- Open Access
Nonstandard finite difference schemes for a fractional-order Brusselator system
© Ongun et al.; licensee Springer 2013
- Received: 24 December 2012
- Accepted: 27 March 2013
- Published: 12 April 2013
In this paper, we discuss numerical methods for fractional order problems. Some nonstandard finite difference schemes are presented and investigated. The application in the simulation of a fractional-order Brusselator system is hence presented. By means of some numerical experiments, we show the effectiveness of the proposed approach.
- Fractional Order
- Fractional Derivative
- Fractional Calculus
- Fractional Differential Equation
- Discrete Representation
Recently, fractional calculus has gained an increasing popularity due to the wide range of applications in fields including engineering, chemistry, finance, physics, seismology and so on.
Although the discussion on derivatives of noninteger order dates back to almost as far as the development of the classical theory of integer-order differential calculus, only at the end of the nineteenth century it has been realized the great enhancements that could be achieved by exploiting the power of fractional calculus; by means of fractional differential equations (FDEs) it is indeed possible to describe, in a natural way, real-life phenomena with memory effect and systems exhibiting anomalous diffusion .
The publication of some cornerstone books completely devoted to fractional calculus (we just cite here the works of Oldham and Spainer , Samko, Kilbas and Marichev , Miller and Ross , Podlubny , Diethelm  and Mainardi ) has successively played a considerable role in disseminating the subject. A variety of recent books [8–13] have also been published to illustrate applications of FDEs and methods for their solution.
In most cases, the solution of a FDE does not exist in terms of a finite number of elementary functions; it is therefore fundamental to device numerical methods in order to practically evaluate approximated solutions by means of difference schemes or other alternative approaches (e.g., see [14–19]).
A major difficulty in the numerical treatment of FDEs is the presence of the long and persistent memory, which is related to the nonlocal nature of fractional derivative operators. From a practical point of view, storing and taking into account all the past history of the solution is usually a very demanding task.
In the presence of nonlinearities, these difficulties are amplified by the need of solving, at each step, some nonlinear algebraic systems whenever implicit schemes are adopted to cope with stability issues.
Nonstandard finite difference (NSFD) schemes have been introduced [20, 21] with the aim of avoiding full implicit schemes, which are computationally expensive, but at the same time preserving some of the main essential physical properties of the solution such as, for instance, positivity, monotonicity or convergence towards a stable steady-state.
The central aim of this work is to apply NSFD schemes within the context of fractional-order problems and study their potentials in replicating some of the main properties of the true solution [22–25]. In particular, the paper is concerned with the numerical simulation, by means of some ad hoc devised NSFD schemes, of a fractional-order Brussellator system; the stability properties of the system are analyzed and we show that the proposed NSFD methods allow to preserve stability in the numerical solution.
The paper is organized as follows. In Section 2, we briefly review the main definitions concerning fractional derivatives and FDEs and we introduce NSFD methods. Section 3 is devoted to discuss the main problems in applying NSFD schemes to fractional-order problems. In Section 4, we analyze stability of the fractional-order Brusselator system, and we present some suitably devised NSFD for this system. By means of some numerical simulations, in Section 5, we show the stability preserving properties of the proposed schemes and we compare the results with those provided by a classical method. Finally, some concluding remarks are given in Section 6.
In this section, some basic definitions and properties in the theory of the fractional calculus are presented; moreover, we introduce the main aspects concerning nonstandard discretization methods.
2.1 Fractional derivatives and FDEs
where is the Euler gamma function, is the smallest integer such that and and denote the standard derivatives of integer order.
which does not have a clear physical meaning.
This method has been extensively studied in literature (e.g., see [18, 26]) and the numerical solution obtained by (6) converges to true solution with order 1 as . Method (6) will form the basis on which NSFD methods will be devised in the subsequent section.
We introduce the following result concerning the weights of the GL discretization scheme (6), which will be used later on.
Proof The proof is an immediate consequence of the recursive relationship stated in (3). □
2.2 Nonstandard discretization
to ensure that the discrete representation in (8) converges to the corresponding continuous derivative as . Examples of denominator functions fulfilling (10) are h, , , and so forth.
