Explicit and implicit numerical investigations of one-dimensional heat equation based on spline collocation and Thomas algorithm

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Soft Computing Pub Date : 2024-07-24 DOI:10.1007/s00500-024-09925-3
Saumya Ranjan Jena, Archana Senapati
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Abstract

This study uses the cubic spline method to solve the one-dimensional (1D) (one spatial and one temporal dimension) heat problem (a parametric linear partial differential equation) numerically using both explicit and implicit strategies. The set of simultaneous equations acquired in both the explicit and implicit method may be solved using the Thomas algorithm from the tridiagonal dominating matrix, and the spline offers a continuous solution. The results are implemented with very fine meshes and with relatively small-time steps. Using mesh refinement, it was possible to find better temperature distribution in the thin bar. Five numerical examples are used to support the efficiency and accuracy of the current scheme. The findings are also compared with analytical results and other results in terms of error and error norms \({L}_{2}\) and \({L}_{\infty }\). The Von-Neuman technique is used to analyse stability. The truncation error of both systems is calculated and determined to have a convergence of order \(O\left( {h + \Delta t^{2} } \right).\)

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基于样条配位和托马斯算法的一维热方程显式和隐式数值研究
本研究采用立方样条法,利用显式和隐式两种策略对一维(1D)(一个空间维度和一个时间维度)热问题(参数线性偏微分方程)进行数值求解。在显式和隐式方法中获得的同步方程组可使用托马斯算法从三对角支配矩阵求解,而样条线则提供连续解。这些结果是通过非常精细的网格和相对较小的时间步骤实现的。通过细化网格,可以发现薄棒中更好的温度分布。五个数值实例证明了当前方案的效率和准确性。研究结果还与分析结果以及误差和误差规范 \({L}_{2}\) 和 \({L}_{\infty }\) 方面的其他结果进行了比较。Von-Neuman 技术用于分析稳定性。计算并确定这两个系统的截断误差具有 \(O\left( {h +\Delta t^{2} } \right).\) 的收敛性。
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来源期刊
Soft Computing
Soft Computing 工程技术-计算机:跨学科应用
CiteScore
8.10
自引率
9.80%
发文量
927
审稿时长
7.3 months
期刊介绍: Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists and engineers engaged in research and development in this fast growing field.
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