Abstract

This article proposes a space–time meshless approach based on the transient radial polynomial series function (TRPSF) for solving convection–diffusion equations. We adopted the TRPSF as the basis function for the spatial and temporal discretization of the convection–diffusion equation. The TRPSF is constructed in the space–time domain, which is a combination of n–dimensional Euclidean space and time into an n + 1–dimensional manifold. Because the initial and boundary conditions were applied on the space–time domain boundaries, we converted the transient problem into an inverse boundary value problem. Additionally, all partial derivatives of the proposed TRPSF are a series of continuous functions, which are nonsingular and smooth. Solutions were approximated by solving the system of simultaneous equations formulated from the boundary, source, and internal collocation points. Numerical examples including stationary and nonstationary convection–diffusion problems were employed. The numerical solutions revealed that the proposed space–time meshless approach may achieve more accurate numerical solutions than those obtained by using the conventional radial basis function (RBF) with the time-marching scheme. Furthermore, the numerical examples indicated that the TRPSF is more stable and accurate than other RBFs for solving the convection–diffusion equation.

Highlights

  • The convection–diffusion equation is extensively used in many contexts in scientific and engineering problems, such as those related to groundwater pollution [1], gas flow after-treatment systems [2], and methane reformation in catalytic reactors [3]

  • This study proposed the transient radial polynomial series function (TRPSF) for solving convection–diffusion equations

  • The following findings were obtained: This study presented a novel spatial and temporal discretization scheme for the convection–diffusion equation by using the proposed TRPSF

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Summary

Introduction

The convection–diffusion equation is extensively used in many contexts in scientific and engineering problems, such as those related to groundwater pollution [1], gas flow after-treatment systems [2], and methane reformation in catalytic reactors [3]. To increase the accuracy of results, optimal parameters are required for each proposed numerical scheme. The Kansa method [17,18] that directly adopts the MQ RBF has been indicated to be an effective numerical tool for approximating PDE solutions. Tsai et al [27] proposed the golden-section search algorithm to select the optimal shape parameter of the MQ RBF to solve PDEs. Ng et al [28] utilized a new higher–order. MQ RBF in conjunction with the time-marching scheme [29,30,31] is an effective numerical tool for solving time-dependent PDEs, the time interval must be minimized to increase the result accuracy.

Mathematical Formulation
Validation and Convergence Analysis
Diffusion Equation Modeling in Two Dimensions
Modeling the Two-Dimensional Convection–Diffusion Equation
Conclusions
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