Article
Kyungpook Mathematical Journal 2020; 60(3): 629-645
Published online September 30, 2020
Copyright © Kyungpook Mathematical Journal.
Uniformly Convergent Numerical Method for Singularly Perturbed Convection-Diffusion Problems
Derartu Ayansa Turuna, Mesfin Mekuria Woldaregay, Gemechis File Duressa*
Department of Mathematics, Ambo University, Ambo, Ethiopia
e-mail : dereayansa@gmail.com
Department of Applied Mathematics, Adama Science and Technology University, Adama, Ethiopia
e-mail : msfnmkr02@gmail.com
Department of Mathematics, Jimma University, Jimma, Ethiopia
e-mail : gammeef@gmail.com
Received: July 17, 2019; Revised: April 23, 2020; Accepted: May 4, 2020
Abstract
A uniformly convergent numerical method is developed for solving singularly perturbed 1-D parabolic convection-diffusion problems. The developed method applies a non-standard finite difference method for the spatial derivative discretization and uses the implicit Runge-Kutta method for the semi-discrete scheme. The convergence of the method is analyzed, and it is shown to be first order convergent. To validate the applicability of the proposed method two model examples are considered and solved for different perturbation parameters and mesh sizes. The numerical and experimental results agree well with the theoretical findings.
Keywords: convection-diffusion problem, method of line, non-standard finite difference, singular perturbation.
1. Introduction
The convection-diffusion-reaction equation is consists of three processes [19]. The first process is convection and is due to the movement of materials from one region to another. The second process is diffusion and is due to the movement of materials from a region of high concentration to a region of low concentration. The third process is reaction and is due to the decay, absorption and reaction of substances with other components. The convection-diffusion-reaction PDE provides a very useful and important mathematical model in a wide range of applications in sciences and engineering. Applications include the water quality problem in river networks [11], simulation of oil extraction from under-ground reservoirs [9], convective heat transport problems with large Peclet numbers [8], electromagnetic field problems in moving media [13], financial modeling of option pricing [2], turbulence models [16], drift diffusion equations of semiconductor device modeling [23], atmospheric pollution [24], fluid flow with high Reynolds numbers [20] and ground water transport [1]. In many of these applications, the unknown variables in the governing PDEs represent physical quantities that cannot take negative values such as pollutants, population, and concentration of chemical compounds [3].
Differential equations whose highest order derivative(s) is multiplied by a small perturbation parameter
In the case of singularly perturbed problems, the use of numerical methods developed for solving regular problems leads to errors in the solution that depend on the value of the parameter ε. Errors of the numerical solution depend on the distribution of mesh points and become small only when the effective mesh-size in the layer is much less than the value of the parameter ε [18]. Such numerical methods turn out to be inapplicable for singularly perturbed problems. Because of this, there is an interest in the development of numerical methods where solution errors are independent of the parameter or that converge ε-uniformly. When the solutions of a PDE are ε-uniformly convergent, the methods and solutions are called robust [10]. Some ε-uniform numerical schemes developed for the considered problem can be found in [4, 5, 6, 12, 14, 28].
It is well known that classical numerical methods for solving singular perturbation problems are unstable and fail to give accurate results when the perturbation parameter ε is small. The accuracy and convergence of the methods need attention, because the treatment of singular perturbation problems is not trivial, and the solution depends on perturbation parameter and mesh size h [7]. This suggests that numerical treatment of singularly perturbed 1-D parabolic convection-diffusion problems should be improved. The presence of the singular perturbation parameter ε, leads to oscillations or divergence in the computed solutions while using classical numerical methods. To avoid these oscillations or divergence, an unacceptably large number of mesh points are required when ε is very small, which is not practical. So, to overcome this drawback associated with classical numerical methods, we develop a method based on the method of line (MOL) using a non-standard finite difference method in a spatial direction together with an implicit Runge-Kutta method of order two and three in the temporal direction, which treat the problem without creating an oscillation. Thus, this paper presents an accurate and ε-uniformly convergent numerical method for solving singular perturbation 1-D parabolic convection-diffusion problem.
The paper is organized as follows. In Section 1 a brief introduction about the problem is given, in Section 2 the definition of the problem and the behavior of its analytical solution is given. In Section 3, the discretization of the spatial domain and techniques of non-standard finite differences are discussed, and the ε-uniform convergence of the semi-discrete problem is proved. Next, the Runge-Kutta method is used for the system of IVPs resulting from the spatial discretization and the convergence of the discrete scheme is discussed. In Section 4, numerical examples and results are given to validate the theoretical analysis and finally in Section 5, the conclusion of the work done is given.
