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The purpose of this paper is to present the basic ideas of magnetic nanoparticles' usage in the breast cancer treatment, which is called magnetic fluid hyperthermia. The proposed approach offers a relatively simple methodology of energy deposition, allowing an adequate temperature control at the target tissue, in this case a cancerous one. By means of a numerical method the authors aim to investigate two heating effects caused by varying magnetic fields, i.e. to compare the power density heating effects of eddy currents and magnetic nanoparticles.

In order to numerically investigate the combination of the overheating effect of magnetic nanoparticles and eddy currents, the Finite Element Method solver based on FEniCS project has been prepared. To include the magnetic fluid in the model it has been assumed that power losses in the magnetic nanoparticles are completely converted into heat, according to experimentally developed formula. That formula can be interpreted as the hysteresis losses with regard to the volume of magnetic fluid. Finally, the total power density has been calculated as the product of the sum of power density from eddy currents and hysteresis losses. That methodology has been applied to calculate the effectiveness of magnetic fluid hyperthermia with regard to the female breast phantom.

The paper presents the methodology which can be used in magnetic fluid hyperthermia therapy planning and Computer Aid Diagnosis (CAD). Furthermore, it is shown how to overcome one of the most serious engineering challenges connected with hyperthermia, i.e. achieving adequate temperature in deep tumors without overheating the body surface.

The obtained results connected with the assessment of eddy currents effect suggest that during hyperthermia treatment the configuration which consists of an exciting coil and human body, plays a curial role. Moreover, the authors believe that these results will help to predict the skin surface overheating that accompanies deep heating. The presented methodology can be used by engineers in the development of Computer Aid Diagnosis systems.

In a given patient's situation a number of choices must be made to determine the parameters of the hyperthermia treatment. These include the need of multiple‐point temperature measurements for accurate and thorough monitoring. Treatment planning will require accurate characterization of the applicator deposition pattern and the tissue parameters, as well as the numerical techniques to predict the resultant heating pattern. The presented paper shows how to overcome these problems from the numerical point of view at least.

The purpose of this paper is to present the synthesis of magnetic fluid characteristics, like diameter of nanoparticles (NPs) and their concentration, in order to obtain a prescribed temperature rate. An evolution strategy algorithm is used in the optimization procedure, while three‐dimensional finite‐element (FE) modelling is used for magnetic field and thermal field analysis in transient conditions.

FE analysis has been used in order to compute the magnetic and thermal field in a suitable model of the tumor region. The power density due to NP has been accordingly derived.

The NP distribution, giving a prescribed thermal response, is synthesized.

The proposed method can be used to design a therapeutic treatment based on magnetic fluid hyperthermia.

The paper belongs to a streamline of innovative studies on computational hyperthermia.

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.

Magnetic fluid hyperthermia experiment requires a uniform magnetic field in order to control the heating rate of a magnetic nanoparticle fluid for laboratory tests. The automated optimal design of a real-life device able to generate a uniform magnetic field suitable to heat cells in a Petri dish is presented. The paper aims to discuss these issues.

The inductor for tests has been designed using finite element analysis and evolutionary computing coupled to design of experiments technique in order to take into account sensitivity of solutions.

The geometry of the inductor has been designed and a laboratory prototype has been built. Results of preliminary tests, using a previously synthesized and characterized magneto fluid, are presented.

Design of experiment approach combined with evolutionary computing has been used to compute the solution sensitivity and approximate a 3D Pareto front. The designed inductor has been tested in an experimental set-up.

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.

The purpose of the paper is to propose a cost‐effective method of non‐parametric optimisation in order to explore shapes of a magnetic pole, in the search for the optimal one fulfilling a prescribed objective function.

The boundary of the magnetic field region to synthesize is considered as a moving boundary separating two materials (air and ferrite). An objective‐function dependent velocity field is defined, in order to update the position of nodes located along the unknown boundary. Specifically, a uniform magnetic field within the controlled region is aimed at.

The application of the proposed method to the design of a magnet for magnetic‐fluid hyperthermia made it possible to reduce the field deviation with a little computational effort.

Instead of using a standard algorithm of numerical minimisation to find the optimal search direction, a field‐dependent velocity proportional to the objective function value is exploited. This way, the motion of the boundary towards the optimal shape is automatically driven: in principle, in fact, the velocity reaches the zero value at the optimum.

Thanks to the kinematic law governing the movement of the boundary to synthesize, the overall computational cost is low. Moreover, the non‐parametric approach to the shape synthesis preserves the advantage of a broad search space.

Magnetization is one of the most important parameters of magnetic fluids. The shape of the magnetization curve often determines the application of a fluid in a device. On the basis of the magnetization curve, it is also possible to estimate, for example, the distribution and size of the particles in a magnetic fluid carrier fluid. The aim of this paper is to present a new approach for estimating the magnetization curve.

