Advanced Theoretical and Applied Studies of Fractional Differential Equations 2013
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A. H. Bhrawy, "A New Numerical Algorithm for Solving a Class of Fractional AdvectionDispersion Equation with Variable Coefficients Using Jacobi Polynomials", Abstract and Applied Analysis, vol. 2013, Article ID 954983, 9 pages, 2013. https://doi.org/10.1155/2013/954983
A New Numerical Algorithm for Solving a Class of Fractional AdvectionDispersion Equation with Variable Coefficients Using Jacobi Polynomials
Abstract
We propose JacobiGaussLobatto collocation approximation for the numerical solution of a class of fractionalinspace advectiondispersion equation with variable coefficients based on Caputo derivative. This approach has the advantage of transforming the problem into the solution of a system of ordinary differential equations in time this system is approximated through an implicit iterative method. In addition, some of the known spectral collocation approximations can be derived as special cases from our algorithm if we suitably choose the corresponding special cases of Jacobi parameters and . Finally, numerical results are provided to demonstrate the effectiveness of the proposed spectral algorithms.
1. Introduction
Spectral methods have emerged as powerful techniques used in applied mathematics and scientific computing to numerically solve differential equations [1]. Also, they have became increasingly popular for solving fractional differential equations (see, for instance, [2–6]). The main idea of spectral methods is to put the solution of the problem as a sum of certain basis functions and then to choose the coefficients in the sum in order to minimize the difference between the exact solution and approximate one as well as possible. Spectral collocation method has an exponential convergence rate, which is valuable in providing highly accurate solutions to nonlinear differential equations even using a small number of grids. Moreover, the choice of collocation points is very useful for the convergence and efficiency of the collocation approximation [7, 8].
In recent years, considerable interest in fractional partial differential equations has been motivated because of their growing applications in electromagnetics, acoustics, viscoelasticity, electrochemistry, and material science [9, 10]. Several analytical algorithms have been investigated for treating these equations analytically to obtain closedform solutions such as variational iteration method, Fourier transform method, homotopy analysis method, the method of separation of variables, Adomian decomposition method, and Laplace transform method [9, 11–14]. However, there are only few types of these equations in which the analytical solutions are available. Therefore, numerical means have to be used in general.
In numerous physical models, an equation commonly used to describe transport diffusive problems is the classical advectiondiffusion (or dispersion) equation which may be generalized to the fractional ones to cover other very interesting physical models. The advectiondispersion equation, which is based on Fick's law, is commonly used to simulate contaminant transport in porous media [15]. The space, time and timespace fractional advectiondispersion equations are presented as a reliable model to simulate the transport of passive tracers carried by fluid flow in a porous media and are used in groundwater hydrology [13, 16]. Moreover, they have been introduced to describe other important physical phenomena (see [17–21]).
In the last few years, theory and numerical analysis of fractional partial differential equations have received an increasing attention. In this direction, Rihan [22] proposed the method for approximating timefractional parabolic partial differential equations in the Caputo sense. An explicit Euler method, an implicit Euler method, and the fractional CranckNicholson method for solving fractional differential equations are discussed in [23–26]. An explicit difference approach for solving space fractional diffusion equation has been proposed in [27]. Ding et al. [28] investigated a class of weighted finite difference method for tackling a class of timedependent fractional differential equations based on shifted Grünwald formula. K. Wang and H. Wang [29] and Huang et al. [16] proposed a fast numerical scheme for fractional timedependent advectiondiffusion and advectiondispersion equations based on finite difference method, respectively. Recently, the SincLegendre spectral method has been developed in [30] for the fractional convectiondiffusion. Jiang and Lin [31] proposed a new method for a class of fractional advectiondispersion in the reproducing kernel space. Furthermore, Liu et al. [32] proposed an efficient implicit numerical method for a class of fractional advectiondispersion models in which they discussed five fractional models. In the area of numerical methods of fractional partial differential equations, little work has been done by spectral methods compared to finite difference and finite element methods. This partially motivates our interest in such methods.
