Abstract

In this paper, a new numerical algorithm for solving the time fractional convection–diffusion equation with variable coefficients is proposed. The time fractional derivative is estimated using the L_{1} formula, and the spatial derivative is discretized by the sinc-Galerkin method. The convergence analysis of this method is investigated in detail. The numerical solution is 2-alpha order accuracy in time and exponential rate of convergence in space. Finally, some numerical examples are given to show the effectiveness of the numerical scheme.

Highlights

  • 1 Introduction In the last few decades, fractional differential equations have been widely applied in various fields of science and engineering to model many phenomena [1,2,3,4,5,6,7,8,9,10,11]

  • In [38], a new reliable algorithm based on the sinc function is employed for the time fractional diffusion equation

  • 4 Convergence analysis we show that the approximate solution unM(x) converges to the exact solution un(x) of (12) at an exponential rate

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Summary

Introduction

In the last few decades, fractional differential equations have been widely applied in various fields of science and engineering to model many phenomena [1,2,3,4,5,6,7,8,9,10,11]. We consider the following time fractional convection–diffusion equation with variable coefficients:. In [34], Nagy applied the sinc-Chebyshev collocation method for numerical investigation of the time fractional nonlinear Klein–Gordon equation. In [35], Saadatmandi et al proposed the sinc-Legendre collocation method for a class of fractional convection–diffusion equations with variable coefficients. In [38], a new reliable algorithm based on the sinc function is employed for the time fractional diffusion equation. In [39], Jalilian et al adopted an algorithm based on sinc basis functions for the numerical solution of the nonlinear fractional integro-differential equation of pantograph type. We apply the sinc-Galerkin method to solve the time fractional convection–diffusion equation with variable coefficients.

Preliminaries
Temporal discretization
Numerical experiments
Conclusion

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