An efficient spline-based DQ method for 2D/3D Riesz space-fractional convection–diffusion equations

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-06-14 DOI:10.1016/j.jocs.2024.102364
Xiaogang Zhu, Yaping Zhang
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Abstract

This paper proposes an efficient spline-based DQ method for the 2D and 3D convection–diffusion equations (CDEs) with Riesz fractional derivative in space, which have been widely used to describe the anomalous solute transport in complex media. Firstly, a spline-based differential quadrature (DQ) formula is developed to approximate the Riesz derivative by using cubic B-splines as trial functions, which allows us to approximate the fractional derivatives with high accuracy and small computational cost. We then utilize it to discretize the fractional derivatives in the governing equation and a cubic B-spline DQ scheme is further established by applying the finite difference (FD) scheme to the resulting system of ordinary differential equations. A brief implementation of the proposed DQ method is also presented. To examine the effectiveness of this spline-based DQ method, numerical tests are finally done on some benchmark problems and the simulation of rotating Gaussian hill in convection-dominated flow governed by fractional derivatives. The advantages in computational accuracy and efficiency are illustrated by comparing the results with the other algorithms in open literature.

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基于样条线的二维/三维里兹空间分数对流扩散方程高效 DQ 方法
二维和三维对流扩散方程(CDEs)在空间具有 Riesz 分导数,被广泛用于描述复杂介质中的溶质异常输运,本文提出了一种基于样条的高效 DQ 方法。首先,我们开发了一种基于样条曲线的微分正交(DQ)公式,通过使用三次 B 样条曲线作为试函数来逼近 Riesz 导数,从而以较高的精度和较小的计算成本逼近分数导数。然后,我们利用它对支配方程中的分数导数进行离散化,并通过将有限差分(FD)方案应用于由此产生的常微分方程系统,进一步建立立方 B 样条 DQ 方案。此外,还简要介绍了所提出的 DQ 方法的实现过程。为了检验这种基于样条线的 DQ 方法的有效性,最后在一些基准问题上进行了数值测试,并模拟了在分数导数支配的对流中的旋转高斯山。通过与公开文献中其他算法的结果比较,说明了该方法在计算精度和效率方面的优势。
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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