Levent Gurel
Dept. of Electrical and Electronics Engineering
Director, Computational Electromagnetics Research Center (BiLCEM)
Bilkent University, TR-06800 Bilkent, Ankara Turkey
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Levent Gürel (S'87-M'92-SM'97-F'09) received the B.Sc. degree from the Middle East Technical University (METU), Ankara, Turkey, in 1986, and the M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign (UIUC) in 1988 and 1991, respectively, all in electrical engineering.

He joined the Thomas J. Watson Research Center of the International Business Machines Corporation, Yorktown Heights, New York, in 1991, where he worked as a Research Staff Member on the electromagnetic compatibility (EMC) problems related to electronic packaging, on the use of microwave processes in the manufacturing and testing of electronic circuits, and on the development of fast solvers for interconnect modeling. Since 1994, he has been a faculty member in the Department of Electrical and Electronics Engineering of the Bilkent University, Ankara, where he is currently a Professor.

He was a Visiting Associate Professor at the Center for Computational Electromagnetics (CCEM) of the UIUC for one semester in 1997. He returned to the UIUC as a Visiting Professor in 2003-2005, and as an Adjunct Professor after 2005. He founded the Computational Electromagnetics Research Center (BiLCEM) at Bilkent University in 2005, where he is serving as the Director.

Prof. Gürel's research interests include the development of fast algorithms for computational electromagnetics (CEM) and the application thereof to scattering and radiation problems involving large and complicated scatterers, antennas and radars, frequency-selective surfaces, high-speed electronic circuits, optical and imaging systems, nanostructures, and metamaterials. He is also interested in the theoretical and computational aspects of electromagnetic compatibility and interference analyses. Ground penetrating radars and other subsurface scattering applications are also among his research interests. Since 2006, his research group has been breaking several world records by solving extremely large integral-equation problems, most recently the largest involving as many as 540 million unknowns.

Among the recognitions of Prof. Gürel's accomplishments, the two prestigious awards from the Turkish Academy of Sciences (TUBA) in 2002 and the Scientific and Technical Research Council of Turkey (TUBITAK) in 2003 are the most notable.

He is a member of the USNC of the International Union of Radio Science (URSI) and the Chairman of Commission E (Electromagnetic Noise and Interference) of URSI Turkey National Committee. He served as a member of the General Assembly of the European Microwave Association (EuMA) during 2006-2008.

He is currently serving as an associate editor for Radio Science, IEEE Antennas and Wireless Propagation Letters, Journal of Electromagnetic Waves and Applications (JEMWA), and Progress in Electromagnetics Research (PIER).

Prof. Gürel served as the Chairman of the AP/MTT/ED/EMC Chapter of the IEEE Turkey Section in 2000-2003. He founded the IEEE EMC Chapter in Turkey in 2000. He served as the Cochairman of the 2003 IEEE International Symposium on Electromagnetic Compatibility. He is the organizer and General Chair of the CEM’07 and CEM’09 Computational Electromagnetics International Workshops held in 2007 and 2009, technically sponsored by IEEE AP-S.

Hierarchical Parallelization of the Multilevel Fast Multipole Algorithm (MLFMA)

It is possible to solve electromagnetics problems several orders of magnitude faster by using MLFMA. Without exaggeration, this means accelerating the solutions by thousands or even millions of times, compared to the Gaussian elimination. However, it is quite difficult to parallelize MLFMA. This is because of the already-too-complicated structure of the MLFMA solver. Recently, we have developed a hierarchical parallelization scheme for MLFMA. This novel parallelization scheme is both efficient and effective. This way, we have been able to parallelize MLFMA over hundreds of processors. By using distributed-memory architectures, this accomplishment translates into an ability to use more memory and to solve much larger problems than it was possible before. Unlike previous parallelization techniques, with the novel hierarchical partitioning strategy, the tree structure of MLFMA is distributed among processors by partitioning both clusters and samples of fields at each level. Due to the improved load-balancing, the hierarchical strategy offers a higher parallelization efficiency than previous approaches, especially when the number of processors is large. We demonstrate the improved efficiency on scattering problems discretized with millions of unknowns. We present the effectiveness of our algorithm by solving very large scattering problems.

Solution of World’s Largest Integral-Equation Problems

Accurate simulations of real-life electromagnetics problems with integral equations require the solution of dense matrix equations involving millions of unknowns. Solutions of these extremely large problems cannot be achieved easily, even when using the most powerful computers with state-of-the-art technology. However, with MLFMA and parallel MLFMA, we have been able to obtain full-wave solutions of scattering problems discretized with hundreds of millions of unknowns. Some of the complicated real-life problems (such as, scattering from a realistic aircraft) involve geometries that are larger than 1000 wavelengths. Accurate solutions of such problems can be used as reference data for high-frequency techniques. Solutions of extremely large canonical benchmark problems involving sphere and NASA Almond geometries will be presented, in addition to the solution of complicated objects, such as metamaterial problems, red blood cells, and dielectric photonic crystals. For example, by solving the world’s largest and most complicated metamaterial problems (without resorting to homogenization), we demonstrate how the transmission properties of metamaterial walls can be enhanced with randomly-oriented unit cells. Also, we present a comparative study of scattering from healthy red blood cells (RBCs) and diseased RBCs with deformed shapes, leading to a method of diagnosis of blood diseases based on scattering statistics of RBCs. We will present solutions of extremely large problems involving more than 500 million unknowns.

Novel and Effective Preconditioners for Iterative Solvers

Solutions of extremely large matrix equations require iterative solvers. MLFMA accelerates the matrix-vector multiplications performed with every iteration. Despite the acceleration provided by MLFMA, the number of iterations should also be kept at a minimum, especially if the dimension of the matrix is in the order of millions. This is exactly where the preconditioners are needed. We have developed several novel preconditioners that can be used to accelerate the solution of various problems formulated with different types of integral equations. For example, it is well known that the electric-field integral equation (EFIE) is worse conditioned than the magnetic-field integral equation (MFIE) for conductor problems. Therefore, the preconditioners that we develop for EFIE are crucial for the solution of extremely large EFIE problems. For dielectric problems, we formulate several different types of integral equations to investigate which ones have better conditioning properties. Furthermore, we develop effective preconditioners specifically for dielectric problems. In this talk, we will review three classes of preconditioners:

1. Sparse near-field preconditioners
2. Approximate full-matrix preconditioners
3. Schur complement preconditioning for dielectric problems

We will present our efforts to devise effective preconditioners for MLFMA solutions of difficult electromagnetics problems involving both conductors and dielectrics, such as the block-diagonal preconditioner (BDP), incomplete LU (ILU) preconditioners, sparse approximate inverse (SAI) preconditioners, iterative near-field (INF) preconditioner, approximate MLFMA (AMLFMA) preconditioner, the approximate Schur preconditioner (ASP), and the iterative Schur preconditioner (ISP).