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This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics.

The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry.

3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent.

The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

Structural performance of additively manufactured parts is deposition path-dependent because of the induced material anisotropy. Hence, this paper aims to contribute a novel idea of concurrently performing the deposition path planning and the structural topology optimization for additively manufactured parts.

The concurrent process is performed under a unified level set framework that: the deposition paths are calculated by extracting the iso-value level set contours, and the induced anisotropic material properties are accounted for by the level set topology optimization algorithm. In addition, the fixed-geometry deposition path optimization problem is studied. It is challenging because updating the zero-value level set contour cannot effectively achieve the global orientation control. To fix this problem, a level set-based multi-step method is proposed, and it is proved to be effective.

The proposed concurrent design method has been successfully applied to designing additively manufactured parts. The majority of the planned deposition paths well match the principle stress direction, which, to the largest extent, enhances the structural performance. For the fixed geometry problems, fast and smooth convergences have been observed.

The concurrent deposition path planning and structural topology optimization method is, for the first time, developed and effectively implemented. The fixed-geometry deposition path optimization problem is solved through a novel level set-based multi-step method.

Seeks to posit a new procedure to optimize the geometry of an electric motor for hybrid vehicles (HV), based on an optimization model implementing the complex specifications, environment‐sizing constraints and vehicle‐operating points.

In this work, induction machines (IM) were studied for automotive traction applications, more specifically, for HV. The necessary models and methods to analyse and design such electric machines were developed. A limited number of finite element computations was used to establish a non‐linear electromagnetic model of the machine. This model was then adapted to study the sensitivity of the design for the most significant geometric variations. Thus, one can easily adapt a machine for new sizing requirements.

This modelling methodology was extended to take into account a great number of parametric variations. This model was used for constrained optimisation on the geometry of a machine. To achieve this, a new “reset model optimisation” method was proposed, combining fast computations, precision and simplicity. This methodology was used to design an IM with HV‐sizing considerations.

The methodology is based on an improved equivalent circuit of the IM where the FEM accuracy (saturation) is combined with the potential of analytical modelling (simple and quick).

This half‐analytical, half‐FEM methodology is well adapted for research projects where the specifications are frequently reviewed.

The paper aims to investigate tool wear mechanism and tool geometry optimization of drilling PCB fixture hole.

An experimental study was carried out to investigate the chip formation and tool wear mechanism of drilling PCB fixture holes. Two types of drill with different types of chip-split groove were used in this study. The performances of these two types of drill bots were evaluated by tool wear and the shapes of chips.

The chips of drilling fixture holes contain aluminum chips from the cover board, copper chips from the copper foil, discontinuous glass fiber and resin from the CFRP. Feed rate and drilling speed have a great influence on the chip morphology. Abrasive wear of the drill lip is the main reason of the fixture drill bit in drilling PCB, and micro-chipping is observed on the tool nose and chisel edge. The influence of distance between the chip-split groove and drill point center on the axial force and torque is not obvious.

In this paper, hole wall roughness and drilling temperature were not analyzed in the optimization of drilling parameters. The future research work should consider them.

This paper investigated the mechanism of burr formation and tool wear in drilling of PCB fixture holes. Tool geometry was optimized by adding chip-split grooves.

The aim of this paper is to investigate performance improvements of a monolithic solid oxide fuel cell geometry through an entropy generation analysis.

The analysis of entropy generation rates makes it possible to identify the phenomena that cause the main irreversibilities in the fuel cell, to understand their causes and to propose changes in the design and operation of the system. The various contributions to entropy generation are analyzed separately in order to identify which geometrical parameters should be considered as the independent variables in the optimization procedure. The local entropy generation rates are obtained through 3D numerical calculations, which account for the heat, mass, momentum, species and current transport. The system is then optimized in order to minimize the overall entropy generation and increase efficiency.

In the optimized geometry, the power density is increased by about 10 per cent compared to typical designs. In addition, a 20 per cent reduction in the fuel cell volume can be achieved with less than a 1 per cent reduction in the power density with respect to the optimal design.

The physical model is based on a simple composition of the reactants, which also implies that no chemical reactions (water gas shift, methane steam reforming, etc.) take place in the fuel cell. Nevertheless, the entire procedure could be applied in the case of different gas compositions.

Entropy generation analysis allows one to identify the geometrical parameters that are expected to play important roles in the optimization process and thus to reduce the free independent variables that have to be considered. This information may also be used for design improvement purposes.

In this paper, entropy generation analysis is used for a multi‐physics problem that involves various irreversible terms, with the double use of this physical quantity: as a guide to select the most relevant design geometrical quantities to be modified and as objective function to be minimized in the optimization process.

This paper presents a new numerical approach for shape optimization of resistors with complex geometry. The aim of this method is to develop a systematic modification of resistor design in combination with the computation of the electric field, in order to optimize some chosen properties like homogenization of the power dissipation on the resistor surface.

