ﻻ يوجد ملخص باللغة العربية
FEpX is a modeling framework for computing the elastoplastic deformations of polycrystalline solids. Using the framework, one can simulate the mechanical behavior of aggregates of crystals, referred to as virtual polycrystals, over large strain deformation paths. This article presents the theory, the finite element formulation, and important features of the numerical implementation that collectively define the modeling framework. The article also provides several examples of simulating the elastoplastic behavior of polycrystalline solids to illustrate possible applications of the framework. There is an associated finite element code, also referred to as FEpX, that is based on the framework presented here and was used to perform the simulations presented in the examples. The article serves as a citable reference for the modeling framework for users of that code. Specific information about the formats of the input and output data, the code architecture, and the code archive are contained in other documents.
In the analysis of composite materials with heterogeneous microstructures, full resolution of the heterogeneities using classical numerical approaches can be computationally prohibitive. This paper presents a micromechanics-enhanced finite element fo
In order to accelerate implementation of hyperelastic materials for finite element analysis, we developed an automatic numerical algorithm that only requires the strain energy function. This saves the effort on analytical derivation and coding of str
This article takes the form of a tutorial on the use of a particular class of mixed finite element methods, which can be thought of as the finite element extension of the C-grid staggered finite difference method. The class is often referred to as co
We present experimental results and numerical Finite Element analysis to describe surface swelling due to the creation of buried graphite-like inclusions in diamond substrates subjected to MeV ion implantation. Numerical predictions are compared to e
The stochastic partial differential equation approach to Gaussian processes (GPs) represents Matern GP priors in terms of $n$ finite element basis functions and Gaussian coefficients with sparse precision matrix. Such representations enhance the scal