Advances in Numerical Analysis

Volume 2014 (2014), Article ID 456142, 10 pages

http://dx.doi.org/10.1155/2014/456142

Research Article

A Meshless Method for the Numerical Solution of a Two-Dimension IHCP

F. Parzlivand and A. M. Shahrezaee

Department of Mathematics, Alzahra University, Vanak, Tehran 19834, Iran

Received 25 February 2014; Accepted 10 July 2014; Published 3 August 2014

Academic Editor: Ting-Zhu Huang

Copyright © 2014 F. Parzlivand and A. M. Shahrezaee. 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.

This paper uses the collocation method and radial basis functions (RBFs) to analyze the solution of a two-dimension inverse heat conduction problem (IHCP). The accuracy of the method is tested in terms of Error and RMS errors. Also, the stability of the technique is investigated by perturbing the additional specification data by increasing the amounts of random noise. The results of numerical experiments are compared with the analytical solution in illustrative examples to confirm the accuracy and efficiency of the presented scheme.

1. Introduction

In many industrial applications one wishes to determine the temperature on the surface of a body, where the surface itself is inaccessible for measurements. In this case, it is necessary to determine the surface temperature from a measured temperature history at a fixed location inside the body. This is called an IHCP and has been an interesting subject recently [1]. Inverse problems have practical implications in thermal transport systems which involve conduction, convection, and radiation. In thermal radiation [2], for example, identifying the distribution of the radiation source has been stimulated by a wide range of applications, including thermal control in space technology, combustion, high-temperature forming and coating technology, solar energy utilization, high-temperature engines, and furnace technology [3]. The importance of inverse heat conduction problems and appropriate solution algorithms are established in numerous works and the books (see, e.g., [1, 4–7] and the references therein). Several techniques have been proposed for solving a one-dimensional IHCP [8–14]. Among the methods proposed for higher dimensional IHCP, boundary element [15], finite difference [16], and finite element [17] have been widely adopted for problems in two-dimension. Besides, the sequential function specification method [1, 15] and differential method [18] have also been used in solving the IHCP. There is, however, still a need on numerical scheme for two-dimensional IHCP. The traditional mesh-dependent finite difference and finite element methods are so far the principal numerical tool of choice for the modeling and simulation of the IHCP. A major disadvantage of these methods, however, is their mesh-dependent characteristics which normally requires enormous computational effort and induces numerical instability when large number of grids or elements are required.

In this paper, a two-dimensional IHCP is solved by RBFs as a truly meshless/meshfree method. A meshfree method does not require a mesh to discretize the domain or boundary of the problem under consideration and the approximate solution is constructed entirely based on a set of scattered nodes. It is considered as the main advantage of these methods over the mesh-dependent techniques.

In the recent rapid development of meshless computational schemes, the RBF has been successfully developed as an efficient meshless scheme for solving various kinds of partial differential equations (PDEs) and ordinary differential equations (ODEs). For example, see [19–25]. Also some applications of this approach in solving inverse problems can be found in [20, 21, 26, 27].

2. Problem Formulation

In this section, we consider the following two-dimension IHCP, in the dimensionless form:

and the overspecified condition:

where is known, represent the final time of interest for the time evolution of the problem, and , , , , and are known functions in their domain satisfying the compatibility conditions while and remain to be determined from some interior temperature measurements.

The existence and uniqueness of the solution of this problem are discussed in [28]. The problem (1)–(2) may be divided into two separate problems.

The first problem is

This problem may be analyzed as a direct problem, for the portion of the body from to with known boundary conditions. There is a unique stable solution to the direct problem (3)–(8) and may be found in [29]. The second problem is the following IHCP:

Therefore, for solving the inverse problem (1)–(2), we will investigate the direct problem (3)–(8) and IHCP (9)–(14). In the next section, the above problems will be considered; the heat in the body and heat at the boundary will be obtained by solving these problems numerically.

3. Radial Basis Functions

Radial basis functions are very efficient instruments for interpolating a scattered set of points. The use of the radial basis function for solving partial differential equations has some advantages over mesh-dependent methods, such as finite difference methods, finite element methods, spectral methods, finite volume methods, and boundary element methods. The use of radial basis functions as a meshless method for numerical solution of partial differential equations is based on the collocation method. Because of the collocation technique, this method does not need to evaluate any integral.

