Printer Friendly

Finite Volume Element Approximation for the Elliptic Equation with Distributed Control.

1. Introduction

In recent years, the optimization with partial differential equation constraints (PDEs) has received a significant impulse. Because of wide applicability of the field, a lot of theoretical results have been developed. Generally, it is difficult to obtain the analytical solutions for optimal control problems with PDEs. Factually, only approximate solutions or numerical solutions can be expected. Therefore, many numerical methods have been proposed to solve the problems.

Finite element method is an important numerical method for the problems of partial differential equations and widely used in the numerical solution of optimal control problems. There are extensive studies in convergence of finite element approximation for optimal control problems. For example, priori error estimates for finite element discretization of optimal control problems governed by elliptic equations are discussed in many publications. In [1], a new approach to error control and mesh adaptivity is described for the discretization of the optimal control problems governed by elliptic partial differential equations. In [2], the error estimates for semilinear elliptic optimal controls in the maximum norm are presented. Chen and Liu present a priori error analysis for mixed finite element approximation of quadratic optimal control problems [3]. In [4], a priori error analysis for the finite element discretization of the optimal control problems governed by elliptic state equations is considered. Hou and Li investigate the error estimates of mixed finite element methods for optimal control problems governed by general elliptic equations and derive [L.sup.2] and [H.sup.1] error estimates for both the control and state variables [5].

The finite volume element method has been one of the most commonly used numerical methods for solving partial differential equations. The advantages of the method are that the computational cost is less than finite element method, and the mass conservation law is maintained. So it has been extensively used in computational fluid dynamics [6-12]. However, there are only a few published results on the finite volume element method for the optimal control problems. In [13], the authors discussed distributed optimal control problems governed by elliptic equations by using the finite volume element methods. The variational discretization approach is used to deal with the control and the error estimates are obtained in some norms. In [14], the authors considered the convergence analysis of discontinuous finite volume methods applied to distributed optimal control problems governed by a class of second-order linear elliptic equations.

In this paper, we will investigate the finite volume element method for the general elliptic optimal control problem with Dirichlet or Neumann boundary conditions. The variational discretization approach is used to deal with the control, which can avoid explicit discretization of the control and improve the approximation. In addition, we discuss the optimal control problems in polygonal domains with corner singularities. In this situation, the solution does not admit integrable second derivatives. The desired convergence results of finite volume element schemes cannot be expected. Two effective methods are proposed to compensate the negative effects of the corner singularities. The corresponding results will be reported in the future.

The rest of the paper is organized as follows. In Section 2, the model problem and the finite volume element schemes are introduced. Section 3 presents the error estimates of the finite volume element schemes. In Section 4, numerical results are supplied to justify the theoretical analysis. Brief conclusions are given in Section 5.

2. Problem Statement and Discretization

2.1. Model Problem. In this paper, we consider the following second-order elliptic partial differential equation:

-[nabla] x (A[nabla]y) + [c.sub.0]y = Bu + f, in [OMEGA], (1)

where [OMEGA] [subset] [R.sup.2] is a bounded convex polygon with boundary [partial derivative][OMEGA], A = {[a.sub.ij](x)} is a 2 x 2 symmetric and uniformly positive definite matrix, [c.sub.0] >0 is a sufficient smooth function defined on [OMEGA], B denotes the linear and continuous control operator, Bu [member of] [L.sup.2]([OMEGA]), and u and f have enough regularity so that this problem has a unique solution when we combine either homogeneous Dirichlet or Neumann boundary conditions on [partial derivative][OMEGA].

In addition, we use the following notations for the inner products and norms on [L.sup.2]([OMEGA]), and [L.sup.[infinity]]([OMEGA])

[mathematical expression not reproducible], (2)

The corresponding weak formulation for (1) is

Find y [member of] H such that a(y, v) = (Bu + f, v),

[for all]v [member of] H, (3)

where

[mathematical expression not reproducible], (4)

and

(Bu + f, v)= [[integral].sub.[OMEGA]] (Bu + f) vdx; (5)

H denotes either depending on the prescribed type of boundary conditions (homogeneous Neumann or Dirichlet).

