2015
Prof. Levent Gürel received the Harrington-Mittra Award in Computational Electromagnetics.

  2014
New book from IEEE Press and Wiley: The Multilevel Fast Multipole Algorithm (MLFMA) for Solving Large-Scale Computational Electromagnetics Problems.

  2014
Another achievement in the series of world record: More than one billion unknown solved.

  2013
Prof. Levent Gürel received 2013 UIUC ECE Distinguished Alumni Award.

  2011
Prof. Gürel is elevated to ACES Fellow Grade.

  2011
Prof. Gürel is invited to address the 2011 ACES Conference in Virginia, USA, as a Plenary Speaker.

  2011
Prof. Levent Gürel is named IEEE Distinguished Lecturer.

   2009
Prof. Levent Gürel is elevated to IEEE Fellow grade


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Research Activities


  • Parallelization of MLFMA

    Fast and accurate solutions of large-scale scattering problems involving three-dimensional closed conductors with arbitrary shapes are possible using the multilevel fast multipole algorithm (MLFMA). With an efficient parallelization of MLFMA, scattering problems that are discretized with tens of millions of unknowns are easily solved on a cluster of computers. We extensively investigate the parallelization of MLFMA, identify the bottlenecks, and provide remedial procedures to improve the efficiency of the implementations. The accuracy of the solutions is demonstrated on a scattering problem involving a sphere of radius 110 lambda discretized with 41,883,638 unknowns, the largest integral-equation problem solved to date. In addition to canonical problems, we also present the solution of real-life problems involving complicated targets with large dimensions. click for more...


  • Solutions of Electromagnetics Problems Involving Tens of Millions of Unknowns

    By achieving the parallelization of MLFMA, which is not trivial at all, we have been able to solve extremely large electromagnetics problems involving tens of millions of unknowns.  Since we are using carefully formulated integral equations, the solutions are not only efficient, but also accurate.  Hence, accurate solutions of previously unsolvable problems are useful both in real-life applications and for benchmarking purposes.  Each solved problem involving tens of millions of unknowns was the largest of its class (hence, a world record) at the time.  Examples of solutions of such extremely large problems are presented here.


  • Hierarchical Parallelization of MLFMA

    We developed a novel hierarchical partitioning strategy for the efficient parallelization of the multilevel fast multipole algorithm (MLFMA) on distributed-memory architectures to solve large-scale problems in electromagnetics. Unlike previous parallelization techniques, 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. In addition, we present the effectiveness of our algorithm by solving very large scattering problems involving a conducting sphere of radius 210 wavelengths and a complicated real-life target with a maximum dimension of 880 wavelengths. Both of the objects are discretized with more than 200 million unknowns. click for more...


  • Solutions of Electromagnetics Problems Involving Hundreds of Millions of Unknowns

    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. Hence, many electromagnetics problems in the literature have been solved by resorting to various approximation techniques without controllable error. In this paper, we present full-wave solutions of scattering problems discretized with hundreds of millions of unknowns by employing a parallel implementation of the multilevel fast multipole algorithm. Various examples involving canonical and complicated objects are presented in order to demonstrate the feasibility of accurately solving large-scale problems on relatively inexpensive computing platforms.click for more...


  • Approximate Multilevel Fast Multipole Algorithm (AMLFMA) as a Preconditioner

    An iterative inner-outer scheme for the efficient solution of large-scale electromagnetics problems involving perfectly-conducting objects formulated with surface integral equations. Problems are solved by employing the multilevel fast multipole algorithm (MLFMA) on parallel computer systems. In order to construct a robust preconditioner, we develop an approximate MLFMA (AMLFMA) by systematically increasing the efficiency of the ordinary MLFMA. Using a flexible outer solver, iterative MLFMA solutions are accelerated via an inner iterative solver, employing AMLFMA and serving as a preconditioner to the outer solver. The resulting implementation is tested on various electromagnetics problems involving both open and closed conductors. We show that the processing time decreases significantly using the proposed method, compared to the solutions obtained with conventional preconditioners in the literature. click for more...


  • Computational Study of Scattering From Healthy and Diseased Red Blood Cells

    Comparative study of scattering from healthy red blood cells (RBCs) and diseased RBCs with deformed shapes. Scattering problems involving three-dimensional RBCs are formulated accurately with the electric and magnetic current combined-field integral equation and solved efficiently by the multilevel fast multipole algorithm. We compare scattering cross section values obtained for different RBC shapes and different orientations. This way, we determine strict guidelines to distinguish deformed RBCs from healthy RBCs and to diagnose various diseases using scattering cross section values. The results may be useful for designing new and improved flow cytometry procedures. click for more...


  • Numerical Techniques:
    • Modeling of complicated geometries on computers
    • Development of flexible simulation environments
    • Development of powerful software packages
    • Fast solvers (both frequency-domain and time-domain solvers)
      • Fast multipole method (FMM)
      • Finite-difference time-domain (FDTD)
      • Method of moments (MoM)
      • Fast solution of near-resonant structures
      • ntegrating in-house software packages with other commercial software
    • Modeling EM phenomena using circuit-theory concepts by employing parasitic extraction, AWE, and SPICE.

  • Radars:
    • RCS (Radar Cross Section) computations (and measurements)
    • Stealth studies
    • GPR (Ground Penetrating Radar) simulations
    • EM scattering from targets with arbitrary geometries, such as aircraft, missiles, ships, any electronic equipment and furniture in a room, human forms, buildings, and geographical features

  • EMC (Electromagnetic Compatibility):
    • Simulations
    • Measurements (precompliance and compliance)
    • Military and commercial EMC standards
    • EMC education
    • Measurement of GSM base stations
    • Radiation from PCB and chip geometries
    • Crosstalk and signal-integrity issues
    • Radiation from and coupling through apertures on enclosures and shields

  • Antennas:
    • Frequency-independent (broadband) antenna design
    • Antennas installed on platforms (aircraft, ships, satellites, telephones)
    • Printed antennas, conformal antennas (GPS, aircraft, missile, GSM)
    • Antenna measurements
    • Radiation from antenna structures, computation of antenna patterns
    • Synthesis of broadband antennas and antenna arrays