The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

Since large power transformers are custom-made, and their design process is a labor-intensive task, their design process is split into different parts. In tendering, the price calculation is based on the preliminary design of the transformer. Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Most of the published algorithms are using a copper filling factor based winding model to calculate the main dimensions of the transformer during this first, preliminary design step. Therefore, these cost optimization methods are not considering the detailed winding layout and the conductor dimensions. However, the knowledge of the exact conductor dimensions is essential to calculate the thermal behaviour of the windings and make a more accurate stray loss calculation. The paper presents a novel, evolutionary algorithm-based transformer optimization method which can determine the optimal conductor shape for the windings during this examined preliminary design stage. The accuracy of the presented FEM method was tested on an existing transformer design. Then the results of the proposed optimization method have been compared with a validated transformer design optimization algorithm.

A method of finding the temperature dependences of physical parameters of metals is proposed and discussed. The method is based on the combination of measurement of the surface temperature of the material and subsequent solution of the inverse problem using the multi-parametric optimization. Particular steps and algorithms are illustrated with an example.

Purpose: – The purpose of this paper is to propose and analyze a combined heat treatment of metal materials, consisting in classic induction pre-heating and/or post-heating and full heating by laser beam. This technology is prospective for some kinds of surface hardening and welding because its application leads to lowering of temperature gradients at the heated spots, which substantially reduces local residual mechanical strains and stresses. Design/methodology/approach: – The task was solved like the 3D hard-coupled problem for electromagnetic field, temperature field and field of displacements. It was solved numerically using the techniques based on the FEM. For solution was used commercial software COMSOL Multiphysics, some parts were solved using own scripts in the software Agros. Findings: – In the paper are shown results of the numerical solution and experimental measured data. Due the work the authors found that the influence of the pre-heating and post-heating really leads to limit the temperature gradients and from other measurements is clear that also to improving of the welding. Originality/value: – The paper presents fully 3D nonlinear and nonstationary mathematical model of hybrid laser welding, its numerical solution experimental verification.