纤维增强塑料构件多尺度分析的FE-DMN方法
An FE-DMN method for the multiscale analysis of fiber reinforced plastic components
摘要：
在这项工作中，我们提出了一个完全耦合的多尺度策略，由短纤维增强复合材料制成的组件，其中每个高斯点的宏观有限元模型配备了一个深材料网络（DMN），它涵盖了不同的纤维取向状态变化的组件内。这些DMN需要确定的线性弹性预估代表性的体积元素，并作为高保真度的替代品的全场模拟与非弹性成分的微观结构。我们讨论了如何扩展直接DMN来考虑不同的纤维方向，并提出了一个简化的采样策略，显着加快了训练过程。为了实现并发多尺度模拟，有效地评估DMN是至关重要的。我们讨论了专门的技术，利用稀疏性和高性能线性代数模块，并展示了工业规模的三维组件上所提出的方法的力量。事实上，DMN能够显著加速双尺度模拟，提供几个量级的可能加速。
英文摘要：
In this work, we propose a fully coupled multiscale strategy for components made from short fiber reinforced composites, where each Gauss point of the macroscopic finite element model is equipped with a deep material network (DMN) which covers the different fiber orientation states varying within the component. These DMNs need to be identified by linear elastic precomputations on representative volume elements, and serve as high-fidelity surrogates for full-field simulations on microstructures with inelastic constituents. We discuss how to extend direct DMNs to account for varying fiber orientation, and propose a simplified sampling strategy which significantly speeds up the training process. To enable concurrent multiscale simulations, evaluating the DMNs efficiently is crucial. We discuss dedicated techniques for exploiting sparsity and high-performance linear algebra modules, and demonstrate the power of the proposed approach on an industrial-scale three-dimensional component. Indeed, the DMN is capable of accelerating two-scale simulations significantly, providing possible speed-ups of several magnitudes.
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