Now, we consider the following optimal control problem for state variable y and the control variable u:

[mathematical expression not reproducible], (6)

over all H x [L.sup.2]([OMEGA]) subject to elliptic state problem (3) and the control constraints

[u.sub.a] (x) [less than or equal to] u(x) [less than or equal to] [u.sub.b] (x), (7)

where [y.sub.[OMEGA]] [member of] [L.sup.2]([OMEGA]) is a given desired state and [lambda] [greater than or equal to] 0 is a regularization parameter. We define the set of admissible control by

[U.sub.ad] = {u [member of] [L.sup.2] ([OMEGA]) : [u.sub.a] (x) [less than or equal to] u [less than or equal to] [u.sub.b](x)}, (8)

where [U.sub.ad] is a nonempty, closed, and convex subset of [L.sup.2]([OMEGA]), [u.sub.a](x) [less than or equal to] [u.sub.b](x).

From standard arguments for elliptic equations, we can obtain the following propositions.

Proposition 1. For fixed control u [member of] [L.sup.2]([OMEGA]), the state equation (3) admits a unique solution y [member of] H. Moreover, there is a constant C, which does not depend on Bu + f, such that

[[parallel] u [parallel].sub.1] [less than or equal to] C[parallel]Bu + f[parallel]. (9)

Proposition 2. Let [U.sub.ad] be a nonempty, closed, bounded, and convex set, [y.sub.[OMEGA]] in [L.sup.2]([OMEGA]) and [lambda] > 0; then the optimal control problem (6) admits a unique solution ([bar.y], [bar.u]).

This proof follows standard techniques [15]. The adjoint state equation for [bar.z] [member of] H is given by

a ([bar.z], w) = ([bar.y] - [y.sub.[OMEGA]], w), [for all]w [member of] H, (10)

where the equation is the weak formulation of the following elliptic problem:

-[for all] x (A[for all][bar.z]) + [c.sub.0][bar.z] = [bar.y] - [y.sub.[OMEGA]], in [OMEGA], (11)

with homogeneous Neumann or Dirichlet boundary conditions.

Proposition 3. The necessary and sufficient optimality conditions for (6) and (7) can be expressed as the variational inequality

([lambda][bar.u] + [B.sup.*][bar.z], u - [bar.u]) [greater than or equal to] 0, [for all]u [member of] [U.sub.ad]. (12)

Further, the variational inequality is equivalent to

[mathematical expression not reproducible], (13)

where [mathematical expression not reproducible] denotes the orthogonal projection in [L.sup.2]([OMEGA]) onto the admissible set of the control and [B.sup.*] is the adjoint operator of B.

2.2. Discretization. Now we describe the finite volume element discretization of the optimal control problem (6).

We consider a quasi-uniform triangulation [T.sub.h]. Divide [bar.[OMEGA]] into a sum of finite number of small triangles K such that they have no overlapping internal region and a vertex of any triangle does not belong to a side of any other triangle. At last, we can obtain a triangulation such that [mathematical expression not reproducible].

We then construct a dual mesh [T.sup.*.sub.h] related to [T.sub.h]. Let [P.sub.0] be a node of a triangle, [P.sub.i] (i = 1, 2, ..., 6) the adjacent nodes of [P.sub.0], and [M.sub.i], the midpoint of [P.sub.0][P.sub.i]. Choose the barycenter [Q.sub.i]; of triangle [DELTA][P.sub.0][P.sub.i][P.sub.i+1] ([P.sub.7] = [P.sub.1]) as the node of the dual mesh. Connect successively [M.sub.1], [Q.sub.1], ..., [M.sub.6], [Q.sub.6], [M.sub.1] to form a polygonal region V, called a control volume. Figure 1 presents a sketch of a control volume.

Let [U.sub.h] be the trial function space defined on the triangulation [T.sub.h],

[U.sub.h] = {v [member of] C ([OMEGA]) : v|[.sub.K] is linear for all K [member of] [T.sub.h]}, (14)

and [V.sub.h] be the test function space defined on the dual mesh [T.sup.*.sub.h],

[V.sub.h] = {v [member of] [L.sup.2] ([OMEGA]) : v|[sub.v] is constant for all V [member of] [T.sup.*.sub.h]}. (15)

In this way, we have

[mathematical expression not reproducible], (16)

where [[phi].sub.i] are the standard node basis functions with the nodes [x.sub.i] and are the characteristic functions of the control volume [V.sub.i].

