A new space–time localized meshless method based on coupling radial and polynomial basis functions for solving singularly perturbed nonlinear Burgers’ equation

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

In this paper, the singularly perturbed nonlinear Burgers’ problem (SPBP) with small kinematic viscosity 0<ϵ1 is solved using a new Space–Time Localized collocation method based on coupling Polynomial and Radial Basis Functions (STLPRBF). To our best knowledge, it is the first time that the solution of SPBP is accurately approximated using the space–time meshless method without applying any adaptive refinement technique. The method is based on solving the problem without distinguishing between space and time variables, which eliminates the need for time discretization schemes. To address the inherent non-linearity of the problem, the method employs an iterative algorithm based on quasilinearization technique. The efficiency and accuracy of the proposed method are demonstrated by solving different examples of one- and two-dimensional SPBP with very small ϵ up to 1010. Additionally, the numerical convergence of the method with respect to ϵ and also to the number of collocation points has been investigated. The comparison of the STLPRBF results with other published ones is presented.
基于径向基函数和多项式基函数耦合的新型时空局部无网格方法,用于求解奇异扰动非线性布尔格斯方程
本文采用基于耦合多项式和径向基函数(STLPRBF)的新时空局部配位法求解了具有小运动粘度 0<ϵ≪1 的奇异扰动非线性布尔格斯问题(SPBP)。据我们所知,这是首次在不应用任何自适应细化技术的情况下使用无网格时空法精确逼近 SPBP 的解。该方法是在不区分空间和时间变量的情况下求解问题,因此无需时间离散化方案。为了解决该问题固有的非线性问题,该方法采用了基于准线性化技术的迭代算法。通过求解ϵ 非常小(10-10)的一维和二维 SPBP 的不同示例,证明了所提方法的效率和准确性。此外,还研究了该方法的数值收敛性与ϵ 和配位点数量的关系。此外,还将 STLPRBF 的结果与其他已发表的结果进行了比较。
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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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