论文标题
一种低级别的方法,用于参数依赖性的流体结构相互作用与超弹性的离散化
A Low-rank Method for Parameter-dependent Fluid-structure Interaction Discretizations With Hyperelasticity
论文作者
论文摘要
流体结构相互作用模型用于研究材料在不同雷诺数下与不同流体相互作用的方式。不仅要针对不同的流体检查相同的模型,而且还可以针对不同的固体进行检查,从而优化了更好的构造材料的选择。该需求的可能答案是与参数依赖性离散化。此外,低级技术可以降低计算与参数依赖性流体结构相互作用离散所需的复杂性。 低级别方法已应用于参数依赖性线性流体结构的交流。所涉及的运算符的线性允许将结果方程式转换为单个矩阵方程。该解决方案通过低级别方法近似。在本文中,我们提出了一种新方法,该方法将该框架扩展到非线性参数依赖性流体结构相互作用问题,并通过牛顿迭代。参数集分为不相交子集。在每个子集上,计算与上部参数相关的问题的牛顿近似,并作为整个子集中牛顿步骤的初始猜测。这个牛顿步骤产生一个矩阵方程,该方程可以通过低级别方法近似。与将牛顿迭代应用于连续的单独问题相比,所得的方法需要少量的牛顿步骤。在考虑的实验中,提出的方法允许计算低级别近似值的速度比直接方法快二十倍。
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 upper 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.