Research Article  Open Access
YueQian Wu, XinQing Sheng, XingYue Guo, HaiJing Zhou, "Study on the Accuracy Improvement of the SecondKind Fredholm Integral Equations by Using the BuffaChristiansen Functions with MLFMA", International Journal of Antennas and Propagation, vol. 2016, Article ID 2417402, 7 pages, 2016. https://doi.org/10.1155/2016/2417402
Study on the Accuracy Improvement of the SecondKind Fredholm Integral Equations by Using the BuffaChristiansen Functions with MLFMA
Abstract
Former works show that the accuracy of the secondkind integral equations can be improved dramatically by using the rotated BuffaChristiansen (BC) functions as the testing functions, and sometimes their accuracy can be even better than the firstkind integral equations. When the rotated BC functions are used as the testing functions, the discretization error of the identity operators involved in the secondkind integral equations can be suppressed significantly. However, the sizes of spherical objects which were analyzed are relatively small. Numerical capability of the method of moments (MoM) for solving integral equations with the rotated BC functions is severely limited. Hence, the performance of BC functions for accuracy improvement of electrically large objects is not studied. In this paper, the multilevel fast multipole algorithm (MLFMA) is employed to accelerate iterative solution of the magneticfield integral equation (MFIE). Then a series of numerical experiments are performed to study accuracy improvement of MFIE in perfect electric conductor (PEC) cases with the rotated BC as testing functions. Numerical results show that the effect of accuracy improvement by using the rotated BC as the testing functions is greatly different with curvilinear or plane triangular elements but falls off when the size of the object is large.
1. Introduction
Surface integral equations (SIEs) are widely used for computing electromagnetic scattering in reallife problems. Integral equations (IEs) are termed as the firstkind Fredholm IEs, such as the EFIE for PEC cases and the PoggioMillerChangHarringtonWuTsai (PMCHWT) equations or the combined tangential formulation (CTF) for dielectric cases. The firstkind Fredholm IEs are well known to have good accuracy but bad condition number; besides this, IEs are termed as the secondkind Fredholm IEs, such as the MFIE for PEC cases and the combined normal formulation (CNF) or the normal Muller formulation (NMF) for dielectric cases. The secondkind Fredholm IEs are less accurate but significantly better convergence [1–7].
Application of the secondkind integral equations is limited especially when high accuracy is required in reallife application. In recent years, much work has been done to improve the numerical accuracy of the secondkind IEs. Some of them focus on accurate calculation of the impedance elements by using the proper basis functions [8–12]. Besides these, other good methods are also mentioned by the way of improving the discretization schemes [13–16]. Careful examination shows that the secondkind IEs have identity operators, which will cause large discretization error [17, 18]. Recently, the rotated BC function is used as testing function in the secondkind IEs for both the PEC and dielectric cases to achieve a better accuracy. It is proved in [19, 20] that the discretization error caused by the identity operators can be greatly reduced by using the rotated BC function as testing functions. It is investigated that the secondkind IEs can achieve comparable or even better accuracy to the firstkind IEs while keeping their fast convergence speed [19]. The discretization procedure which uses the rotated BC as testing function for the secondkind IEs is shown with spherical examples in [19]. A later work generalized this technique for arbitrary shaped objects by handling near singularities with singular extraction technique [20]. However, the size of the objects is severely limited with the conventional MoM which is implemented to solve the integral equation; numerical performance of using the rotated BC as the testing function for large objects is not well studied.
In this paper, the MLFMA is employed to accelerate matrixvector multiplication in the iterative solution of the MFIE with the rotated BC as the testing function (NBCMFIE). Since, for PEC cases, there is only the operator which has the identity operator, only PEC cases are studied. Accuracy and convergence rate of the proposed NBCMFIE is studied in the case of scattering by cube and spherical objects with different radius. It is demonstrated that the accuracy of the MFIE can be improved significantly by using this discretization scheme. For some cases, this proposed procedure even achieves better accuracy than the EFIE with RaoWiltonGlisson (RWG) [21] as the testing function (RWGEFIE). Compared with the conventional MFIE (RWGMFIE), better convergence in iterative solution can be achieved by NBCMFIE. It should be noted that different effects of the geometry modeling with planar or curvilinear triangular patches are studied in this paper.
The rest of this paper is organized as follows: A brief description of the formulation of NBCMFIE is presented in Section 2. Various discretization schemes of SIEs with PEC scattering objects are compared in Section 3. Based on the numerical results, a discussion is presented in Section 4. Finally, Section 5 gives our conclusions.