3.1. Definition of the Space Radial Basis Functions

Let , denote the Euclidean norm and let be a continuous function with . A radial basis function on is a function of the form

which depended only on the distance between and a fixed point [30]. So that the radial basis function is radially symmetric about the center . Some best-known RBFs are listed in Table 1, where and is a free positive parameter, often referred to as the shape parameter, to be specified by the user. Despite many research works, which have been done to find algorithms for selecting the optimum values of [31–33], the optimal choice of shape parameter is an open problem, which is still under intensive investigation.

Table 1: Some well-known functions that generate RBFs.

Name of radial basis function
Definition

Multiquadric (MQ)

Inverse quadratic (IQ)

Inverse multiquadric (IMQ)

Gaussian (GA)

Thin plate splines (TPS)

The standard radial basis functions are categorized into two major classes [34, 35].

Class  1. Infinitely smooth RBFs.

These basis functions are infinitely differentiable and involve a parameter (such as multiquadric (MQ), inverse multiquadric (IMQ), and Gaussian) which needs to be selected so that the required accuracy of the solution is attained.

Class  2. Infinitely smooth (except at centers) RBFs.

These basis functions are not infinitely differentiable. These basis functions are shape parameter free and have comparatively less accuracy than the basis functions discussed in Class 1. Examples are thin plate splines.

4. Numerical Procedures

Now we use the RBFs for discretization of both time and space variables. Let , , be a set of scattered nodes. Then, the function of two spatial dimensions to be interpolated can be represented by RBFs as

where for a radial function and ; , , and are unknown constants that must be found.

4.1. The Application of RBFs in IHCP (9)–(14)

For this problem, let , ; , ; and , . Now (9)–(14) are approximated using (16). The collocation technique is used for finding unknown constants .

We collocate (9) in points ; , ; we have

Now, collocation (10) in points yields

By collocating (12) in points , we obtain

By collocation (13) in points , we can write

and by collocation (14) in points ; , , we have

Equations (17)–(21) give a system of linear algebraic equations with the unknown coefficients . Solving this linear system, the approximate temperature distribution of the problem (9)–(14) is obtained. Knowing the temperature distribution, we can easily determine the function describing the boundary condition:

4.2. The Application of RBFs in Direct Problem (3)–(8)

Now, let , , and . The unknown function in (3)–(8) can be approximated by using (16). The collocation technique is used for finding unknown coefficients .

We collocate (3) in points ; we get

Now, collocation (4) in points , yields

By collocation (5) in points , we obtain

By collocation (6) in points , we can write

By collocation (7) in points , we have

and by collocation (8) in points , we have

Equations (23)–(28) give a system of linear algebraic equations with the unknown coefficients . Solving this linear system, the approximate solution of the problem (3)–(8) is obtained.

5. Convergence Analysis and Error Bound

This section covers the error analysis of the proposed method. Also the sufficient conditions are presented to guarantee the convergence of RBFs, when applied to solve the differential equations.

5.1. Approximation Error

Here, we are concerned with the error of the approximation of a given three-variate function by its expansion in terms of radial basis functions.

Suppose that , where (also note that the solution ); the inner product in this space is defined by

and the norm is as follows:

Let be the set of radial basis functions and

and let be an arbitrary element in . Since is a finite dimensional vector space, has the unique best approximation out of such as [36], such that

Moreover, since , there exist unique coefficients such that

Theorem 1. Let be a Hilbert space and let be a closed subspace of such that and is any basis for . Let be an arbitrary element in and let be the unique best approximation to out of . Then [36]

where

5.2. Convergence Analysis

We have the following theorem about the convergence of RBFs interpolation.

Theorem 2. Assume are nodes in which is convex; let

when for any satisfies ; we have

where is RBFs, the constant depends on the RBFs, is space dimension, and and are nonnegative integer.

Proof. A complete proof is given by authors [37, 38].

It can be seen that not only RBFs themselves but also any of their order derivatives has a good convergence.