Let [I.sub.h] and [I.sup.*.sub.h]; be the interpolation projections onto the trial function space [U.sub.h] and test function space [V.sub.h], respectively. By the interpolation theory, we have for w [member of] [U.sub.h] [intersection] [H.sup.2]

[mathematical expression not reproducible]. (17)

Then the finite volume element schemes for (3), (10), and (13) are defined as follows:

[mathematical expression not reproducible], (18)

[mathematical expression not reproducible], (19)

[mathematical expression not reproducible], (20)

where

[mathematical expression not reproducible]. (21)

In order to present the error estimates, we first introduce some lemmas in preparation of the proof for the main convergence theorem.

?.1. Some Lemmas. According to [16], we have the following lemma, which indicates that the bilinear form [a.sub.h](x, [I.sup.*.sub.h] x) is coercive on [U.sub.h].

Lemma 4. [a.sub.h](x, [I.sup.*.sub.h] x) is positive definite for small enough h; namely, there exist [h.sub.0] > 0, [alpha] > 0 such that for 0 < k [less than or equal to] [k.sub.0]

[a.sub.h](x, [I.sup.*.sub.h] x) [greater than or equal to] [alpha] [[parallel]v[parallel].sup.2.sub.1], [for all]v [member of] [U.sub.h]. (22)

We seldom have a symmetric bilinear form [a.sub.h](x, [I.sup.*.sub.h] x) even though a(x, x) is symmetric. The following lemma is used to measure how far the bilinear form [a.sub.h](x, [I.sup.*.sub.h] x) is from being symmetric [17].

Lemma 5. There exist positive constants C, [h.sub.0] such that, for u, w [member of] [U.sub.h] and 0 < k [less than or equal to] [k.sub.0], we have

[mathematical expression not reproducible]. (23)

Furthermore, we introduce the auxiliary functions [[bar.y].sup.h] [member of] [U.sub.h] and [[bar.y].sup.h] [member of] [U.sub.h] which are the solutions of the following problems:

[mathematical expression not reproducible]. (24)

For the problems, we can obtain the following results.

Lemma 6. Let [[bar.y].sub.h] and [[bar.z].sub.h] be the solution of (18) and (19) and [[bar.y].sup.h], [[bar.z].sup.h] be the solution of (24). Then, we have

[mathematical expression not reproducible], (25)

[mathematical expression not reproducible]. (26)

Proof. Combining (18) and (24), we have

[mathematical expression not reproducible]. (27)

By taking v = [[bar.y].sup.h] - [[bar.y].sub.h] and using Lemma 4, we have

[mathematical expression not reproducible], (28)

where Lemma 4 is used. At last, we can obtain (25) with Cauchy-Schwarz inequality. Equation (26) can be obtained similarly.

The results in [18] can easily be extended to cover the elliptic equations with homogeneous Neumann boundary conditions. Now we list the useful theoretical results in the following lemma.

Lemma 7. Let [bar.y] and [bar.z] be the solution of (4) and (10), respectively, and [[bar.y].sup.h], [[bar.z].sup.h] be the solution of (24), [bar.u], f, [y.sub.[OMEGA]] [member of] [H.sup.1]([OMEGA]), and A [member of] [W.sup.2,[infinity]]. Then there exists a positive constant C > 0 and [h.sub.0] >0 such that for 0 < h [less than or equal to] [h.sub.0]

[mathematical expression not reproducible]. (29)

3.2. [L.sup.2] Error Estimate

Theorem 8. Assume that [bar.u] and [[bar.u].sub.h] are the solutions of (6) and (20), respectively, [bar.u], f, [y.sub.[OMEGA]] [member of] [H.sup.1]([OMEGA]), and A [member of] [member of] [W.sup.2,[infinity]]. Then there exists a positive constant C > 0 and [h.sub.0] > 0 such that for 0 < h [less than or equal to] [h.sub.0]

[parallel] [bar.u] [[bar.u].sup.h] [parallel] [less than or equal to] [Ch.sup.2]. (30)