2. Outline of the MLFMA Enhanced the MFIE with the Rotated BC as the Testing Function
Consider the case of scattering by a given PEC object; based on the principle of equivalence, a set of integral equations in terms of equivalent electric current can be derived [22]: the EFIE and MFIE. The boundary of the PEC body is taken as the equivalent surface, with the incident plane wave denoted as . Hence,where and denote the free space wave impedance and solid angle (). The identity operator is ; operators and are defined as follows:where and which is free space Green’s function.
As shown in Figure 1, by using the rotated BC as the testing function, an original large triangular patch is first divided into six small patches. Then the RWG basis function and the BC function can be expressed as the superposition of the RWG function defined on the barycentric refinement of the original triangular path:and definitions of the weighting factors , , , , and can be found in [23].
(a) RWG function
(b) BC function
By expanding the electric current on the object surface using the RWG basis functions and applying as the testing function, we can obtain the matrix form of (1): with and are sparse transformation matrices mapping the RWG function and the BC function defined on the original mesh to the RWG function on the refined mesh, and their columns are composed by and in (5). For the conventional MFIE with the RWG basis function as its testing function, there is an identity operator in the final impedance matrix:which causes large discretization error and makes the MFIE less accurate. From (7) we can know that when the rotated BC is used as the testing function, there also exists an identity operator but has the form ofCompared with the identity operator in (9), (10) is weakly tested and propagates its inaccuracy less in final results.
Due to the computational and storage complexity of , MoM is conventionally limited and difficult to be used for dealing with electrically largescale problems. Various acceleration methods including MLFMA and fast Fourier transform (FFT) are developed for matrixvector multiplication during iterative solution steps [21, 24]. In this paper, MLFMA is employed to accelerate the computing and reduce the memory requirements for the impedance matrix in (7).
When MLFMA is employed, near interaction matrix is computed and stored explicitly in set up step. For far interactions between the basis and testing elements, MLFMA calculates them in a groupbygroup manner consisting of three stages called aggregation, translation, and disaggregation [24]. In MLFMA, far interactions can be factorized aswhere the disaggregation term , the aggregation terms , and the translation term are explicitly expressed as follows:where and and are the basis functions residing in groups or centered at and respectively. denotes the spherical Hankel function of the second kind, is the Legendre polynomial of degree , and is the number of multipole expansion terms; here it is determined bywith being diagonal length of the cubical box. And then construct the MLFMA tree in a multilevel way.
3. Numerical Results
To demonstrate the accuracy, efficiency, and capability of the presented algorithm, a series of numerical experiments are performed in this section. All the computations are performed on a computer platform LiuhuiII at the Center for Electromagnetic Simulation, Beijing Institute of Technology. It has 2 Intel X5650 2.66 GHz CPUs with 12 cores for each CPU, 96 GB memory. The GMRES solver is employed and the convergence criterion is set to 0.001.
We consider various discretization schemes for SIEs. Table 1 summarizes 6 discretization schemes, where the first and second columns show the choices of the basis functions and the testing functions; the third column shows the equations; the last column shows the abbreviations for different discretization schemes. For example, the abbreviation “NCBCMFIE” means we choose the CRWG basis functions to expand the current densities and the to test MFIE. CBC means the BC basis function which is defined on curvilinear patch. Similarly, CRWG represents the curvilinear RWG.

Firstly, we consider plane wave scattering by a PEC sphere with a radius of 1 m. We compute the normalized root mean square (RMS) error of the bistatic radar cross section (RCS) for this sphere problem; the reference value is obtained with the Mie series. The normalized RMS error is defined as
The frequency of the incident plane wave is 75 MHz and 150 MHz, respectively. These two examples are exactly the same as Figure 4 in [19]; what is more, different effects by using the curvilinear triangular patches and planar triangular patches are studied. Figures 2(a) and 2(b) show the normalized RMS error as a function of the number of unknowns for various approaches with the planar and curvilinear patches, respectively. In Figure 3, faster iterative convergence is investigated for the NCBCMFIE. Then we increase the frequency of incident wave to 150 MHz; the same results can be obtained obviously from Figure 4 as the frequency of the incident wave is 75 MHz. We can observe from these figures the following:(1)The accuracy of the EFIE (RWGEFIE and CRWGEFIE) is always better than the MFIE (RWGMFIE and CRWGMFIE), which is the same as known to all. However, when the rotated BC function is used as the testing function, the MFIE (NBCMFIE and NCBCMFIE) can achieve comparable or even better accuracy than that of EFIE.(2)When the curvilinear patches are employed, all CRWGMFIE, CRWGEFIE, and NCBCMFIE can achieve better accuracy than those with the planar triangular patches. The NCBCMFIE with curvilinear has the best accuracy, even better than that of the CRWGEFIE. On one hand, the discretization error of the identity operator is suppressed when the rotated BC is used as the testing function; on the other hand, when the size of the objects is relatively small, the curvilinear patches will affect the accuracy of the final results greatly.(3)The numbers of iterations for solving final matrix equation system for the NCBCMFIE/NBCMFIE and CRWGMFIE/RWGMFIE keep constant as a function of number of unknowns, but for CRWGEFIE/RWGEFIE, the number of iterations increases fast. The NBCMFIE converges even faster than that of RWGMFIE. We can conclude that NBCMFIE can achieve better accuracy than both RWGEFIE and RWGMFIE and maintain fast convergence of the secondkind integral equation at the same time.
