Large-scale 3D random polycrystals for the finite element method: Generation, meshing and remeshing

By R. Quey, P. Dawson, F. Barbe. 2011

1,188 citations recorded by the source corpus.

Abstract A methodology is presented for the generation and meshing of large-scale three-dimensional random polycrystals. Voronoi tessellations are used and are shown to include morphological properties that make them particularly challenging to mesh with high element quality. Original approaches are presented to solve these problems: (i) “geometry regularization”, which consists in removing the geometrical details of the polycrystal morphology, (ii) “multimeshing” which consists in using simultaneously several meshing algorithms to optimize mesh quality, and (iii) remeshing, by which a new mesh is constructed over a deformed mesh and the state variables are transported, for large strain applications. Detailed statistical analyses are conducted on the polycrystal morphology and mesh quality. The results are mainly illustrated by the high-quality meshing of polycrystals with large number of grains (up to 10 5 ), and the finite element method simulation of a plane strain compression of e = 1.4 of a 3000-grain polycrystal. The presented algorithms are implemented and distributed in a free (open-source) software package: Neper.

Subfields: Computer Graphics and Computer-Aided Design, Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/e29662aa08cf939623dab8bafc92af5ec48071a1

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In Computer Graphics and Computer-Aided Design

In Materials Chemistry

In Mechanical Engineering

Citations in the corpus

Generation of 3D representative volume elements for heterogeneous materials: A review (2018, methodology): “Some of the most popular are QHULL by Barber et al. [218], VORO++ by Rycroft et al. [219] or NEPER by Quey et al. [201].”
Virtual and smoothed finite elements: A connection and its application to polygonal/polyhedral finite element methods (2015, methodology): “Engng 2015; 104:1173–1199 DOI: 10.1002/nme ACKNOWLEDGEMENTS The authors would also like to acknowledge Dr. Romain Quey for his generous support with the Neper software.”
Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity (2020, methodology): “One of the most intuitive pooling is to infer the grain connectivity graph from an micro-CT image (Jaquet et al., 2013; Wang et al., 2016a) or realization of micro-structures generated from software packages such as Neper or Cubit (Quey et al., 2011; Salinger et al., 2016).”
A low order 3D virtual element formulation for finite elasto–plastic deformations (2017, methodology): “(54) TheVoronoi meshes for this and all following examples were generated by the open-source software package Neper [44].”
Optimal polyhedral description of 3D polycrystals: Method and application to statistical and synchrotron X-ray diffraction data (2018, methodology): “[26], we proposed a correction method, called “regularization”, which replaces small edges and faces by single vertices.”