Silvio Ikuyo Nabeta

Modified nodal analysis applied to electric circuits coupled with FEM in the simulation of a universal motor

In the last few years, several papers considering the finite element method coupled with electric circuits were published. Many of these works have been used the theory of mesh analysis to analyze the electric circuit. However, when we use a circuit with many current and voltage sources, it is not always easy to use this analysis. Aiming to avoid this problem, this paper presents the application of the modified nodal analysis in the analysis of coupled circuits, as a continuation of the previously presented work, where the use of nonlinear ferromagnetic devices was not considered. The analysis of a universal motor at stall is presented to validate this new approach.

A wavelet-based algebraic multigrid preconditioner for sparse linear systems

This work considers the use of discrete wavelet transform (DWT), based in filters, in the construction of the hierarchy of matrices in the algebraic multigrid method (AMG). The two-dimensional DWT is applied to produce an approximation of the matrix in each level of the wavelets multiresolution decomposition process. In this procedure an operator is created, formed only by lowpass filters, that is applied to the rows and columns of the matrix capturing this approximation. This same operator is used as an intergrid transfer in the AMG. Wavelet-based algebraic multigrid method (WAMG) was implemented, with Daubechies wavelets of orders 6, 4 and 2, and used as a preconditioner for the generalized minimal residual method (GMRES). Numerical results are presented comparing this new approach with the standard algebraic multigrid as preconditioner for sparse linear systems.

An adaptive method applied to the diffuse element approximation in optimization process

This paper presents a new method to accelerate the optimization process of stochastic methods. This method is an adaptive procedure of the Diffuse Element Approximation, used to approximate the objective function. The robustness of this new approach is verified on a two parameters function optimization and the Problem 25 of TEAM Workshop.

A time-stepped finite-element simulation of a symmetrical short-circuit in a synchronous machine

The of this work is to propose and apply the principle of superposition for the numerical simulation of a symmetrical short-circuit in an unloaded synchronous machine. For this purpose a linear, two-dimensional, time-stepped Finite-Element Method coupled with electric circuits is used which permits one to take into account the rotor movement and the end-winding impedances. Computed values of subtransient, transient and steady-state reactances, as well as, subtransient and transient time-constraints on D-axis are compared with those obtained by the short-circuit test and the Standstill Frequency Response (SSFR) test simulation. >

Diffuse-element method and quadtrees: two "ingredients" for an adaptive response surface

A new algorithm to obtain the response surface of objective functions is presented. The diffuse-element method and quadtrees are used in the implementation of this algorithm. The approximation of a two-parameter analytical function and the optimization of a switched reluctance motor are performed to show the robustness of this new approach.

A non-linear time-stepped finite-element simulation of a symmetrical short-circuit in a synchronous machine

The aim of this work is to carry out a two-dimensional, non-linear, time-stepped finite-element simulation of a symmetrical short-circuit in a synchronous machine. Computed results of currents and rotorical torque are presented as comparison between computed reactances and test values. >

A multi‐objective analysis of a special switched reluctance motor

Purpose – The design of electrical machines includes the computation of several requirements and, in general, the improvement of one requirement implies in a degradation of another one: this is a typical multi‐objective scenario. The paper focuses on the multi‐optimization analysis of a special switched reluctance motor.Design/methodology/approach – Two design requirements were analyzed: the average torque and the ripple torque. The electromagnetic field computation was performed by the finite element method and the torque was computed by the Coulombs Virtual Work for several positions. This allows us to calculate the average torque and the ripple torque. Three different methods were used to obtain the Pareto set: a min‐max approach, the non‐dominated sorting genetic algorithm (NSGA) and the strength Pareto evolutionary algorithm (SPEA). In order to save the computation time, the objective functions (the average torque and the ripple torque) were replaced with surrogate functions. Kriging models were use...

A Wavelet-Based Algebraic Multigrid Preconditioning for Iterative Solvers in Finite-Element Analysis

A new approach for algebraic multigrid, based on wavelets, is presented as an efficient preconditioner for iterative solvers applied to the solution of linear systems issued from finite-element analysis. It can be applied to complex systems in which the coefficient matrix violates the M-matrix property, as those arising from ungauged edge-based AV finite-element formulation. When used as a preconditioner for the biconjugate gradient stabilized method it is shown that the proposed technique is more efficient than incomplete Cholesky preconditioner

An aspect of modelling a permanent magnet motor by using the finite-element method coupled with circuit equations

This paper shows the activities developed to study some aspects of a permanent magnet motor by using the finite-element method coupled with circuit equations, implemented by the software FLUX2D. The emphasis is the eddy currents and losses in the permanent magnets, damper bars and in the iron rotor pole pieces.

A Two-Level Genetic Algorithm for Large Optimization Problems

Many local two-level algorithms have been proposed for accelerating the electromagnetic optimization by stochastic algorithms. These algorithms use a combination of a coarse and a fine model in the optimization procedure. Despite the good results, the global convergence properties represent an important drawback of these approaches. A global two-level algorithm had been proposed to deal with the convergence problems, but the requirement to refine the global surrogate model in each step can demand high computational time. This paper introduces a global two-level genetic algorithm that uses single predefined coarse and fine surrogate models, which are defined as an artificial neural network nonlinear regression of a preliminary set of finite element simulations. The benchmark test problem, Hartmann 6, and the problem dealing with the eight-parameter design of superconducting magnetic energy storage have been analyzed..