An algebraic multigrid method for solving very large electromagnetic systems

The focus of this study is on the efficient solution for three-dimensional (3D) time-harmonic eddy current problems discretised by the finite-element method (FEM) with thin elements. The systems of equations arising from the finite-element formulations are solved by the geometric multigrid preconditioned conjugate gradient (MGCG) method. Numerical examples show that the MGCG method has stable convergence, which does not deteriorate for elements with high aspect ratios and its computation time is much less than that of the conjugate gradient method with incomplete Cholesky factorisation as the preconditioner (ICCG).

Computer Solution of Large Linear Systems

A partial preconditioned conjugate gradient method for large eigenproblems

The multifrontal method is applied for solving a large system of linear equations resulting from the use of edge-based finite element method (FEM). The finite element method combined with the perfectly matched layers (PML) is given for simulation of microwave filters and the algorithm of multifrontal method is described in detail. The reflection and insertion losses of millimeter wave filter are analyzed as the examples and the obtained results are compared with those obtained from literature. In order to demonstrate the efficiency of the multifrontal method, the computational time is compared with that of both symmetric successive overrelaxation (SSOR) preconditioned conjugate gradient (PCG) and conjugate gradient methods (CG) for the thick-iris waveguide bandpass filer.

The focus of this paper is on the efficient solution of 3-D time-harmonic eddy current problems discretized by the finite element method with bad quality meshes. The sys tems of equations arising from the finite element formula tions are solved by a geometric multigrid preconditioned conjugate gradient method. The efficiency of the proposed method will be analyzed by numerical examples.

We are interested in the design of efficient algebraic multigrid (AMG) methods for the solution of large sparse systems of linear equations arising from finite element (FE) discretization of second-order elliptic partial differential equations (PDEs). In particular, we introduce the concept of so-called “edge matrices”, which–in the present context–are extracted from the individual element matrices. This allows for the construction of spectrally equivalent approximations of the original stiffness matrix that can be utilized in the framework of AMG. The edge matrices give rise to modify the definition of “strong” and “weak” connections (edges), which provides a basis for selecting the coarse-grid nodes in algebraic multigrid methods. Moreover, a reproduction of edge matrices on coarse levels offers the opportunity to combine classical coarsening algorithms with effective (energy minimizing) interpolation principles involving small-sized “computational molecules” (small collections of edge matrices). This yields a flexible and robust new variant of AMG, which we refer to as AMGm.

ABSTRACTAn accurate and efficient 3D finite‐difference (FD) forward algorithm for DC resistivity modelling is developed. In general, the most time‐consuming part of FD calculation is to solve large sets of linear equations: Ax=b, where A is a large sparse band symmetric matrix. The direct method using complete Choleski decomposition is quite slow and requires much more computer storage. We have introduced a row‐indexed sparse storage mode to store the coefficient matrix A and an incomplete Choleski conjugate‐gradient (ICCG) method to solve the large linear systems. By taking advantage of the matrix symmetry and sparsity, the ICCG method converges much more quickly and requires much less computer storage. It takes approximately 15 s on a 533 MHz Pentium computer for a grid with 46 020 nodes, which is approximately 700 times faster than the direct method and 2.5 times faster than the symmetric successive over‐relaxation (SSOR) conjugate‐gradient method. Compared with 3D finite‐element resistivity modelling with the improved ICCG solver, our algorithm is more efficient in terms of number of iterations and computer time. In addition, we solve for the secondary potential in 3D DC resistivity modelling by a simple manipulation of the FD equations. Two numerical examples of a two‐layered model and a vertical contact show that the method can achieve much higher accuracy than solving for the total potential directly with the same grid nodes. In addition, a 3D cubic body is simulated, for which the dipole–dipole apparent resistivities agree well with the results obtained with the finite‐element and integral‐equation methods. In conclusion, the combination of several techniques provides a rapid and accurate 3D FD forward modelling method which is fundamental to 3D resistivity inversion.

This study presents an efficacy comparison of iterative solution methods for solving the compressible-fluid Reynolds equation in modeling air- or gas-lubricated bearings. A direct fixed-point iterative (DFI) method and Newton’s method are employed to transform the Reynolds equation in a form that can be solved iteratively. The iterative solution methods examined are the Gauss–Seidel method, the successive over-relaxation (SOR) method, the preconditioned conjugate gradient (PCG) method, and the multigrid method. The overall solution time is affected by both the transformation method and the iterative method applied. In this study, Newton’s method shows its effectiveness over the straightforward DFI method when the same iterative method is used. It is demonstrated that the use of an optimal relaxation factor is of vital importance for the efficiency of the SOR method. The multigrid method is an order faster than the PCG and optimal SOR methods. Also, the multigrid and PCG methods involve an extended coding work and are less flexible in dealing with gridwork and boundary conditions. Consequently, a compromise has to be made in terms of ease of use as well as programming effort for the solution of the compressible-fluid Reynolds equation.

