Research Article  Open Access
Fenghui Huang, "A TimeSpace Collocation Spectral Approximation for a Class of Time Fractional Differential Equations", International Journal of Differential Equations, vol. 2012, Article ID 495202, 19 pages, 2012. https://doi.org/10.1155/2012/495202
A TimeSpace Collocation Spectral Approximation for a Class of Time Fractional Differential Equations
Abstract
A numerical scheme is presented for a class of time fractional differential equations with Dirichlet's and Neumann's boundary conditions. The model solution is discretized in time and space with a spectral expansion of Lagrange interpolation polynomial. Numerical results demonstrate the spectral accuracy and efficiency of the collocation spectral method. The technique not only is easy to implement but also can be easily applied to multidimensional problems.
1. Introduction
Fractional differential equations have attracted in recent years considerable interest because of their ability to model complex phenomena. For example, fractional derivatives have been used successfully to model frequencydependent damping behavior of many viscoelastic materials. They are also used in modeling of many chemical processes, mathematical biology, and many other problems in engineering. Related equations of importance are fractional diffusion equations, the fractional advectiondiffusion equation for anomalous diffusion with sources and sinks, and the fractional FokkerPlanck equation for anomalous diffusion in an external field, and so forth.
In this paper, we consider the following time fractional differential equation (TFDE) where is a linear differential operator. , are given constants, , is a given continuous function, is a time fractional derivative which is defined in the Caputo sense The use of Caputo derivative in the above equation is partly because of the convenience to specify the initial conditions [1].
The TFDE (1.1) includes a few special cases: time fractional diffusion equation, time fractional reactiondiffusion equation, time fractional advectiondiffusion equations, and their respective corresponding integerorder partial differential equations.
There are many analytical techniques for dealing with the TFDE, such as integral transformation method (including Laplace’s transform, Fourier’s transform, and Mellin’s transform) [1–5], operational calculus method [6], Adomian decomposition method [7], iteration method and series method [8], and the method of separating variables [9].
One of the key issues with numerical solution of the TFDE (1.1) is design of efficient numerical schemes for time fractional derivative. Until now, most numerical algorithms have relied on the finite difference (FD) methods to discretize the fractional derivatives, and the numerical accuracy always dependent on the order of the fractional derivatives. On the other hand, those FD methods have been generally limited to simple cases (low dimension or small integration) and are very difficult to improve the numerical accuracy [10–14]. Some numerical schemes using loworder finite elements (FE) have also been proposed [15–17]. The fractional derivatives are defined using integrals, so they are nonlocal operators. This nonlocal property means that the next state of a system not only depends on its current state but also on its historical states starting from the initial time. This nonlocal property is good for modeling reality, but they require a large number of operations and a large memory storage capacity when discretized with loworder FD and FE schemes. From this point, the “global method”—the nonlocal methods, like the spectral method—is well suited to discretize the nonlocal operators like fractionalorder derivatives. These methods naturally take the global behavior of the solution into account and thus do not result in an extra computational cost when moving from an integer order to a fractionalorder model. For example, Hanert has proposed a pseudospectral method based on Chebyshev basis functions in space and MittagLeffler basis functions in time to discretize the timespace fractional diffusion equation [18, 19]. Li and Xu have proposed a Galerkin spectral method based on Lagrangian basis functions in space and Jacobi basis functions in time for time fractional diffusion equation [20].
In this paper, we propose a timespace collocation spectral method to discretize the TFDEs (1.1), which is easier to implement and apply to multidimensional problems than the existing Galerkin spectral. Another advantage of the present scheme is that the method can easily handle all kinds of boundary conditions.
2. Analytical Solution of the TFDE in a Bounded Domain
In this section, we present some analytical solutions of the TFDE which will be found helpful in the comprehension of the nature of such a problem.
We consider the TFED (1.1) with initial condition and Dirichlet boundary conditions or Neumann boundary conditions
For the case that and , by applying the finite sine (cosine) and Laplace transforms to (1.1) with initial condition (2.1), the analytical solutions for the problem can be obtained [5] as for homogeneous Dirichlet boundary conditions, and for homogeneous Neumann boundary conditions. Where denotes a oneparameter MittagLeffler function which is defined by the series expansion
Obviously, if we fix the variable , that is, is a function of the variable , we can see the solution is not smooth on . According to (2.4) and (2.5), its first derivative behaves like and the highorder derivative behaves like near .
3. Collocation Spectral Method
First, we give the properties of the Caputo fractional derivative [1] as where is the RiemannLiouville fractional integral of order which is defined by
By the above properties, we can transform the initial value problem (1.1) into the following Volterra integral equation equivalently:
For the singular behavior of the exact solution near which we have mentioned in the special case (the exact solution (2.4) or (2.5) behaves near ), the direct application of the spectral methods is difficult. To overcome this difficulty, we use the technique in [21], that is, applying the transformation to make the solution smooth. Then (3.3) is transformed to the equation where
To apply the theory of orthogonal polynomials, we set then the singular problems (3.5) can be rewritten as where , and
For the collocation methods, (3.8) holds at the GaussLobatto collocation points and Jacobi collocation points with Jacobi weight functions on , namely,
By using the following variable change: we can rewrite (3.10) as follows:
We first use , ; to indicate the approximate value for , then we can use to approximate the function , where is the th Lagrange interpolation polynomial associated with the collocation points and is the th Lagrange interpolation polynomial associated with the collocation points .
Using a point Gauss quadrature formula relative to the Jacobi weights , (3.12) can be approximated by where the set coincides with the collocation points on .
Then the collocation spectral method is to seek of the form (3.13) such that satisfies the above collocation equations (3.14) for .
