Computational Electromagnetics Research Center (BİLCEM)

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Browsing Computational Electromagnetics Research Center (BİLCEM) by Subject "Antennas"

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    Accuracy: The Frequently Overlooked Parameter in the Solution of Extremely Large Problems
    (IEEE, 2011) Ergul, O.; Gürel, Levent
    We investigate error sources and their effects on the accuracy of solutions of extremely large electromagnetics problems with parallel implementations of the multilevel fast multipole algorithm (MLFMA). Accuracy parameters and their effects on the accuracy of MLFMA solutions are studied for large-scale problems discretized with hundreds of millions of unknowns. We show that some error sources are more dominant and should be suppressed for more accurate solutions; identifying less-effective error sources may allow us to derive more efficient implementations. Based on our analysis, we determine a set of benchmark problems that can be used to compare the accuracy of solvers for large-scale computations. A benchmarking tool is provided at www.cem.bilkent.edu.tr/ benchmark.
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    Advanced partitioning and communication strategies for the efficient parallelization of the multilevel fast multipole algorithm
    (IEEE, 2010) Ergül O.; Gürel, Levent
    Large-scale electromagnetics problems can be solved efficiently with the multilevel fast multipole algorithm (MLFMA) [1], which reduces the complexity of matrix-vector multiplications required by iterative solvers from O(N 2) to O(N logN). Parallelization of MLFMA on distributed-memory architectures enables fast and accurate solutions of radiation and scattering problems discretized with millions of unknowns using limited computational resources. Recently, we developed a hierarchical partitioning strategy [2], which provides an efficient parallelization of MLFMA, allowing for the solution of very large problems involving hundreds of millions of unknowns. In this strategy, both clusters (sub-domains) of the multilevel tree structure and their samples are partitioned among processors, which leads to improved load-balancing. We also show that communications between processors are reduced and the communication time is shortened, compared to previous parallelization strategies in the literature. On the other hand, improved partitioning of the tree structure complicates the arrangement of communications between processors. In this paper, we discuss communications in detail when MLFMA is parallelized using the hierarchical partitioning strategy. We present well-organized arrangements of communications in order to maximize the efficiency offered by the improved partitioning. We demonstrate the effectiveness of the resulting parallel implementation on a very large scattering problem involving a conducting sphere discretized with 375 million unknowns. ©2010 IEEE.
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    Analysis of Lossy Dielectric Objects with the Multilevel Fast Multipole Algorithm
    (IEEE, 2011) Ergul, O.; Gurel, Levent
    Rigorous solutions of electromagnetics problems involving lossy dielectric objects are considered. Problems are formulated with two recently developed formulations, namely, the combined-tangential formulation (CTF) and the electric and magnetic current combined-field integral equation (JMCFIE), and solved iteratively using the multilevel fast multipole algorithm (MLFMA). Accuracy and efficiency of solutions are compared for different objects and conductivity values. We show that iterative solutions of CTF are significantly accelerated as the conductivity increases and CTF becomes a good alternative to JMCFIE in terms of efficiency. Considering also its high accuracy, CTF seems to be a suitable formulation for the analysis of lossy dielectric objects.
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    Benchmark Solutions of Large Problems for Evaluating Accuracy and Efficiency of Electromagnetics Solvers
    (IEEE, 2011) Ergul, O.; Gürel, Levent
    We present a set of benchmark problems involving conducting spheres and their solutions using a parallel implementation of the multilevel fast multipole algorithm (MLFMA). Accuracy of the implementation is tested by comparing the computational results with analytical Mie-series solutions. Reference solutions are made available on an interactive website to evaluate and compare the accuracy and efficiency of fast solvers. We also demonstrate the capabilities of our solver on real-life problems involving complicated targets, such as the Flamme.
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    Computational analysis of complicated metamaterial structures using MLFMA and nested preconditioners
    (IEEE, 2007-11) Ergül, Özgür; Malas, Tahir; Yavuz, Ç; Ünal, Alper; Gürel, Levent
    We consider accurate solution of scattering problems involving complicated metamaterial (MM) structures consisting of thin wires and split-ring resonators. The scattering problems are formulated by the electric-field integral equation (EFIE) discretized with the Rao-Wilton- Glisson basis functions defined on planar triangles. The resulting dense matrix equations are solved iteratively, where the matrix-vector multiplications that are required by the iterative solvers are accelerated with the multilevel fast multipole algorithm (MLFMA). Since EFIE usually produces matrix equations that are ill-conditioned and difficult to solve iteratively, we employ nested preconditioners to achieve rapid convergence of the iterative solutions. To further accelerate the simulations, we parallelize our algorithm and perform the solutions on a cluster of personal computers. This way, we are able to solve problems of MMs involving thousands of unit cells.