2. Statement of the Problem
A singularly perturbed 1-D parabolic convection-diffusion problem on the domain
where ε is the perturbation parameter such that 0 < ε ≪ 1, the coefficient functions
In this paper, we assume the case
2.1. Properties of Analytical Solution
Next, we see some of the properties of analytical solutions to the problem.
Let
so that the data matches at the two corners points (0,0) and (1,0).
Let
Since we considered a right boundary layer problem using compatibility conditions, we deduce that there exists a constant
For details, the interested reader can refer to page 105 of [25].
To show the bounds of the solution
Since
Since
which implies
where
At the initial value:
At the boundary points:
For the differential operator:
which implies that
Hence, the proof is completed.
3. Formulation of Numerical Scheme
3.1. Discretization in Spatial Direction
On the spatial domain [0, 1], we introduce uniform mesh with mesh length
First we rewrite Eq.
Solving Eq.
where
Using the upwind finite difference for the first derivative, we obtain the scheme as
Now combining the Eqs.
Using
In this discretization Eq.
where
where
and
respectively.
Now we need to show the semi-discrete operator
Using the assumption, we get
At the boundary points we have
On the discretized domain
From Lemma 3.1, using the semi-discrete maximum principle, we obtain
3.2. Convergence Analysis for Semi-discrete Scheme
In the above two lemmas we proved that the semi-discrete operator
Hence, we obtain the bound as:
Above we used the estimate
Above we used the estimate
where
Using the boundedness of the derivatives of the solution in Lemma 2.4, with Eq.
Since
This complete the proof.
where
3.3. Discretization in Temporal Direction
On the time domain [0,
with the initial condition
where
Then using the boundedness of the solution, Lemma 3.4 implies
This shows that the discretization in temporal direction is consistent and global error is bounded. Now we use Eq.
Using boundedness of the solution, Theorem 3.2, and Eq.
4. Numerical Experiments and Discussion
To validate the established theoretical results in this paper, we perform experiments using the proposed numerical scheme on the problem of the form given in Eq.
The exact solution is not known for the first example, therefore maximum nodal errors are calculated using the double mesh principle given in [27] as
where
The rate of convergence of the method is calculated using the formula
The solution of the problems given in Example 4.1 and 4.2 has a boundary layer at the right side of the
5. Conclusion
In this paper, an ε-uniform numerical method has been developed for solving singularly perturbed 1-D parabolic convection-diffusion problems with a boundary layer on the right side of the domain. The developed method is based on the method of a line that constitutes the non-standard finite-difference for the spatial discretization and an implicit Runge-Kutta method of order 2 and 3 is used in the temporal direction for the system of initial value problem resulting from the spatial discretization. The stability and convergence of the proposed scheme are analyzed. Two model examples have been considered to validate the theoretical analysis by taking different values for the perturbation parameter ε. The computational results are presented in tables and figures. The proposed numerical scheme is first-order convergent. The performance of the scheme is investigated by comparing the results with prior studies. The proposed method gives more accurate and ε-uniformly convergent numerical results.
Acknowledgements
References
- J.. Bear, and A.. Verruijt. Modeling groundwater flow and pollution,
, Springer Science & Business Media, 2012. - F.. Black, and M.. Scholes.
The pricing of options and corporate liabilities . J. Polit. Econ..,81 (3)(1973), 637-654. - B. M.. Chen-Charpentier, and H. V.. Kojouharov.
An unconditionally positivity preserving scheme for advection-diffusion reaction equations . Math. Comput. Modeling.,57 (9-10)(2013), 2177-2185. - M.. Chandru, P.. Das, and H.. Ramos.
Numerical treatment of two-parameter singularly perturbed parabolic convection diffusion problems with non-smooth data . Math. Methods Appl. Sci..,41 (14)(2018), 5359-5387. - C.. Clavero, J. C.. Jorge, and F.. Lisbona.
A uniformly convergent scheme on a nonuniform mesh for convection-diffusion parabolic problems . J. Comput. Appl. Math..,154 (2)(2003), 415-429. - A.. Das, and S.. Natesan.
Uniformly convergent hybrid numerical scheme for singularly perturbed delay parabolic convection-diffusion problems on Shishkin mesh . Appl. Math. Comput..,271 (2015), 168-186. - E. P.. Doolan, J. J. M.. Miller, and W. H. A.. Schilders. Uniform numerical methods for problems with initial and boundary layers,
, Boole Press, Dublin, 1980. - D. K.. Edwards, J. T.. Gier, K. E.. Nelson, and R. D.. Roddick.
Spectral and directional thermal radiation characteristics of selective surfaces for solar collectors . Solar Energy.,6 (1)(1962), 1-8. - R. E.. Ewing.
Problems arising in the modeling of processes for hydrocarbon recovery . The Mathematics of Reservoir Simulation.,(1983) SIAM, 3-34. - P. A.. Farrell, A. F.. Hegarty, J. J. H.. Miller, E.. O'Riordan, and G. I.. Shishkin.
Robust computational techniques for boundary layers . Applied Mathematics (Boca Raton) 16,, Chapman & Hall, 2000:168-186. - A. D.. Goodwin, C. F.. Meares, L. H.. DeRiemer, C. I.. Diamanti, R. L.. Goode, J. J.. Baumert, D. J.. Sartoris, R. L.. Lantieri, H. D.. Fawcett, and Clinical studies with In-111. BLEDTA.
a tumor-imaging conjugate of bleomycin with a bifunctional chelating agent . J. Nuclear Medicine.,22 (9)(1981), 787-792. - S.. Gowrisankar, and S.. Natesan.
Robust numerical scheme for singularly perturbed convection-diffusion parabolic initial-boundary-value problems on equidistributed grids . Comput. Phys. Commun..,185 (2014), 2008-2019. - P. F.. Hahn, D. D.. Stark, S.. Saini, J. M.. Lewis, J.. Wittenberg, and J.. Ferrucci.
Ferrite particles for bowel contrast in MR imaging: design issues and feasibility studies . Radiology.,164 (1)(1987), 37-41. - MK.. Kadalbajoo, V.. Gupta, and A.. Awasthi.
A uniformly convergent B-spline collocation method on a nonuniform mesh for singularly perturbed one dimensional time-dependent linear convection-diffusion problem . J. Comput. Appl. Math..,220 (1-2)(2008), 271-289. - H.. Lamba, and AM.. Stuart.
Convergence results for the MATLAB $ODE23$ routine . BIT.,38 (4)(1998), 751-780. - B. E.. Launder, D. B.. Spalding, The numerical computation of turbulent. flows, and Numerical prediction of. flow.
The numerical computation of turbulent flows, Numerical prediction of flow, heat transfer, turbulence and combustion .,Array (1983), 96-116. - R. E.. Mickens, and Nonstandard finite difference. schemes. Applications of nonstandard finite difference schemes,
, World Scientific, Atlanta, 1999:1-54. - J. J. H.. Miller, E.. O'Riordan, and G. I.. Shishkin. Fitted numerical methods for singular perturbation problems,
, World Scientific Publishing, Singapore, 2012. - Ndivhuwo. M..
Numerical solution of 1-D convection-diffusion-reaction equation , Thesis, 2013. - W. L.. Oberkampf, T. G.. Trucano, C.. Hirsch, . Verification, and . validation.
and predictive capability in computational engineering and physics . Appl. Mech. Rev..,57 (5)(2004), 345-384. - R.. O'Malley, and Singular perturbation methods for ordinary differential. equations.
Singular perturbation methods for ordinary differential equations . Applied Mathematical Sciences 89,, Springer-Verlag, New York, 1991. - K. C.. Patidar.
On the use of nonstandard finite difference methods . J. Difference Equ. Appl..,11 (2005), 735-758. - S. J.. Polak, C. Den. Heijer, W.. Schilders, and P.. Markowich.
Semiconductor device modeling from the numerical point of view . Int. J. Numer. Meth. Engin..,24 (4)(1987), 763-838. - J. A.. Pudykiewicz.
Application of adjoint tracer transport equations for evaluating source parameters . Atmospheric environment.,32 (17)(1998), 3039-3050. - H. G.. Roos, M.. Stynes, L.. Tobiska, and Robust numerical methods for singularly perturbed differential. equations. Robust numerical methods for singularly perturbed differential equations, convection-diffusion-reaction and flow problems,
, Springer-Verlag, Berlin, Heidelberg, 2008. - L. F.. Shampine, and M. W.. Reichelt.
The matlab ode suite, SIAM . J. Scientific Computing.,18 (1)(1997), 1-22. - M. M.. Woldaregay, and G. F.. Duressa.
Uniformly convergent numerical method for singularly perturbed delay parabolic differential equations arising in computational neuroscience . Kragujevac J. Math..,46 (1)(2022), 65-84. - C.. Yanping, and L.. Li-Bin.
An adaptive grid method for singularly perturbed time-dependent convection-diffusion problems . Commun. Comput. Phys..,20 (5)(2016), 1340-1358.