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Integral spectral Tchebyshev approach for solving space RiemannLiouville and Riesz fractional advectiondispersion problems
Advances in Difference Equations volume 2017, Article number: 284 (2017)
Abstract
The principal aim of this paper is to analyze and implement two numerical algorithms for solving two kinds of space fractional linear advectiondispersion problems. The proposed numerical solutions are spectral and they are built on assuming the approximate solutions to be certain double shifted Tchebyshev basis. The two typical collocation and PetrovGalerkin spectral methods are applied to obtain the desired numerical solutions. The special feature of the two proposed methods is that their applications enable one to reduce, through integration, the fractional problem under investigation into linear systems of algebraic equations, which can be efficiently solved via any suitable solver. The convergence and error analysis of the double shifted Tchebyshev basis are carefully investigated, aiming to illustrate the correctness and feasibility of the proposed double expansion. Finally, the efficiency, applicability, and high accuracy of the suggested algorithms are demonstrated by presenting some numerical examples accompanied with comparisons with some other existing techniques discussed in the literature.
Introduction
Fractional calculus is a very important branch of mathematical analysis. This branch is basically interested in investigating the properties of derivatives and integrals of noninteger orders (called fractional derivatives and integrals). Fractional ordinary differential equations (FODEs) and fractional partial differential equations (FPDEs) have attracted considerable interest from a large number of researchers due to their ability to model a lot of phenomena in engineering, control theory, chemical physics, stochastic processes, anomalous diffusion, rheology, biology, and other sciences, such as medicine and neuronal dynamics. Due to the growing interest in these kinds of differential equations, obtaining approximate solutions of them is of fundamental importance and hence it is very useful to develop numerical techniques for solving these types of equations.
The approach of employing spectral methods is very effective in handling ordinary differential equations as well as fractional differential equations. This approach is basically built on assuming the approximate solutions to be linear combinations of certain basis functions. These basis functions may be orthogonal or nonorthogonal. Approximations by orthogonal basis functions occupy a considerable part in the literature. In fact, there are three commonly used spectral methods, namely the collocation, the tau, and the Galerkin method and its variants. The collocation method is very effective in a wide range of practical problems, particularly in the nonlinear ones (see for example [1]). The tau method is useful for treating boundary value problems of complicated boundary conditions (see for example [2]). In the Galerkin and PetrovGalerkin methods, one chooses two sets of basis functions, which are called ‘trial functions’ and ‘test functions’. These two sets are identical in the Galerkin method, however, in the PetrovGalerkin method, they are not identical, so the main advantage of employing the PetrovGalerkin method is its flexibility in choosing test functions. The Galerkin and PetrovGalerkin methods have been applied successfully in various situations. For example, the authors in [3, 4] have constructed efficient spectral Galerkin algorithms and PetrovGalerkin algorithms for handling various even and oddorder boundary value problems. One of the advantages of the Galerkin and PetrovGalerkin methods is that they enable one to investigate carefully the algebraic systems resulting from their applications and their structures and complexities.
There are three types of FPDEs, namely space FPDEs, time FPDEs and spacetime FPDEs. One of the most important fractional differential equation is the fractional advectiondiffusion equation (FADE). Due to the importance of this equation (see, for example, [5]), a variety of papers with different numerical techniques have been proposed to handle it. For example, Shen et al. in [6] derived the fundamental solution for the spacetime RieszCaputo FADE with an initial condition. The authors in [7] have derived some analytical solutions for the multiterm timespace CaputoRiesz FADEs on a finite domain. The Adomian decomposition method is employed in [8] for solving an intermediate fractional advectiondispersion equation. The finite element method is used to handle the fractional advectiondispersion equation.
In this study, we are concerned with introducing numerical integral spectral solutions for two kinds of the FADEs. We apply the collocation and PetrovGalerkin methods for this purpose. The main idea behind the proposed approach is to solve the integrated form of the equation. The advantage of using this approach is that its use enables one to reduce the solution of the equation with its boundary and initial conditions into a system of linear or nonlinear algebraic equations. The linear system can be efficiently solved using the Gaussian elimination solver or by any other suitable solver, while the nonlinear system can be solved with the aid of Newton’s iterative method.
