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Complex variable methods for shape sensitivity of finite element models

机译:有限元模型形状敏感性的复变量方法

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Shape sensitivity analysis of finite element models is useful for structural optimization and design modifications. Complex variable methods for shape sensitivity analysis have some potential advantages over other methods. In particular, for first order sensitivities using the complex Taylor series expansion method (CTSE), the implementation is straightforward, only requiring a perturbation of the finite element mesh along the imaginary axis. That is, the real valued coordinates of the mesh are unaltered and no other modifications to the software are required. Fourier differentiation (FD) provides higher order sensitivities by conducting an FFT analysis of multiple complex variable analyses around a sampling radius in the complex plane. Implementation of complex variable sensitivity methods requires complex variable finite element software such that complex nodal coordinates can be used to implement a perturbation in the shape of interest in the complex domain. All resulting finite element outputs such as displacements, strains and stresses become complex and accurate derivatives of all finite element outputs with respect to the shape parameter of interest are available. The methodologies are demonstrated using two-dimensional finite element models of linear elasticity problems with known analytical solutions. It is found that the error in the sensitivities is primarily defined by the error in the finite element solution not the error in the sensitivity method. Hence, more accurate sensitivities can be obtained through mesh refinement.
机译:有限元模型的形状敏感性分析对于结构优化和设计修改很有用。与其他方法相比,用于形状敏感性分析的复杂变量方法具有一些潜在的优势。特别地,对于使用复数泰勒级数展开方法(CTSE)的一阶灵敏度,实现是简单明了的,只需要沿虚轴对有限元网格进行微扰即可。也就是说,网格的实际值坐标不变,并且不需要对软件进行其他修改。傅立叶微分(FD)通过对复杂平面中采样半径周围的多个复杂变量分析进行FFT分析来提供更高阶的灵敏度。复杂变量灵敏度方法的实现需要复杂变量有限元软件,以便可以使用复杂节点坐标在复杂域中以目标形状实现扰动。所有得到的有限元输出(例如位移,应变和应力)都变得复杂,并且可以获取所有有限元输出相对于目标形状参数的精确导数。使用具有已知解析解的线性弹性问题的二维有限元模型演示了该方法。发现灵敏度的误差主要由有限元解决方案中的误差而不是灵敏度方法中的误差定义。因此,可以通过网格细化获得更准确的灵敏度。

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