6. Test Examples

In order to illustrate the performance of the RBFs method in solving IHCPs and justify the accuracy and efficiency of the method presented in this paper, we consider the following examples. For two examples, the true solutions are available. We tested the accuracy and stability of the method presented in this paper by performing the mentioned method for different values of , , and . To study the convergence behavior of the RBFs method, we applied the following laws.

(1)The error Error is described using

(2)The root mean square (RMS) is described using

where are interpolate nodes, is the exact value, and is the RBFs approximation.

In order to investigate the stability of the numerical method, the additional specification data has been perturbed as

where is the relative (percentage) noise level and is a random number between .

6.1. Example  1

We solve the problem (1)–(2) with [28]: , , , , , and for which the exact solution is and .

Table 2 shows the comparison between the exact solution, RBFs solution and approximate solution result from method in [28] by Tikhonov regularization 0th, 1st, and 2nd, and SVD with noiseless data. Table 3 shows this comparison with noisy data. Also, Table 4 shows the Error and RMS error values for and on the intervals , , and for various values of , , and . The corresponding results obtained for on the intervals , , and are presented in Table 5. Figure 1 shows the plot of error for and on the intervals , , and . It can be obtained from Tables 4 and 5 and Figure 2 that the accuracy increases with the increase of the number of collocation points.

Table 2: The comparison between the exact, Tikhonov, SVD, and GA-RBFs solution for with noiseless data for Example  1.

Exact
Tikhonov 0
Tikhonov 1
Tikhonov 2
SVD
GA-RBFs
GA-RBFs

()
()

0.1
0.955989
0.977515
0.953963
0.953359
0.977522
0.956061
0.955988

0.2
0.941638
0.961234
0.937549
0.937005
0.961242
0.941742
0.941636

0.3
0.917877
0.936400
0.912705
0.912258
0.936409
0.918004
0.917875

0.4
0.884946
0.903014
0.879430
0.879118
0.903023
0.885090
0.884943

0.5
0.843172
0.861074
0.837723
0.837584
0.861083
0.843326
0.843169

0.6
0.792974
0.810581
0.787585
0.787658
0.810590
0.793124
0.792971

0.7
0.734852
0.751535
0.729016
0.729339
0.751544
0.734981
0.734850

0.8
0.669388
0.683935
0.662016
0.662626
0.683945
0.669479
0.669387

0.9
0.597236
0.607783
0.586884
0.587521
0.607793
0.597279
0.597935

Table 3: The comparison between the exact, Tikhonov, SVD, and GA-RBFs solution for with noisy data () for Example  1.

Exact
Tikhonov 0
Tikhonov 1
Tikhonov 2
SVD
GA-RBFs
GA-RBFs

()
()

0.1
0.955989
0.989553
0.959390
0.958599
0.989560
0.956861
0.955980

0.2
0.941638
0.971284
0.940893
0.940179
0.971291
0.941096
0.941640

0.3
0.917877
0.945030
0.914534
0.913948
0.945037
0.919012
0.917873

0.4
0.884946
0.910791
0.880313
0.879905
0.910798
0.880111
0.884940

0.5
0.843172
0.868567
0.838231
0.838050
0.868575
0.843935
0.843171

0.6
0.792974
0.818358
0.788287
0.788384
0.818366
0.793001
0.792979

0.7
0.734852
0.760165
0.730482
0.730907
0.760172
0.735098
0.734855

0.8
0.669388
0.693986
0.664816
0.665618
0.693994
0.669153
0.669390

0.9
0.597236
0.619823
0.591288
0.592518
0.619830
0.597291
0.597938

Table 4: Error and RMS errors for and on the intervals , , and for Example  1.

Method
Error

GA-RBFs

()
RMS

Error

RMS

Error

IMQ-RBFs

()
RMS

Error

RMS

Error

MQ-RBFs

()
RMS

Error

RMS

Error

IQ-RBFs

()
RMS

Error

RMS

Error

Table 5: Error and RMS errors for on the intervals , , and for Example  1.

Method
Error

GA-RBFs

()
RMS

Error

IMQ-RBFs

()
RMS

Error

MQ-RBFs

()
RMS

Error

IQ-RBFs

()
RMS

Error

(a) for

(b) for , ,

(c) for

(d) for , ,

Figure 1: Graph of absolute error for and by using GA-RBF with for Example  1.

