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This paper aims to present the optimal design of an inductor used to heat a magnetic nanoparticle fluid injected in a cell culture inside a Petri dish.

The inductor design is driven by means of a multi-objective optimization algorithm that generalizes the migration-non-dominated sorting genetic algorithm (NSGA); it is called self-adapting migration-NSGA.

The optimized device is able to synthesize a uniform magnetic field in a nanoparticle fluid, substantially helping its heating capability. The ultimate scope is to assist the cancer therapy based on magnetic fluid hyperthermia (MFH).

The optimal design of an inductor for MFH applications has been carried out by applying an improved version of migration-based NSGA-II algorithm including automatic stop and a self-adapting concept. The modified optimization algorithm is suitable to find better optimal solutions with respect to a standard version of NSGA-II.

The purpose of this paper is to present a modified version of the non-dominated sorted genetic algorithm with an application in the design optimization of a power inductor for magneto-fluid hyperthermia (MFH).

The proposed evolutionary algorithm is a modified version of migration-non-dominated sorting genetic algorithms (M-NSGA) that now includes the self-adaption of migration events- non-dominated sorting genetic algorithms (SA-M-NSGA). Moreover, a criterion based on the evolution of the approximated Pareto front has been activated for the automatic stop of the computation. Numerical experiments have been based on both an analytical benchmark and a real-life case study; the latter, which deals with the design of a class of power inductors for tests of MFH, is characterized by finite element analysis of the magnetic field.

The SA-M-NSGA substantially varies the genetic heritage of the population during the optimization process and allows for a faster convergence.

The proposed SA-M-NSGA is able to find a wider Pareto front with a computational effort comparable to a standard NSGA-II implementation.

In electrochemotherapy, flexible electrodes, composed by an array of needles, are applied to human tissues to treat large surface tumors. The positioning of the needles in the tissue depends on the surface curvature. The parallel needle case is preferred, as their relative inclinations strongly affect the actual distribution of electric field. Nevertheless, in some case, small inclinations are unavoidable. The purpose of this paper is to study the electric field distribution for non-parallel needles.

The effect of electrode position is evaluated systematically by means of numerical models and experiments on phantoms for two different angles (5° and 30°) and compared with the case of parallel needles. Potato model was used as phantom, as this tissue becomes dark after few hours from electroporation. The electroporation degree was gauged from the color changings on the potatoes.

The distribution of electric field in different needle configuration is found by means of finite element analysis (FEA) and experiments on potatoes. The electric field level of inclined needles was compared with parallel needle case. In particular, the electric field distribution in the case of inclined needles could be very different with respect to the one in the case of parallel needles. The degree of enhancement for different inclinations is visualized by potato color intensity. The FEA suggested that the needle parallelism has to be maintained as possible as if the tips are closer to each other, the electric field intensity could be different with respect to the one in the case of parallel needles.

This paper analyzes the effect of inclined electrodes considering also the non-linearity of tissues.

This paper aims to investigate different multi-objective optimization (MOO) approaches for design and control of electromagnetic devices. The main goal of MOO is to find the set of design variables or control parameters which will provide the best possible values of typical conflicting objective functions.

In the research studies, standard genetic algorithm (GA), non-dominated sorting GA (NSGA-II), migration NSGA algorithm and alternance method of optimal control theory are discussed and compared.

The test practical problems of multi-criteria optimization of induction heating processes with respect to chosen quality criteria confirm the effectiveness of application of considered MOO approaches both for the problems of design and control.

This paper represents and investigates different MOO approaches for design and control of electrotechnological systems.

The purpose of this paper is to show how the EStra-Many method works on optimization problems characterized by high-dimensionality of the objective space. Moreover, a comparison with a more classical approach (a constrained bi-objective problem solved by means of NSGA-II) is done.

The six reactances of a compensation network (CN) for a wireless power transfer system (WPTS) are synthesized by means of an automated optimal design. In particular, an evolutionary algorithm EStra-Many coupled with a sorting strategy has been applied to an optimization problem with four objective functions (OFs). To assess the obtained results, a classical genetic algorithm NSGA-II has been run on a bi-objective problem, constrained by two functions, and the solutions have been analyzed and compared with the ones obtained by EStra-Many.

The proposed EStra-Many method identified a solution (CN synthesis) that enhances the WPTS, considering all the four OFs. In particular, to assess the synthesized CN, the Bode diagram of the frequency response and a circuital simulation were evaluated a posteriori; they showed good performance of the CN, with smooth response and without unwanted oscillations when fed by a square wave signal with offset. The EStra-Many method has been able to find a good solution among all the feasible solutions, showing potentiality also for other fields of research, in fact, a solution nondominated with respect to the starting point has been identified. From the methodological viewpoint, the main finding is a new formulation of the many-objective optimization problem based on the concept of degree of conflict, which gives rise to an implementation free from hierarchical weights.

The new approach EStra-Many used in this paper showed to properly find an optimal solution, trading-off multiple objectives. The compensation network so synthesized by the proposed method showed good properties in terms of frequency response and robustness. The proposed method, able to deal effectively with four OFs, could be applied to solve problems with a higher number of OFs in a variety of applications because of its generality.

This paper aims to present an approach to the design of the compensation networks (CNs) based on a genetic optimization algorithm. The algorithm is applied to CNs with T-topology and considers the effects of the parasitic series resistances of their inductive components. The effectiveness of the algorithm is verified using Bode diagrams and simulation results.

The paper at first describes the problem and the approach followed to reach a set of optimal solutions, then explains the optimization algorithm, reports the obtained solutions and selects the optimal CNs. Finally, the actual performance of the wireless power transfer system (WPTS) when the selected CNs are used are checked.

