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arXiv 2510.00511math.NAcs.NA
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一种用于龙格-库塔间断伽辽金方法的多分辨率限制器

A multi-resolution limiter for the Runge-Kutta discontinuous Galerkin method

University of Electronic Science and Technology of China(电子科技大学);Capital Normal University(首都师范大学)

Hua Shen, Bangwei She

AI总结:

本研究提出一种用于RKDG方法的多分辨率限制器,通过比较DG多项式导数与相邻单元基线动态选最优多项式,无需调参且具尺度不变性,数值算例验证了其准确性与鲁棒性。

摘要:

我们提出了一种新型多分辨率(MR)限制器,用于在一般非结构网格上求解双曲守恒律的龙格-库塔间断伽辽金(RKDG)方法。与仅检测解的间断性并将单元二分为良好单元或问题单元的经典限制器不同,所提出的MR限制器还考虑了导数间断性,将单元划分为若干组。该方法的运作方式是:从高阶到低阶,将局部DG多项式的导数与由相邻单元平均值构建的基线进行逐次比较。如果DG多项式的k阶导数大于基线,则将阶数降至k-1,并将对应的k阶项设为0;否则,保留剩余的k阶DG多项式来表示最终解。仅当所有导数都大于基线时,才使用TVD斜率限制器重建解。通过这种方式,该限制器可根据局部解的光滑度动态选择最优多项式,无需调整依赖于问题的参数。值得注意的是,它还具备大多数经典限制器所不具备的尺度不变性。一系列数值算例验证了所提出的MR限制器的准确性和鲁棒性。

英文摘要:

We propose a novel multi-resolution (MR) limiter for the Runge-Kutta discontinuous Galerkin (RKDG) method for solving hyperbolic conservation laws on a general unstructured mesh. Unlike classical limiters, which detects only solution discontinuities to dichotomize cells into good or troubled, the proposed MR limiter also takes into account the derivative discontinuities to divide cells into several groups. The method operates by performing a successive comparison of the local DG polynomial's derivatives, from high-order to low-order, against a baseline constructed from neighboring cell averages. If a $k$th-order derivative of the DG polynomial is larger than the baseline, then we reduce the order to $k-1$ and set the corresponding $k$th-order terms to be 0; Otherwise, the remaining $k$th-order DG polynomial is used to represent the final solution. Only if all the derivatives are larger than the baseline, a TVD slope limiter is used to reconstruct the solution. In this manner, the limiter dynamically selects an optimal polynomial suited to the local solution smoothness without problem-dependent parameter to tune. Notably, it also possesses a scale-invariance property that is absent in most classical limiters. A series of numerical examples demonstrate the accuracy and robustness of the proposed MR limiter.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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