The main purpose of the this paper is to construct the solution of a class of space fractional advectiondispersion equation with variable coefficients using JacobiGaussLobatto collocation (JGLC) approximation, based on JacobiGaussLobatto quadrature knots, combined with an implicit iterative method for treating the time discretization. More precisely, implementing the JGLC approximation to the spatial variable of the fractional advectiondispersion equation and the corresponding boundary conditions reduces the problem to the time integration of a system of ordinary differential equations in respect to the time variable. To the best of our knowledge, such algorithm has not been implemented for solving space fractional initialboundary problems.
The plan of the paper is as follows. In the next section, we introduce basic properties of Jacobi polynomials. In Section 3, the way of constructing the GaussLobatto collocation technique for space fractional advectiondispersion equation with variable coefficients is described using the Jacobi polynomials, and in Section 4 the proposed method is applied to two problems. Finally, some concluding remarks are given in Section 5.
2. Preliminaries
In this section, we give some definitions and properties of the fractional calculus (see, e.g., [9, 18, 33, 34]) and Jacobi polynomials (see, e.g., [35–37]).
For to be the smallest integer that exceeds , Caputo's fractional derivative operator of order is defined as where is the RiemannLiouville fractional integral operator of order and is defined as
Form (1), the Caputo fractional derivative of is given by where and are ceiling and floor functions. Also, and . Caputo's fractional differentiation is a linear operation; that is, where and are constants.
Let , , and be the standard Jacobi polynomial of degree . We have that
For integer , the thorder derivative of Jacobi polynomials is Let ; then we define the weighted space as usual, equipped with the following inner product and norm: The set of Jacobi polynomials forms a complete orthogonal system, and
Let ; then the shifted Jacobi polynomial of degree on the interval is defined by .
With the aid of (5), we demonstrate that
Next, let ; then we define the weighted space in the usual way, with the following inner product and norm:
The set of shifted Jacobi polynomials is a complete orthogonal system. Moreover, due to (8), we have
3. Jacobi Spectral Collocation Method
Since the Jacobi spectral collocation method approximates the initialboundary problems in physical space and it is a global method, it is very easy to implement and adapt to various problems, including variable coefficient and nonlinear problems (see, for instance, [7, 38]). In this section, a new algorithm for solving timedependent space fractional advectiondispersion equation is proposed based on JacobiGaussLobatto spectral collocation approximation and an implicit iterative method in finite spacetime domain.
In this section, we consider the space fractional advectiondispersion equations with space and time variable coefficients [19, 23, 28, 39, 40]: subject to the boundary conditions: and the initial value: where is the drift of the process, that is, the mean advective velocity, is the coefficient of dispersion, is the fractional order in the Caputo sense, , and is a source function. In particular, if , (12) is the classical convectiondiffusion equation with a source term which has commonly been used to describe the Brownian motion of particles [41]. Moreover, if , it reduces to the space fractional diffusion equation (cf. [42–46]).
Now we introduce the JacobiGaussLobatto quadratures in two different intervals , and . Denote by , , and , , the nodes and Christoffel numbers of the standard (shifted) JacobiGaussLobatto quadratures on the intervals and , respectively. Then one can clearly deduce that and if denotes the set of all polynomials of degree at most , then it follows that, for any , we have
We define the discrete inner product and norm as follows: Obviously, Thus, for any , the norms and coincide.
Associating with this quadrature rule, we denote by the JacobiGaussLobatto interpolation (cf. [47]):
We now derive an efficient algorithm for solving spacefractional advection diffusion equation (12)–(14). We expand the numerical approximation in terms of Jacobi polynomials: If we make use of the orthogonality property of Jacobi polynomials with respect to the weight functions and the discrete inner product (17), then we get and accordingly, (20) takes the following form: or equivalently
The first order spatial derivative of the spectral solution can be approximated by the JGLC points
According to equation (24) can be written in the following form: where
The fractional derivative of order in the Caputo sense for the Jacobi polynomials is given by where
The spatial partial fractional derivatives of order for the spectral solution (20) can be evaluated at the JGLC points . Hence, we have where for , and is defined in (29).