This approach gave interesting results and made available a number of different sizing solutions of complex networks in a very short time. Most of the obtained solutions outperform the widely used series-series compensation. An accurate post processing of the obtained result is mandatory to discriminate the solutions that could be implemented from those that in a real system would originate uncontrolled high frequency current oscillation.

This paper offers a rather new approach to solve the problem of sizing the CNs of a dynamic WPTS. This approach makes available a large number of optimal solutions to the problem in a short time, without solving complex system of equations.

The purpose of this paper is to describe main ideas and demonstrate results of the research activities carried out by the authors in the field of design concepts of induction mass heating technology based on multiple-criteria optimization. The main goal of the studies is the application of different optimization methods and numerical finite element method (FEM) codes for field analysis to solve the multi-objective optimization problem that is mathematically formulated in terms of the most important optimization criteria, for example, maximum temperature uniformity, maximum energy efficiency and minimum scale formation.

Standard genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA) and alternance method of parametric optimization based on the optimal control theory are applied as effective tools for the practice-oriented problems for multiple-criteria optimization of induction heaters’ design based on non-linear coupled electromagnetic and temperature field analysis. Different approaches are used for combining FEM codes for interconnected field analysis and optimization algorithms into the automated optimization procedure.

Optimization procedures are tested and investigated for two- and three-criteria optimization problems solution on the examples of induction heating of a graphite disk, induction heating of aluminum and steel billets prior to hot forming.

Solved problems are based on the design of practical industrial applications. The developed optimization procedures are planned to be applied to the wide range of real-life problems of the optimal design and control of different electromagnetic devices and systems.

The paper describes main ideas and results of the research activities carried out by the authors during past years in the field of multiple-criteria optimization of induction heaters’ design based on numerical coupled electromagnetic and temperature field analysis. Implementing the automated procedure that combines a numerical FEM code for coupled field analysis with an optimization algorithm and its subsequent application for designing induction heaters makes the proposed approach specific and original. The paper also demonstrates that different optimization strategies used (standard GA, NSGA-II and the alternance method of optimal control theory) are effective for real-life industrial applications for multiple-criteria optimization of induction heaters design.

The paper presents the Finite Element (FE) evaluation of the magnetic field emitted by a Wireless Power Transfer Systems used to charge the battery of electrical vehicles. An original approach for reducing the mesh size of the 3D FE model is used.

A minicar equipped with a circular coil is considered, while the transmitting coil is coherent with the Society of Automotive Engineers (SAE) standard. The different shape of the coils and a possible misalignment are considered as possible sources of emitted magnetic field, which a person could be exposed to. To this end, a FE model is implemented. Because of the complexity of the mesh, a suitable 3D model is used. This model is previously validated and then used for evaluating the magnetic field around the Wireless Power Transfer Systems (WPTS).

The magnetic flux density around the WPTS is calculated and compared with the International Commission on Non-Ionizing Radiation Protection (ICNIRP) limits.

The proposed 3D model, whose validation is shown in the paper, is able to compute the magnetic field with high accuracy despite the presence of a conductive and ferromagnetic thin structure, the steel layer related to the car frame, which would need a very fine mesh with a large number of elements to solve Maxwell equations.

An inductor for the uniform heating of the extremity of a ferromagnetic steel tube for stress relieving is considered. The main goal of the study is to investigate the possibility to achieve a reasonable design of the inductor when dealing with many design variables.

Genetic optimization algorithms are used for this purpose, demonstrating the applicability of these techniques to the design of induction heating inductors. Genetic algorithms provide to the designer several optimal solutions belonging to Pareto Front, and this way they allow choosing the solution that better fits the technological requirements. In any case, the designer has to adapt the chosen solution to fit in with the real possibilities in industrial application.

The study demonstrates that automatic optimization methods may help the designer of the induction heating system to solve complex problems with very conflicting technological requirements.

In the paper, a problem with a high number of design variables is solved. Moreover, the goals of the optimization process are strongly conflicting, and the proposed problem is a challenging one.

The paper aims to propose a a field-circuit method for investigating the magnetic behavior of a wireless power transfer system (WPTS) for the charge of batteries of electric vehicles. In particular, a 3D model for finite element analysis (FEA) for the field simulation of a WPTS is developed. Specifically, the effects of aluminum shield and steel layer, representing the car frame, on the self and mutual inductances are investigated. An equivalent electric circuit is then built, and the relevant lumped parameters are identified by means of the FEAs.

The finite element model is used to evaluate self and mutual inductances in several transmitting-receiving coil configurations and relative positions. In particular, the FEA simulates the aluminum and steel layers as shell elements in a 3D domain. The self and mutual inductance values in the aligned coil case are also used as input parameters in a circuit model to evaluate the onload current.

The use of shell elements in FEA substantially reduces the number of mesh elements needed to simulate the eddy currents in the steel and aluminum layer, so putting the ground for low-cost field analysis. Moreover, the FEA gives an accurate computation of the self and mutual inductance to be used in a circuit model, which, in turn, provides a fast update of the onload induced current.

To save computational time, the use of 2D shell elements to model thin conductive regions introduces a simplified FEA that could be used in the WPTS simulation. Moreover, the dynamic behavior of WPTS, i.e. the operation when the receiving coil is moving with respect to the transmitting one, is considered. Because of the lumped parameters’ dependence upon the relative positions of the two coils, the proposed method allows identifying the circuit parameters for several configurations so substantially reducing the computational burden.