Search results

The first purpose of this paper is to identify – by an inverse problem – the unknown material characteristics in a permanent magnet synchronous machine in order to obtain a numerical model that is a realistic representation of the machine. The second purpose is to optimize the machine geometrically – using the accurate numerical model – for a maximal torque to losses ratio. Using the optimized geometry, a new machine can be manufactured that is more efficient than the original.

A 2D finite element model of the machine is built, using a nonlinear material characteristic that contains three parameters. The parameters are identified by an inverse problem, starting from torque measurements. The validation is based on local BH‐measurements on the stator iron.

Geometrical parameters of the motor are optimized at small load (low‐stator currents) and at full load (high‐stator currents). If the optimization is carried out for a small load, the stator teeth are chosen wider in order to reduce iron loss. An optimization at full load results in a larger copper section so that the copper loss is reduced.

The identification of the material parameters is influenced by the tolerance on the air gap – shown by a sensitivity analysis in the paper – and by 3D effects, which are not taken into account in the 2D model.

The identification of the material parameters guarantees that the numerical model describes the real material properties in the machine, which may be different from the properties given by the manufacturer because of mechanical stress and material degradation.

The optimization is more accurate because the material properties, used in the numerical model, are determined by the solution of an inverse problem that uses measurements on the machine.

Magnetic material properties of an electromagnetic device (EMD) can be recovered by solving a coupled experimental numerical inverse problem. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the EMD need to be perfectly modeled using a complex numerical model. However, these fine models demand a high computational time. Alternatively, less accurate coarse models can be used with a demerit of the high expected recovery errors. The purpose of this paper is to present an efficient methodology to reduce the effect of stochastic modeling errors in the inverse problem solution.

The recovery error in the electromagnetic inverse problem solution is reduced using the Bayesian approximation error approach coupled with an adaptive Kriging-based model. The accuracy of the forward model is assessed and adapted a priori using the cross-validation technique.

The adaptive Kriging-based model seems to be an efficient technique for modeling EMDs used in inverse problems. Moreover, using the proposed methodology, the recovery error in the electromagnetic inverse problem solution is largely reduced in a relatively small computational time and memory storage.

The proposed methodology is capable of not only improving the accuracy of the inverse problem solution, but also reducing the computational time as well as the memory storage. Furthermore, to the best of the authors knowledge, it is the first time to combine the adaptive Kriging-based model with the Bayesian approximation error approach for the stochastic modeling error reduction.

The aim of this paper is to reduce the evaluations number of the fine model within the output space mapping (OSM) technique in order to reduce their computing time.

In this paper, n-level OSM is proposed and expected to be even faster than the conventional OSM. The proposed algorithm takes advantages of the availability of n models of the device to optimize, each of them representing an optimal trade-off between the model error and its computation time. Models with intermediate characteristics between the coarse and fine models are inserted within the proposed algorithm to reduce the number of evaluations of the consuming time model and then the computing time. The advantages of the algorithm are highlighted on the optimization problem of superconducting magnetic energy storage (SMES).

A major computing time gain equals to three is achieved using the n-level OSM algorithm instead of the conventional OSM technique on the optimization problem of SMES.

The originality of this paper is to investigate several models with different granularities within OSM algorithm in order to reduce its computing time without decreasing the performance of the conventional strategy.

The purpose of this paper is to show with the help widely used analytical and application-oriented benchmark problems that a novel and relatively uncommon optimization method, lambda optimization, can be successfully applied to the solution of optimization problems in electromagnetics. Furthermore an improvement to the method is proposed and its effectiveness is validated.

An adaptive probability factor is used within the framework of lambda optimization.

It is shown that in the framework of lambda optimization (LO) the use of an adaptive probability factor can provide high-quality solutions with small standard deviation on the selected benchmark problem.

Although the chosen benchmarks are considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.

This paper introduces and validates the use of adaptive probability factor in order to improve the balance between the explorative and exploitative characteristics of the LO algorithm.

The purpose of this paper is to solve an inverse problem of structure recognition arising in eddy current testing (ECT) – type NDT. For this purpose, the space mapping (SM) technique with an extraction based on the Gauss‐Newton algorithm with Tikhonov regularization is applied.

