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Electromagnetic noise of permanent magnet synchronous motor (PMSM) seriously affects the sound quality of electric vehicles (EVs). This paper aims to present a comprehensive process for the electromagnetic noise analysis and optimization of a water-cooled PMSM.

First, the noises of an eight-pole 48-slot PMSM in at speeds up to 10,000 rpm are measured. Furthermore, an electromagnetic-structural-acoustic model of the PMSM is established for multi-field coupling simulations of electromagnetic noises. Finally, the electromagnetic noise of the PMSM is optimized by using the multi-objective genetic algorithm, where a multi-objective function related to the slot width of PMSM stator is defined for radial electromagnetic force (REF) optimization.

The experimental results show that main electromagnetic noises are the 8n-order (n = 1, 2, 3, …) and 12-order noises. The simulated results show that the REFs are mainly generated by the 8n-order (n = 1, 2, 3, 4, 5, 6) vibrations, especially those of the 8th, 16th, 24th and 32th orders. The 12-order noise is a mechanical noise, which might be caused by the bearings and other structures of the PMSM. Comparing the simulated results before and after optimization, both the REFs and electromagnetic noises are effectively reduced, which suggests that an appropriate design of stator slot is important for reducing electromagnetic noise of the PMSM.

In view of applications, the methods proposed in this paper can be applied to other types of PMSM for generation mechanism analysis of electromagnetic noise, optimal design of PMSM and thereby noise improvement of EVs.

Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.

The purpose of this paper is to investigate the use of multidisciplinary optimization (MDO) formulations within space‐mapping techniques in order to reduce their computing time.

The aim of this work is to quantify the interest of using MDO formulations within space mapping techniques. A comparison of three MDO formulations is carried out in a short time by using an analytical model of a safety transformer. This comparison reveals the advantage of two formulations in terms of robustness and computing time among the three MDO formulations. Then, the best formulations are investigated within output space mapping, using both analytical and FE models of the transformer.

A major computing time gain equal to 5.5 is achieved using the Individual Disciplinary Feasibility formulation within the output space‐mapping technique in the case of the safety transformer.

The MultiDisciplinary Feasibility formulation is the common formulation used within space‐mapping technique because it is the most conventional way to perform MDO. The originality of this paper is to investigate the Individual Disciplinary Feasibility formulation within output space‐mapping technique in order to allow the parallelization of calculation and to achieve a major reduction of computing time.

The purpose of this paper is to elaborate the effective method of adaptation of the external penalty function to the genetic algorithm.

In the case of solving the optimization tasks with constraints using the external penalty function, the penalty term has a larger value than the primary objective function. The sigmoidal transformation is introduced to solve this problem. A new method of determining the value of the penalty coefficient in subsequent iterations associated with the changing penalty has been proposed. The proposed approach has been applied to the optimization of an electromagnetic linear actuator, and the mathematical model of the devices contains equations of the magnetic field, by taking into account the nonlinearity of ferromagnetic material.

The proposed new approach of the penalty function method consists in the reduction of the external penalty function in successive penalty iterations instead of its increase as it is in the classical method. In addition, the method of normalization of constraints during the formulation of optimization problem has a significant impact on the obtained results of optimization calculations.

The proposed approach can be applied to solve constrained optimization tasks in designing of electromagnetic devices.

This paper aims the application of a novel hybrid algorithm, called MeTEO, based on the combination of three heuristics inspired by artificial life to the optimization of electrodes voltages of multistage depressed collector.

The flock-of-starlings optimization (FSO), the particle swarm optimization (PSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO+BCA has been used to refine the FSO-found solutions, exploiting their better performances in local search.

The optimization of the voltage of the electrodes of multistage depressed collector are efficiently handled with a moderate computational effort.

The development of an efficient method for the solution of a complicated electromagnetic optimization problem, exploiting the different characteristic of different approaches based on evolutionary computation algorithm.

The paper shows that the combination of stochastic methods having different exploration properties with appositely developed FE electromagnetic simulator allows us to produce effective solutions of multimodal electromagnetic optimization problems, with an acceptable computational cost.

Shape and material parameters have major influence on the performance of electromagnetic components. Optimization of these parameters is therefore vital in electromagnetic design. Reduction of the radar cross section (RCS) for aircraft and frequency selective surfaces are two well known examples. Shape and materials optimization is performed for different scatterers in 2D.

Continuum design sensitivities for microwave problems are applied for the gradient‐based optimization of scatterers' shape and material parameters. The goal function is chosen to be an average of the monostatic RCS for a sector of incident angles over a frequency band. Numerical tests are presented for 2D scatterers and, specifically, a perfectly electrically conducting scatterer and an absorber on the front edge of an airplane wing are considered. The results are compared with theoretical findings and results in the open literature.

It is demonstrated that a dense frequency sampling of the goal function over a wide frequency band relaxes the requirements on the angular resolution. The broad band requirements on the RCS also avoids corrugations without the resorting to regularization methods and penalty terms added to the goal function. The optimization algorithm refines, in a small number of iterations, the initial geometry of the scatterer to an optimized design with strongly reduced RCS.

Shape and material parameters have major influence on the performance of electromagnetic components. Optimization of these parameters for scatterers demonstrates that a densely evaluated goal function over a broad frequency band has the advantages of: lowering the requirements on angular resolution; avoiding corrugations; and regularizing the problem by the broad frequency band requirements which often are naturally included in the performance specification of electromagnetic devices.

The purpose of this paper is to develop a novel technique for the pre-design of a printed circuit board (PCB) of a DC-DC power converters where the placement of electric components can cancel the electromagnetic emissions through subtractive coupling and in this sense to minimize the stray magnetic and electric fields at a specific location. For this work the location of interest is a current transducer used for control purposes positioned in the center of a DC-DC Cuk converter board as a constrain.

The methodology of design is based on the development of an interface software platform through MatLab script coding which interconnects the solution of a numerical analysis software and an optimization technique. The numerical analysis software is based on finite element calculations where quasi-static field analysis are performed to calculate the radiated electric and magnetic fields. The optimization technique is conducted by genetic algorithms (GAs).

The results for the proposed procedure for PCB design show a significant reduction in radiated electromagnetic (EM) field at the susceptible device in the PCB. Even when the optimization procedure is applied only for the sensor center, the field reduction is extended for a wide region around the sensor. The proposed technique not only reduces the fundamental field component but also all the harmonic contents for the electromagnetic field. It is demonstrated that it is possible to cancel the emissions by means of varying the location and orientation of the passive elements avoiding the utilization of electromagnetic interference filters and complex modulations.

The novelty of the design procedure falls in the fitness function programming where an interface software platform is built through MatLab scripting to connect a 3D-FE analysis and the GA. The finite element analysis address the radiated EM calculation while the GA focus in the minimization of it. This computational platform has the flexibility to be easily adapted for the PCB design of any power electronic converter where the radiated EM compliance is required as well as extended to perform emissions minimization outside or/and inside the PCB.

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.

The aim of this paper is to explore the potential of standard quantum particle swarm optimization algorithms to solve single objective electromagnetic optimization problems.

A modified quantum particle swarm optimization (MQPSO) algorithm is designed.

The MQPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic design problems. The numerical results as reported have demonstrated that the proposed approach can find better final optimal solution at an initial stage of the iterating process as compared to other tested stochastic methods. It also demonstrates that the proposed method can produce better outcomes by using almost the same computation cost (number of iterations). Thus, the merits or advantages of the proposed MQPSO method in terms of both solution quality (objective function values) and convergence speed (number of iterations) are validated.

The improvements include the design of a new position updating formula, the introduction of a new selection method (tournament selection strategy) and the proposal of an updating parameter rule.

This paper presents a probabilistic optimal design for electromagnetic systems. A 2D magnetostatic finite element model is constructed for a reliability‐based topology optimization (RBTO). Permeability, coercive force, and applied current density are considered as uncertain variables. The uncertain variable means that the variable has a variance on a certain design point. In order to compute reliability constraints, a performance measure approach is widely used. To find reliability index easily, the limit‐state function is linearly approximated at each iteration. This approximation method is called the first‐order reliability method, which is widely used in reliability‐based design optimizations. To show the effectiveness of the proposed method, RBTO for the electromagnetic systems is applied to magnetostatic problems.