## 1 Introduction

Fractional order integral and derivative are the generalizations of classical integral and derivative concepts which are examined in detail by Leibniz and Newton. The concepts of fractional integral and derivative are as old as integer order integral and derivative concepts, and the fractional derivative expression is first mentioned by Leibniz's letter to L’Hospital in 1695 [1]. In the letter, Leibniz's question was ’Can the integer order derivatives be generalized to fractional order derivative’. This is known as the first emergence of the concept of fractional differential. In addition to Leibniz, many scientists such as Liouville, Riemann, Weyl, Lagrange, Laplace, Fourier, Euler and Abel have worked on the same subject [2]. Many definitions are given in the literature for fractional derivative. Some of these are Riemann-Liouville, Caputo, Grünwald-Letnikov, Wely, Riesy fractional derivatives [3]. Some studies have shown that these definitions are equivalent under certain conditions. There is more than one derivative definition in the fractional analysis, making it possible to use the most suitable one according to the problem and thus to get the best solution for this problem.

However, if the derivative is described as how the derivative of the fractional order is defined, the expression that when the order is selected being equal to the integer is the same as the integer order of the derivative.

The description of Caputo fractional derivative was first introduced by the Italian mathematician M.Caputo in the 1960s to eliminate the problem of the calculation of Riemann-Liouville definition of initial values in Laplace transform applications. The fundamental advantage of the Caputo approach is the fact that the appropriate initial conditions defined for Caputo fractional differential equations are identical. Therefore, in recent studies in the literature, in the exact and approximate solutions of fractional differential equations, instead of Riemann-Liouville fractional derivative operator, Caputo fractional derivative operator has been more preferred. Recently, studies on the solution of fractional differential equations have increased. There are several studies about fractional problems and their computational accuracy in the literature [4, 5, 6]. Since the exact solution for many of the fractional differential equations are not found, various methods have been developed to find approximate or numerical solutions.

*f*(

*t*) function continuously and can be differentiated, and for

*n*− 1 ≤

*γ*<

*n*Caputo means for the fractional order derivatives;

Finite difference methods are widely used in the solution of many linear and nonlinear partial differential equations. In general, the following way is followed in applying a finite difference method to a partial differential equation:

The given solution area of the problem is divided into meshes with geometric shapes and approximate solution for the problem is calculated on the nodes of each mesh. Proper finite difference approaches are obtained by using Taylor series instead of derivatives in differential equations. Thus, the present problem of solution of the differential equation has been converted into the problem of the solution of an algebraic system of equation consisting of difference equations. Thus the algebraic equation system obtained now may be solved easily by one of the direct or iterative methods.

## 2 Numerical Solution of the Model Problem

*ν*is the viscosity parameter,

*u*(

*x*,

*t*) represents the speed of fluid media at time-space position (

*x*,

*t*) and

*et al*[10] have proposed an implicit difference scheme with

*L*1 algorithm which is a specific time discretization of the Caputo fractional derivative. A numerical method focused on a finite difference scheme in terms of time and the Chebyshev spectral collocation method in terms of space is used to get approximate solutions of the Burgers’ equation in [11]. Saad and Al-Sharif [16] have applied variational iteration method for solving the equation considering several initial conditions. Asgari and Hosseini [13] have focused on generalized time fractional Burger type equation, they put forward two semi implicit Fourier pseudospectral approximations for seeking solutions of the equation. As a different view to the mentioned equation, Khan et al [14] are used the generalized version of the differential transform method and homotopy perturbation method. Lombard and Matignon [15] present an article for better understanding the competition between nonlinear effects and nonlocal relaxation.

