Abstract and Applied Analysis

Volume 2013 (2013), Article ID 562140, 13 pages

http://dx.doi.org/10.1155/2013/562140

Research Article

Numerical Solution of the Fractional Partial Differential Equations by the Two-Dimensional Fractional-Order Legendre Functions

Fukang Yin, Junqiang Song, Yongwen Wu, and Lilun Zhang

College of Computer, National University of Defense Technology, Changsha 410073, China

Received 13 May 2013; Revised 8 September 2013; Accepted 8 September 2013

Academic Editor: Santanu Saha Ray

Copyright © 2013 Fukang Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

A numerical method is presented to obtain the approximate solutions of the fractional partial differential equations (FPDEs). The basic idea of this method is to achieve the approximate solutions in a generalized expansion form of two-dimensional fractional-order Legendre functions (2D-FLFs). The operational matrices of integration and derivative for 2D-FLFs are first derived. Then, by these matrices, a system of algebraic equations is obtained from FPDEs. Hence, by solving this system, the unknown 2D-FLFs coefficients can be computed. Three examples are discussed to demonstrate the validity and applicability of the proposed method.

1. Introduction

Fractional partial differential equations play a significant role in modeling physical and engineering processes. Therefore, there is an urgent need to develop efficient and fast convergent methods for FPDEs. Recently, several different techniques, including Adomian’s decomposition method (ADM) [1, 2], homotopy perturbation method (HPM) [3–5], variational iteration method (VIM) [6–8], spectral methods [9–13], orthogonal polynomials method [14–17], and wavelets method [18–21] have been presented and applied to solve FPDEs.

The method based on the orthogonal functions is a wonderful and powerful tool for solving the FDEs and has enjoyed many successes in this realm. The operational matrix of fractional integration has been determined for some types of orthogonal polynomials, such as Chebyshev polynomials [16], Legendre polynomials [22], Laguerre polynomials [23–25], and Jacobi polynomials [26]. Moreover, the operational matrix of fractional derivative for Chebyshev polynomials [9] and Legendre polynomials [9, 14] also has been derived. However, since these polynomials using integer power series to approximate fractional ones, it cannot accurately represent properties of fractional calculus. Recently, Rida and Yousef [27] presented a fractional extension of the classical Legendre polynomials by replacing the integer order derivative in Rodrigues formula with fractional order derivatives. The defect is that the complexity of these functions made them unsuitable for solving FDEs. Subsequently, Kazem et al. [28] presented the orthogonal fractional order Legendre functions based on shifted Legendre polynomials to find the numerical solution of FDEs and drew a conclusion that their method is accurate, effective, and easy to implement.

Benefiting from their “exponential-convergence” property when smooth solutions are involved, spectral methods have been widely and effectively used for the numerical solution of partial differential equations. The basic idea of spectral methods is to expand a function into sets of smooth global functions, called the trial functions. Because of their special properties, the orthogonal polynomials are usually chosen to be trial functions. Spectral methods can obtain very accurate approximations for a smooth solution while only need a few degrees of freedom. Recently, Chebyshev spectral method [9], Legendre spectral method [10], and adaptive pseudospectral method [11] were proposed for solving fractional boundary value problems. Moreover, generalized Laguerre spectral algorithms and Legendre spectral Galerkin method were developed by Baleanu et al. [12] and Bhrawy and Alghamdi [13] for fractional initial value problems, respectively.

Motivated and inspired by the ongoing research in orthogonal polynomials methods and spectral methods, we construct two-dimensional fractional-order Legendre functions and derive the operational matrices of integration and derivative for the solution of FPDEs. To the best of the authors’ knowledge, such approach has not been employed for solving FPDEs.

The rest of the paper is organized as follows. In Section 2, we introduce some mathematical preliminaries of the fractional calculus theory and fractional-order Legendre functions. In Section 3, a basis of 2D-FLFs is defined and some properties are given. Section 4 is devoted to the operational matrices of fractional derivative and integration for 2D-FLFs. Some numerical examples are presented in Section 5. Finally, we conclude the paper with some remarks.

