Siemens (Czechia)

The digital transformation associated with the Industry 4.0 concept fundamentally changes the functioning of human resources management and places new demands on the competencies of HR managers. The main objective of this paper is to explore the impact of HR Managers 4.0 competencies as a moderating factor in the relationship between HRM 4.0 practices and selected corporate and employee performance indicators. The research is based on quantitative data collected through a questionnaire survey conducted among HR managers and directors of HR departments. Descriptive statistics, factor analysis and simple and multiple linear regression were used to analyse the data. The results indicate the importance of competencies such as effective communication, complex problem-solving, and strategic and analytical thinking, which positively influence productivity, especially in the areas of employee adaptation, training and development. The study contributes to the development of knowledge on the role of HR managers' competencies in the digital environment.

The energy efficiency of machines is nowadays an intensively studied problem. The efficiency of the induction motor is dominantly influenced by the rotor’s and stator winding’s temperature. The main goal of the research presented in this paper is to develop a methodology based on Computational Fluid Dynamics (CFD) analysis of internal and external aerodynamics, which is necessary for the optimisation of cooling of the induction motors. In this paper, the theoretical, as well as the numerical study of the internal and external aerodynamics of the induction motor, is described and verified by the experimental measurements. In the CFD-based numerical study, the Reynolds-averaged Navier–Stokes (RANS) turbulence modelling approach was applied to the flow field simulations inside and outside the induction motor. The complexity of the solved problem is increased not only by the geometric asymmetry but also by the flow’s asymmetric character caused by the fan’s rotation to cool the motor casing. This increases demand, especially on computational resources, as it is impossible to create a simplified numerical model incorporating symmetry. The volume flow of the cooling air and velocity between ribs was measured for the experimental study. Comparing the results of the Computational Fluid Dynamics (CFD) simulations and data obtained from the experimental measurement, we concluded that the results of CFD simulations are in good relationship with the results of experimental measurement and analytical approximations. An experimentally validated CFD model of the induction motor, the so-called digital twin, will be in the future used for virtual optimisation of the new designs concerning minimising losses and maximising efficiency, respectively.

The article deals with the influence of the partial anisotropy of electrotechnical sheets metal for electrical rotating machine. Standard EN 10106: 2015 specifies tolerances for individual types of electrotechnical sheets metal. Different features in different directions cause asymmetry in magnetic circuit of the asynchronous motor. The effect of the asymmetry of the magnetic circuit is analogical to the effect of the unsymmetrical supply, the deformation of the rotating magnetic field, which causes the higher vibrations, the higher shaft voltage and the torque characteristics deformation. The idea of limiting this phenomenon is to alternately mechanically rotate the plates by 90∘ and thus to balance in total the magnetic conductivity in one direction. In the article, the difference between losses in different sheet metal rotation is verified according to the standard. Furthermore, dynamic hysteresis curves were measured for different sheet metal rotation. Using the Finite Element Method the effect on the torque characteristic was verified.

An adaptive quadratic polynomial neural unit (QNU) controller for optimization of a conventional Smart Microgrid control loop is studied and proposed. The parameters associated with the studied grid plants are considered to be known in this study, with the fact that the load is unknown and time-variant. A sample-by-sample real-time recurrent learning algorithm of an additional QNU controller is derived, with its performance tested and discussed as a result of this paper.