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为了预测二维平纹机织复合材料的弹性性能,提出了基于有限元重合网格法的域分解方法。域分解方法与传统代表体元法的有限元建模不同,前者不再建立精细的纤维与基体模型,而是分别建立二维平纹机织复合材料单胞的整体域与纤维域,整体域是真实基体体积与纤维体积的叠加,两区域网格独立剖分,互不影响。采用MSC.Nastran中的多节点约束在纤维节点与基体节点之间建立位移协调来模拟纤维和基体单元的位移函数关系,实现了纤维域和基体域的耦合计算。研究表明,域分解方法大大简化了机织复合材料细观力学建模的复杂性,降低了建模时间,采用域分解方法预测的二维平纹机织复合材料弹性常数与试验值吻合较好,充分说明了该预测模型与方法的正确性。研究了不同纤维体积分数下,域分解方法预测二维平纹机织复合材料的弹性常数的变化趋势,结果表明,随纤维体积分数增加,模量呈上升趋势,泊松比呈降低趋势。
In order to predict the elastic properties of two-dimensional plain weave composites, a domain decomposition method based on the finite element coincidence grid method is proposed. The domain decomposition method is different from the traditional finite element modeling of the representative body element method. The former no longer establishes the fine fiber and matrix model, but sets up the whole domain and the fiber domain of the two-dimensional plain weave composite material unit cell respectively, the whole domain is Real matrix volume and fiber volume superposition, the two regional grid independent subdivision, does not affect each other. The multi-node constraint in MSC.Nastran was used to establish the displacement coordination between the fiber nodes and the substrate nodes to simulate the displacement function relationship between the fiber and the matrix elements. The coupling calculation between the fiber domain and the matrix domain was realized. The research shows that the method of domain decomposition greatly simplifies the complexity of mesomechanical modeling of woven composites and reduces the modeling time. The elastic constants of two-dimensional woven woven composites predicted by domain decomposition method agree well with the experimental values, It fully illustrates the correctness of the forecasting model and method. The variation trend of the elastic constants of two-dimensional plain weave woven composites predicted by domain decomposition method under different fiber volume fractions was studied. The results show that the modulus increases with the increase of fiber volume fraction, and the Poisson’s ratio decreases.