归一化时间分数Cahn-Hilliard方程

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Hyun Geun Lee , Soobin Kwak , Seokjun Ham , Youngjin Hwang , Junseok Kim
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引用次数: 0

摘要

我们提出了一个标准化的时间分数Cahn-Hilliard (TFCH)方程,结合时间分数导数来模拟相分离过程中的记忆效应。我们使用标准化的时间分数导数,这是卡普托分数导数的一种形式,以提高模型的灵活性和物理解释。这种归一化允许对分数阶进行更一致的解释,从而可以在不同阶导数之间进行公平的比较。为了求解归一化的TFCH方程,我们采用了基于傅里叶谱法的高效计算方案,保证了较高的精度和计算效率。此外,我们深入研究了归一化TFCH方程的动力学行为,并重点研究了分数阶时间导数对相域演化和形态的影响。数值仿真结果表明了该方法在复杂相分离动力学建模中的通用性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The normalized time-fractional Cahn–Hilliard equation
We present a normalized time-fractional Cahn–Hilliard (TFCH) equation by incorporating time-fractional derivatives to model memory effects in phase separation processes. We use a normalized time-fractional derivative, which is a form of the Caputo fractional derivative, to improve the flexibility and physical interpretation of the model. This normalization allows for a more consistent interpretation of fractional orders, which enables fair comparisons across different orders of the derivative. To solve the normalized TFCH equation, we use an efficient computational scheme based on the Fourier spectral method, which ensures high accuracy and computational efficiency. Furthermore, we conduct a thorough investigation into the dynamic behavior of the normalized TFCH equation and focus on how varying the fractional-order time derivative influences the evolution and morphology of phase domains. Numerical simulations demonstrate the versatility and effectiveness of the proposed method in modeling complex phase separation dynamics.
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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