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arXiv 2103.07706math.NAcs.NA
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球面上非线性浅水方程的时间并行积分与相位平均

Time parallel integration and phase averaging for the nonlinear shallow water equations on the sphere

Hiroe Yamazaki, Colin J Cotter, Beth Wingate

AI总结:

本文针对球面非线性旋转浅水方程,提出相位平均技术,结合有限元离散与并行黎曼和实现,验证了最优平均窗口,并证明其时间离散与平均误差远小于半隐式方法。

摘要:

我们描述了一项针对球面上非线性旋转浅水方程的相位平均技术的概念验证开发与应用,该方程使用相容有限元方法进行离散化。相位平均包括对线性波算子指数中的相位偏移进行非线性平均。相位平均旨在捕捉在变换变量中时间上更平滑的解中的慢动态,从而可以采用更大的时间步长。我们克服了阻碍研究相位平均及其实现推进的两个关键技术挑战:1) 我们开发了一种特定于有限元的稳定矩阵指数,2) 我们开发了一种并行的有限平均程序。遵循Peddle等人(2019)的研究,我们考虑有限宽度的相位平均窗口,因为方程具有有限的时间尺度分离。在我们的数值实现中,平均积分被黎曼和替代,其中每一项都可以并行评估。这为时间步进方法创造了并行化的机会,我们在此利用它来计算我们的解。在此,我们关注数值解的稳定性和准确性。我们确认存在一个最优的平均窗口,与理论一致。关键的是,我们观察到,与应用于相同空间离散化的半隐式方法相比,组合的时间离散化和平均误差远小于时间离散化误差。对并行方面的评估将在后续工作中进行。

英文摘要:

We describe a proof-of-concept development and application of a phase averaging technique to the nonlinear rotating shallow water equations on the sphere, discretised using compatible finite element methods. Phase averaging consists of averaging the nonlinearity over phase shifts in the exponential of the linear wave operator. Phase averaging aims to capture the slow dynamics in a solution that is smoother in time (in transformed variables) so that larger timesteps may be taken. We overcome the two key technical challenges that stand in the way of studying the phase averaging and advancing its implementation: 1) we have developed a stable matrix exponential specific to finite elements and 2) we have developed a parallel finite averaging proceedure. Following Peddle et al (2019), we consider finite width phase averaging windows, since the equations have a finite timescale separation. In our numerical implementation, the averaging integral is replaced by a Riemann sum, where each term can be evaluated in parallel. This creates an opportunity for parallelism in the timestepping method, which we use here to compute our solutions. Here, we focus on the stability and accuracy of the numerical solution. We confirm there is an optimal averaging window, in agreement with theory. Critically, we observe that the combined time discretisation and averaging error is much smaller than the time discretisation error in a semi-implicit method applied to the same spatial discretisation. An evaluation of the parallel aspects will follow in later work.

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