The paper is organized as follows. In the next section, some necessary definitions and mathematical preliminaries of fractional calculus are presented. Besides, some properties of shifted Tchebyshev polynomials are given. Sections 3 and 4 are devoted to solving FADEs by implementing and presenting two numerical algorithms based on the application of the collocation and PetrovGalerkin methods. Section 5 focuses on investigating the convergence and error analysis of the suggested Tchebyshev double expansion. The numerical results and comparisons are displayed in Section 6. Finally, some conclusions are reported in Section 7.
Some fundamental properties of fractional calculus
This section is devoted to presenting some fundamental definitions and preliminary facts of the fractional calculus theory. For fundamentals of this branch, the reader is referred to [9].
Definition 1
The RiemannLiouville fractional integral operator \(I^{\alpha }\) of order α on the usual Lebesgue space \(L_{1}[0, 1]\) is defined as
The following properties are satisfied by the operator \(I^{\alpha }\):
where \(\alpha ,\beta \geqslant0\), and \(\nu>1\).
Definition 2
The left and right handed RiemannLiouville fractionalorder operators are defined, respectively, as follows:
where \(m1\leqslant\gamma< m\), \(m\in\mathbb{N}\).
Definition 3
For a function f defined on the interval \(I=[a,b]\), the left and right handed Caputo fractionalorder derivatives are defined as
where \(m1\leqslant\gamma< m\), \(m\in\mathbb{N}\).
It is worthwhile to mention here that the operator \({}_{0}^{C}D_{t}^{\alpha }\) satisfies the following fundamental properties, for \(n1\leqslant \alpha < n\):
where \(\lceil \alpha \rceil\) is the wellknown ceiling function.
Property 1
See [10]
The relation between the RiemannLiouville fractional derivative and the Caputo fractional derivative is given by
Definition 4
The Riesz fractionalorder derivative of order γ is defined (see [11]) as
where \(c = \frac{1}{2} \sec\frac{\gamma\pi}{2}\).
For a function f defined over the interval \(I=[a,b]\), where \(f(a)=f(b)=0\), we extend the function to be \(f(x)=0\), for all \(x>b\) and \(x< a\). Thus we have [6]
For several other properties of fractional derivatives and integrals, see [9, 12].
Some properties of shifted Tchebyshev polynomials
Let \(T_{n}(t)\); \(t\in[1,1]\), denote the standard first kind Tchebyshev polynomial of degree n. It is well known that the set \(\{T_{n}(t); t\in[1,1]\}_{n\ge0}\) form a complete orthogonal system for \(L^{2}_{w}(1,1)\), where \(w=\frac{1}{\sqrt{1t^{2}}}\). Moreover, Tchebyshev polynomials satisfy the following orthogonality relation:
We denote by \(T_{j}^{\tau}(t)\) the shifted Tchebyshev polynomials defined on \((0,\tau)\) as
The shifted Tchebyshev polynomials form a complete orthogonal system for \(L_{w}^{2}(0,\tau)\), where \(w=\frac{1}{\sqrt{\tau tt^{2}}}\). The orthogonality relation satisfied by \(T_{i}^{\tau}(t)\) is
The linearization of the product of two Tchebyshev polynomials is given by (see [13])
These polynomials (see [13]) have the following two analytic forms:
and
Now if we let \(f(t)\in L^{2}[0,\tau]\), then \(f(t)\) may be expanded in terms of a shifted Tchebyshev basis as
where
For more properties of Tchebyshev polynomials and their shifted ones, see for example [13].
Integral transforms for two kinds of the space fractional advectiondispersion equations
This section is devoted to transforming two kinds of space FADEs into their integral forms in order to treat them numerically by our proposed techniques in the upcoming section.
The first kind of space fractional advectiondispersion problem
Consider the following space righthanded RiemannLiouville fractional advectiondispersion problem (see [14]):
governed by the nonhomogeneous boundary conditions
and the initial condition
where \(u(x,t)\) represents the concentration, \(k(x,t)\) and \(v(x,t)\) are the dispersion coefficients, and \(f(x,t,u)\) is the source term which may be linear or nonlinear.