If we apply the JacobiGaussLobatto collocation method of (12) without the two assigned abscissas and ; , which will be necessary used as two points from the collocation nodes for enforcing the boundary conditions (13), and using (30), then (12) may be written as
Let us denote that Thus, (32) can be rewritten in the following simple form: Let us assume that Then (34) and using the twopoint boundary conditions (13) generate a system of ordinary differential equations in time. with the initial values which may be written in the following matrix form: where
The system of ordinary differential equations (38) in time may be solved using any standard technique to find and then from (22).
4. Numerical Results
In order to check the accuracy and reliability of the proposed algorithm, we present two numerical examples using the proposed algorithm. In the first example, we compute the space fractional diffusion equation to check the accuracy, and space fractional advectiondispersion equation with variable coefficients is solved in the second example which confirms the good accuracy of our method. Comparing the results obtained by various choices of Jacobi parameters and and results presented elsewhere reveals that the present method is very effective and convenient for all choices of and .
Example 1. Consider the space fractional diffusion equation (see, [42, 48, 49]): with the initial condition: and the boundary conditions: The exact solution to this problem is
In Table 1, we list the maximum absolute errors using JGLC method with three choices of the Jacobi parameters and and various choices of the .
We contrast our results with the corresponding results for the backward Euler finite difference scheme (BEFD [49]), the fractional CrankNicholson approach (CN [50]), and the extrapolated fractional CrankNicholson approach (Extra CN [50]) which we have presented in the fifth, sixth, and seventh columns of Table 1. We should note that for all values of and , the proposed method is always more accurate than the results of CN [50], Extra CN [50], and BEFD [49], which shows the spectral accuracy of our method.
In Figures 1 and 2, the analytical solutions and the numerical solutions for , , and , , are shown, respectively. Consequently, we see that all numerical solutions are in complete agreement with the analytical solutions.
Example 2. Consider the space fractional advectiondispersion equation with variable coefficients: where with the initial condition: and the boundary conditions: The exact solution to this problem is
For the sake of comparison of some different values of Jacobi parameters and , we introduce in Table 2 the maximum absolute errors between the exact and numerical solutions using Jacobi GaussLobatto collocation method with , , and various choices of . Consequently, we conclude that all numerical solutions are in good agreement with the analytical solutions in all choices of and .

In case of Chebyshev polynomials of the first kind , the spacetime graphs of approximate solutions at for the two choices and are shown in Figures 3 and 4, respectively. From these figures, it can be seen that the numerical solutions are in excellent agreement with the exact solutions. Numerical simulation is given in Figure 5 to compare the curves of exact solution and approximate solution (in case of Legendre polynomials and ) for with and . Moreover, the curves of exact solution and approximate solution (in case of Chebyshev polynomials of the second kind and ) for with and are sketched in Figure 6. Consequently, we see that the curves of the exact and approximate solutions almost coincide for all chosen values of and .
The obtained results of this example show that the Jacobi GaussLobatto collocation method is simple and very accurate for all values of and . Also by selecting limited GaussLobatto collocation points, excellent numerical results are obtained.
5. Conclusion and Future Work
In this paper, we have proposed the Jacobi GaussLobatto collocation spectral approximation for tackling fractionalinspace advectiondispersion equation subject to initialboundary conditions. Applying the collocation method has reduced the problem to system of ordinary differential equations in time. This system may be solved by an implicit iterative technique. One of the main advantages of the proposed method is the Legendre GaussLobatto collocation approximation, and the four kinds of Chebyshev GaussLobatto collocation approximations may be obtained as special cases of the proposed Jacobi GaussLobatto collocation approximation by taking the corresponding special cases of the Jacobi parameters and . The numerical results given in Section 4 demonstrate the good accuracy of proposed algorithm.
The implementation of Jacobi GaussLobatto collocation spectral approximation for timespace fractional advectiondispersion equations may also constitute another line of our future lines of research. We also conclude that this algorithm can be useful in dealing with coupled nonlinear partial differential equations.
Conflict of Interests
The author declares that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
This work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia under Grant no. (130141D1433). The author, therefore, acknowledges the DSR technical and financial support.
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Copyright © 2013 A. H. Bhrawy. 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.