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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.

Human performance, particularly that of the warfighter, has been the subject of a large amount of research during the past few decades. For example, in the Medline database of medical and psychological research, 1,061 papers had been published on the topic of “military performance” as of October 2003. Because warfighters are often pushed to physiological and mental extremes, a study of their performance provides a unique glimpse of the interplay of a wide variety of intrinsic and extrinsic factors on the functioning of the human brain and body. Unfortunately, it has proven very difficult to build performance models that can adequately incorporate the myriad of physiological, medical, social, and cognitive factors that influence behavior in extreme conditions. The chief purpose of this chapter is to provide a neurobiological (neurochemical) framework for building and integrating warfighter performance models in the physiological, medical, social, and cognitive areas. This framework should be relevant to all other professionals who routinely operate in extreme environments. The secondary purpose of this chapter is to recommend various performance metrics that can be linked to specific neurochemical states and can accordingly strengthen and extend the scope of the neurochemical model.

This paper aims to focus on the accurate analysis of the fractional heat transfer in a two-dimensional (2D) rectangular monolayer tissue with three different kinds of lateral boundary conditions and the quantitative evaluation of the degree of thermal damage and burn depth.

A symplectic method is used to analytically solve the fractional heat transfer dual equation in the frequency domain (s-domain). Explicit expressions of the dual vector can be constructed by superposing the symplectic eigensolutions. The solution procedure is rigorously rational without any trial functions. And the accurate predictions of temperature and heat flux in the time domain (t-domain) are derived through numerical inverse Laplace transform.

Comparison study shows that the maximum relative error is less than 0.16%, which verifies the accuracy and effectiveness of the proposed method. The results indicate that the model and heat source parameters have a significant effect on temperature and thermal damage. The pulse duration (Δt) of the laser heat source can effectively control the time to reach the peak temperature and the peak slope of the thermal damage curve. The burn depth is closely correlated with exposure temperature and duration. And there exists the delayed effect of fractional order on burn depth.

A symplectic approach is presented for the thermal analysis of 2D fractional heat transfer. A unified time-fractional heat transfer model is proposed to describe the anomalous thermal behavior of biological tissue. New findings might provide guidance for temperature prediction and thermal damage assessment of biological tissues during hyperthermia.

The purpose of this paper is to present an inverse analysis procedure based on a coupled numerical formulation through which the coefficients describing non‐linear thermal properties of blood perfusion may be identified.

The coupled numerical technique involves a combination of the dual reciprocity boundary element method (DRBEM) and a genetic algorithm (GA) for the solution of the Pennes bioheat equation. Both linear and quadratic temperature‐dependent variations are considered for the blood perfusion.

The proposed DRBEM formulation requires no internal discretisation and, in this case, no internal nodes either, apart from those defining the interface tissue/tumour. It is seen that the skin temperature variation changes as the blood perfusion increases, and in certain cases flat or nearly flat curves are produced. The proposed algorithm has difficulty to identify the perfusion parameters in these cases, although a more advanced genetic algorithm may provide improved results.

The coupled technique allows accurate inverse solutions of the Pennes bioheat equation for quantitative diagnostics on the physiological conditions of biological bodies and for optimisation of hyperthermia for cancer therapy.

The proposed technique can be used to guide hyperthermia cancer treatment, which normally involves heating tissue to 42‐43°C. When heated up to this range of temperatures, the blood flow in normal tissues, e.g. skin and muscle, increases significantly, while blood flow in the tumour zone decreases. Therefore, the consideration of temperature‐dependent blood perfusion in this case is not only essential for the correct modelling of the problem, but also should provide larger skin temperature variations, making the identification problem easier.

This bibliography is offered as a practical guide to published papers, conference proceedings papers and theses/dissertations on the finite element (FE) and boundary element (BE) applications in different fields of biomechanics between 1976 and 1991. The aim of this paper is to help the users of FE and BE techniques to get better value from a large collection of papers on the subjects. Categories in biomechanics included in this survey are: orthopaedic mechanics, dental mechanics, cardiovascular mechanics, soft tissue mechanics, biological flow, impact injury, and other fields of applications. More than 900 references are listed.

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.

This study aims to investigate a critical review on the applications of fluid-structure interaction (FSI) in porous media.

Transport phenomena in porous media are of continuing interest by many researchers in the literature because of its significant applications in engineering and biomedical sectors. Such applications include thermal management of high heat flux electronic devices, heat exchangers, thermal insulation in buildings, oil recovery, transport in biological tissues and tissue engineering. FSI is becoming an important tool in the design process to fully understand the interaction between fluids and structures.

This study is structured in three sections: the first part summarizes some important studies on the applications of porous medium and FSI in various engineering and biomedical applications. The second part focuses on the applications of FSI in porous media as related to hyperthermia. The third part of this review is allocated to the applications of FSI of convection flow and heat transfer in engineering systems filled with porous medium.

To the best knowledge of the present authors, FSI analysis of turbulent flow in porous medium never been studied, and therefore, more attention should be given to this area in any future studies. Moreover, more studies should also be conducted on mixed convective flow and heat transfer in systems using porous medium and FSI.

The wall of the blood vessel is considered as a flexible multilayer porous medium, and therefore, rigid wall analysis is not accurate, and therefore, FSI should be implemented for accurate predictions of flow and hemodynamic stresses.