In order to proceed in developing our two spectral algorithms for treating (3.1)(3.3), we make use of the following transformation:
and we take into consideration the following relations:
to convert equation (3.1), subject to the nonhomogeneous boundary conditions (3.2) and the initial condition (3.3), into the following modified one:
subject to the homogeneous Dirichlet boundary conditions
and the initial condition
where
and
Note 1
If \(\beta=1\), \(\gamma=2\), equation (3.8) has the following form:
Now, the integral form of equation (3.5) governed by the conditions (3.6) and (3.7) is
and
The second kind of space fractional advectiondispersion problem
Consider the space RiemannLiouville fractional advectiondispersion problem with Riesz space fractional derivatives (see [15]):
governed by the nonhomogeneous boundary conditions
and the initial condition
where \(u(x,t)\) represents the concentration, \(\kappa_{\beta}\) and \(\kappa_{\gamma}\) represent the dispersion coefficient and the average fluid velocity, respectively, and \(f(x,t,u)\) is the source term which may be linear or nonlinear.
Now, with the aid of the transformation formula
equation (3.13), subject to the nonhomogeneous boundary conditions (3.14) and the initial condition (3.15), is turned into the following modified equation:
subject to the homogeneous Dirichlet boundary conditions
and the initial condition
where
where
and
Note 2
If \(\beta=1\), \(\gamma=2\), equation (3.20) has the following form:
Now, the integral form of equation (3.17) governed by the conditions (3.18) and (3.19) is
and
Numerical spectral treatment for two kinds of space fractional linear advectiondispersion problems
This section is concerned with explaining in detail two spectral algorithms for numerically solving two kinds of space fractional linear advectiondispersion problems. First, we select a unified double Tchebyshev expansion as basis functions, and then apply the two wellknown spectral methods, namely the collocation and PetrovGalerkin methods.
Choice of the basis functions
We choose the following two families of orthogonal polynomials:
It is not difficult to show that the polynomials \(\{\phi_{i}(x)\} _{i\ge 0}\) are linearly independent and orthogonal with respect to the weight function \(w(x)=\frac{1}{x^{5/2}(\ellx)^{5/2}}\) on \([0,\ell]\). Moreover, it is also clear that each member of them fulfills the boundary conditions (3.6). The orthogonality relation for \(\{\phi_{i}(x)\}_{i\ge 0}\) is
Now, we define the following two spaces:
where \(H_{w(x,t)}^{2}(\Omega)\), \(\Omega=(0,\ell)\times(0,\tau]\) is the Sobolev space defined in [16], and
Now, let \(g(x,t)\in V\). Then this function can be expanded in the following double expansion:
and the coefficients \(c_{ij}\) are given by the formula
Numerically, \(g(x,t)\) can be approximated by the truncated double series
In the following, we are going to state and prove three important theorems concerned with the basis functions \(\phi_{i}(x)\) and \(\psi_{j}(t)\). In the first, we give a new formula for the fractional derivatives of the polynomials \(\phi_{i}(x)\) in the sense of RiemannLiouville, while the second gives the Riesz fractional derivatives for the same basis. In the third theorem, an integral formula for \(\psi_{j}(t)\) is given.
Theorem 1
Let \(\alpha \in(1,2)\). Then the following fractional derivative (in the RiemannLiouville sense) relation is valid:
Proof
The power form representation for \(T_{i}^{\ell}(x)\) given in (2.11) enables one to write \(\phi_{i}(x)\) as
If the operator \({}_{0}^{R}D_{x}^{\alpha}\) is applied to both sides of (4.9), and making use of the formula
then we get
If we make use of the relation
then after performing some rather lengthy manipulations, we get the desired equation (4.8). □
Note 3
By writing \(a_{k}\lessapprox b_{k}\), we mean that there exists a generic constant C such that \(a_{k}< C\, b_{k}\) for large k.
Lemma 1
For \(x\in[0,1]\) and \(0<\alpha<1\), we have
Proof
From relation 2.7 and knowing that \(\phi_{i}(0)=0\), we have
so it suffices to prove the lemma for Caputo’s definition:
Noting that \(\phi_{i}(x)=(12x)\cos(i \theta)i\sqrt{xx^{2}} \sin (i \theta)\), where \(\theta=\arccos(2x1)\), and since \(\sqrt{xx^{2}}\leq\frac{1}{2}\), we get
and since \(x^{1\alpha}\leq1\), then the lemma is proved. □
Lemma 2
For \(x\in[0,1]\) and \(1<\gamma<2\), we have
Proof
The proof of this lemma is similar to the proof of Lemma 1. □
Theorem 2
For \(\beta\in(0,2)\{1\}\), the following Riesz fractional derivatives relation is valid:
Proof
Based on the two analytic forms of \(T_{i}^{\ell}(x)\) given in (2.11) and (2.12) and if we perform similar manipulations as in the proof of Theorem 1, then we get the desired result. □
Similarly we can prove the following estimates for the Riesz fractional derivatives of \(\phi_{i}(x)\).