Other than by fulfilling the consistency condition (10), there are however some other criteria for choosing a suitable denominator function. The main aim is to achieve what is called the dynamic consistency: the solution of the discrete model (9) must satisfy some properties of particular importance for the original continuous model (7) (e.g., positivity, monotonicity, fixed points and so on).
ensures this desirable property for any value of h.
to ensure that the steady-state of the discrete model preserves the same stability properties of the original continuous model.
when the GL discretization (6) is exploited as the underlying method for the construction of NSFD schemes.
Although all the above functions satisfy the consistency condition (12), not all of them preserve the first order convergence of (6). Indeed, it is immediate to observe that to achieve this further goal it is necessary that p in (12) satisfies . Since most of the listed functions do not fulfill this requirement, it is obvious to expect a drop in the convergence order which could reduce to α, when , instead of being 1.
Both of them fulfill the consistency condition (12) but just preserves order of convergence 1 since . Moreover, while satisfies for any values of h the stability requirement (13) only when λ is of small or moderate size, the function fulfills (13) in any circumstance. Thus, is expected to better work for preserving stability whereas seems more suitable for achieving higher accuracy.
where , are activator and inhibitor variables and a, μ are external parameters (the relationship between them determines the system dynamics) .
which can be easily determined as . The following result allows to summarize the dynamics of this equilibrium point .
Theorem 2 There exists a marginal value such that the equilibrium E is locally asymptotically stable if and it is unstable if .
where and .
For consider the parabola and introduce the marginal value , . When the system has oscillatory, but stable modes; when unstable and more complicated dynamics arise . □
The value of α is thus an additional bifurcation parameter, which switches the stable and unstable states of the system and changes the form of the limit cycle. When , the system has a unique limit cycle when and it has a stable limit cycle for .
Since system (14) does not have a general solution in closed form, numerical methods must be used to approximate its solutions; a major requirement is that the numerical schemes preserve the dynamics of the system.
To discretize the fractional-order nonlinear system (14), we propose and discuss some NSFD schemes applied in combination with the truncation of the GL operator as stated in (11).
In our tests as the denominator function, we will use the function introduced in Section 3.
and the denominator function will be used.
When , this is a fully explicit scheme and it is the counterpart for FDEs of the classical forward Euler method. Anyway in our experiments, we will use both the denominator functions and to compare the behavior and the corresponding schemes will be denoted respectively as NSFD 3a and NSFD 3b.
To study positivity of the numerical approximations let us assume , and . A straightforward analysis allows us to identify the conditions under which positive iterations and are obtained. We summarize these conditions as follows:
Scheme 1: and .
Scheme 2: and .
Scheme 3: and .
We present in this section some numerical simulations. To compare the results obtained with the NSFD schemes investigated in this paper, we use the reference solution provided by the Adams-Bashforth-Moulton (ABM) method implemented in , whose stability properties have been investigated in .
Errors and EOC with some values of N at for , and
As expected from the discussion in Section 3, a drop in the order of convergence is achieved by using the denominator function whilst the function allows to preserve order 1. Moreover, all the schemes allow to obtain quite accurate results.
In this paper, some NSFD schemes have been investigated for the numerical solution of the fractional-order Brusselator differential system. Some different denominator functions and nonlocal terms have been proposed and the results have been compared with a classical Adams-Bashforth-Moulton method for FDEs. From the numerical experiments, we observed that NSFD schemes allow to replicate quite well the behavior of the true solution, and hence they are a useful tool for detecting the main stability properties for the problem under investigation and for similar problems.
M.Y. Ongun and D. Arslan would like to acknowledge the partly financial support received from the Scientific Research Project Commission, SDU, Turkey, Project No: 2695-YL-11. The work of R. Garrappa has been carried out under the PRIN-MIUR project 2009F4NZJP.
