Numerically predicting the performance of heterogenous structures without scale separation represents a significant challenge to meet the critical requirements on computational scalability and efficiency -- adopting a mesh fine enough to fully account for the small-scale heterogeneities leads to prohibitive computational costs while simply ignoring these fine heterogeneities tends to drastically over-stiffen the structure's rigidity. This study proposes an approach to construct new material-aware shape (basis) functions per element on a coarse discretization of the structure with respect to each curved bridge nodes (CBNs) defined along the elements' boundaries. Instead of formulating their derivation by solving a nonlinear optimization problem, the shape functions are constructed by building a map from the CBNs to the interior nodes and are ultimately presented in an explicit matrix form as a product of a B\'ezier interpolation transformation and a boundary-interior transformation. The CBN shape function accomodates more flexibility in closely capturing the coarse element's heterogeneity, overcomes the important and challenging issues of inter-element stiffness and displacement discontinuity across interface between coarse elements, and improves the analysis accuracy by orders of magnitude; they also meet the basic geometric properties of shape functions that avoid aphysical analysis results. Extensive numerical examples, including a 3D industrial example of billions of degrees of freedom, are also tested to demonstrate the approach's performance in comparison with results obtained from classical approaches.
翻译:以数字方式预测不同结构的性能,而不进行比例分化,这是满足计算可缩缩缩率和效率的关键要求的重大挑战 -- -- 采用足以充分说明小规模异差问题的网格罚款,从而充分说明小规模异差性,从而导致令人望而却步的计算成本,而只是忽视这些细微异性,往往使结构的僵硬性大为过于紧张。本研究报告建议采用一种方法,在沿元素边界界定的每个弯曲桥结点(CBNs)上粗糙的离散结构结构结构每个要素构建新的物质认知形状(basis)功能。采用这一方法,不是通过解决非线性优化问题来制定它们的衍生,而是通过绘制从CBNs到内部结点的地图来构建形状功能,最终以明确的矩阵形式展示出结构的僵硬性变化和边界内变异性。CBN形状的功能在更灵活地捕捉取结构结构结构结构的分解,克服重要和具有挑战性的比较问题,而是通过不线性化的深度分析,同时展示了深度的稳定性和深度分析结果。