Lemma 3
For \(x\in[0,1]\) and \(0<\beta<1\), we have
Lemma 4
For \(x\in[0,1]\) and \(1<\gamma<2\), we have
Theorem 3
If \(\psi_{j}(t)\) is defined as in (4.2), then the following integral formula holds:
Proof
The proof is easily obtained by integrating relation (2.11) over the interval \([0,t]\). □
Numerical algorithms for handling equation (3.11)
This section is devoted to describing in detail two numerical algorithms for handling equation (3.11). The first algorithm depends on the application of the typical collocation method, while the second depends on the application of the PetrovGalerkin method. The main idea behind the two proposed algorithms is based on making use of Theorems 1 and 3 along with the application of the collocation and PetrovGalerkin methods in order to transform equation (3.11) into a system of linear or nonlinear algebraic equations in the unknown expansion coefficients \(c_{ij}\). The linear system is solved using Gauss elimination, and the nonlinear one is solved via Newton’s iterative method.
The collocation approach
Consider the following approximate solution of equation (3.11):
Now, let \(v(x,t)\) and \(k(x,t)\in V\). Then these two functions can be expanded in the following double expansions:
and the coefficients \(v_{p q}\) and \(k_{p q}\) are given by the formulas
Now consider the following approximations:
and
In order to apply the collocation method, and due to (4.18), we note that the residual of (3.11) takes the form
and therefore
Now, making use of the two power form representations of the polynomials \(\phi_{i}(x)\) and \(\psi_{j}(t)\), and the two relations in (2.10), (4.8) and (4.17) enable us to write the residual \(R(x,t)\) in the following form:
where \(\zeta=qj\) and \(\eta=q+j\). Now, we apply the typical collocation method to equation (4.27). In fact, if this equation is collocated at the following set of points: \(\{ (\frac{i \ell}{M+2},\frac{j \tau}{M+2} ): 1\leq i,j\leq M+1 \}\), then we obtain a system of algebraic equations of dimension \((M+1)^{2}\) in the unknown expansion coefficients \(\{c_{ij}: 0\leq i,j\leq M\}\). This linear algebraic system is solved by the Gaussian elimination procedure or by any suitable solver, while the nonlinear system is solved with the aid of Newton’s iterative method. Hence, the desired spectral solution can be obtained.
PetrovGalerkin approach
This section is devoted to introducing an alternative algorithm for finding a spectral solution to equation (3.11). The main advantage of employing the PetrovGalerkin method is its flexibility in choosing test functions, since we can choose a suitable set of functions that is not identical to the set of trial functions. Now, we choose the test functions to be
Then the application of the PetrovGalerkin method leads to
where \(R(x,t)\) is defined as in (4.27).
Now, equation (4.28) can be written alternatively as
where \(F_{m,n}= \int_{0}^{\tau} \int_{0}^{\ell}F(x,t,g) \rho_{mn}(x,t) \,dx\,dt\). Hence, a system of equations of dimension \({(M+1)^{2}}\) in the unknown expansion coefficients \(c_{ij}\) is generated. This system can be efficiently solved by the Gauss elimination technique, and hence an approximate spectral solution can be obtained.
Numerical algorithms for handling equation (3.23)
In this section, we describe how the collocation and PetrovGalerkin procedures can be employed to handle equation (3.23). As we have done in Section 4.2.1, collocation and PetrovGalerkin are employed along with Theorems 2 and 3 to convert equation (3.23) into a system of linear algebraic equations in the unknown expansion coefficients \(c_{ij}\).