2. Preliminaries and Notations

2.1. Fractional Calculus Theory

Some necessary definitions and Lemma of the fractional calculus theory [29, 30] are listed here for our subsequent development.

Definition 1. A real function , , is said to be in the space , , if there exists a real number , such that , where , and it is said to be in the space if and only if , .

Definition 2. Riemann-Liouville fractional integral operator () of order , of a function , is defined as

where is the well-known Gamma function. Some properties of the operator can be found, for example, in [29, 30].

Definition 3. The fractional derivative of in the Caputo sense is defined as

where , denotes a continuous (but not necessarily differentiable) function.

Lemma 4. Let , , , , . Then

2.2. Fractional-Order Legendre Functions

In this section, we introduce the fractional-order Legendre functions which were first proposed by Kazem et al. [28]. The normalized eigenfunctions problem for FLFs is

which is a singular Sturm-Liouville problem. The fractional-order Legendre polynomials, denoted by , are defined on the interval and can be determined with the aid of following recurrence formulae:

and the analytic form of of degree is given by

where and . The orthogonality condition is

where is the weight function and is the Kronecker delta. For more details, please see [28].

3. 2D-FLFs

In this section, the definitions and theorems of 2D-FLFs are given by Liu’s method described in [31].

3.1. Definitions and Properties of the 2D-FLFs

Definition 5. Let be the fractional Legendre polynomials on ; we call the two-dimensional fractional Legendre polynomials on .

Theorem 6. The basis is orthogonal on with the weight function .

Proof. Let or

Theorem 7. Consider

3.2. 2D-FLFs Expansion

Definition 8. A function of two independent variables which is integrable in square can be expanded as

where

Theorem 9. If the series converges uniformly to on the square , then we have

Proof. By multiplying on both sides of (10), where and are fixed and integrating termwise with regard to and on , then

Finally one can get (11).

If the infinite series in (10) is truncated, then it can be written as

where and are given by

where , , and .

According to the definition of FLFs, one can find that fractional Legendre polynomials are identical to Legendre polynomials shifted to when using the transform , . Therefore, in a similar method described in [31], we can easily get the convergence and stability theorems of proposed method.

Lemma 10. If the function is a continuous function on and the series converges uniformly to , then is the 2D-FLFs expansion of .

Proof (by contradiction). Let

Then there is at least one coefficient such that . However,

Lemma 11. If two continuous functions defined on have the identical 2D-FLFs expansions, then these two functions are identical.

Proof. Suppose that and can be expanded by 2D-FLFs as follows:

By subtracting the above two equations with each other, one has

Then Lemma 11 can be proved.

Theorem 12. If the 2D-FLFs expansion of a continuous function converges uniformly, then the 2D-FLFs expansion converges to the function .

Proof. Theorem 12 can be proved by Theorems 7 and 9.

Theorem 13. If the sum of the absolute values of the 2D-FLFs coefficients of a continuous function forms a convergent series, then the 2D-FLFs expansion is absolutely uniformly convergent, and converges to the function .

Proof. Consider

Then converges uniformly to the function .

Theorem 14. If a continuous function , defined on , has bounded mixed partial derivative , then the 2D-FLFs expansion of the function converges uniformly to the function.

Proof. Let be a function defined on such that

where is a positive constant and

By employing the transform and , one can obtain

Consequently, in a similar method described in [31], Theorem 14 can be proved.

4. Operational Matrices of 2D-FLFs

4.1. Integration Operational Matrices of 2D-FLFs

Lemma 15. The Riemann-Liouville fractional integration of order of the 2D-FLFs can be obtained in the form of

Proof. Consider

Lemma 16. Let ; then one has

Proof. Using previous Lemma 15 and (6), one can have

Theorem 17. Let be the 2D-FLFs vector defined in (16); then one has

where is the operational matrix of Riemann-Liouville fractional integration of order , and has the form as follows:

in which is matrix and the elements are defined as follows:

and is identity matrix.

Proof. Using (29) and orthogonality property of FLFs, one can get

where and are two matrices defined as

Now by substituting above equations in (32), Theorem 12 can be proved.

In a similar way as previous, one can obtain the operational matrix of Riemann-Liouville fractional integration with respect to variable .

