848 research outputs found
Solutions of large-scale electromagnetics problems involving dielectric objects with the parallel multilevel fast multipole algorithm
Fast and accurate solutions of large-scale electromagnetics problems involving homogeneous dielectric objects are considered. Problems are formulated with the electric and magnetic current combined-field integral equation and discretized with the Rao-Wilton-Glisson functions. Solutions are performed iteratively by using the multi-level fast multipole algorithm (MLFMA). For the solution of large-scale problems discretized with millions of unknowns, MLFMA is parallelized on distributed-memory architectures using a rigorous technique, namely, the hierarchical partitioning strategy. Efficiency and accuracy of the developed implementation are demonstrated on very large problems involving as many as 100 million unknowns
Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method
Computational tools for characterizing electromagnetic scattering from
objects with uncertain shapes are needed in various applications ranging from
remote sensing at microwave frequencies to Raman spectroscopy at optical
frequencies. Often, such computational tools use the Monte Carlo (MC) method to
sample a parametric space describing geometric uncertainties. For each sample,
which corresponds to a realization of the geometry, a deterministic
electromagnetic solver computes the scattered fields. However, for an accurate
statistical characterization the number of MC samples has to be large. In this
work, to address this challenge, the continuation multilevel Monte Carlo
(CMLMC) method is used together with a surface integral equation solver. The
CMLMC method optimally balances statistical errors due to sampling of the
parametric space, and numerical errors due to the discretization of the
geometry using a hierarchy of discretizations, from coarse to fine. The number
of realizations of finer discretizations can be kept low, with most samples
computed on coarser discretizations to minimize computational cost.
Consequently, the total execution time is significantly reduced, in comparison
to the standard MC scheme.Comment: 25 pages, 10 Figure
Accurate and efficient algorithms for boundary element methods in electromagnetic scattering: a tribute to the work of F. Olyslager
Boundary element methods (BEMs) are an increasingly popular approach to model electromagnetic scattering both by perfect conductors and dielectric objects. Several mathematical, numerical, and computational techniques pullulated from the research into BEMs, enhancing its efficiency and applicability. In designing a viable implementation of the BEM, both theoretical and practical aspects need to be taken into account. Theoretical aspects include the choice of an integral equation for the sought after current densities on the geometry's boundaries and the choice of a discretization strategy (i.e. a finite element space) for this equation. Practical aspects include efficient algorithms to execute the multiplication of the system matrix by a test vector (such as a fast multipole method) and the parallelization of this multiplication algorithm that allows the distribution of the computation and communication requirements between multiple computational nodes. In honor of our former colleague and mentor, F. Olyslager, an overview of the BEMs for large and complex EM problems developed within the Electromagnetics Group at Ghent University is presented. Recent results that ramified from F. Olyslager's scientific endeavors are included in the survey
Analysis of microstrip antennas by multilevel matrix decomposition algorithm
Integral equation methods (IE) are widely used in conjunction with Method of Moments (MoM) discretization for the numerical analysis of microstrip antennas. However, their application to large antenna arrays is difficult due to the fact that the computational requirements increase rapidly with the number of unknowns N. Several techniques have been proposed to reduce the computational cost of IE-MoM. The Multilevel Matrix Decomposition Algorithm (MLMDA) has been implemented in 3D for arbitrary perfectly conducting surfaces discretized in Rao, Wilton and Glisson linear triangle
basis functions . This algorithm requires an operation count that is proportional to N·log2N. The performance of the algorithm is much better for planar or piece-wise planar objects than for general 3D problems, which makes the algorithm particularly well-suited for the analysis of microstrip antennas. The memory requirements are proportional to N·logN and very low. The main advantage of the MLMDA compared with other efficient techniques to solve integral equations is that it does not rely on specific mathematical properties of the Green's functions being used. Thus, we can apply the method to interesting configurations governed by special Green's functions like multilayered media. In fact, the MDA-MLMDA method can
be used at the top of any existing MoM code. In this paper we present the application to the analysis of large printed antenna arrays.Peer ReviewedPostprint (published version
Weak scalability analysis of the distributed-memory parallel MLFMA
Distributed-memory parallelization of the multilevel fast multipole algorithm (MLFMA) relies on the partitioning of the internal data structures of the MLFMA among the local memories of networked machines. For three existing data partitioning schemes (spatial, hybrid and hierarchical partitioning), the weak scalability, i.e., the asymptotic behavior for proportionally increasing problem size and number of parallel processes, is analyzed. It is demonstrated that none of these schemes are weakly scalable. A nontrivial change to the hierarchical scheme is proposed, yielding a parallel MLFMA that does exhibit weak scalability. It is shown that, even for modest problem sizes and a modest number of parallel processes, the memory requirements of the proposed scheme are already significantly lower, compared to existing schemes. Additionally, the proposed scheme is used to perform full-wave simulations of a canonical example, where the number of unknowns and CPU cores are proportionally increased up to more than 200 millions of unknowns and 1024 CPU cores. The time per matrix-vector multiplication for an increasing number of unknowns and CPU cores corresponds very well to the theoretical time complexity
A hybrid MLFMM-UTD method for the solution of very large 2-D electromagnetic problems
The multilevel fast multipole method (MLFMM) is combined with the uniform theory of diffraction (UTD) to model two-dimensional (2-D) scattering problems including very large scatterers. The discretization of the very large scatterers is avoided by using ray-based methods. Reflections are accounted for by image source theory, while for diffraction a new MLFMM translation matrix is introduced. The translation matrix elements are derived based on a technique that generalizes the use of UTD for arbitrary source configurations and that efficiently describes the field over extended regions of space. O(n) scaling of the computational time and memory requirements is achieved for relevant structures, such as large antenna arrays in the presence of a wedge. The theory is validated by means of several illustrative numerical examples and is shown to remain accurate for non-line-of-sight (NLoS) scattering problems
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