Proof. Let us test (12) with [[bar.u].sup.h], and (20) with [bar.u], and sum up the two inequalities; we have

[mathematical expression not reproducible]. (31)

We further get

[mathematical expression not reproducible], (32)

where

[mathematical expression not reproducible]. (33)

Combining the above equations, we can obtain

[mathematical expression not reproducible]. (34)

According to Lemmas 5, 6, and 7, we have

[mathematical expression not reproducible]. (35)

[mathematical expression not reproducible]. (36)

Using Lemmas 5 and 6, we conclude

[mathematical expression not reproducible]. (37)

Combining (34)-(37) and using Lemma 7, we can obtain the desirable result

[parallel] [bar.u] [[bar.u].sup.h] [parallel] [less than or equal to] [Ch.sup.2]. (38)

Theorem 9. Assume that [bar.y], [bar.z] are the solutions of (6) and (11), respectively, and [[bar.y].sup.h], [[bar.z].sup.h] are the solutions of (18) and (19), respectively, [bar.u], f, [y.sub.[OMEGA]] [member of] [H.sup.1]([OMEGA]), and A [member of] [W.sup.2,[infinity]]. Then there exists a positive constant C > 0 such that

[parallel] [bar.y] [[bar.y].sup.h] [parallel] [less than or equal to] [Ch.sup.2],

[parallel] [bar.z] [[bar.z].sup.h] [parallel] [less than or equal to] [Ch.sup.2]. (39)

Proof. Using the triangle inequality, we have

[mathematical expression not reproducible]. (40)

From Lemma 6 and Theorem 8, we can obtain

[mathematical expression not reproducible]. (41)

Using Lemma 7, we can obtain the desired result

[parallel] [bar.y] [[bar.y].sup.h] [parallel] [less than or equal to] [Ch.sup.2]. (42)

Similarly, we have

[parallel] [bar.z] [[bar.z].sup.h] [parallel] [less than or equal to] [Ch.sup.2]. (43)

3.3. [H.sup.1] Error Estimate

Theorem 10. Assume that [bar.y], [bar.z] are the solutions of (6) and (11), respectively, and [[bar.y].sup.h], [[bar.z].sup.h] are the solutions of (18) and (19), respectively, [bar.u], f, [y.sub.[OMEGA]] [member of] [L.sup.2]([OMEGA]), and A [member of] [W.sup.1,[infinity]]. Then there exists a positive constant C > 0 such that

[[parallel] [bar.y] [[bar.y].sup.h] [parallel].sub.1] [less than or equal to] [Ch.sup.2],

[[parallel] [bar.z] [[bar.z].sup.h] [parallel].sub.1] [less than or equal to] [Ch.sup.2]. (44)

Using the triangle inequality, we have

[mathematical expression not reproducible]. (45)

From Lemma 4, we can obtain

[mathematical expression not reproducible]. (46)

By using Lemma 7 and Theorems 8 and 9, we can obtain the desired result

[[parallel] [bar.y] [[bar.y].sup.h] [parallel].sub.1] [less than or equal to] Ch. (47)

Similarly, we have

[[parallel] [bar.z] [[bar.z].sup.h] [parallel].sub.1] [less than or equal to] Ch. (48)

Remark 11. In the case [U.sub.ad] = [L.sup.2]([OMEGA]), the projection equations (13) and (20) become [bar.u] = -[B.sup.*] z/[lambda] and [[bar.u].sup.h] = -[B.sup.*] [[bar.z].sup.h]/A, respectively. Using the above theorem, we can obtain the following error estimate:

[[parallel] [bar.u] [[bar.u].sup.h] [parallel].sub.1] [less than or equal to] Ch. (49)

3.4. [L.sup.[infinity]] Error Estimate

Theorem 12. Assume that [[bar.y].sup.h], [[bar.z].sup.h], [[bar.u].sup.h] are the solutions of (18), (19), and (20), respectively, [bar.u], f, [y.sub.[OMEGA]] [member of] [H.sup.1]([OMEGA]), and A [member of] [member of] [W.sup.2,[infinity]]. Then there exists a positive constant C >0 such that

[mathematical expression not reproducible]. (50)