The proposed method is an iterative method based on the measurement of magnetic induction on a test stand. To determine the magnetization curve, a numerical simulation of the magnetic field distributions for the preliminary magnetization curve should also be performed. Numerical simulations for modified forms of the magnetization curve are performed until the difference between the results obtained by the measurement and numerical simulation are the smallest.

This paper presents the results of magnetization curve research for ferrofluids and magnetorheological fluids.

The discussed method shows the possibilities of using numerical simulations of magnetic field distribution to determine the magnetic properties of magnetic fluids. This method may be an alternative for estimating the magnetization curve of the magnetic fluid compared to other methods.

The purpose of this paper is to focus on applications related to the hyperthermia treatment of cancer, with heating imposed either by a laser in the near-infrared range or by radiofrequency waves. The particle filter algorithms are compared in terms of computational time and solution accuracy.

The authors extend the analyses performed in their previous works to compare three different algorithms of the particle filter, as applied to the hyperthermia treatment of cancer. The particle filters examined here are the sampling importance resampling (SIR) algorithm, the auxiliary sampling importance resampling (ASIR) algorithm and Liu & West’s algorithm.

Liu & West’s algorithm resulted in the largest computational times. On the other hand, this filter was shown to be capable of dealing with very large uncertainties. In fact, besides the uncertainties in the model parameters, Gaussian noises, similar to those used for the SIR and ASIR filters, were added to the evolution models for the application of Liu & West’s filter. For the three filters, the estimated temperatures were in excellent agreement with the exact ones.

This work may help medical doctors in the future to prescribe treatment protocols and also opens the possibility of devising control strategies for the hyperthermia treatment of cancer.

The natural solution to couple the uncertain results from numerical simulations with the measurements that contain uncertainties, aiming at the better prediction of the temperature field of the tissues inside the body, is to formulate the problem in terms of state estimation, as performed in this work.

The purpose of this study is to show that the methods of the numerical simulation can be a very effective tool for a proper choice of control parameters of artificial hyperthermia. An electromagnetic field induced by two external electrodes and a temperature field resulting from electrodes action in a 3D domain of biological tissue is considered. An important problem is the appropriate directing of heat in the region of tumor, so as to avoid damaging healthy cells surrounding the tumor. Recently, to concentrate the heat on the tumor, magnetic nanoparticles, which are introduced into the tumor, were used. The nanoparticles should be made of material that ensures appropriate magnetic properties and has a high biocompatibility with the biological tissue. External electric field causes the heat generation in the tissue domain.

The distribution of electric potential in the domain considered is described by the Laplace system of equations, while the temperature field is described by the Pennes’ system of equations. These problems are coupled by source function being the additional component in the Pennes’ equation and resulting from the electric field action. The boundary element method is applied to solve the coupled problem connected with the heating of biological tissues.

The aim of investigations is to determine an electric potential of external electrodes and the number of nanoparticles introduced to a tumor region to obtain the artificial hyperthermia state. The tests performed showed that the proposed tool to solve the inverse problem provides correct results.

In the paper the steady state bioheat transfer problem is considered, so the thermal damage is a function of the temperature only. Therefore, the solution can be considered as the maximum ablation zone of cancer. Additionally, the choice of appropriate parameters will be affected on the position and shape of the tumor and the electrodes.

In the paper the inverse problem has been solved using the evolutionary algorithm, gradient method and hybrid algorithm which is a combination of the two previous.

Field excited flux switching machines (FEFSM) are preferred over induction and synchronous machines due to the confinement of all excitation sources on the stator leaving a robust rotor. This paper aims to perform coupled electromagnetic thermal analysis and stress analysis for single phase FEFSM as, prolonged high-speed operational time with core and copper losses makes it prone to stress and thermal constraints as temperature rise in machine lead to degraded electromagnetic performance whereas the violation of the principle stress limit may result in mechanical deformation of the rotor.

This paper presents the implementation of coupled electromagnetic-thermal and rotor stress analysis on single-phase FEFSM with non-overlap winding configurations using finite element analysis (FEA) methodology in JMAG V. 18.1. three-dimensional (3D) magnetic loss analysis is performed and extended to 3D thermal analysis to predict temperature distribution on various parts of the machine whereas Stress analysis predicts mechanical stress acting upon edges and faces of the rotor.

Analysis reveals that temperature distribution and rotor stress on the machine is within acceptable limits. A maximum temperature rise of 37.7°C was noticed at armature and field windings, temperature distribution in stator near pole proximity was 35°C whereas no significant change in rotor temperature was noticed. Furthermore, principal stress at the speed of 3,000 rpm and 30,000 rpm was found out to be 0.0305 MPa 3.045 MPa, respectively.

The designed machine will be optimized for improvement of electromagnetic performance followed by hardware implementation and experimental testing in the future.

The model is developed for axial fan applications.

Thermal analysis is not being implemented on FEFSM for axial fan applications which is an important analysis to ensure the electromagnetic performance of the machine.