- Kempfle S, Schäfer I, Beyer H: Fractional calculus via functional calculus: theory and applications. Nonlinear Dyn. 2002, 29(1–4):99–127.View ArticleGoogle Scholar
- Oldham KB, Spanier J: The Fractional Calculus. Academic Press, New York; 1974.Google Scholar
- Samko SG, Kilbas AA, Marichev OI: Fractional Integrals and Derivatives. Gordon & Breach, Yverdon; 1993. (Theory and applications, edited and with a foreword by S. M. Nikol’skiĭ, translated from the 1987 Russian original, revised by the authors)Google Scholar
- Miller KS, Ross B A Wiley-Interscience Publication. In An Introduction to the Fractional Calculus and Fractional Differential Equations. Wiley, New York; 1993.Google Scholar
- Podlubny I Mathematics in Science and Engineering 198. In Fractional Differential Equations. Academic Press, San Diego; 1999.Google Scholar
- Diethelm K Lecture Notes in Mathematics 2004. In The Analysis of Fractional Differential Equations. Springer, Berlin; 2010.View ArticleGoogle Scholar
- Mainardi F: Fractional Calculus and Waves in Linear Viscoelasticity. Imperial College Press, London; 2010.View ArticleGoogle Scholar
- Baleanu D, Diethelm K, Scalas E, Trujillo JJ Series on Complexity, Nonlinearity and Chaos 3. In Fractional Calculus. World Scientific, Hackensack; 2012.Google Scholar
- Butzer PL, Westphal U: An introduction to fractional calculus. In Applications of Fractional Calculus in Physics. Edited by: Hilfer R. World Scientific, Singapore; 2000:1–85.View ArticleGoogle Scholar
- Caponetto R, Dongola G, Fortuna L, Petráš I Series on Nonlinear Science, Series A 72. In Fractional Order Systems: Modeling and Control Applications. World Scientific, Singapore; 2010.Google Scholar
- Kilbas AA, Srivastava HM, Trujillo JJ North-Holland Mathematics Studies 204. In Theory and Applications of Fractional Differential Equations. Elsevier, Amsterdam; 2006.Google Scholar
- Magin R: Fractional Calculus in Bioengineering. Begell House Publishers, Redding; 2006.Google Scholar
- Sabatier J, Agrawal O, Machado JT: Advances in Fractional Calculus: Theor. Developments and Appl. in Physics and Engineering. Springer, Berlin; 2007.View ArticleGoogle Scholar
- Diethelm K, Ford NJ, Freed AD: A predictor-corrector approach for the numerical solution of fractional differential equations. Nonlinear Dyn. 2002, 29(1–4):3–22.MathSciNetView ArticleGoogle Scholar
- Diethelm K, Ford N, Freed A, Luchko Y: Algorithms for the fractional calculus: a selection of numerical methods. Comput. Methods Appl. Mech. Eng. 2005, 194(6):743–773.MathSciNetView ArticleGoogle Scholar
- Garrappa R, Popolizio M: On the use of matrix functions for fractional partial differential equations. Math. Comput. Simul. 2011, 81(5):1045–1056.MathSciNetView ArticleGoogle Scholar
- Garrappa R, Popolizio M: On accurate product integration rules for linear fractional differential equations. J. Comput. Appl. Math. 2011, 235(5):1085–1097.MathSciNetView ArticleGoogle Scholar
- Lubich C: Discretized fractional calculus. SIAM J. Math. Anal. 1986, 17(3):704–719.MathSciNetView ArticleGoogle Scholar
- Moret I, Novati P: On the convergence of Krylov subspace methods for matrix Mittag-Leffler functions. SIAM J. Numer. Anal. 2011, 49(5):2144–2164.MathSciNetView ArticleGoogle Scholar
- Mickens RE, Smith A: Finite-difference models of ordinary differential equations: influence of denominator functions. J. Franklin Inst. 1990, 327: 143–149.MathSciNetView ArticleGoogle Scholar
- Mickens RE: Nonstandard Finite Difference Models of Differential Equations. World Scientific, River Edge; 1994.Google Scholar
- Baleanu D, Mohammadi H, Rezapour S: Positive solutions of an initial value problem for nonlinear fractional differential equations. Abstr. Appl. Anal. 2012., 2012: Article ID 837437Google Scholar