Theorem 18. Let be the 2D-FLFs vector defined in (16); one has

where is the operational matrix of Riemann-Liouville fractional integration of order , and has the form as follows:

in which is matrix and the elements are defined as follows:

4.2. Derivative Operational Matrices of 2D-FLFs

Lemma 19. The FLFs Caputo fractional derivative of can be obtained in the form of

where when and in other case .

Proof. Consider

Lemma 20. Let , ; then one has

Proof. Using previous Lemma 19 and (6), one can have

Theorem 21. Let be the 2D-FLFs vector defined in (16); one has

where is the operational matrix of Caputo fractional derivative of order , and has the form as follows:

in which is matrix and the elements are defined as follows:

and is a identity matrix.

Proof. Using (41) and the orthogonality property of FLFs, one can have

where and are two matrices defined as

Now by substituting above equations in (44), Theorem 21 can be proved.

In a similar way as above, one can get Caputo fractional derivative of order with respect to variable .

Theorem 22. Let be the 2D-FLFs vector defined in (16); one can have

where is the operational matrix of Caputo fractional derivative of order , and has the form as follows:

in which is matrix and the elements are defined as follows:

5. Applications and Results

Consider the following FPDEs:

where and are linear operator and nonlinear operator; respectively. and are the Caputo fractional derivatives of order and , respectively; is a known analytic function.

By employing operator on both sides of (49) and then using the Lemma 4, one can have

We first express unknown function and derivative term as

Now for the nonlinear part, by employing the nonlinear term approximation method described in [32] and then by using transform , , one can get the 2D-FLFs expansion of nonlinear term as

where is coefficient matrix of nonlinear term which must be computed and its order is .

For the linear part, we have

where is a matrix of order .

After substituting (51)–(53) into (50), one can obtain

According to the Wu’s [33] technology for determining the initial iteration value, the initial iteration value is chosen as . The coefficient matrix can be computed by using the MATLAB function fsolve( ) or the method described in [34].

Now, the present method is applied to solve the linear and nonlinear FPDEs, and their results are compared with the solution of other methods. The accuracy of our approach is estimated by the following error functions:

Example 23. Consider the one-dimensional linear inhomogeneous fractional Burger’s equation [35]:

with the initial condition and the exact solution being .

By employing 2D-FLFs method, one can get

where . Then we can get .

Figures 1(a) and 1(b) show the numerical results for with , and with , , respectively. It should be found that the accuracy of 2D-FLFs method is very high while only a small number of 2D-FLFs are needed.

(a)

(b)

(c)

(d)

Figure 1: Numerical results for Example 23 when = 0.25, 0.50.

Example 24. Consider nonlinear fractional Klein-Gordon equation [36, 37]:

subject to the initial conditions

and . The exact solution of (58) is .

By employing 2D-FLFs method with and , one can have

The numerical results of Example 24 for different values of and are shown in Figure 2. In addition, and errors are presented in Table 1. From Table 1, one can conclude that the solutions of 2D-FLFs method are in good agreement with the exact results. Compared with homotopy analysis method (HAM) [36] and homotopy perturbation method (HPM) [37], 2D-FLFs method can get high accuracy solution while only need a few terms of 2D-FLFs.

Table 1: Errors of Example 24 for different values of and with .

Error

(a)

(b)

(c)

(d)

Figure 2: Numerical results of Example 24 for different values of and .

Example 25. Consider the nonlinear time-fractional advection partial differential equation [37–39]

subject to the initial condition

Figure 3 gives the approximation solutions of (61) for with , and with , . Moreover, Table 2 shows the approximate solutions for (61) obtained for different values of using the fractional variational iteration method (FVIM) [39] and 2D-FLFs method. The values of are the only case for which we know the exact solution . It should be noted that only the fourth-order term of the FVIM was used in evaluating the approximate solutions for Table 2. From Table 2, it clearly appears that 2D-FLFs method is more accurate than FVIM and the obtained results are in good agreement with exact solution.

Table 2: Numerical values when = 0.50, 0.75, and 1.0 for (61).

