超弹性参数相关流固耦合离散的低秩方法

Peter Benner, Thomas Richter, Roman Weinhandl
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引用次数: 0

摘要

流固耦合模型用于研究材料在不同雷诺数下与不同流体的相互作用。对同一模型的研究不仅适用于不同的流体,也适用于不同的固体,这样可以更好地优化建筑材料的选择。根据参数进行离散化是满足这一需求的一个可行方法。此外,低秩技术可以降低计算与参数相关的流固耦合离散近似所需的复杂性。低秩方法已被应用于与参数相关的线性流固耦合离散化。由于所涉及的算子具有线性,因此可以将所得到的方程转化为单个矩阵方程。用低秩方法可以近似求解。在本文中,我们提出了一种新方法,通过牛顿迭代将这一框架扩展到非线性参数相关流固耦合问题。参数集被分割成互不相交的子集。在每个子集上,计算与中位参数相关问题的牛顿近似值,并将其作为整个子集上一个牛顿步骤的初始猜测。这个牛顿步骤会产生一个矩阵方程,其解可以用低秩方法来近似。与对不同问题连续进行牛顿迭代的直接方法相比,该方法所需的牛顿步数更少。在实验中,所提出的方法计算低阶近似值的速度比直接方法快 20 倍。
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A low‐rank method for parameter‐dependent fluid‐structure interaction discretizations with hyperelasticity
Fluid‐structure interaction models are used to study how a material interacts with different fluids at different Reynolds numbers. Examining the same model not only for different fluids but also for different solids allows to optimize the choice of materials for construction even better. A possible answer to this demand is parameter‐dependent discretization. Furthermore, low‐rank techniques can reduce the complexity needed to compute approximations to parameter‐dependent fluid‐structure interaction discretizations. Low‐rank methods have been applied to parameter‐dependent linear fluid‐structure interaction discretizations. The linearity of the operators involved allows to translate the resulting equations to a single matrix equation. The solution is approximated by a low‐rank method. In this paper, we propose a new method that extends this framework to nonlinear parameter‐dependent fluid‐structure interaction problems by means of the Newton iteration. The parameter set is split into disjoint subsets. On each subset, the Newton approximation of the problem related to the median parameter is computed and serves as initial guess for one Newton step on the whole subset. This Newton step yields a matrix equation whose solution can be approximated by a low‐rank method. The resulting method requires a smaller number of Newton steps if compared with a direct approach that applies the Newton iteration to the separate problems consecutively. In the experiments considered, the proposed method allows to compute a low‐rank approximation up to twenty times faster than by the direct approach.
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