Enhancing the Convergence of the Multigrid-Reduction-in-Time Method for the Euler and Navier–Stokes Equations

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-06-21 DOI:10.1007/s10915-024-02596-0
Meiyuan Zhen, Xuejun Ding, Kun Qu, Jinsheng Cai, Shucheng Pan
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Abstract

Excessive spatial parallelization can introduce a performance bottleneck due to the communication overhead. While time-parallel method multigrid-reduction-in-time (MGRIT) provides an alternative to enhance concurrency, it generally requires large numbers of iterations to converge or even fails when applied to advection-dominated problems. To enhance the convergence of MGRIT, we propose the use of consecutive-step coarse-grid operators in MGRIT, rather than the standard rediscretized coarse-grid operators. The consecutive-step coarse-grid operator is defined as the multiplication of several fine-grid operators, which is able to track the advective characteristic more accurately than the standard rediscretized one. Numerical results show that multilevel MGRIT using the proposed operator is more efficient than the one using the standard rediscretized operator when applied to linear advection problems. Moreover, we perform time-parallel computing of the Euler equations and the Navier–Stokes equations by using the proposed method. Spatial coarsening is also considered. Compared with the MGRIT using the standard rediscretization approach, the developed method demonstrates enhanced robustness and efficiency in handling complex flow problems, including cases involving multidimensional shock waves and contact discontinuities.

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增强欧拉和纳维-斯托克斯方程多网格-时间还原法的收敛性
由于通信开销,过度的空间并行化会带来性能瓶颈。虽然时间并行方法多网格-时间还原(MGRIT)为增强并发性提供了一种选择,但它通常需要大量迭代才能收敛,甚至在应用于平流主导问题时会失败。为了提高 MGRIT 的收敛性,我们建议在 MGRIT 中使用连续步粗网格算子,而不是标准的重新具体化粗网格算子。连续步粗网格算子被定义为多个细网格算子的乘法,它能比标准的重新具体化算子更精确地跟踪平流特性。数值结果表明,当应用于线性平流问题时,使用所提出的算子的多级 MGRIT 比使用标准再具体化算子的多级 MGRIT 更有效。此外,我们还利用提出的方法对欧拉方程和纳维-斯托克斯方程进行了时间并行计算。我们还考虑了空间粗化问题。与使用标准再具体化方法的 MGRIT 相比,所开发的方法在处理复杂流动问题(包括涉及多维冲击波和接触不连续性的情况)时表现出更强的鲁棒性和更高的效率。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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