In analyzing large scale structures it is necessary to take into account the fine scale heterogeneity for accurate failure prediction. Resolving fine scale features in the numerical model drastically increases the number of degrees of freedom, thus making full fine-scale simulations infeasible, especially in cases where the model needs to be evaluated many times. In this paper, a methodology for fine scale modeling of large scale structures is proposed, which combines the variational multiscale method, domain decomposition and model order reduction. Addressing applications where the assumption of scale separation does not hold, the influence of the fine scale on the coarse scale is modelled directly by the use of an additive split of the displacement field. Possible coarse and fine scale solutions are exploited for a representative volume element (RVE) to construct local approximation spaces. The local spaces are designed such that local contributions of RVE subdomains can be coupled in a conforming way. Therefore, the resulting global system of equations takes the effect of the fine scale on the coarse scale into account, is sparse and reduced in size compared to the full order model. Several numerical experiments show the accuracy and efficiency of the method.
翻译:在分析大型结构时,有必要考虑到用于准确故障预测的精细规模差异性; 解决数字模型中的精细规模特点会大大增加自由度的数量,从而使完全的精细模拟变得不可行,特别是在模型需要多次评估的情况下; 在本文件中,提出了大规模结构的精细规模模型方法,该方法结合了多种不同规模方法、域分解和减少模型顺序。 在假设比例分离不起作用的情况下处理应用问题,微小规模对粗缩规模的影响是直接通过使用异位场的添加法来模拟的。 可能为具有代表性的量元素(RVE)开发粗略和精细规模的解决方案,以构建本地近距离空间。 本地空间的设计可以使RVE子域的本地贡献能够以一致的方式结合。 因此,由此产生的全球方程系统与全序模型相比,微尺度对粗尺度的影响是分散和缩小的。 几个数字实验显示了方法的准确性和效率。