(a) for with

(b) for with

(c) for with

(d) for with

Figure 2: Graph of absolute error for by using different RBFs for Example  2 with , , .

6.2. Example  2

As the second example, consider (1)–(2) with [28]: , , , , , and for which the exact solution is and .

Table 6 shows the comparison between the exact solution, RBFs solution and approximate solution result from method in [28] by Tikhonov regularization 0th, 1st, and 2nd, and SVD with noiseless data. Table 7 shows this comparison with noisy data. Furthermore, Table 8 shows the and RMS error values for and on the intervals , , and for various values of , , and . The corresponding results obtained for on the intervals , , and are presented in Table 9. It can be obtained from Tables 8 and 9 that the accuracy increases with the increase of the number of collocation points. Also absolute error with different radial basis functions is depicted in Figure 2.

Table 6: The comparison between the exact, Tikhonov, SVD, and GA-RBFs solution for with noiseless data for Example  2.

Exact
Tikhonov 0
Tikhonov 1
Tikhonov 2
SVD
GA-RBFs
GA-RBFs

()
()

0.1
0.095591
0.099151
0.091232
0.091254
0.099229
0.095594
0.095591

0.2
0.190879
0.194718
0.188768
0.188793
0.194778
0.190958
0.190879

0.3
0.283933
0.286510
0.282289
0.282316
0.286555
0.284056
0.283932

0.4
0.374149
0.374528
0.371793
0.371824
0.374561
0.374295
0.374148

0.5
0.460627
0.454773
0.457291
0.457315
0.458794
0.460774
0.460626

0.6
0.542503
0.539243
0.538753
0.538791
0.539256
0.542638
0.542502

0.7
0.618958
0.615939
0.616208
0.616251
0.615946
0.619078
0.618957

0.8
0.689228
0.688861
0.689647
0.689695
0.688864
0.689332
0.689227

0.9
0.752612
0.758009
0.759070
0.759124
0.758011
0.752690
0.752612

Table 7: The comparison between the exact, Tikhonov, SVD, and GA-RBFs solution for with noisy data () for Example  2.

Exact
Tikhonov 0
Tikhonov 1
Tikhonov 2
SVD
GA-RBFs
GA-RBFs

()
()

0.1
0.095591
0.101437
0.097458
0.097523
0.101531
0.095599
0.095591

0.2
0.190879
0.198779
0.195645
0.195653
0.198852
0.190983
0.190879

0.3
0.283933
0.291784
0.289378
0.289331
0.291838
0.284318
0.283938

0.4
0.374149
0.380451
0.378655
0.378557
0.380490
0.374808
0.374150

0.5
0.460627
0.464780
0.463477
0.463330
0.464807
0.460512
0.460621

0.6
0.542503
0.544772
0.543844
0.543652
0.544789
0.542840
0.542509

0.7
0.618958
0.620427
0.619756
0.619522
0.620437
0.619578
0.618957

0.8
0.689228
0.691744
0.691212
0.690939
0.691750
0.689809
0.689221

0.9
0.752612
0.758723
0.758213
0.757905
0.758728
0.752831
0.752612

Table 8: Error and RMS errors for and on the intervals , , and for Example  2.

Method
Error

GA-RBFs

()
RMS

Error

RMS

Error

IMQ-RBFs

()
RMS

Error

RMS

Error

MQ-RBFs

()
RMS

Error

RMS

Error

IQ-RBFs

()
RMS

Error

RMS

Error

Table 9: Error and RMS errors for on the intervals , , and for Example  2.

Method
Error

GA-RBFs
RMS

()
Error

IMQ-RBFs
RMS

()
Error

MQ-RBFs
RMS

()
Error

IQ-RBFs
RMS

()
Error

7. Conclusion

In the paper, an application of the RBFs for the solution of a two-dimension IHCP is presented. Problem consists in the calculation of temperature distribution in the domain, as well as in the reconstruction of functions describing the temperature on the boundary, when the temperature measurements in the domain are known. The present study successfully applies the numerical method to IHCPs and has been found stable with respect to small perturbation in the input data. The results of computing by radial basis function method are compared to that by the method in [28]. The results of numerical examples demonstrate that this method is more accurate than the method in [28].

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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