指数梯度网格上基于三角五次b样条的时变两参数奇摄动问题研究

IF 2.9 3区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Sangeetha C, Aswin V S, Ashish Awasthi
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引用次数: 0

摘要

本文给出了求解具有Dirichlet边界条件的双参数奇摄动抛物型对流-扩散-反应方程的新视角。该方法集成了Crank-Nicolson (CN)格式来离散时间导数,并应用三角五次b样条(TQBS)方法来近似指数梯度网格上的状态变量及其空间导数。通过细致的收敛性分析,建立了空间上的四阶和时间上的二阶参数一致收敛。为了验证理论主张和评估方法的有效性,用数值算法求解了四个测试实例,为所提出的数值方案的参数一致收敛性提供了切实的证据。此外,本文还将所提出的方法与Shishkin网格进行了图形比较,为其有效性和参数均匀收敛性提供了经验证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Study on Time-Dependent Two Parameter Singularly Perturbed Problems via Trigonometric Quintic B-Splines on an Exponentially Graded Mesh

A Study on Time-Dependent Two Parameter Singularly Perturbed Problems via Trigonometric Quintic B-Splines on an Exponentially Graded Mesh

This paper presents a fresh perspective on solving two-parameter singularly perturbed parabolic convection-diffusion-reaction equations with Dirichlet boundary conditions. The methodology integrates the Crank–Nicolson (CN) scheme for discretizing temporal derivatives and applies the Trigonometric Quintic B-splines (TQBS) approach to approximate both the state variable and its spatial derivatives on an exponentially graded mesh. Through a meticulous convergence analysis, the study establishes a parameter-uniform convergence of fourth order in space and second order in time. To verify the theoretical claims and evaluate the method's efficacy, four test examples are solved using the numerical algorithm, offering tangible evidence of the parameter-uniform convergence of the proposed numerical scheme. Additionally, the paper includes a graphical comparison of the proposed method with Shishkin meshes, providing empirical evidence of its efficacy and parameter-uniform convergence.

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来源期刊
CiteScore
5.70
自引率
6.90%
发文量
276
审稿时长
5.3 months
期刊介绍: The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems. The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.
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