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国际多尺度计算工程期刊
影响因子: 1.016 5年影响因子: 1.194 SJR: 0.452 SNIP: 0.68 CiteScore™: 1.18

ISSN 打印: 1543-1649
ISSN 在线: 1940-4352

国际多尺度计算工程期刊

DOI: 10.1615/IntJMultCompEng.v9.i4.20
pages 365-377

HYBRID COMPUTING MODELS FOR LARGE-SCALE HETEROGENEOUS 3D MICROSTRUCTURES

Kai Schrader
Bauhaus-Universität Weimar, Institute of Structural Mechanics, D-99423 Weimar, Germany
Carsten Konke
Bauhaus-Universität Weimar, Institute of Structural Mechanics, Germany

ABSTRACT

In recent years design and assessment of engineering structures are done in numerical simulation environments, applying state-of-the-art models from CAD, computational mechanics and visual analytics. Over the last two decades there has been a strong trend toward integration of theoretical and numerical models from material science on different scales up to the atomic lattice into simulation models for engineering applications, by applying multiscale models in combination with homogenization techniques or concurrent multiscale models. Especially for investigating new and heterogeneous materials, multiscale models can be applied to study material physics, such as damage initiation and propagation, on appropriate scales and integrate this information into large-scale engineering models. A major drawback of multiscale models in materials science is their enormous demand for computing power with respect to computing time and main memory. This paper suggests a method to split a heterogeneous material model, consisting of a matrix material and embedded inclusions with interfacial transition zones, into zones of elastic and inelastic behavior and to customize the discretization methods for these two zones in an appropriate way. We propose the application of structured and unstructured meshes in a hybrid fashion and to solve the resulting equation systems with several million degrees of freedom by iterative solver techniques. In order to consider the damage evolution behavior, a regularized anisotropic damage model is used and the incremental-iterative solution for this problem is based on sequential linear analysis, following the sawtooth concept of Rots et al. (2006).

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