Proof. Using the projection equations (13) and (20), we have

[mathematical expression not reproducible]. (51)

Similarly, we have

[[parallel] [bar.z] [[bar.z].sup.h] [parallel].sub.[infinity]] [less than or equal to] [Ch.sup.2] log 1/h. (52)

4. Numerical Experiments

In this section, we report some numerical results of finite volume element schemes for the elliptic optimal control problems. To illustrate the theoretical analysis, the following rate of convergence r is defined:

r = [log.sub.2] ([parallel] [u.sub.2h] - u [parallel]/[parallel] [u.sub.h] - u [parallel]), (53)

where [u.sub.h] is the numerical solution with space step size h and u the analytical solution. The rate approaching the number 2 would indicate second-order accuracy in space.

4.1. Experiment 1. To validate the finite volume element schemes for the solution of elliptic optimal control problems, test example is needed for which the exact solutions are known in advance [15]. We consider the problems with homogeneous Neumann boundary condition,

[mathematical expression not reproducible], (54)

subject to

- [DELTA]y + y = u + f, in [OMEGA],

[nabla] y x n = 0, on [partial derivative][OMEGA], (55)

where Q denotes unit square [0,1] x [0,1], [U.sub.ad] = [L.sup.2]([OMEGA]), n is the outer unit normal vector, and f = 1 - [sin.sup.2](2[pi][x.sub.1])[sin.sup.2](2[pi][x.sub.2]). Under these settings, the optimal control is

[bar.u](x) = [sin.sup.2] (2[pi][x.sub.1]) [sin.sup.2] (2[pi][x.sub.2]). (56)

The adjoint state is

[bar.z](x) = [sin.sup.2] (2[pi][x.sub.1]) [sin.sup.2] (2[pi][x.sub.2]), (57)

and the associated state is

[bar.y](x) = 1. (58)

Then we can determine the function [y.sub.[OMEGA]] accordingly.

Errors of finite volume element schemes in [L.sup.[infinity]], [L.sup.2], and [H.sup.1] norm are computed. Data are listed in Tables 1-3. In Tables 1 and 3, errors in [H.sup.1] norm have optimal convergence order for both control and adjoint state. These results confirm our theoretical error analysis (44). In Table 2, due to additional smoothness of the state, the [H.sup.1] error is O([h.sup.2]). The convergence results in Tables 1-3 demonstrate second-order accuracy in [L.sup.[infinity]] and [L.sup.2] norm for the control, state, and adjoint state.

Figure 2 depicts the development of the [L.sup.[infinity]], [L.sup.2], and [H.sup.1] error for the control, state, and adjoint state under uniform refinement of the mesh. From the figure, the expected order O([h.sup.2]) in [L.sup.[infinity]] and [L.sup.2] norm for the control is observed, and the order O(k) in [H.sup.1] norm is shown. Additionally, we observe convergence of order O([h.sup.2]) in [L.sup.[infinity]] and [L.sup.2] norm for state and adjoint state. Because of better smoothness of state, the order O([h.sup.2]) in [H.sup.1] norm is also observed.

We perform a simulation with space size h = 1/32 for this problem. Figure 3 presents the computed state, optimal control, and adjoint state. Examination of Figure 3 shows that the approximate solutions coincide with the true solutions. At the same time, the relationship between the control and adjoint state is preserved well.

4.2. Experiment 2. Now, we consider the optimal control problem with homogeneous Dirichlet boundary condition and control constraint,

[mathematical expression not reproducible], (59)

subject to

-[DELTA]y = u, in [OMEGA],

y = 0, on [partial derivative][OMEGA], (60)

where [OMEGA] denotes the unit circle, [U.sub.ad] = {u [member of] [L.sup.2]([OMEGA]) : -0.2 [less than or equal to] u [less than or equal to] 0.2}, [y.sub.[OMEGA]](x) = (1 - ([x.sup.2.sub.1] + [x.sup.2.sub.2]))[x.sub.1], and [lambda] = 0.1.

