Unconditionally energy stable second-order numerical schemes for the Functionalized Cahn-Hilliard gradient flow equation based on the SAV approach

被引:8
作者
Zhang, Chenhui [1 ]
Ouyang, Jie [1 ]
机构
[1] Northwestern Polytech Univ, Sch Math & Stat, Xian 710129, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive time scheme; FCH gradient flow equation; SAV approach; Second-order scheme; Unconditional energy stability; Unique solvability;
D O I
10.1016/j.camwa.2020.12.003
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we devise and analyse three highly efficient second-order accurate (in time) schemes for solving the Functionalized Cahn-Hilliard (FCH) gradient flow equation where an asymmetric double-well potential function is considered. Based on the Scalar Auxiliary Variable (SAV) approach, we construct these schemes by splitting the FCH free energy in a novel and ingenious way. Utilizing the Crank-Nicolson formula, we firstly construct two semi-discrete second-order numerical schemes, which we denote by CN-SAV and CN-SAV-A, respectively. To be more specific, the CN-SAV scheme is constructed based on the fixed time step, while the CN-SAV-A scheme is a variable time step scheme. The BDF2-SAV scheme is another second-order scheme in which the fixed time step should be used. It is designed by applying the second-order backward difference (BDF2) formula. All the constructed schemes are proved to be unconditionally energy stable and uniquely solvable in theory. To the best of our knowledge, the CN-SAV-A scheme is the first unconditionally energy stable, second-order scheme with variable time steps for the FCH gradient flow equation. In addition, an effective adaptive time selection strategy introduced in Christlieb et al., (2014) is slightly modified and then adopted to select the time step for the CN-SAV-A scheme. Finally, several numerical experiments based on the Fourier pseudo-spectral method are carried out in two and three dimensions, respectively, to confirm the numerical accuracy and efficiency of the constructed schemes. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码:16 / 38
页数:23
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