### 2.1 Crank Nicolson Finite Difference Scheme

*x*≤ 1 is discretized into regular grids with equal length Δ

*x*in the

*x*-direction and also with equal time intervals Δ

*t*over time

*t*such that

*x*=

_{j}*j*Δ

*x*,

*j*= 1(1)

*M*− 1 and

*t*=

_{n}*n*Δ

*t*,

*n*= 0(1)

*N*and the numerical solution of

*u*at the grid point (

*j*Δ

*x*,

*n*Δ

*t*) will denote by

*uu*and

_{x}*u*respectively:

_{xx}*S*= Δ

*t*Γ(2 −

^{γ}*γ*).

## 3 Numerical Results

*L*

_{2}

*L*

_{∞}

**Example 1.**

*Firstly, we are going to take time fractional Burgers equation (1) given with the following appropriate boundary conditions*

*and the initial condition as*

*The term f*(

*x*,

*t*)

*is of the form [9]*

*The analytic solution for the present problem is described as*

*A comparison for the exact and approximate solutions got by the Crank-Nicolson methods for fractional Burgers equation for the values of γ* = 0.5, Δ*t* = 0.00025 *and t _{f}* = 1

*for various values of M is presented in Table 1. As one could clearly see from this table, the decreasing values of the error norms L*

_{2}

*and L*

_{∞}

*validate this comment. In Table 2, the approximate solutions obtained for values of M have been compared with those in Ref. [9]. Those error norms for each values of M got by this method are smaller than those presented in Ref. [9].*

The error norms *L*_{2} and *L*_{∞} of the time fractional Burgers equation problem using the Crank Nicolson finite difference method with *ν* = 1.0, Δ*t* = 0.00025 and *t _{f}* =1 for various values of

*M*

N = 10 | N = 20 | N = 40 | M = 80 | |
---|---|---|---|---|

L_{2} × 10^{3} | 22.64087326 | 5.44483424 | 1.22007333 | 0.16846258 |

L_{∞} × 10^{3} | 30.49445082 | 7.70051177 | 1.72552915 | 0.23826679 |

A comparison of the errors for Example 1 at *t _{f}* =1

N = 40 | N = 80 | N = 100 | ||||
---|---|---|---|---|---|---|

Present | [9] | Present | [9] | Present | [9] | |

L_{2} × 10^{3} | 1.22007333 | 1.224329 | 0.16846258 | 0.177703 | 0.04239382 | 0.052299 |

L_{∞} × 10^{3} | 1.72552915 | 1.730469 | 0.23826679 | 0.253053 | 0.05996900 | 0.076541 |

*The solutions obtained at different times in Fig. 1 were given graphically with exact solutions. The results obtained at different times were found to be too close to each other on the graphics.*

*In Table 3, solutions for different viscosity values were given for values of γ* = 0.5, Δ*t* = 0.00025, *t _{f}* =1.0,

*N*= 40.

*It was observed that the errors increased gradually at small values of viscosity.*

The error norms *L*_{2} and *L*_{∞} of Example 1 for *γ*=0.5, Δ*t*=0.00025, *t _{f}* =1.0,

*N*=40 and various values of

*ν*

ν = 1 | ν = 0.5 | ν = 0.1 | ν = 0.01 | ν = 0.001 | |
---|---|---|---|---|---|

L_{2} × 10^{3} | 0.41761157 | 0.51259161 | 1.03928112 | 2.13880314 | 6.46231244 |

L_{∞} × 10^{3} | 0.59040780 | 0.72342731 | 1.51280185 | 4.65707275 | 22.95302718 |

*Table 4 presents the error norms obtained at various selected values of γ.*

The error norms *L*_{2} and *L*_{∞} of Example 1 for *N*=120, *t _{f}* = 1.0, Δ

*t*= 0.00025 for different values of

*γ*

γ = 0.1 | γ = 0.25 | γ = 0.75 | γ = 0.9 | |
---|---|---|---|---|

L_{2} × 10^{3} | 0.02411976 | 0.02490518 | 0.02669547 | 0.02579288 |

L_{∞} × 10^{3} | 0.03409905 | 0.03521115 | 0.03774592 | 0.03646791 |

**Example 2.**

*Secondly, we will consider the model problem with the appropriate boundary conditions*