The exact solution of the problem is not known in advance. So we use the numerical results computed on a grid with h = 1/256 as reference solutions. The [L.sup.[infinity]], [L.sup.2], and [H.sup.1] errors for state, control, and adjoint state of the above problems have been computed. They are displayed in Tables 4-6 for the finite volume element schemes. Examination of the tables shows that the error measures of the schemes diminish approximately quadratically for the error in [L.sup.[infinity]] and [L.sup.2] norm and linearly for the error in [H.sup.1] norm, which are consistent with our theoretical analysis.

In Figure 4, the development of the [L.sup.[infinity]], [L.sup.2], and [H.sup.1] error for control, state, and adjoint state under uniform refinement of the mesh is shown. Here, the expected order O([h.sup.2]) in [L.sup.[infinity]] and [L.sup.2] norm for the control is observed. Again, we observe convergence of order O([h.sup.2]) in [L.sup.[infinity]] and [L.sup.2] norm for state and adjoint state, which is consistent with our expectation of the order of convergence. The errors in [H.sup.1] norm confirm our error estimation (11). Figure 5 displays the numerical solution computed by the finite volume element schemes with h = 1/16. The results are nearly the same as those in [19]. The relationship between the control and adjoint state is also preserved well.

4.3. Experiment 3. Now we consider the optimal control problem (59) with [OMEGA] = [(-1,1).sup.2] \ ([-1,0] x [0,1]) denoting an L-shaped domain, [U.sub.ad] = {u [member of] [L.sup.2]([OMEGA]) : -0.2 [less than or equal to] u [less than or equal to] 0.2}. Further, we set [y.sub.[OMEGA]] = 1 - ([x.sup.2.sub.1] + [x.sup.2.sub.2]) and [lambda] = 0.1.

In this situation, the solution does not admit integrable second derivatives. The desired convergence results of finite volume element schemes cannot be expected. So we only present the numerical solutions of the finite volume element schemes in Figure 6, which are nearly the same as those in [19]. On one hand, the desired convergence results may be obtained by using graded meshes and postprocessing [20], which will need more computational cost. On the other hand, we can modify finite volume element schemes near the corner to obtain the second-order accuracy. The related results will be reported in the future.

5. Conclusions

In this article, we have investigated the finite volume element discretizations of optimal control problems governed by linear elliptic partial differential equations and subject to pointwise control constraints. Optimal order [L.sup.2], [H.sup.1], and [L.sup.[infinity]] error estimates for the considered problems are obtained and numerical experiments validate the theoretical results. In addition, we discuss the optimal control problems in polygonal domains with corner singularities. Two effective methods are proposed to compensate the negative effects of the corner singularities. The corresponding results will be reported in the future.

https://doi.org/10.1155/2018/4753792

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 project is partially supported by the Fundamental Research Funds for the Central Universities (Nos. KYZ201565 and KJQN201839) and the National Natural Science Foundation of China (Nos. 11701283,11426134, and 11471166).

References

[1] R. Becker, H. Kapp, and R. Rannacher, "Adaptive finite element methods for optimal control of partial differential equations: basic concept," SIAM Journal on Control and Optimization, vol. 39, no. 1, pp. 113-132, 2000.

[2] N. Arada, E. Casas, and F. Troltzsch, "Error estimates for the numerical approximation of a semilinear elliptic control problem," Computational Optimization and Applications, vol. 23, no. 2, pp. 201-229, 2002.

[3] Y. Chen and W. Liu, "Error estimates and superconvergence of mixed finite element for quadratic optimal control," International Journal of Numerical Analysis & Modeling, vol. 3, no. 3, pp. 311-321, 2006.

[4] A. Kroner and B. Vexler, "A priori error estimates for elliptic optimal control problems with a bilinear state equation," Journal of Computational and Applied Mathematics, vol. 230, no. 2, pp. 781-802, 2009.

[5] T. Hou and L. Li, "Error estimates of mixed methods for optimal control problems governed by general elliptic equations," Advances in Applied Mathematics and Mechanics, vol. 8, no. 6, pp. 1050-1071, 2016.

[6] Z. Zhang, "Error estimates for finite volume element method for the pollution in groundwater flow," Numerical Methods for Partial Differential Equations, vol. 25, no. 2, pp. 259-274, 2009.

[7] Q. Wang, Z. Zhang, and Z. Li, "A Fourier finite volume element method for solving two-dimensional quasi-geostrophic equations on a sphere," Applied Numerical Mathematics, vol. 71, pp. 1-13, 2013.