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    Effective preconditioners for large integral-equation problems
    (IET, 2007-11) Malas, Tahir; Ergül, Özgür; Gürel, Levent
    We consider effective preconditioning schemes for the iterative solution of integral-equation methods. For parallel implementations, the sparse approximate inverse or the iterative solution of the near-field system enables fast convergence up to certain problem sizes. However, for very large problems, the near-field matrix itself becomes too crude approximation to the dense system matrix and preconditioners generated from the near-field interactions cannot be effective. Therefore, we propose an approximation strategy to the multilevel fast multipole algorithm (MLFMA) to be used as a preconditioner. Our numerical experiments reveal that this scheme significantly outperforms other preconditioners. With the combined effort of effective preconditioners and an efficiently parallelized MLFMA, we are able to solve targets with tens of millions of unknowns in a few hours.
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    Efficient surface integral equation methods for the analysis of complex metamaterial structures
    (IEEE, 2009) Yla-Oijala, P.; Ergül, Özgür; Gürel, Levent; Taskinen, M.
    Two approaches, the multilevel fast multipole algorithm with sparse approximate inverse preconditioner and the surface equivalence principle algorithm, are applied to analyze complex three-dimensional metamaterial structures. The efficiency and performance of these methods are studied and discussed.
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    Improving iterative solutions of the electric-field integral equation via transformations into normal equations
    (Taylor and Francis, 2012-04-03) Ergül, Özgür; Gürel, Levent
    We consider the solution of electromagnetics problems involving perfectly conducting objects formulated with the electric-field integral equation (EFIE). Dense matrix equations obtained from the discretization of EFIE are solved iteratively by the generalized minimal residual (GMRES) algorithm accelerated with a parallel multilevel fast multipole algorithm. We show that the number of iterations is halved by transforming the original matrix equations into normal equations. This way, memory required for the GMRES algorithm is reduced by more than 50%, which is significant when the problem size is large.
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    Parallel-MLFMA solution of CFIE discretized with tens of millions of unknowns
    (Institution of Engineering and Technology, 2007) Ergül, Özgür; Gürel, Levent
    We consider the solution of large scattering problems in electromagnetics involving three-dimensional arbitrary geometries with closed surfaces. The problems are formulated accurately with the combined-field integral equation and the resulting dense matrix equations are solved iteratively by employing the multilevel fast multipole algorithm (MLFMA). With an efficient parallelization of MLFMA on relatively inexpensive computing platforms using distributed-memory architectures, we easily solve large-scale problems that are discretized with tens of millions of unknowns. Accuracy of the solutions is demonstrated on scattering problems involving spheres of various sizes, including a sphere of radius 110 λ discretized with 41,883,638 unknowns, which is the largest integral-equation problem ever solved, to the best of our knowledge. In addition to canonical problems, we also present the solution of real-life problems involving complicated targets with large dimensions.
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    Rigorous solutions of scattering problems involving red blood cells
    (IEEE, 2010) Ergül, Özgür; Arslan-Ergül, Ayça; Gürel, Levent
    We present rigorous solutions of scattering problems involving healthy red blood cells (RBCs) and diseased RBCs with deformed shapes. Scattering cross-section (SCS) values for different RBC shapes and different orientations are obtained accurately and efficiently using a sophisticated simulation environment based on the electric and magnetic current combinedfield integral equation and the multilevel fast multipole algorithm. Using SCS values, we determine strict guidelines to distinguish deformed RBCs from healthy RBCs and to diagnose related diseases.
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    The solution of large EFIE problems via preconditioned multilevel fast multipole algorithm
    (Institution of Engineering and Technology, 2007) Malas, Tahir; Gürel, Levent
    We propose an effective preconditioning scheme for the iterative solution of the systems formulated by the electric- field integral equation (EFIE). EFIE is notorious for producing difficult-to-solve systems. Especially, if the target is complex and the utilized frequency is high, it becomes a challenge to solve these dense systems with even robust solvers such as full GMRES. For this purpose, we use an inner-outer solver scheme and use an approximate multilevel fast multipole algorithm for the inner solver to provide a very efficient approximation to the dense linear system matrix. We explore approximation level and inner-solver accuracy to optimize the efficiency of the inner-outer solution scheme. We report the solution of large EFIE systems of several targets to show the effectiveness of the proposed approach.