The attractiveness of functionally graded composites lies in the possibility of a gradual spatial change of their properties such as hardness, strength and wear resistance. The purpose of this paper is to discuss the use of electromagnetic buoyancy to separate the reinforcement particles during the casting process of such a composite.

The basic problem encountered in the process of casting composites is to obtain electromagnetic buoyancy and simultaneously to avoid a flow of the liquid metal which destroys the desired composite structure. In this paper the authors present the methodology of numerical optimization of inductor geometry in order to homogenize the electromagnetic force field distribution.

The optimization method based on searching the solution subspace created by applying knowledge of the modelled process physics proved better than the universal local optimization methods. These results were probably caused by the complex shape of the criterion function hypersurface characterized by the presence of local minima.

Due to their characteristics, functionally graded composites are of great interest to the automotive, aerospace and defense industries. In the case of metal matrix composites casting techniques (as the presented one) are the most effective methods of producing functionally graded materials.

The paper presents the optimization of a new process of casting functionally graded composites in a low-frequency alternating electromagnetic field. The process involves problems that did not occur previously in the area of electromagnetic processing of materials. The paper proposes the use of special design of inductors to homogenize the electromagnetic force field.

High torque ripple is the significant challenge of the synchronous reluctance machine in household electric appliances, electric vehicles and so on. This paper aims to present an optimized design of a synchronous reluctance rotor structure to reduce the torque ripple with improving the average torque by the particle swarm optimization (PSO) algorithm.

The optimization of rotor geometries has been investigated. Most of the rotor parameters such as the width of iron parts, the width of barriers along d and q axes and the endpoint angle of barriers are optimized by a new method using the PSO algorithm. After optimization, the resulted optimum design along with the initial design is simulated by two-dimensional finite element method and results are compared. At the end, a prototype is constructed and tested. Results of the experiment are compared with the simulation results where acceptable adoption is yielded.

Minimizing the torque ripple without losing the average torque is an important achievement of the synchronous reluctance motor (SynRM) optimization; furthermore, the finite element analysis and experimental results indicate that the torque ripple of the SynRM with the optimized rotor is reduced significantly. Also, increasing the number of optimization parameters can effectively obtain an accurate shape of the SynRM barrier.

Because of the high number of parameters in synchronous reluctance rotors, the majority of proposed optimizations did not use all geometric parameters of the rotor and tried to simplify the optimization by ignoring several optimization parameters or reducing the number of flux barriers. In this optimization, most of the rotor parameters have been used to achieve the precise barrier shape with the aim of reducing the torque ripple in SynRM.

The purpose of this study is to optimize high-strength gears produced by powder metallurgical process and to provide a material model to predict the tooth root bending fatigue strength. Powder metal (PM) technology offers great opportunities for the reduction of the carbon footprint and improvement of the cost efficiency of gear production. PM gears can achieve flank load-carrying capacities comparable to wrought steel gears if the loaded volume is fully densified. Still, the tooth root strength is of particular importance.

The tooth root stresses can be minimized by optimizing the tooth root geometry. This usually leads to a target conflict, as fully optimized tooth root geometries cannot be manufactured by generating processes such as hobbing, generating-grinding or rolling. To use the increase in tooth root load-carrying capacity of fully optimized root geometry on PM gears, a non-generating method for surface densifying is needed. The shot-peening process is used as an alternative densification process for PM gears. The properties of both shot peened and cold-rolled PM gears are analyzed and compared. To quantify the effect of both manufacturing processes, the tooth root bending fatigue strength will be evaluated and compared to wrought gears.

From the fatigue strength perspective, a material model is developed, which is able to predict local endurable stress amplitudes. The model is gained through regression varying carbon content, density and size effect on bending specimens.

It is transferable to PM gears of the same material using a load transfer coefficient.

Total artificial hearts (TAHs) are required for the treatment of cardiovascular diseases. In order to replace the native heart a TAH must provide a sufficient perfusion of the human body, prevent blood damage and meet the implantation constraints. Until today there is no TAH on the market which meets all constraints. So the purpose of this paper is to design a drive in such a way that the operated TAH meets all predefined constraints.

The drive is designed in terms of weight and electric losses. In setting up a cost function containing those constraints, the drive design can be included in a optimization process. When reaching the global minimum of the cost function the optimum drive design is found. In this paper the optimization methods manual parameter variation and differential evolution are applied.

At the end of the optimization process the drive's weight amounts to 460 g and its mean losses sum up to 10 W. This design meets all predefined constraints. Further it is proposed to start the optimization process with a parameter variation to reduce the amount of optimization parameters for the time consuming differential evolution algorithm.

This TAH has the potential to provide a therapy for all patients suffering from cardiovascular diseases as it is independent of donor organs.

The optimization-based design process yields an optimum drive for a TAH in terms of weight and electrical losses. In this way a TAH is developed which meets all implantation constraints and provides sufficient perfusion of the human body at the same time.