多层潮汐模型的加权范数预处理

C. Cotter, R. Kirby, Hunter Morris
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引用次数: 0

摘要

我们推导了一个模拟潮汐的线性化旋转浅水系统,它可以被混合有限元离散化。与以前的模型不同,这个模型允许按密度分层的多层。与单层情况\cite{kirby2021preconditioning}一样,加权范数预条件给出了一种(接近)参数鲁棒的方法来求解每个时间步的结果线性系统,但模型中各层之间的全对全耦合对效率提出了重大挑战。忽略层间耦合给出的预条件会随着层数的增加而迅速退化。通过对耦合层的矩阵的仔细分析,我们推导出一种鲁棒方法,该方法需要求解一个仅涉及相邻层之间耦合的重新表述系统。利用Firedrake获得的数值结果证实了这一理论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weighted-norm preconditioners for a multi-layer tide model
We derive a linearized rotating shallow water system modeling tides, which can be discretized by mixed finite elements. Unlike previous models, this model allows for multiple layers stratified by density. Like the single-layer case~\cite{kirby2021preconditioning} a weighted-norm preconditioner gives a (nearly) parameter-robust method for solving the resulting linear system at each time step, but the all-to-all coupling between the layers in the model poses a significant challenge to efficiency. Neglecting the inter-layer coupling gives a preconditioner that degrades rapidly as the number of layers increases. By a careful analysis of the matrix that couples the layers, we derive a robust method that requires solving a reformulated system that only involves coupling between adjacent layers. Numerical results obtained using Firedrake confirm the theory.
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