(a) With planar patches
(b) With curvilinear patches
Then we increase the frequency of the incident plane wave to 0.6 GHz and 1.2 GHz. The computed normalized RMS error as a function of unknowns is plotted in Figures 5 and 6, respectively. Since the size of the PEC sphere is relatively large now, the MLFMA is employed to speed matrixvector multiplication in these iterative solution steps. From these two figures we can conclude the following:(1)As observed in Figures 5 and 6, when we increase the frequency of the incident plane wave, the accuracy of the RWGEFIE is still better than the RWGMFIE. What is different is that the BCMFIE can achieve better accuracy than the RWGMFIE, but less accurate than that of the RWGEFIE. With the size of the object increasing, the discretization error of the identity operator can be improved in the MFIE by using the BC function as the testing function. However, as shown in (10), the identity operator does not vanish but still exists in MFIE. With the increment of the size, the discretization error of the identity operator in (10) increases. Hence the BCMFIE is less accurate than the RWGEFIE.(2)With the increment of the number of unknowns (increase in mesh density), the iteration numbers of NBCMFIE with curvilinear and planar patches are almost the same.
To demonstrate various discretization schemes further, the last case is a PEC cube with 2 m edges; the frequency of the incident wave is 300 MHz. This problem is discretized with various mesh sizes and solved with the RWGMFIE, RWGEFIE, and NBCMFIE. Figure 7 presents the normalized RMS error of RCS with various discretization schemes for this cube problem. We use the result of the RWGEFIE with 1/50λ discretization mesh size as the reference result. Similar to the previous large spherical examples, the NBCMFIE improves the accuracy of the MFIE by using the BC functions but gets less accurate result than the result of the RWGEFIE.
4. Discussion
As was shown in the section of numerical results, the accuracy of the MFIE is highly improved by using the as the testing function while maintaining high convergence rate. Especially when the object size is small, we can get the same conclusion as shown in [19]; the NCBCMFIE can get more accurate result than the CRWGEFIE. When the size of object becomes larger, the accuracy of the MFIE can be improved, but less accurate than EFIE, because there are two aspects which lead to high accuracy: the BC function and the curvilinear patch. The effect of the curvilinear patch gets weaker with the size increasing, so the accuracy of the NCBCMFIE is almost the same as that of the NBCMFIE when the incident frequency is high. Cube case shows the same conclusion. From our analysis, there are two benefits for using the rotated BC functions:(1)During the process of calculating the impedence elements, the identity operator can be more accurately calculated which is shown in (10).(2)During the process of solving the matrix equation, the matrices and map the relationship between the test functions and the basis functions from the space of the barycentric meshes to the space of the original meshes, which leads to well conditioned matrix. That is why this discretization scheme can achieve good convergence rate. In contrast, using the rotated RWG functions as the testing functions can fulfill benefit but also makes the matrices illconditioned.
5. Conclusion
In this paper, numerical performance of the MFIE with the rotated BC function as the testing function is studied and the MLFMA is employed to accelerate iterative solution for the electrical large objects. A series of numerical experiments on PEC cube and spheres with different radius show that, compared with using the RWG as the testing functions, the discretization error of the identity operator can indeed be suppressed by using the rotated BC as testing functions. When the size of the sphere is not very large, the NCBCMFIE can achieve accurate numerical solutions that are comparable to (or even better than) the existing solutions of CRWGEFIE, because the discretization error for the identity operator is sharply decreased and the geometry modeling is more accurate. However, the effect of the accuracy improvement by using the rotated BC function as testing function for the MFIE falls off when the radius of the sphere becomes large. When the rotated BC function is used instead of the conventional RWG function, the discretization error of the identity operator although being improved still exists.
Competing Interests
The authors declare that they have no competing interests.
Acknowledgments
This work was supported by Major State Basic Research Development Program of China (Grant no. 2013CB328904), National Natural Science Foundation of China (Grant no. 61431014), and CAEP (Grant no. 2015B0403094).
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Copyright © 2016 YueQian Wu 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.