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首页> 外文期刊>Journal of Materials Research >Skeletonization-based beam finite element models for stochastic bicontinuous materials: Application to simulations of nanoporous gold
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Skeletonization-based beam finite element models for stochastic bicontinuous materials: Application to simulations of nanoporous gold

机译:基于骨架的随机双连续材料梁有限元模型:在纳米多孔金模拟中的应用

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摘要

An efficient representative volume element generation strategy is developed in modeling nanoporous materials. It uses periodic 3D beam finite element (FE) models derived from skeletonization of spinodal-like stochastic microstructures produced by a leveled random field. To mimic stiffening with agglomeration of the mass at junctions, an increased Young's modulus is assigned to the elements within the junction zone. The effective Young's modulus, Poisson's ratio, and universal anisotropy index are computed. A good agreement of the Young's modulus predictions with those obtained from experimental results for phase volume fractions 0.20 phi(B) 0.50 is observed. Moreover, the elastic anisotropy index of the generated beam networks shows sufficient proximity to isotropy. Finally, it is demonstrated that, as compared to the simulation statistics of voxel-FE models, for the beam-FE models over 500-fold computational acceleration with 250-fold less memory requirement is provided.
机译:在建模纳米多孔材料中,开发了一种有效的代表性体积元素生成策略。它使用周期性3D光束有限元(FE)模型,该模型是由水平随机场产生的类似旋节状随机微结构的骨架化衍生的。为了模拟结点处的团聚引起的变硬,将增加的杨氏模量分配给结点区域内的元素。计算有效杨氏模量,泊松比和通用各向异性指数。观察到杨氏模量预测与从相体积分数0.20 hi(B)<0.50的实验结果获得的结果吻合良好。此外,所产生的梁网络的弹性各向异性指数显示出与各向同性的足够接近性。最后,证明了,与voxel-FE模型的仿真统计数据相比,对于Beam-FE模型,可以提供500倍的计算加速速度和250倍以下的内存需求。

著录项

  • 来源
    《Journal of Materials Research》 |2018年第20期|3371-3382|共12页
  • 作者单位

    Univ Wuppertal, Sch Mech Engn & Safety Engn, Chair Solid Mech, D-42119 Wuppertal, Germany;

    Atilim Univ, Dept Mfg Engn, TR-06830 Ankara, Turkey;

    Univ Wuppertal, Sch Mech Engn & Safety Engn, Chair Solid Mech, D-42119 Wuppertal, Germany;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
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