The use of porous media theory in biomedical applications received a great attention by many investigators in the literature (Khanafer and Vafai, 2006a; Al-Amiri et al., 2014; Lasiello et al., 2016a, Lasiello et al., 2016b; Lasiello et al., 2015; Chung and Vafai, 2013; Mahjoob and Vafai, 2009; Yang and Vafai, 2008; Yang and Vafai, 2006; Ai and Vafai, 2006). A comprehensive review was conducted by Khanafer and Vafai (2006b) summarizing various studies associated with magnetic field imaging and drug delivery. The authors illustrated that the tortuosity and porosity had a profound effect on the diffusion process within the brain. AlAmiri et al. (2014) conducted a numerical study to investigate the effect of turbulent pulsatile flow and heating technique on the thermal distribution within the arterial wall. The results of that investigation illustrated that local heat flux variation along the bottom layer of the tumor was greater for the low-velocity condition. Yang and Vafai (2006) presented a comprehensive four-layer model to study low-density lipoprotein transport in the arterial wall coupled with a lumen (Figure 1). All the four layers (endothelium, intima, internal elastic lamina and media) were modeled as a homogenous porous medium.

Future studies on the applications of FSI in porous media are recommended in this review.

Inverting electroheat problems involves synthesizing the electromagnetic arrangement of coils and geometries to realize a desired heat distribution. To this end two finite element problems need to be solved, first for the magnetic fields and the joule heat that the associated eddy currents generate and then, based on these heat sources, the second problem for heat distribution. This two-part problem needs to be iterated on to obtain the desired thermal distribution by optimization. Being a time consuming process, the purpose of this paper is to parallelize the process using the graphics processing unit (GPU) and the real-coded genetic algorithm, each for both speed and accuracy.

This coupled problem represents a heavy computational load with long wait-times for results. The GPU has recently been demonstrated to enhance the efficiency and accuracy of the finite element computations and cut down solution times. It has also been used to speedup the naturally parallel genetic algorithm. The authors use the GPU to perform coupled electroheat finite element optimization by the genetic algorithm to achieve computational efficiencies far better than those reported for a single finite element problem. In the genetic algorithm, coding objective functions in real numbers rather than binary arithmetic gives added speed and accuracy.

The feasibility of the method proposed to reduce computational time and increase accuracy is established through the simple problem of shaping a current carrying conductor so as to yield a constant temperature along a line. The authors obtained a speedup (CPU time to GPU time ratio) saturating to about 28 at a population size of 500 because of increasing communications between threads. But this far better than what is possible on a workstation.

By using the intrinsically parallel genetic algorithm on a GPU, large complex coupled problems may be solved very quickly. The method demonstrated here without accounting for radiation and convection, may be trivially extended to more completely modeled electroheat systems. Since the primary purpose here is to establish methodology and feasibility, the thermal problem is simplified by neglecting convection and radiation. While that introduces some error, the computational procedure is still validated.

The methodology established has direct applications in electrical machine design, metallurgical mixing processes, and hyperthermia treatment in oncology. In these three practical application areas, the authors need to compute the exciting coil (or antenna) arrangement (current magnitude and phase) and device geometry that would accomplish a desired heat distribution to achieve mixing, reduce machine heat or burn cancerous tissue. This process presented does it more accurately and speedily.

Particularly the above-mentioned application in oncology will alleviate human suffering through use in hyperthermia treatment planning in cancer treatment. The method presented provides scope for new commercial software development and employment.

Previous finite element shape optimization of coupled electroheat problems by this group used gradient methods whose difficulties are explained. Others have used analytical and circuit models in place of finite elements. This paper applies the massive parallelization possible with GPUs to the inherently parallel genetic algorithm, and extends it from single field system problems to coupled problems, and thereby realizes practicable solution times for such a computationally complex problem. Further, by using GPU computations rather than CPU, accuracy is enhanced. And then by using real number rather than binary coding for object functions, further accuracy and speed gains are realized.

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 electromagnetic fields have a great influence on the behaviour of all the living systems. The as low as reasonably achievable (ALARA) principle imposes, in case of long exposures to low (i.e. power systems) or high frequency (i.e. microwave systems or cell phones) fields, some limitations to the radiated fields by the industrial equipment. On the other hand, some benefits can be taken from the effects of the electromagnetic fields on the living being: the hyperthermal technique is well known for the treatment of the cancer. Either we want to be protected from the fields, or we want to take benefit of the positive effects of these fields, all the effects thermal as well as genetic have to be well known. Like in any industrial application, the electromagnetic field computation allows a better knowledge of the phenomena, and an optimised design. Hence, there is a very important challenge for the techniques of computation of electromagnetic fields. The major difficulties that appear are: (1) related to the material properties – the “material” (the human body) has very unusual properties (magnetic permeability, electric permittivity, electric conductivity), these properties are not well known and depend on the activity of the person, and this material is an active material at the cell scale; (2) related to the coupling phenomena – the problem is actually a coupled problem: the thermal effect is one of the major effects and it is affected by the blood circulation; (3) related to the geometry – the geometry is complex and one has to take into account the environment. The problems that we have to face with are – the identification of the properties of the “material”, the coupled problem solution and the representation of the simulated phenomena.