4. Numerical Results with a Collocation Spectral Approximation
In order to demonstrate the effectiveness of the proposed timespace collocation spectral method, some examples are now presented with Dirichlet boundary conditions, Neumann boundary conditions, and mixed boundary conditions.
For completeness sake, the implementation is briefly described here. To simplify the computation, we rewrite the above collocation equations (3.14) into the following: where
In our numerical tests, we use the Chebyshev GaussLobatto collocation points with the associated weights in the space. The other kinds GaussLobatto collocation points (such as Legendre GaussLobatto collocation points) also can be used. The advantage of GaussLobatto points is that they include the boundary points, which means we can apply boundary conditions there. For the time, the Jacobi Gauss collocation points are used for with the associated weights and other kinds of the Jacobi collocation points also suit to be used.
Let us set let be a matrix of by , and is a matrix of by .
4.1. Implementation of Dirichlet Boundary Conditions
The Dirichlet boundary conditions are directly applied in (4.1) and give numerical solutions on boundary in the following way:
We set where
Thus, the numerical scheme (4.1) leads to a system of equation of the form where , is a matrix of by .
4.2. Implementation of Neumann Boundary Conditions
The Neumann boundary conditions (2.3) at and can be approximated as Equation (4.10) can be written as follows: Solving (4.11) for and , we get
Then the last right term of (4.1) can be written as follows: where
Let where
Then the numerical scheme (4.1) leads to a system of equation of the form where , is a matrix of by .
Remark 4.1. The implementation for the mixed boundary conditions also can be derived by (4.6) and (4.12).
4.3. Numerical Experiments
In this subsection, the proposed numerical scheme is applied to several test problems to show the efficiency and spectral accuracy.
In each example, we have calculated errors and errors given by the following formulas: where is the numerical approximation solutions of the exact solutions .
Example 4.2 (Dirichlet Boundary Conditions). In this example, we consider the following time fractional diffusion equations: where The exact solution is given by
In Figure 1, we plot the exact and numerical solutions, and, in Figure 2, we represent the associated errors field. Here we set , , and . The results in Figures 1 and 2 denote that the numerical solution using the proposed collocation spectral method is excellent in agreement with the exact solution at the whole domain. The efficiency of this collocation spectral method can be further confirmed by Figures 3–4, which are the comparison of the exact solution and numerical solution when the space variable or time variable is fixed for various and ; here .
(a)
(b)
(a)
(b)
(a)
(b)
The main purpose of the numerical test is to check the convergence behavior of numerical solutions with respect to the polynomial degrees and for several , especially the convergence in time because of the fractional derivative in time. In order to investigate the spatial accuracy when increases, we take larger enough so that the time discretization errors are negligible compared with the spatial discretization errors. Similary, for the temporal accuracy, we must keep large enough to preclude spatial errors. errors and errors in semilog scale varying with the polynomial degree (see (a)) or (see (b)) are shown in Figures 5 and 6 for with , and Figure 7 for with . It is clear that the spectral convergence is achieved both of spatial and temporal errors. This indicates that the convergence in space and time of the timespace collocation spectral method is exponential, even though for the classical diffusion equation ().
(a)
(b)
(a)
(b)
(a)
(b)
Example 4.3 (Neumann Boundary Conditions). Consider the time fractional advectiondiffusion equation with Neumann boundary conditions We choose the suitable source term to obtain the exact solution
In Table 1, errors and errors varying with and are given, and it indicates the efficiency of the present technique.

In Figure 8, we plot errors and errors in semilog scale. Figures 8(a) and 8(b) are concerned with the spatial errors with and the temporal errors with , respectively. As expected, the spatial and temporal spectral convergence is achieved. Our greatest interest is to check the convergence in time because of the fractional derivative in time. So we plot the errors and errors as functions of for several more values in Figure 9, where . This graph shows that the spectral convergence in time can be reached, and that the timespace collocation spectral method also works well for the classical advectiondiffusion equation.
(a)
(b)
(a)
(b)
(c)
(d)
Example 4.4 (Mixed Boundary Conditions). Consider the time fractional reactionsubdiffusion equation The exact solution is given by
In Figure 10, we plot the visual fields of the exact and numerical solutions. Comparing Figures 10(a) and 10(c), and Figures 10(b) and 10(d), we see that the numerical solution is in good agreement with the exact solution; the shapes on the space variable are similar to Sine function. In addition, we can see that the solutions decay over time from the initial state. Comparing Figures 10(a) and 10(b) with Figures 10(c) and 10(d), (a) and (b) decay faster in the beginning and the trend is the opposite of (c) and (d). This is consistent with the behavior of the function . For , decays faster than , whereas for , the trend is just the opposite.
(a)
(b)
(c)
(d)
We plot in Figure 11 the errors and errors versus with in 11(a) and versus with in 11(b). As can be seen in Figure 11, the proposed method provides spatial and temporal spectral convergence for errors and errors.
(a)
(b)
5. Conclusion
This paper proposes a timespace collocation spectral method for a class of time fractional differential equations with Caputo derivatives. The proposed method also works well for the corresponding classical integerorder partial differential equations (), and it differs from (and is simpler than) the existing timespace spectral methods which are based on the PetrovGalerkin or DualPetrovGalerkin formulation. The main advantage of the present scheme is that it gives very accurate convergency by choosing less number of grid points and the problem can be solved up to big time, and the storage requirement due to the time memory effect can be considerably reduced. At the same time, the technique is also simpler and easier to apply to multidimensional problems than the existing Galerkin spectral method such as the methods in [19, 20].
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Copyright
Copyright © 2012 Fenghui Huang. 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.