Mesh; Domain Discretization

2021 ◽  
pp. 1-35
Author(s):  
Chunlin Wu ◽  
Liangliang Zhang ◽  
Huiming Yin

Abstract The paper extends the recent work (JAM, 88, 061002, 2021) of the Eshelby's tensors for polynomial eigenstrains from a two dimensional (2D) to three dimensional (3D) domain, which provides the solution to the elastic field with continuously distributed eigenstrain on a polyhedral inclusion approximated by the Taylor series of polynomials. Similarly, the polynomial eigenstrain is expanded at the centroid of the polyhedral inclusion with uniform, linear and quadratic order terms, which provides tailorable accuracy of the elastic solutions of polyhedral inhomogeneity by using Eshelby's equivalent inclusion method. However, for both 2D and 3D cases, the stress distribution in the inhomogeneity exhibits a certain discrepancy from the finite element results at the neighborhood of the vertices due to the singularity of Eshelby's tensors, which makes it inaccurate to use the Taylor series of polynomials at the centroid to catch the eigenstrain at the vertices. This paper formulates the domain discretization with tetrahedral elements to accurately solve for eigenstrain distribution and predict the stress field. With the eigenstrain determined at each node, the elastic field can be predicted with the closed-form domain integral of Green's function. The parametric analysis shows the performance difference between the polynomial eigenstrain by the Taylor expansion at the centroid and the 𝐶0 continuous eigenstrain by particle discretization. Because the stress singularity is evaluated by the analytical form of the Eshelby's tensor, the elastic analysis is robust, stable and efficient.


2020 ◽  
Vol 383 ◽  
pp. 123121 ◽  
Author(s):  
Michael Mansour ◽  
Prafull Khot ◽  
Péter Kováts ◽  
Dominique Thévenin ◽  
Katharina Zähringer ◽  
...  

Author(s):  
Hailong Chen ◽  
Yile Hu ◽  
Benjamin W. Spencer

In this paper, reformulation of classical bond-based peridynamic thermomechanical model for irregular domain decomposition and its MOOSE-based implicit formulation are presented. First, the irregular grid based peridynamic thermomechanical model is formulated and model parameters are derived. Following this, an implicit formulation for the solution of static or quasi-static problems is presented. Some aspects of the MOOSE-based implementation are given. After that, the formulation is verified against benchmark solutions for thermomechanic problems. Crack initiation and propagation in circular (2D) and cylindrical (3D) nuclear fuels at high temperature are studied using irregular grids.


Author(s):  
Horacio Antonio Flo´rez Guzma´n

A computer code for solving the equations of mass diffusion has been developed and applied to study the molecular-level mixing between two fluids inside a pipe. First, one fluid occupies the entire volume within the pipe, and then a second miscible fluid is forced into the pipe, developing a mixing process through the interface between the fluids. This phenomenon occurs as the combination of molecular diffusion, variation of velocity over the cross-section and turbulence. The code developed for this study is based on the finite element method for domain discretization and standard finite difference schemes for temporal discretization. Comparison with experimental data shows that the code is able to reproduce the physical trends and gives good predictions for engineering applications. A grid independence analysis is presented for all computations.


2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Bowen Zheng ◽  
Grace X. Gu

AbstractDefects in graphene can profoundly impact its extraordinary properties, ultimately influencing the performances of graphene-based nanodevices. Methods to detect defects with atomic resolution in graphene can be technically demanding and involve complex sample preparations. An alternative approach is to observe the thermal vibration properties of the graphene sheet, which reflects defect information but in an implicit fashion. Machine learning, an emerging data-driven approach that offers solutions to learning hidden patterns from complex data, has been extensively applied in material design and discovery problems. In this paper, we propose a machine learning-based approach to detect graphene defects by discovering the hidden correlation between defect locations and thermal vibration features. Two prediction strategies are developed: an atom-based method which constructs data by atom indices, and a domain-based method which constructs data by domain discretization. Results show that while the atom-based method is capable of detecting a single-atom vacancy, the domain-based method can detect an unknown number of multiple vacancies up to atomic precision. Both methods can achieve approximately a 90% prediction accuracy on the reserved data for testing, indicating a promising extrapolation into unseen future graphene configurations. The proposed strategy offers promising solutions for the non-destructive evaluation of nanomaterials and accelerates new material discoveries.


2020 ◽  
Vol 1473 ◽  
pp. 012010
Author(s):  
Kallur V Vijayakumar ◽  
A S Hariprasad

2006 ◽  
Vol 128 (4) ◽  
pp. 898-903 ◽  
Author(s):  
Tianxiang Liu ◽  
Geng Liu ◽  
Qin Xie ◽  
Q. Jane Wang

When contact problems are solved by numerical approaches, a surface profile is usually described by a series of discrete nodes with the same intervals along a coordinate axis. Contact computation based on roughness datum mesh may be time consuming. An adaptive-surface elasto-plastic asperity contact model is presented in this paper. Such a model is developed in order to reduce the computing time by removing the surface nodes that have little influence on the contact behavior of rough surfaces. The nodes to be removed are determined by a prescribed threshold. The adaptive-surface asperity contact model is solved by means of the element-free Galerkin-finite element coupling method because of its flexibility in domain discretization and versatility in node arrangements. The effects of different thresholds on contact pressure distribution, real contact area, and elasto-plastic stress fields in contacting bodies are investigated and discussed. The results show that this model can help reduce about 48% computational time when the relative errors are about 5%.


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