[8] Q. Wang, S. Lin, and Z. Zhang, "Numerical methods for a fluid mixture model," International Journal for Numerical Methods in Fluids, vol. 71, no. 1, pp. 1-12, 2013.

[9] Q. Wang, Z. Zhang, X. Zhang, and Q. Zhu, "Energy-preserving finite volume element method for the improved Boussinesq equation," Journal of Computational Physics, vol. 270, pp. 58-69, 2014.

[10] R. Ruiz-Baier and H. Torres, "Numerical solution of a multidimensional sedimentation problem using finite volume-element methods," Applied Numerical Mathematics, vol. 95, pp. 280-291, 2015.

[11] C. Chen and W. Liu, "A two-grid finite volume element method for a nonlinear parabolic problem," International Journal of Numerical Analysis & Modeling, vol. 12, no. 2, pp. 197-210, 2015.

[12] C. Bi and C. Wang, "A posteriori error estimates of finite volume element method for second-order quasilinear elliptic problems," International Journal of Numerical Analysis & Modeling, vol. 13, no. 1, pp. 22-40, 2016.

[13] X. Luo, Y. Chen, and Y. Huang, "Some error estimates of finite volume element approximation for elliptic optimal control problems," International Journal of Numerical Analysis & Modeling, vol. 10, no. 3, pp. 697-711, 2013.

[14] R. Sandilya and S. Kumar, "Convergence analysis of discontinuous finite volume methods for elliptic optimal control problems," International Journal of Computational Methods, vol. 13, no. 2, 1640012, 20 pages, 2016.

[15] F. Troltzsch, Optimal Control of Partial Differential Equations: Theory, Methods and Applications, vol. 112, American Mathematical Society, 2010.

[16] R. Li, Z. Chen, and W. Wu, Generalized difference methods for differential equations, vol. 226 of Monographs and Textbooks in Pure and Applied Mathematics, Marcel Dekker, Inc., 2000.

[17] S. Chou and Q. Li, "Error estimates in [L.sup.2], [H.sup.1] and [L.sup.[infinity]] in covolume methods for elliptic and parabolic problems: a unified approach," Mathematics of Computation, vol. 69, no. 229, pp. 103-120, 2000.

[18] R. E. Ewing, T. Lin, and Y. Lin, "On the accuracy of the finite volume element method based on piecewise linear polynomials," SIAM Journal on Numerical Analysis, vol. 39, no. 6, pp. 1865-1888, 2002.

[19] M. Hinze, R. Pinnau, M. Ulbrich, and S. Ulbrich, Optimization with PDE Constraints, Springer, 2009.

[20] T. Apel, A. Rosch, and D. Sirch, "[L.sup.[infinity]] error estimates on graded meshes with application to optimal control," SIAM Journal on Control and Optimization, vol. 48, no. 3, pp. 1771-1796, 2009.

Quanxiang Wang (iD), (1) Tengjin Zhao, (2) and Zhiyue Zhang (iD) (2)

(1) College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

(2) Jiangsu Key Laboratory for NSLSCS, School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China

Correspondence should be addressed to Zhiyue Zhang; zhangzhiyue@njnu.edu.cn

Received 1 February 2018; Revised 21 April 2018; Accepted 3 September 2018; Published 1 November 2018

Guest Editor: Omar Abu Arqub

Caption: FIGURE 1: Control volume with barycenter as internal point.

Caption: FIGURE 2: The [L.sup.[infinity]], [L.sup.2], and [H.sup.1] error for the control, state, and adjoint state under uniform refinement of the mesh.

Caption: FIGURE 3: Numerical results of Experiment 1: optimal state, optimal control, and corresponding adjoint state.

Caption: FIGURE 4: The [L.sup.[infinity]], [L.sup.2], and [H.sup.1] error for the control, state, and adjoint state under uniform refinement of the mesh.

Caption: FIGURE 5: Numerical results of Experiment 2: optimal state, optimal control, and corresponding adjoint state.