The collocation approach
Now, consider the following approximate spectral solution of equation (3.23):
In order to apply the collocation method, and due to (4.30), we note that the residual of (3.23) takes the form
The latter formula, along with the aid of the two power form representations of the polynomials \(\phi_{i}(x)\) and \(\psi_{j}(t)\), and the two relations in (4.14) and (4.17), enable one to write the residual \(R(x,t)\) in the following form:
Now, we apply the typical collocation method. In fact, the residual \(R(x,t) \) is enforced to vanish at the following set of points: \(\{ (\frac{i \ell}{M+2},\frac{j \tau}{M+2} ): 1\leq i,j\leq M+1 \}\). Then we obtain a linear algebraic system of equations of dimension \((M+1)^{2}\) in the unknown expansion coefficients \(\{c_{ij}: 0\leq i,j\leq M\}\) which is solved by Gauss elimination solver.
PetrovGalerkin approach
To apply the PetrovGalerkin method to equation (3.23), we choose the test functions to be
and therefore the application of the PetrovGalerkin method leads to
where \(R(x,t)\) is defined as in (4.32). Now, equation (4.33) can be written alternatively as
where
Equation (4.34) generates a linear system of dimension \((M+1)^{2}\) in the unknown expansion coefficients \(c_{ij}\), which is efficiently solved using Gauss elimination solver.
Convergence and error analysis of the suggested double expansion
In this section we concentrate on investigating the convergence and error analysis of the suggested double Tchebyshev expansion. Three important theorems are stated and proved for this purpose. The first theorem shows that the double Tchebyshev expansion of a function \(u(x,t)\) converges uniformly to \(u(x,t)\). The second and third theorems discuss the error analysis of the full discretization scheme for the two problems (3.5) and (3.17).
First, the following lemma is useful.
Lemma 5
See [17], p.742
Let \(u(x)\) be a function such that \(u(k)=a_{k}\). Suppose that the following assumptions are satisfied:

1.
\(u(x)\) is a continuous, positive, decreasing function for \(x\geq n\).

2.
\(\sum a_{n}\) is convergent, and \(R_{n}=\sum_{k=n+1}^{\infty}a_{k}\).
Then
Theorem 4
A function
with \(f^{(3)}(x)\leq M_{1}\), \(h^{(3)}(x)\leq M_{2}\) can be expanded as an infinite sum of the basis \(\{\phi_{i}(x) \psi_{j}(t)\}_{0\le i,j\le M}\), and the series converges uniformly to \(g(x,t)\). Moreover, the following inequality is satisfied by the expansion coefficients in (4.18):
Proof
Relation (4.6) implies that
We make use of the two substitutions:
to convert the coefficients \(c_{ij}\) in (5.2) into the form
Application of the integration by parts three times to the right hand side of equation (5.3) enables one to write \(c_{ij}\) in the form
where
In virtue of the two assumptions \(f^{(3)}(x)\leq M_{1}\), \(h^{(3)}(t)\leq M_{2}\) and the wellknown inequality \(T_{i}^{\ell}(x)\leq1\), and since
we get the theorem. □
Theorem 5
Let \(\mathcal {E}_{M}\) be the residual of equation (3.5), i.e.,
If \(f_{1}(x, t, z)\) satisfies the Lipschitz condition in the variable z, and \(v(x,t)\) and \(k(x,t)\) are bounded functions, then we have the following estimate:
Proof
We have
Now, noting that
and based on Theorem 4, Lemmas 1 and 2 and the hypothesis of the theorem, we get the desired result. □
Theorem 6
Let \(\mathcal {F}_{M}\) be the residual of equation (3.17), i.e.,
If \(f_{1}(x, t, z)\) satisfies the Lipschitz condition in the variable z, then we have the following estimate:
Proof
The steps of the proof are similar to those followed in the proof of Theorem 5, but based on Lemmas 3 and 4 instead of Lemmas 1 and 2. □
Now, the following theorem investigates the stability of the suggested double Tchebyshev expansion.
Theorem 7
Under the assumptions of Theorem 4, we have \(\g_{M+1}(x,t)g_{M}(x,t)\_{\omega }\lessapprox M^{4}\).
Proof
We have
With the aid of the identity
we get
Now, based on the result of Theorem 4, and following [18], and after some manipulations, we get the desired result. □
Numerical results and comparisons
This section is devoted to presenting some numerical results obtained by the application of the two proposed numerical methods. All the results are obtained using the software Mathematica 9. The results are accompanied by a comparison with results from the literature, obtained by applying some other numerical techniques.