Gauss-Seidel preconditioned conjugate gradient (GS-PCG) method based on element-by-element (EBE) is derived and the calculation process of finite element method (FEM) is presented in this paper. The corresponding FEM program are developed and implemented on the magnetic field calculations which produced by straight conductor carrying current and 3-phases induction motor respectively. The validity and accuracy of the method have been verified by comparing the result with the analytical solution. Compared with Jacobi preconditioned conjugate gradient (J-PCG) method based on EBE-FEM, less computational time and cost are needed for the proposed method with same accuracy. The proposed method can be applied to electromagnetic field analysis of transformer and motor, which may refer large scale numerical computation.

The purpose of this paper is to discuss parallel preconditioning techniques to solve the elliptic portion (since it dominates computation) of the bidomain model, a non-linear system of partial differential equations that is widely used for describing electrical activity in the heart. Specifically, we assessed the performance of parallel multigrid preconditioners for a conjugate gradient solver. We compared two different approaches: the Geometric and Algebraic Multigrid Methods. The implementation is based on the PETSc library and we reported results for a 6-node Athlon 64 cluster. The results suggest that the algebraic multigrid preconditioner performs better than the geometric multigrid method for the cardiac bidomain equations.KeywordsCoarse GridMultigrid MethodParallel SpeedupConjugate Gradient IterationParallel GeometricThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Classical iterative methods when applied to the partial solution of the generalized eigenvalue problem Ax =λBx, may yield very poor convergence rates particularly when ill‐conditioned problems are considered. In this paper the preconditioned conjugate gradient (CG) method via the minimization of the Rayleigh quotient and the reverse power method is employed for the partial eigenproblem. The triangular splitting preconditioners employed are obtained from an incomplete Choleski factorization and a partial Evans preconditioner. This approach can dramatically improve the convergence rate of the basic CG method and is applicable to any symmetric eigenproblem in which one of the matrices A, B is positive definite. Because of the renewed interest in CG techniques for FE work on microprocessors and parallel computers, it is believed that this improved approach to the generalized eigenvalue problem is likely to be very promising.

A geometric multigrid method for the efficient solution of time-harmonic 3-D eddy-current problems is presented. A finite element method with a scalar potential and a vector potential is used to describe the problem. Numerical examples show that using the right smoother in the multigrid, a good convergence of solutions, which does not deteriorate for bad quality meshes can be obtained. The computation time for solving the eddy-current problem of the multigrid method is much faster than that of the conjugate gradient method with incomplete Cholesky factorization as preconditioner.

We present a parallelized geometric multigrid (GMG) method, based on the cell-based Vanka smoother, for higher order space-time finite element methods (STFEM) to the incompressible Navier–Stokes equations. The STFEM is implemented as a time marching scheme. The GMG solver is applied as a preconditioner for generalized minimal residual iterations. Its performance properties are demonstrated for 2D and 3D benchmarks of flow around a cylinder. The key ingredients of the GMG approach are the construction of the local Vanka smoother over all degrees of freedom in time of the respective subinterval and its efficient application. For this, data structures that store pre-computed cell inverses of the Jacobian for all hierarchical levels and require only a reasonable amount of memory overhead are generated. The GMG method is built for the deal.II finite element library. The concepts are flexible and can be transferred to similar software platforms.

The temperature stress due to temperature changes is often more than the concrete tensile strength during construction period and operation period in massive concrete structures in hydraulic engineering, so the analysis of temperature stress, temperature control and measures to prevent the cracks become very important. The solution of large sparse linear equations is essential to finite element simulation in simulation analysis of temperature field. However, the traditional direct method will cost too much memory and calculation time, and the nodes is limited. The symmetric successive over relaxation (SSOR)-preconditioned conjugate gradient (PCG) method is a very efficient iterative method for solving large sparse linear equations. Construction process simulation and temperature filed calculation during construction period about Caohe river landing rectangle aqueduct of Beijing-Shijiazhuang emergency water supply engineering of middle-route of south-to-north water transfer project are carried out by FEM code using SSOR-PCG method, then the calculation result and monitoring value are compared. The comparison result shows that temperature field during construction can be accurately simulated by simulation calculation, which is helpful for guiding construction and choosing construction scheme, and worthy to popularize to other similar projects.

This paper presents a symmetric coupling between the finite element and boundary element methods for analysing open microwave devices. The proposed finite element boundary element (FEBE) method is based on the E-field vector Helmholtz equation. Second-order hierarchical Curl-conforming vector finite elements over tetrahedrons are used to discretize the interior region. The boundary element (BE) truncation surface is discretized with the second-order divergence-conforming surface elements with triangular support. The symmetry of the BE part is restored through the application of the Calderon-projector. Moreover, the BE computations are accelerated using a single level QR algorithm. This reduces both memory and computational time. The resulted symmetric system of equations is solved with a very efficient preconditioned conjugate gradient (PCCG) method with a p-multiplicative Schwarz (pMUS) preconditioner. Copyright © 2005 John Wiley & Sons, Ltd.