The aim is to have a computationally fast recognition procedure of defects since the monitoring results in a large amount of data points that need to be analyzed by 3D eddy current model. According to the SM optimization, the finite element method (FEM) is used as a fine model, while the model based on an integral method such as the volume integral method (VIM) serves as a coarse model. This approach, being an example of a two‐level optimization method, allows shifting the optimization load from a time consuming and accurate model to the less precise but faster coarse surrogate.

The application of this method enables shortening of the evaluation time that is required to provide the proper parameter estimation of surface defects.

In this work only the specific kinds of surface defects were considered. Therefore, the reconstruction of arbitrary shapes of defects when using real measurement data from ECT system can be treated in further research.

The paper investigated the eddy current inverse problem. According to aggressive space mapping method, a suitable coarse model is needed. In this case, for the purpose of 3D defect reconstruction, the reduced VIM approach was applied. From a practical view point, the authors demonstrated that the two‐level inversion procedures allow saving of up to 50 percent CPU time in comparison with the optimization by means of regularized Gauss‐Newton algorithm in the same FE model.

The purpose of this paper is to set a relation through adaptive multi-level optimization between two physical models with different accuracies; a fast coarse model and a fine time consuming model. The use case is the optimization of a permanent magnet axial flux electrical machine.

The paper opted to set the relation between the two models through radial basis function (RBF). The optimization is held on the coarse model. The deduced solutions are used to evaluate the fine model. Thus, through an iterative process a residue RBF between models responses is built to endorse an adaptive correction.

The paper shows how the use of a residue function permits, to diminish optimization time, to reduce the misalignment between the two models in a structured strategy and to find optimum solution of the fine model based on the optimization of the coarse one. The paper also provides comparison between the proposed methodology and the traditional approach (output space mapping (OSM)) and shows that in case of large misalignment between models the OSM fails.

This paper proposes an original methodology in electromechanical design based on building a surrogate model by means of RBF on the bulk of existing physical model.

The purpose of this paper is to develop an optimal approach for optimizing the dynamic behavior of incremental linear actuators.

First, a parameterized design model is built. Second, a dynamic model is implemented. This model takes into account the thrust force computed from a finite element model. Finally, the multiobjective optimization approach is applied to the dynamic model to optimize control as well as design parameters.

The Pareto front resulting from the optimization approach (or the parallel optimization approach,) is better than the Pareto, which is obtained from the only application of MultiObjective Genetic Algorithm (MOGA) method (or parallel MOGA with the same number of optimization approach objective function evaluations). The only use of MOGA can reach the region near an optimal Pareto front, but it consumes more computing time than the multiobjective optimization approach. At each flowchart stage, parallelization leads to a significant reduction of computing time which is halved when using two-core machine.

In order to solve the multiobjective problem, a hybrid algorithm based on MOGA is developed.

The purpose of this paper is to present a new numerical technique, called adaptive level set method, for use with the finite element method.

A conventional level set method using the smeared Heaviside function has been employed for shape and topology optimizations. The smeared Heaviside function yields an indistinct interface boundary, and so can increase computational time and cause numerical errors. The adaptive level set method does not use the smeared Heaviside function. To coincide with the material interface, it processes the zero level as the boundary data of element meshing.

Usefulness and accuracy of shape optimization using the adaptive level set method are shown by comparison to the conventional level set method. A shape optimization procedure using the adaptive level set method is introduced. Numerical examples are employed to explain how the adaptive level set method is applied.

The adaptive level set method is proposed to relieve the interface problem of the conventional level set method. Shape variation in the optimization is calculated from the continuum sensitivity analysis.

In the development of electromagnetic devices, multiobjective topology optimisation is effective to obtain diverse design candidates for production models. However, multiobjective topology optimisation has not widely been performed because it is difficult to obtain resultant shapes for engineering realisation due to large search spaces. The purpose of this paper is to present a new multiobjective topology optimisation method.

This paper presents a new multiobjective topology optimisation method in which the Immune Algorithm is modified for multiobjecrive optimisation and a shape modification process based on spatial filtering is employed.

The present method shows that better Pareto solutions can be found in comparison with the conventional methods.

A new effective multiobjective topology optimisation is presented. This method enables to diverse design candidates for production models.