- Delavari H, Baleanu D, Sadati J: Stability analysis of Caputo fractional-order nonlinear systems revisited. Nonlinear Dyn. 2012, 67(4):2433–2439.MathSciNetView ArticleGoogle Scholar
- Deng W, Li C, Lü J: Stability analysis of linear fractional differential system with multiple time delays. Nonlinear Dyn. 2007, 48(4):409–416.View ArticleGoogle Scholar
- Jarad F, Abdeljawad T, Baleanu D: Stability of q -fractional non-autonomous systems. Nonlinear Anal., Real World Appl. 2013, 14: 780–784.MathSciNetView ArticleGoogle Scholar
- Garrappa R: On some explicit Adams multistep methods for fractional differential equations. J. Comput. Appl. Math. 2009, 229(2):392–399.MathSciNetView ArticleGoogle Scholar
- Chen M, Clemence DP: Analysis of and numerical schemes for a mouse population model in hantavirus epidemics. J. Differ. Equ. Appl. 2006, 12(9):887–899.MathSciNetView ArticleGoogle Scholar
- Mickens RE: A nonstandard finite-difference scheme for the Lotka-Volterra system. Appl. Numer. Math. 2003, 45(2–3):309–314.MathSciNetView ArticleGoogle Scholar
- Mickens RE: Calculation of denominator functions for nonstandard finite difference schemes for differential equations satisfying a positivity condition. Numer. Methods Partial Differ. Equ. 2007, 23(3):672–691.MathSciNetView ArticleGoogle Scholar
- Roeger LIW: Nonstandard finite-difference schemes for the Lotka-Volterra systems: generalization of Mickens’s method. J. Differ. Equ. Appl. 2006, 12(9):937–948.MathSciNetView ArticleGoogle Scholar
- Roeger LIW: Dynamically consistent discrete Lotka-Volterra competition models derived from nonstandard finite-difference schemes. Discrete Contin. Dyn. Syst., Ser. B 2008, 9(2):415–429.MathSciNetView ArticleGoogle Scholar
- Moaddy K, Momani S, Hashim I: The non-standard finite difference scheme for linear fractional PDEs in fluid mechanics. Comput. Math. Appl. 2011, 61(4):1209–1216.MathSciNetView ArticleGoogle Scholar
- Momani S, Rqayiq AA, Baleanu D: A nonstandard finite difference scheme for two-sided space-fractional partial differential equations. Int. J. Bifurc. Chaos 2012., 22(4): Article ID 1250079Google Scholar
- Radwan AG, Moaddy K, Momani S: Stability and non-standard finite difference method of the generalized Chua’s circuit. Comput. Math. Appl. 2011, 62(3):961–970.MathSciNetView ArticleGoogle Scholar
- Galeone L, Garrappa R: On multistep methods for differential equations of fractional order. Mediterr. J. Math. 2006, 3(3–4):565–580.MathSciNetView ArticleGoogle Scholar
- Wang Y, Li C: Does the fractional Brusselator with efficient dimension less than 1 have a limit cycle? Phys. Lett. A 2007, 363(5–6):414–419.View ArticleGoogle Scholar
- Zhou T, Li C: Synchronization in fractional-order differential systems. Physica D 2005, 212(1–2):111–125.MathSciNetView ArticleGoogle Scholar
- Gafiychuk VV, Datsko B: Stability analysis and limit cycle in fractional system with Brusselator nonlinearities. Phys. Lett. A 2008, 372(29):4902–4904.View ArticleGoogle Scholar
- El-Sayed AMA, El-Mesiry AEM, El-Saka HAA: On the fractional-order logistic equation. Appl. Math. Lett. 2007, 20(7):817–823.MathSciNetView ArticleGoogle Scholar
- Matignon D: Stability results for fractional differential equations with applications to control processing. 2. In Computational Engineering in Systems Applications. IMACS, IEEE-SMC, Lille; 1996:963–968.Google Scholar
- Garrappa, R: Predictor-corrector PECE method for fractional differential equations. MATLAB Central File Exchange [File ID: 32918] (2011)Google Scholar
- Garrappa R: On linear stability of predictor-corrector algorithms for fractional differential equations. Int. J. Comput. Math. 2010, 87(10):2281–2290.MathSciNetView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.