Caption: FIGURE 6: Numerical results of Experiment 3: optimal state, optimal control, and corresponding adjoint state.
TABLE 1: Errors of the control for different error norms.

h        [L.sup.[infinity]]      r      [L.sup.[infinity]]      r
               error                          error

1/8          1.7412E-01         --          4.4468E-02         --
1/16         4.1870E-02        2.05         1.0374E-02        2.09
1/32         1.0476E-02        2.00         2.5558E-03        2.02
1/64         2.6171E-03        2.00         6.3685E-04        2.00

h            [H.sup.1]           r
               error

1/8            2.3006           --
1/16           1.1313          1.02
1/32         5.6371E-01        1.00
1/64         2.8162E-01        1.00

TABLE 2: Errors of the state for different error norms.

h         [L.sup.[infinity]]      r      [L.sup.[infinity]]      r
                error                          error

1/8           1.1429E-03         --          3.7634E-04         --
1/16          2.1927E-04        2.38         8.5181E-05        2.14
1/32          5.1326E-05        2.09         2.0833E-05        2.03
1/64          1.2609E-05        2.03         5.1751E-06        2.01

h             [H.sup.1]           r
                error

1/8           5.3988E-03         --
1/16          1.1332E-03        2.25
1/32          2.7140E-04        2.06
1/64          6.7061E-05        2.02

TABLE 3: Errors of the adjoint state for different error norms.

h         [L.sup.[infinity]]      r
                error

1/8           1.7415E-02         --
1/16          4.1867E-02        2.06
1/32          1.0473E-02        1.99
1/64          2.6159E-03        2.00

h           [L.sup.2]        r        [H.sup.1]        r
              error                     error

1/8        4.4468E-02       --         2.3006         --
1/16       1.0373E-02      2.09        1.1313        1.02
1/32       2.5552E-03      2.02      5.6371E-01      1.00
1/64       6.3655E-04      2.01      2.8162E-01      1.00

TABLE 4: Errors of the control for different error norms.

h          [L.sup.[infinity]]      r
                 error

1/8            4.2814E-03         --
1/16           1.2186E-03        1.81
1/32           2.9931E-04        2.02
1/64           7.6218E-05        1.97

h            [L.sup.2]        r        [H.sup.1]        r
               error                     error

1/8         2.7687E-03       --       2.2775E-02       --
1/16        7.7173E-04      1.84      1.2642E-02      0.85
1/32        1.8098E-04      2.09      5.4512E-03      1.21
1/64        4.0938E-05      2.14      2.2949E-03      1.24

TABLE 5: Errors of the state for different error norms.

h          [L.sup.[infinity]]      r
                 error

1/8            5.2997E-04         --
1/16           1.3047E-04        2.02
1/32           3.2119E-05        2.02
1/64           7.9037E-06        2.02

h          [L.sup.2]       r      [H.sup.1]       r
             error                  error

1/8        3.8069E-04     --      2.9536E-03     --
1/16       1.0869E-04    1.81     1.7383E-03    0.76
1/32       2.7098E-05    2.00     7.6992E-04    1.17
1/64       6.2918E-06    2.10     3.3199E-04    1.21

TABLE 6: Errors of the adjoint state for different error norms.

h        [L.sup.[infinity]]      r
               error

1/8          5.9019E-04         --
1/16         1.6144E-04        1.87
1/32         4.2371E-05        1.93
1/64         1.0588E-05        2.00

h        [L.sup.2]       r      [H.sup.1]       r
           error                  error

1/8      3.9236E-04     --      3.0287E-03     --
1/16     1.1101E-04    1.82     1.7719E-03    0.77
1/32     2.8422E-05    1.97     8.0665E-04    1.13
1/64     6.9125E-06    2.04     3.6516E-04    1.14
COPYRIGHT 2018 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Wang, Quanxiang; Zhao, Tengjin; Zhang, Zhiyue
Publication:International Journal of Differential Equations
Article Type:Report
Date:Jan 1, 2018
Words:5297
Previous Article:Affine Discontinuous Galerkin Method Approximation of Second-Order Linear Elliptic Equations in Divergence Form with Right-Hand Side in [L.sup.1].
Next Article:Applications of Parameterized Nonlinear Ordinary Differential Equations and Dynamic Systems: An Example of the Taiwan Stock Index.
Topics:

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |