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Optimal shape of fibers in composite structure using Inverse variational principles

机译:基于逆变分原理的复合结构中纤维的最佳形状

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Inverse variational principles proved their importance in shape optimization of structures. In this paper they are applied to searching for the optimal shape of fibers in a composite structure. As the boundary element method seems to be more promising than other modern numerical methods applied to the search for optimal shape, in the submitting paper the boundary element method is redefined to enable one to use such an approach, which leads to possibility for the optimal interfacial energies and, hence, to the optimal bearing capacity of the composite structure. Necessary discretization of the domain, which occurs in the finite elements, is suppressed in our case. Standard procedure in the finite elements leads to dependence of the stiffness matrix on the shape of the fibers. In this case, following a basic idea for homogenization and localization, concentration factors have to be calculated in terms of the boundary element method instead. These terms are dependent on the shape of the fibers. It appears that the procedure is still not convergent (we solve a strongly nonlinear problem) and additional constraint has to be involved in the formulation. In order to formulate and solve this problem, the idea of Inverse variational principles is applied here for expressing necessary quantities. The paper concentrates on the calculation of quantities, which are necessary to formulate the optimization problem. The main attention is focused on calculation of concentration factors, which play the most important role in the approach proposed.
机译:逆变分原理证明了它们在结构形状优化中的重要性。在本文中,它们被用于寻找复合结构中纤维的最佳形状。由于边界元素方法似乎比其他现代数值方法更适用于寻找最佳形状,因此在提交的论文中,边界元素方法被重新定义以使人们能够使用这种方法,这导致了最佳界面的可能性。能量,从而达到复合结构的最佳承载能力。在我们的案例中,可以抑制在有限元中出现的必要的离散域。有限元中的标准程序导致刚度矩阵对纤维形状的依赖。在这种情况下,遵循均质化和本地化的基本思想,必须根据边界元素法来计算浓度因子。这些术语取决于纤维的形状。看来该程序仍未收敛(我们解决了一个强烈的非线性问题),并且公式中还必须包含其他约束。为了表述和解决这个问题,这里应用了反变分原理的思想来表达必要的数量。本文着重于数量的计算,这是提出优化问题所必需的。主要注意力集中在浓度因子的计算上,这些浓度因子在所提出的方法中起着最重要的作用。

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