*and the initial condition as*

*The term*

*and the analytic solution for the problem*

*In Table 5, for various values of N, the error norms obtained for Example 2 have been illustrated. In the table, one can obviously see that the improvement in numerical solutions has been observed as expected for the increasing values of N.*

Comparison of results at *t _{f}* = 1.0 for

*γ*= 0.5, Δ

*t*= 0.0001,

*ν*= 1.0 and various mesh sizes

N = 10 | N = 20 | N = 40 | N = 80 | |
---|---|---|---|---|

L_{2} × 10^{3} | 0.81667090 | 0.23594859 | 0.09215464 | 0.05901177 |

L_{∞} × 10^{3} | 1.13678094 | 0.32086237 | 0.12245871 | 0.09790576 |

*In Table 6, the error norms obtained for different values of* Δ*t have been given for values of ν* = 1.0, Δ*x* = 0.025 *and γ* = 0.5. *As the* Δ*t values decreased, it was observed that the errors also decreased accordingly. In Figure 2, the solutions of Problem 2 obtained at different times for values of ν* = 1.0, Δ*x* = 0.025, *γ* = 0.5 *and* Δ*t* = 0.0025 *have been presented together with their analytical values.*

Comparison of results at *t _{f}* = 1.0 for

*ν*=1,

*N*= 40 and various time steps

Δt = 0.0001 | Δt = 0.00025 | Δt = 0.0005 | Δt = 0.001 | Δt = 0.0025 | Δt = 0.005 | |
---|---|---|---|---|---|---|

L_{2} × 10^{3} | 0.09215464 | 0.16142590 | 0.27906906 | 0.51583267 | 1.22775834 | 2.41506007 |

L_{∞} × 10^{3} | 0.12245871 | 0.24577898 | 0.47478371 | 0.93280810 | 2.30696715 | 4.59744900 |

**Example 3.**

*Lastly, we consider the model problem with the boundary conditions*

*and the initial conditions as*

*The function f*(

*x*,

*t*)

*is of the following form*

*The analytical solution of the problem is given by*

*In Table 7, approximate solutions obtained for values of t _{f}* = 1.0

*and*, Δ

*x*= 0.025

*have been given at different*Δ

*t values. It is obvious that the errors get smaller and smaller as*Δ

*t values get smaller and smaller. Figure 3 shows the numerical and exact solutions obtained at different times for Problem 3. It is seen that the results are indiscriminately close to each other on the graphics.*

Comparison of results at *t _{f}* = 1.0 for

*ν*= 1, Δ

*x*= 0.025 and various time steps

Δt = 0.0001 | Δt = 0.00025 | Δt = 0.0005 | Δt = 0.001 | |
---|---|---|---|---|

L_{2} × 10^{3} | 0.08344454 | 0.22252258 | 0.45497994 | 0.92040447 |

L_{∞} × 10^{3} | 0.23648323 | 0.59722584 | 1.19848941 | 2.40107923 |

## 4 Conclusions

As a conclusion, in the present study the numerical solutions of the time fractional Burgers equation have been obtained by using finite difference method based on Crank-Nicolson discretization. The obtained results are compared with analytic and some of the numerical results available in the literature. This comparison has shown that the presented method is efficient and effective and can also be used for a wide range of physical and scientific applications. Moreover to illustrate the accuracy of the present method the error norms *L*_{2} and *L*_{∞} are computed and given in tables. Two test problems have been used to show the accuracy of the present scheme for various values of parameters in the problem. Tables and figures show the results of these various tests and also comparisons with some available results together with the error norms *L*_{2} and *L*_{∞}. Finally the present method has been shown to be applicable for more widely used fractional differential equations.

The present research was supported by the Inonu University Scientific Research Projects under contracts FYL-2018/1082.

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