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A discontinuous Galerkin formulation of a model of gradient plasticity at finite strains

机译:有限应变下梯度塑性模型的不连续Galerkin公式

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The work presented here constitutes an extension to the finite-strain regime of a discontinuous Galerkin based, strain gradient plasticity formulation presented in Djoko et al. [J.K. Djoko, F. Ebobisse, A.T. McBride, B.D. Reddy, A discontinuous Galerkin formulation for classical and gradient plasticity - Part 1: formulation and analysis, Comput. Methods Appl. Mech. Engrg. 196 (2007) 3881-3897; J.K. Djoko, F. Ebobisse, A.T. McBride, B.D. Reddy, A discontinuous Galerkin formulation for classical and gradient plasticity - Part 2: algorithms and numerical analysis, Comput. Methods Appl. Mech. Engrg. 197 (2007) 1-21]. The focus here is on algorithmic and computational aspects of the formulation at finite strains. The adoption of a logarithmic hyperelastic-plastic formulation preserves the essential features of the infinitesimal formulation. This key ingredient allows the predictor-corrector solution algorithms developed for the infinitesimal gradient formulation to be extended readily to the finite-strain regime. The use of low-order elements is essential to contain the computational expense of the formulation but these elements are prone to locking. The method of enhanced assumed strains for geometrically nonlinear problems is utilised to circumvent this limitation. The form of the consistent tangent modulus is derived for the case of gradient plasticity. Two numerical examples are presented to illustrate aspects of the approximation scheme and the algorithm, as well as features of the model of gradient plasticity.
机译:本文介绍的工作构成了Djoko等人提出的基于不连续Galerkin的应变梯度可塑性公式的有限应变形式的扩展。 [J.K.德约科·埃博比塞(A.T.)麦克布赖德Reddy,用于经典和梯度可塑性的不连续Galerkin配方-第1部分:配方和分析,计算机。方法应用。机甲gr 196(2007)3881-3897; J.K.德约科·埃博比塞(A.T.)麦克布赖德Reddy,用于经典和梯度可塑性的不连续Galerkin公式-第2部分:算法和数值分析,计算机。方法应用。机甲gr 197(2007)1-21]。这里的重点是有限应变下配方的算法和计算方面。对数超弹塑性配方的采用保留了无穷小配方的基本特征。该关键成分允许为无穷小梯度公式开发的预测器-校正器解决方案算法易于扩展到有限应变方案。低阶元素的使用对于控制公式的计算开销至关重要,但是这些元素易于锁定。利用用于几何非线性问题的增强假定应变的方法来规避此限制。对于梯度可塑性,导出了相切线模量的形式。给出了两个数值例子来说明近似方案和算法的各个方面,以及梯度可塑性模型的特征。

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