The incorporation of magnetic barium hexaferrite nanoparticles in a transparent polymer matrix of poly(methyl methacrylate) (PMMA) is reported for the first time. The barium hexaferrite nanoplatelets doped with Sc3+, i.e., BaSc0.5Fe11.5O12 (BaHF), having diameters in the range 20 to 130 nm and thicknesses of approximately 5 nm, are synthesized hydrothermally and stabilized in 1-butanol with dodecylbenzenesulfonic acid. This method enables the preparation of monolithic nanocomposites by admixing the BaHF suspension into a liquid monomer, followed by in-situ, bulk free-radical polymerization. The PMMA retains its transparency for loadings of BaHF nanoparticles up to 0.27 wt.%, meaning that magnetically and optically anisotropic, monolithic nanocomposites can be synthesized when the polymerization is carried out in a magnetic field. The excellent dispersion of the magnetic nanoparticles, coupled with a reasonable control over the magnetic properties achieved in this investigation, is encouraging for the magneto-optical applications of these materials.
COBISS.SI-ID: 18777878
The design optimization of an axial flux permanent magnet synchronous machine with a coreless stator and double external rotor is accomplished by using evolutionary optimization with a genetic algorithm and an analytical evaluation of objective functions. On the basis of eight variable geometry parameters, five objective functions are optimized in order to determine the maximum volume torque density and weight torque density, the minimum volume and weight of permanent magnets per Newton-meter, and the minimum machine price per Newton-meter. Based on the geometric parameters for minimum machine price per Newton-meter, a prototype is built for the rated torque. Optimized and analytically evaluated machine characteristics are validated with a finite-element method (FEM) and the measurements of a prototype. Evolutionary optimization with the analytical evaluation of objective functions significantly shortens the computational time required for design optimization in comparison with the FEM.
COBISS.SI-ID: 84086785
This paper presents entropy generation minimisation model of combined heat and power system. The turbine control valves and heater throttle valves were analysed. The high-pressure control valves regulate the mass flow rate of steam into the turbine, whereas the intermediate-pressure and low-pressure control valves the steam pressure of the turbine extracts 3 and 5. The steam of the turbine extracts 3 and 5 is used for the city-wide heating system purposes by means of the peak and basic heaters. The quantity of the extracted steam used for the city-wide heating system is additionally controlled by the throttles regulating the extracted steam into the basic or peak heater. This results in a double throttling of the extracted steam of the turbine, double generated entropy and a double loss of work. If adequate pressure of the extracted steam of the turbines is maintained by means of the turbine control valves the two heaters for the heating system could operate with the throttles open. As a result, the generated entropy of the throttles of the steam admitted to the heater could be avoided and the amount of generated entropy of the turbine control valves reduced.
COBISS.SI-ID: 1024198748
This paper present short-term combined economic and emission hydrothermal optimization, addressing total fuel costs and emissions minimization. This paper uses the fuel cost function with valve-point effect, which increases the degree of optimization problem difficulty. The optimal balance between the addressed objectives, that conflict with each other, can be obtained with appropriate hydro and thermal generation schedules. A surrogate differential evolution is applied in order to satisfy 24-h system demand and final states of hydro power plant reservoirs by minimized total fuel costs and emissions. This paper proposes a novel master%slave model optimization algorithm, where the optimal thermal schedules are obtained within the slave model. The data obtained from the slave model are saved into a matrix, which serves as a surrogate model for a master model, where the hydrothermal optimization with all objectives and constraints is conducted by using a parallel self-adaptive differential evolution algorithm. In order to show the effectiveness of the proposed method, different case studies are used: economic load scheduling, economic emission scheduling, and combined economic emission scheduling. The proposed method is verified on a model consisting of four hydro power plants and three thermal power plants.
COBISS.SI-ID: 18347030
This paper describes a computation model using an artificial neural network (ANN) for a thermoeconomic analysis of district heating (DH) mony flows (MF). The model will compute the results of the MFs in accordance with an energy method or a caloric method and an exergy method at various DH substations. A heat distributer is unable to ensure the same quality of heat to all consumers due to the length of the network. A consumer nearest to the heat source receives heat of a higher quality than the last consumer. As the DH heat is usually calculated using the MF energy method, the available heat quantity that can actually be converted into another form of energy is not taken into account in the calculation. The above indicated deviations, however, are taken into consideration in the calculation of heating costs in accordance with the MF exergy method, giving a more realistic picture of the heating cost evaluation. Considering the first law analysis of thermodynamics, the amount of energy consumed is calculated disregarding the difference between work and heat. The analysis and design of engineering systems based on only the first law is not adequate.
COBISS.SI-ID: 1024193116