Algebraically stable high-order multi-physical property-preserving methods for the regularized long-wave equation

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Xin Li , Xiuling Hu
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引用次数: 0

Abstract

In this paper, based on the framework of the supplementary variable method, we present two classes of high-order, linearized, structure-preserving algorithms for simulating the regularized long-wave equation. The suggested schemes are as accurate and efficient as the recently proposed schemes in Jiang et al. (2022) [20], but share the nice features in two folds: (i) the first type of schemes conserves the original energy conservation, as opposed to a modified quadratic energy in [20]; (ii) the second type of schemes fills the gap of [20] by constructing high-order linear algorithms that preserve both two invariants of mass and momentum. We discretize the SVM systems by employing the algebraically stable Runge-Kutta method together with the prediction-correction technique in time and the Fourier pseudo-spectral method in space. The implementation benefits from solving the optimization problems subject to PDE constraints. Numerical examples and some comparisons are provided to show the effectiveness, accuracy and performance of the proposed schemes.

正则化长波方程的代数稳定高阶多物理属性保留方法
本文基于补充变量法的框架,提出了两类模拟正则化长波方程的高阶、线性化、结构保持算法。所提出的方案与 Jiang 等人(2022 年)[20] 最近提出的方案一样精确高效,但有两个共同点:(i) 第一类方案保留了原始能量守恒,而不是 [20] 中的修正二次能量;(ii) 第二类方案填补了 [20] 的空白,构建了同时保留质量和动量两个不变式的高阶线性算法。我们在时间上采用代数稳定的 Runge-Kutta 方法和预测校正技术,在空间上采用傅里叶伪谱方法,对 SVM 系统进行离散化。该方法的实施得益于求解受 PDE 约束的优化问题。提供的数值示例和一些比较显示了所提方案的有效性、准确性和性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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