolution is governed by convective-diffusive partial differential equations (PDEs). The LS approach circumvents the notoriously difficult problem of generating interface-conforming meshes for geometries subjected to large displacements and to changes in the topology of the domains. Generally, in order to maintain high accuracy when using the LS method, moving interfaces are generally captured by a locally refined FE mesh with the help of mesh adaptation techniques. In a microstructural problem, the large number of interfaces and the fine mesh required in their vicinity make the mesh adaptation process very costly in terms of CPU-time, particularly in 3D Scholtes (2016). In this work, a different adaptation strategy is used. It maintains the benefits of the classical Eulerian LS framework, while enforcing at all times the conformity of the FE mesh to implicit interfaces by means of local remeshing operations, special treatments for vacuum regions have been adopted and will be presented within the generalization of a previous adaptation algorithm presented in Shakoor et al. (2015). Source of errors will be presented and compared for different test cases. Finally, we will illustrate how the new method decreases the requirement in mesh density while maintaining the accuracy at the interfaces, hence reducing the computational cost of the simulations.

[1]  J. Sethian,et al.  Fast methods for the Eikonal and related Hamilton- Jacobi equations on unstructured meshes. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Modeling Polycrystalline Microstructures in 3D , 2004 .

[3]  P. Cloetens,et al.  New opportunities for 3D materials science of polycrystalline materials at the micrometre lengthscale by combined use of X-ray diffraction and X-ray imaging , 2009 .

[4]  Håkan Hallberg,et al.  A modified level set approach to 2D modeling of dynamic recrystallization , 2013 .

[5]  O. C. Zienkiewicz,et al.  Superconvergence and the superconvergent patch recovery , 1995 .

[6]  W. Ludwig,et al.  Coupling Diffraction Contrast Tomography with the Finite Element Method   , 2016 .

[7]  Thierry Coupez,et al.  Parallel meshing and remeshing , 2000 .

[8]  O. C. Zienkiewicz,et al.  The superconvergent patch recovery (SPR) and adaptive finite element refinement , 1992 .

[9]  Pierre-Olivier Bouchard,et al.  An adaptive level‐set method with enhanced volume conservation for simulations in multiphase domains , 2017 .

[10]  Marc Bernacki,et al.  Assessment of simplified 2D grain growth models from numerical experiments based on a level set framework , 2014 .

[11]  N. Bozzolo,et al.  Improvement of 3D mean field models for capillarity-driven grain growth based on full field simulations , 2016, Journal of Materials Science.

[12]  Conyers Herring,et al.  Surface Tension as a Motivation for Sintering , 1999 .

[13]  Benjamin Scholtes,et al.  New finite element developments for the full field modeling of microstructural evolutions using the level set method , 2015 .

[14]  Thierry Coupez,et al.  Level set framework for the numerical modelling of primary recrystallization in polycrystalline materials , 2008 .

[15]  C. Ganino,et al.  2D and 3D simulation of grain growth in olivine aggregates using a full field model based on the level set method , 2018, Physics of the Earth and Planetary Interiors.

[16]  P. Bouchard,et al.  A new body-fitted immersed volume method for the modeling of ductile fracture at the microscale: Analysis of void clusters and stress state effects on coalescence , 2015 .

[17]  Marc Bernacki,et al.  Modeling of dynamic and post-dynamic recrystallization by coupling a full field approach to phenomenological laws , 2017 .

[18]  C. Gruau,et al.  3D tetrahedral, unstructured and anisotropic mesh generation with adaptation to natural and multidomain metric , 2005 .

[19]  B Notarberardino,et al.  An efficient approach to converting three-dimensional image data into highly accurate computational models , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[20]  A. Rollett,et al.  3D reconstruction of microstructure in a commercial purity aluminum , 2006 .

[21]  M. Rosti,et al.  Direct numerical simulation of turbulent channel flow over porous walls , 2014, Journal of Fluid Mechanics.

[22]  Yongjie Zhang,et al.  3D Finite Element Meshing from Imaging Data. , 2005, Computer methods in applied mechanics and engineering.

[23]  Pierre-Olivier Bouchard,et al.  An efficient and parallel level set reinitialization method - Application to micromechanics and microstructural evolutions , 2015 .

[24]  N.-E. Wiberg Superconvergent patch recovery—a key to quality assessed FE solutions , 1997 .

[25]  I. Steinbach Phase-field models in materials science , 2009 .

[26]  N. Bozzolo,et al.  A novel level-set finite element formulation for grain growth with heterogeneous grain boundary energies , 2018, Materials & Design.

[27]  Thierry Coupez,et al.  Level set framework for the finite element modelling of recrystallization and grain growth in polycrystalline materials , 2011 .

[28]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Marc Bernacki,et al.  3D level set modeling of static recrystallization considering stored energy fields , 2016 .

[30]  Harald Garcke,et al.  A MultiPhase Field Concept: Numerical Simulations of Moving Phase Boundaries and Multiple Junctions , 1999, SIAM J. Appl. Math..

[31]  Karim Hitti,et al.  Precise generation of complex statistical Representative Volume Elements (RVEs) in a finite element context , 2012 .

[32]  Hiroshi Imai,et al.  Voronoi Diagram in the Laguerre Geometry and its Applications , 1985, SIAM J. Comput..

[33]  Cyril Gruau Metric generation for anisotropic mesh adaptation, with numerical applications to material forming simulation , 2004 .

[34]  Marc Bernacki,et al.  Advancing layer algorithm of dense ellipse packing for generating statistically equivalent polygonal structures , 2016 .

[35]  Peter Smereka,et al.  Simulations of anisotropic grain growth: Efficient algorithms and misorientation distributions , 2013 .

[36]  S. Osher,et al.  A level set approach for computing solutions to incompressible two-phase flow , 1994 .

[37]  Thierry Coupez,et al.  Finite element model of primary recrystallization in polycrystalline aggregates using a level set framework , 2009 .

[38]  J A Sethian,et al.  Computing geodesic paths on manifolds. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[39]  R. Quey,et al.  Large-scale 3D random polycrystals for the finite element method: Generation, meshing and remeshing , 2011 .

[40]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .