Complexity
Volume 2018 (2018), Article ID 2927080, 6 pages
https://doi.org/10.1155/2018/2927080

Research Article

Error Estimates for the Heterogeneous Multiscale Finite Volume Method of Convection-Diffusion-Reaction Problem

Tao Yu,¹ Peichang Ouyang,¹ and Haitao Cao²

¹Department of Mathematics and Physics, Jinggangshan University, Ji’an 343009, China
²Department of Mathematics and Physics, Hohai University, Changzhou Campus, Changzhou 213022, China

Correspondence should be addressed to Peichang Ouyang; g_fcayang@163.com

Received 10 May 2018; Accepted 10 September 2018; Published 8 October 2018

Academic Editor: Yan-Ling Wei

Copyright © 2018 Tao Yu et al. 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.

Abstract.

Based on the heterogeneous multiscale method, this paper presents a finite volume method to solve multiscale convection-diffusion-reaction problem. The paper constructs an algorithm of the optimal order convergence rate in -norm under periodic medias.

1. Introduction

This paper considers the multiscale method for the following convection-diffusion-reaction problem

where (or ) is a bounded convex polygonal domain with Lipschitz boundary , is a positive parameter which signifies the multiscale nature of (1). This problem is related to groundwater and solute transport in porous media [1].

Optimal order convergence rate of classical finite element method based on piecewise linear polynomials relies on the -norm of . As coefficients vary on a scale of , the solution may also oscillate at the same scale. A direct numerical solution of this multiscale problem is very difficult to derive unless the mesh size is sufficiently smaller. However, it is not feasible in practice since the amount of computation will increase sharply as the amount of calculation increases. On the other hand, from an engineering point of view, the macroscopic features of the solution are often of main interest and importance. According to homogenization theory [2, 3], there is a homogenized equation which can capture the macroscopic properties. In other words, there exist homogenized coefficients , , and so that

Though there exist many classical methods for solving (2), unfortunately, in general, there are no explicit formulae for the homogenized coefficients, except that there are many restrictive assumptions on the media. Thus, developing numerical methods that can capture the effect of small scale on the large scale is an attractive subject. To overcome the above difficulties, many strategies have been established to solve the problems on grids which are coarser than the scale of oscillation; see [4–9] and references therein.

The multiscale finite volume (MSFV) method was first introduced by Jenny et al. for the elliptic problem of highly oscillatory coefficients [9]. Based on a special interpolation from the coarse mesh to the fine mesh, this method captured the effect of small scales on a coarse grid efficiently. It not only fitted into finite volume framework nicely but also kept both the conservation of coarse and fine scales. This method was widely applied in many situations, such as discrete fracture modeling [10], parabolic problem [11], and Maxwell’s equations [12]. Recently, Weinan E. and Engquist established a general efficient methodology—heterogeneous multiscale method (HMM) [13], for the multiscale problems, which consists of two components: (a) selecting a macroscopic solver on a coarse mesh and (b) estimating the missing macroscale data by solving local fine-scale problems. The careful selection of the macro solver and local fine problems is a key issue for the method. A different choice of macroscopic solver will lead to a different heterogeneous multiscale method; see some examples for finite element method [8, 14, 15] and discontinuous finite element method [4, 16]. It is well known that the finite volume method has many advantages, such as keeping conservation and applying to complex regions. Thus, in this paper, we choose the finite volume method (FVM) introduced in [17] as the macroscopic solver. For convenience, the heterogeneous multiscale method taking finite volume method as the macroscopic solver is denoted as HMM-FVM. We will show that our method has optimal order convergence rate in -norm for periodic medias.

The rest of this paper is organized as follows. To solve (1), Section 2 first constructs a HMM-FVM. Then, Section 3 will study the approximate solution and its error estimates for periodic media associated with (1) in detail. We finally conclude the paper in Section 4.

2. HMM-FVM for Convection-Diffusion-Reaction Problem

In this section, we first concisely describe the finite volume method for convection-diffusion-reaction problem in [17]. Then, using the method of [17] as a macroscopic solver of HMM, we derive the HMM-FVM in detail.

Let be a quasi-uniform triangulation of the polygonal domain . The barycenter dual decomposition is constructed by connecting the barycenter to the midpoints of edges of each triangle element. Suppose is a triangle element, denote and , respectively, as an edge and barycenter of and the midpoint of . Assume is a nodal point and is the dual element with respect to (referring to Figure 1). Let be the maximum length of the edges, the set of all nodal points, the vertices of .

(a)

(b)

Figure 1: An element (a) and its dual element (b) with respect to a node .

Let and be the piecewise linear finite element space on , be the interpolation operator from to the piecewise constant space on :

Then, the finite volume method [17] reads as finding such that

where

We next construct our multiscale method with respect to (1).

By the numerical integration for the barycenter quadrature rule, (5) and (6) can be written as

Thus, the barycenter quadrature approximation of the FVM can be read as finding such that

To obtain error estimate, this paper considers the coefficients of scale separation. Assume that the multiscale coefficients , , and have the forms , , and . Furthermore, assume that , , and are smooth in and periodic in with respect to the unit cube .

In the absence of explicit expressions of , , and , , , and can be approximated by

where is the solution of the following microcell problem

and is a cube of size centered at .

Thus, the HMM-FVM can be summarized as the following variational problem: , find satisfying (9)–(11).

3. A Priori Error Estimate

This section deduces the main result of the paper—optimal error estimate (Theorem 2). To this end, we first introduce Lemma 1 and Lemma 2 to derive its modeling error. Then, by regarding the HMM-FVM as a perturbation of the linear finite element method (Theorem 1), we obtain the optimal error estimate of HMM-FVM.

Lemma 1 ([14, 18, 19]). There exists a constant independent of H, , and such that for , ,

where . Here, and are, respectively, the periodic solutions of , with zero mean (i.e., , ).

Lemma 2 ([18, 20]). Given domain with , let defined in be a periodic function in , where is a unit cube and is fixed. Then

where is independent of , , and .

Mathematically, the finite element method for the homogenization problem (2) is equivalent to finding such that

where

Applying barycenter quadrature to (17), the bilinear form can be refined as

Finally, to estimate the priori error estimate, we borrow inf-sup condition of bilinear form given in [17].

Lemma 3 [17]. For ,

The following theorem characterizes the difference between the bilinear form of HMM-FVM and FEM, which will play the key role in the subsequent analysis.

Theorem 1. , we have

where is a positive constant independent of , , and .

Proof. The bilinear form can be split into three terms

where

Denote by

By the standard estimate of [21], we get

Accordingly, the numerical quadrature error is

We next utilize Lemma 1 and Lemma 2 to estimate the modeling error . By the definition of in (11) and Lemma 2, we see that the error between and can be treated as

where and is a positive constant independent of , , and . Let

By Lemma 1 and (27),

Thus, the modeling error can be further estimated as

It remains to estimate the term which concerns three components. We next start this work.
By Green’s formula, the first term of is

The first term of can be derived similarly

The combination of the two gives

Recall the interpolation operator (see Lemma 2.1 in [17]) satisfying

From (29), (33), and (34), the last term can be estimated as

By the estimates of , , and , we finally get

With the preparation of Theorem 1, we summarize the main result in Theorem 2.

Theorem 2. Denote by and the solution of (2) and (9), respectively. Then for sufficient small and , we have

where is a positive constant independent of , , and .

Proof. In order to estimate the error between and , we separate it into two parts

where is the numerical solution of (16), which is FEM formula of the homogenized problem (2).
From the standard error estimate [21], the first part of (38) can be estimated as

It remains to treat the second part of (38). By Lemma 3, we have

According to Theorem 1, can be estimated as

On the other hand, the numerator of in (40) is

Thus,

We see (37) follows immediately by combining the results of (38)–(43).

4. Conclusion

This paper investigates the multiscale convection-diffusion-reaction problem. Based on the heterogeneous multiscale method, we first choose a finite volume method as macroscopic solver elaborately which can keep conservation and can be applied to complex regions. Then, we construct a suitable HMM-FVM strategy to solve the problem. We show that this method possesses the optimal order convergence rate in -norm for periodic medias. As a matter of fact, this method can be applied in practice to multiscale problems without assumptions of periodic and scale separation.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (nos. 11461035 and 11761039), Science and Technology Plan Project of Jiangxi Provincial Education Department (nos. GJJ160749 and GJJ160758) and the Doctoral Starting up Foundation of Jinggangshan University (no. JZB11002).

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