Example 1
Consider the following equation (see [14]):
where
subject to the boundary conditions
and the initial condition
where \(\gamma=1.8\), such that the exact solution of (6.1) is \(u(x,t)=5e^{t}(x^{2}x^{3})\). In [14], the authors assumed that the solution of (6.1) is given by \(u_{M}(x,t)=\sum_{i=0}^{M}a_{i}(t) P_{i}^{(\alpha \cdot \beta )}(x)\). Then they obtained a linear system of differential equations in the unknowns \(a_{i}(t)\), and they solved it using the finite difference method with step size τ. In Table 1 we list the maximum pointwise error of Example 1, while in Table 2, we compare the best errors of Example 1. We mention here that the computational complexity required to solve a linear system of differential equations of dimension 7 with step size \(\frac{1}{300}\) is much smaller than the computational cost to solve a linear system of algebraic equations in the unknown expansion coefficients \(c_{ij}\) of dimension 49. Our computational cost is of order \(\frac{2}{3}(M+1)^{3}\approx78\mbox{,}433\), while their computational cost is of order \(\tau 4^{M+1} \ln4\approx9\mbox{,}564\mbox{,}537\). Let \(E_{C}\) and \(E_{\mathrm{PG}}\) denote, respectively, the maximum pointwise errors resulting from the application of the two methods, namely, the shifted Tchebyshev collocation method (SCCM) and the shifted Tchebyshev PetrovGalerkin method (SCPGM).
Example 2
Consider the following equation (see [19]):
subject to the nonhomogeneous boundary conditions
where \(v=0.2\), \(\sigma= \frac{\Gamma(5\gamma)}{24}\), and the initial condition
The exact solution of (6.3) is \(u(x,t)=e^{t}x^{4}\). In Table 3 we compare our results with the results obtained in [19].
From the results in Table 3, we conclude that our spectral method is more accurate than the fractional difference method used in [19].
Example 3
Consider the space fractional diffusion problem [14]
subject to the homogeneous boundary conditions
and the initial condition
where \(f(x,t)\) is chosen such that the exact solution of equation (6.4) is \(u(x,t)= t^{\gamma}e^{\sigma t}x^{2}(1x)^{2}\). We take \(A=B=2\). We apply SCCM and SCPGM with \(M=7\). In Table 4, we list the maximum pointwise error for different values of σ and γ, while in Table 5, we compare our results with those obtained by the method developed in [14] for the case corresponding to \(\sigma=0.4\), \(\gamma=1.7\), and \(\alpha =\beta =0\).
Example 4
Consider the following Riesz fractional reaction dispersion equation [20, 21]:
subject to the boundary conditions
and the initial condition
where
We apply the PetrovGalerkin Tchebyshev collocation method (PGCCM) to equation (6.5). In Table 6, we compare our results with the results obtained by [20] and [21]. The results in this table demonstrate that our method is more accurate than the methods developed in [20] and [21].
Example 5
Consider the Riesz space fractional diffusion problem
subject to the boundary conditions
and the initial condition
where \(f(x,t)\) is chosen such that the exact solution of equation (6.6) is \(u(x,t)=\frac{(x^{2}+1)}{e^{t}}\). We apply the SCPGM with \(M=14\). In Table 7, we list the pointwise error for different values of β and γ.
Concluding remarks
FADEs are used in groundwater hydrology to model the transport of passive tracers carried by fluid flow in a porous medium. In this research article, two efficient spectral methods are presented and analyzed to solve two kinds of space fractional linear advectiondispersion problems. The spectral collocation and PetrovGalerkin methods are employed to obtain semianalytic solutions for the FADE. Efficient and highly accurate solutions are obtained with a small number of retained modes.
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Doha, E.H., AbdElhameed, W.M., Elkot, N.A. et al. Integral spectral Tchebyshev approach for solving space RiemannLiouville and Riesz fractional advectiondispersion problems. Adv Differ Equ 2017, 284 (2017). https://doi.org/10.1186/s1366201713366
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DOI: https://doi.org/10.1186/s1366201713366
Keywords
 Advectiondispersion Problem
 Fractional Advectiondiffusion Equation (FADEs)
 Collocation Type Method
 PetrovGalerkin Method
 Tchebyshev Polynomials