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Purpose The electrical machines connected to modern electric power grids are non-sinusoidal excited, and their augmented losses, including iron losses, limit their working characteristics. This paper aims to propose a prediction method for iron losses in non-oriented grains (NO) FeSi sheets under non-sinusoidal voltage, involving an inverse classical Preisach hysteresis model and the time-integration of each loss component.Design/methodology/approach The magnetic history management in inverse Preisach model is optimized and a numerical Everett function is identified from measured symmetrical hysteresis cycles. The experimental data for sinusoidal waveforms obtained by a single sheet tester were also used to identify the parameters involved in Bertotti’ losses separation method. The non-sinusoidal magnetic induction waveform, corresponding to a measured voltage in an industrial electrical grid, was the input for Preisach model, the output magnetic field being accurately computed. The hysteresis, classical and excess losses are calculated by time-integration and the total losses are compared with those obtained for sinusoidal excitation.Findings The proposed method allows to estimate the iron losses for non-sinusoidal magnetic induction, using carefully identified parameters of FeSi NO sheets, using experimental data from sinusoidal regimes.Originality/value The method accuracy is assured by using a numerical Everett function, a variable Preisach grid step (adapted for the high non-linearity of FeSi sheets) and high-order fitting polynomials for the microscopic parameters involved in the excess loss estimation. The procedure allows a better design of magnetic cores and an improved estimation of the electric machine derating for non-sinusoidal voltages. [ABSTRACT FROM AUTHOR]

This paper presents a material flow management case study based on our research in modelling, simulation and optimization for a packaging and palletizing system. The studied system allows packaging, palletizing, wrapping and storage of several types of products within a food company. For each type of product to be packaged and stored a material flow system simulation is necessary in order to identify and eliminate the bottlenecks and to obtain an optimized system in terms of productivity. Because the optimization algorithm is the same for different kind of products we will illustrate our research using a single type of product. We will focus the case study on packaging coffee bags in boxes. The boxes are positioned on pallets in order to create a stack that will be wrapped for storage. We used Flexim 7 simulation software for modelling, simulation and optimization of the packaging and palletizing system.

Mesoporous, magnetite based nanoparticles (MNPs) were successfully synthesized by a modified precipitation method. For this purpose, the precursors solution was sprayed into a sodium hydroxide or sodium hydroxide/citrate precipitation bath. In order to determinate the structure and morphology of the as-synthesized mesoporous MNPs the following methods were used: X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), infrared spectra (IR), high-resolution transmission electron microscopy (HRTEM) coupled with selected area electron diffraction (SAED) and energy dispersive spectrometry (EDS). The Brunauer–Emmett–Teller (BET) analysis revealed a mesoporous structure of the samples which has a specific surface area of about 160m2/g (unstabilized Fe3O4 nanoparticles) and 247 - 257 m2/g (for the stabilized Fe3O4 nanoparticles). The average pore size of the MNPs is 2 – 5nm which means that mesoporous materials were obtained.

The efficiency of the magnetic actuation is studied using ideal magnetized nano-ellipsoids under an applied magnetic field. The volumetric magnetic torque is evaluated and the influence of the particle shape, orientation and anisotropy is outlined.