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Fundamental aspects of shape optimization in the context of isogeometric analysis

机译:等几何分析中形状优化的基本方面

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We develop a mathematical foundation for shape optimization problems under state equation constraints where both state and control are discretized by B-splines or NURBS. In other words, we use isogeometric analysis (IGA) for solving the partial differential equation and a nodal approach to change domains where control points take the place of nodes and where thus a quite general class of functions for representing optimal shapes and their boundaries becomes available. The minimization problem is solved by a gradient descent method where the shape gradient will be defined in isogeometric terms. This gradient is obtained following two schemes, optimize first-discretize then and, reversely, discretize first-optimize then. We show that for isogeometric analysis, the two schemes yield the same discrete system. Moreover, we also formulate shape optimization with respect to NURBS in the optimize first ansatz which amounts to finding optimal control points and weights simultaneously. Numerical tests illustrate the theory. (C) 2015 Elsevier B.V. All rights reserved.
机译:我们为状态方程约束下的形状优化问题开发了数学基础,其中状态和控制都通过B样条或NURBS离散化。换句话说,我们使用等几何分析(IGA)来求解偏微分方程,并使用节点方法来更改域,在这些域中,控制点代替了节点,因此可以使用一种非常通用的函数来表示最佳形状及其边界。最小化问题通过梯度下降法解决,其中形状梯度将以等几何学术语定义。该梯度是通过以下两种方案获得的,先优化然后离散化,反之,先离散化然后优化。我们表明,对于等几何分析,这两种方案产生相同的离散系统。此外,我们还在优化的第一ansatz中针对NURBS制定了形状优化,这相当于同时找到了最佳的控制点和权重。数值测试说明了这一理论。 (C)2015 Elsevier B.V.保留所有权利。

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