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arXiv 2202.02068math.NAcs.NA
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平衡律组的平衡自适应紧致逼近Taylor方法

Well-balanced adaptive compact approximate Taylor methods for systems of balance laws

H. Carrillo, E. Macca, C. Pares, G. Russo

摘要:

用于守恒律系统的紧致近似泰勒(CAT)方法由Carrillo和Pares在2019年引入。这些方法基于一种策略,允许一个扩展的高阶Lax-Wendroff方法的非线性系统,而不使用Cauchy-Kovalevskaya程序,具有任意偶数阶的精度2 p和使用(2 p + 1)-点拉伸,其中p是一个任意的正整数。最近在2021年,Carrillo,Macca,Pares,Russo和Zorio引入了一种策略来消除CAT方法产生的不连续性附近的虚假振荡。这种策略导致了所谓的自适应CAT(ACAT)方法,其中精度的顺序,从而宽度的stentrum,适应于局部光滑的解决方案。本文的目标是将CAT和ACAT方法推广到平衡律系统。为此,源项被写为其不定积分的导数,该不定积分被形式上视为通量函数。的方法以及平衡的属性进行了讨论,并提出了一个变种,允许在原则上保持任何固定的解决方案。所得到的方法,然后应用到一些系统,从一个线性标量守恒律的二维欧拉方程与重力,通过Burgers方程与源项和一维浅水方程:在几个数值试验的顺序和良好的平衡性能进行检查。

英文摘要:

Compact Approximate Taylor (CAT) methods for systems of conservation laws were introduced by Carrillo and Pares in 2019. These methods, based on a strategy that allows one to extend high-order Lax-Wendroff methods to nonlinear systems without using the Cauchy-Kovalevskaya procedure, have arbitrary even order of accuracy 2p and use (2p + 1)-point stencils, where p is an arbitrary positive integer. More recently in 2021 Carrillo, Macca, Pares, Russo and Zorio introduced a strategy to get rid of the spurious oscillations close to discontinuities produced by CAT methods. This strategy led to the so-called Adaptive CAT (ACAT) methods, in which the order of accuracy, and thus the width of the stencils, is adapted to the local smoothness of the solution. The goal of this paper is to extend CAT and ACAT methods to systems of balance laws. To do this, the source term is written as the derivative of its indefinite integral that is formally treated as a flux function. The well-balanced property of the methods is discussed and a variant that allows in principle to preserve any stationary solution is presented. The resulting methods are then applied to a number of systems going from a linear scalar conservation law to the 2D Euler equations with gravity, passing by the Burgers equations with source term and the 1D shallow water equations: the order and well-balanced properties are checked in several numerical tests.

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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