FVIM
2D-FLFs
FVIM
2D-FLFs
FVIM
2D-FLFs
Exact

0.25
0.25
0.12422501
0.12225461
0.09230374
0.09224583
0.06250058
0.062500
0.062500

0.50
0.24845002
0.24450922
0.18460748
0.18449165
0.12500117
0.125000
0.125000

0.75
0.37267504
0.36676383
0.27691122
0.27673748
0.18750175
0.187500
0.187500

1.00
0.49690005
0.48901844
0.36921496
0.36898331
0.25000234
0.250000
0.250000

0.50
0.25
0.18377520
0.16584130
0.15148283
0.14985508
0.12507592
0.125000
0.125000

0.50
0.36755040
0.33168259
0.30296566
0.29971016
0.25015184
0.250000
0.250000

0.75
0.55132559
0.49752389
0.45444848
0.44956524
0.37522776
0.375000
0.375000

1.00
0.73510079
0.66336518
0.60593131
0.59942032
0.50030368
0.500000
0.500000

0.75
0.25
0.27227270
0.20678964
0.21407798
0.20119503
0.18881843
0.187500
0.187500

0.50
0.54454540
0.41357929
0.42815596
0.40239005
0.37763687
0.375000
0.375000

0.75
0.81681810
0.62036893
0.64223394
0.60358508
0.56645530
0.562500
0.562500

1.00
1.08909080
0.82715857
0.85631192
0.80478011
0.75527373
0.750000
0.750000

(a)

(b)

Figure 3: Numerical results of Example 25 for different value of .

Example 26. We finally consider the linear time-fractional wave equation:

subject to the initial conditions

Table 3 gives a comparison of the approximate solutions at different values of using the FVIM [39] and 2D-FLFs method. Figure 4 shows the numerical solutions of 2D-FLFs method for (63) at different values of with , . The values of are the only case for which we know the exact solution . As previous, only the fourth-order term of the FVIM was used in evaluating the numerical solutions for Table 3. In the case of , it can be found that absolute error of 2D-FLFs is not bigger than which is very small compared with that obtained by FVIM.

Table 3: Numerical values when = 0.750, 0.875, and 1.000 for (63).

FVIM
2D-FLFs
FVIM
2D-FLFs
FVIM
Exact

0.25
0.25
0.26622298
0.26622021
0.26593959
0.26594005
0.26578827
0.26578827

0.50
0.56489190
0.56488083
0.56375836
0.56376020
0.56315308
0.56315308

0.75
0.89600678
0.89598187
0.89345630
0.89346046
0.89209443
0.89209443

1.00
1.25956762
1.25952332
1.25503343
1.25504082
1.25261232
1.25261232

0.50
0.25
0.28474208
0.28474415
0.28340402
0.28340659
0.28256846
0.28256846

0.50
0.63896831
0.63897662
0.63361610
0.63362636
0.63027383
0.63027383

0.75
1.06267869
1.06269739
1.05063622
1.05065931
1.04311611
1.04311611

1.00
1.55587323
1.55590647
1.53446439
1.53450544
1.52109530
1.52109531

0.75
0.25
0.30690489
0.30690747
0.30361709
0.30361656
0.30139478
0.30139480

0.50
0.72761955
0.72762986
0.71446834
0.71446625
0.70557913
0.70557918

0.75
1.26214400
1.26216719
1.23255378
1.23254905
1.21255304
1.21255316

1.00
1.91047821
1.91051944
1.85787338
1.85786498
1.82231652
1.82231673

(a)

(b)

(c)

(d)

Figure 4: Numerical results of Example 26 for different value of .

6. Conclusion

We define a basis of 2D-FLFs and derived its operational matrices of fractional derivative and integration, which are used to approximate the numerical solution of FPDEs. Compared with other numerical methods, 2D-FLFs method can accurately represent properties of fractional calculus. Moreover, only a small number of 2D-FLFs are needed to obtain a satisfactory result. The obtained results demonstrate the validity and applicability of proposed method for solving the FPFEs.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant no. 11272352). The authors are grateful to the anonymous referees for their comments which substantially improved the quality of this paper.

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