基于广义 SAV 方法的卡恩-希利亚德方程的无条件耗能自适应 IMEX BDF2 方案及其误差估算

IF 2.3 2区 数学 Q1 MATHEMATICS, APPLIED IMA Journal of Numerical Analysis Pub Date : 2024-09-25 DOI:10.1093/imanum/drae057
Yifan Wei, Jiwei Zhang, Chengchao Zhao, Yanmin Zhao
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引用次数: 0

摘要

通过与空间傅立叶谱法相结合,在广义 SAV 方法上对 Cahn-Hilliard 方程的自适应隐式-显式(IMEX)BDF2 方案进行了研究。研究证明,修正的能量耗散规律在离散水平上无条件地得到了保留。在温和的比率限制下,即 A1: $0<r_{k}:=\tau _{k}/\tau _{k-1}< r_{\max }\approx 4.8645$,我们在 $H^{1}$ 规范下建立了严格的误差估计,并在时间上达到了最优的二阶精度。证明涉及离散正交卷积(DOC)核和不等式放大工具。值得注意的是,所提出的自适应时间步长方案只需要在每个时间步长求解一个具有常数系数的线性系统。在我们的分析中,第一步的第一自洽 BDF1 并没有带来 $H^{1}$ 规范的阶次降低。在周期性边界条件下,数值解的 $H^{1}$ 约束可以在没有任何限制(如初始数据均值为零)的情况下得出。最后,我们提供了数值示例来验证我们的理论分析和算法效率。
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An unconditionally energy dissipative, adaptive IMEX BDF2 scheme and its error estimates for Cahn–Hilliard equation on generalized SAV approach
An adaptive implicit-explicit (IMEX) BDF2 scheme is investigated on generalized SAV approach for the Cahn–Hilliard equation by combining with Fourier spectral method in space. It is proved that the modified energy dissipation law is unconditionally preserved at discrete levels. Under a mild ratio restriction, i.e., A1: $0<r_{k}:=\tau _{k}/\tau _{k-1}< r_{\max }\approx 4.8645$, we establish a rigorous error estimate in $H^{1}$-norm and achieve optimal second-order accuracy in time. The proof involves the tools of discrete orthogonal convolution (DOC) kernels and inequality zoom. It is worth noting that the presented adaptive time-step scheme only requires solving one linear system with constant coefficients at each time step. In our analysis, the first-consistent BDF1 for the first step does not bring the order reduction in $H^{1}$-norm. The $H^{1}$ bound of numerical solution under periodic boundary conditions can be derived without any restriction (such as zero mean of the initial data). Finally, numerical examples are provided to verify our theoretical analysis and the algorithm efficiency.
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来源期刊
IMA Journal of Numerical Analysis
IMA Journal of Numerical Analysis 数学-应用数学
CiteScore
5.30
自引率
4.80%
发文量
79
审稿时长
6-12 weeks
期刊介绍: The IMA Journal of Numerical Analysis (IMAJNA) publishes original contributions to all fields of numerical analysis; articles will be accepted which treat the theory, development or use of practical algorithms and interactions between these aspects. Occasional survey articles are also published.
期刊最新文献
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