Two efficient compact ADI methods for the two-dimensional fractional Oldroyd-B model

IF 2.5 2区 数学 Q1 MATHEMATICS, APPLIED Computers & Mathematics with Applications Pub Date : 2025-04-14 DOI:10.1016/j.camwa.2025.04.009
Xinyu Diao, Bo Yu
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Abstract

The objective of this paper is to present efficient numerical algorithms to resolve the two-dimensional fractional Oldroyd-B model. Firstly, two compact alternating direction implicit (ADI) methods are constructed with convergence orders O(τmin{3γ,2β,1+γ2β}+hx4+hy4) and O(τmin{3γ,2β}+hx4+hy4), where γ and β are orders of two Caputo fractional derivatives, τ, hx and hy are the time and space step sizes, respectively. Secondly, the convergence analyses of the proposed compact ADI methods are investigated strictly utilizing the energy estimation technique. Lastly, the two compact ADI methods are implemented to confirm the effectiveness of the convergence analysis. The convergence orders of the two compact ADI methods are separately tested in the direction of time and space, the CPU times are computed compared with the direct compact scheme to demonstrate the efficiency of the derived compact ADI methods, numerical results are also compared with the existing literature. All the numerical simulation results are listed in tabular forms which manifest the validity of the derived compact ADI methods.
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二维分数阶Oldroyd-B模型的两种高效紧致ADI方法
本文旨在提出解决二维分数奥尔德罗伊德-B 模型的高效数值算法。首先,构建了两种收敛阶数分别为 O(τmin{3-γ,2-β,1+γ-2β}+hx4+hy4)和 O(τmin{3-γ,2-β}+hx4+hy4)的紧凑交替方向隐式(ADI)方法,其中γ和β是两个卡普托分数导数的阶数,τ、hx 和 hy 分别是时间步长和空间步长。其次,严格利用能量估计技术研究了所提出的紧凑 ADI 方法的收敛性分析。最后,实现了两种紧凑型 ADI 方法,以确认收敛分析的有效性。两种紧凑型 ADI 方法的收敛阶数分别在时间和空间方向上进行了测试,CPU 计算时间与直接紧凑型方案进行了比较,以证明衍生紧凑型 ADI 方法的效率,数值结果也与现有文献进行了比较。所有数值模拟结果都以表格形式列出,体现了衍生紧凑型 ADI 方法的有效性。
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来源期刊
Computers & Mathematics with Applications
Computers & Mathematics with Applications 工程技术-计算机:跨学科应用
CiteScore
5.10
自引率
10.30%
发文量
396
审稿时长
9.9 weeks
期刊介绍: Computers & Mathematics with Applications provides a medium of exchange for those engaged in fields contributing to building successful simulations for science and engineering using Partial Differential Equations (PDEs).
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