No abstract

Low-voltage circuit breakers provide essential protection for industrial and residential power installations, by taking advantage of the voltage drop at the electrode–plasma interface to force current zero. This is accomplished by using the magnetic force and unbalanced pressure on the arc as the contacts open to push the arc toward a stack of steel plates that break the arc into subarcs and thereby multiply the number of voltage drops. As the fault current can be high, substantial energy can be dissipated, which results in interactions among the arc and solid counterparts in terms of wall ablation and metal evaporation. In this study, ablation experiments are conducted to demonstrate its great influence on the arc voltage and on the pressure field. Significant progress has been accomplished in the computation of arc dynamics through the coupling of fluid motion with electromagnetics, although an important mechanism in arc breaking simulation, the effect of Stefan flow caused by species generation, has not been considered. We report out a numerical approach for taking into account the effect of Stefan flow, particularly for the breakers with high gasifying wall materials. This approach accounts for the diffusion induced convection due to added-in species from the evaporation surfaces, which will largely influence the flow field and the properties of the plasma mixture. Apart from the voltage drop, this mechanism plays an important role in simulating arc interruption. The ability of conducting Stefan flow computation further enhances the understanding of arc behaviors and improves the design of practically oriented low-voltage circuit breakers.

No abstract

Conversion efficiency of a solar energy in the electric is substantially determined not only by the total impurity concentration in solar cell element, but also by impurity chemical and physical state. Gettering processes, which are included in the technology of solar cell manufacturing, are usually used for such impurity redistribution. In order to optimize gettering processes we developed a program tool based on the fundamental physical and chemical laws. The description of physical and chemical behaviour of impurities in silicon is based both on known experimental data, and on calculations of necessary parameters by means of present-day thermodynamic and quantum-chemical methods. Developed tool helps to choose a gettering regime (a temperature profile, time, getter layer thickness) for optimization of these processes for the given initial chemical composition of the silicon wafer. Possibility of analysis of recombination activity of various types of defects in silicon on the basis of carrier lifetime criterion allows to obtain an estimation of efficiency of the gettering processes. Using this program tool we demonstrated that solar cell efficiency can be significantly increased by optimal choice of gettering conditions.

Deposition of semiconductor films is a key process for production of thin-film solar cells, such as CdTe or CIGS cells. In order to optimize photovoltaic properties of the film a comprehensive model of the deposition process should be build, which can relate deposition conditions and film properties. We have developed a multiscale model of deposition of CdTe film in close space sublimation (CSS) process. The model is based on kinetic Monte Carlo method on the rigid lattice, in which each site can be occupied by either Cd or Te atom. The model tabulates the energy of the site as a function of its local environment. These energies were obtained from first-principles calculates and then approximated with analytical formulas. Based on determined energies of each site we performed exchange (diffusion) processes using Metropolis algorithm. In addition the model included adsorption and desorption processes of Cd and Te2 species. The results of the model show that a steady-state structure of the surface layer is formed during film growth. The model can reproduce transition from film deposition to film etching depending on external conditions. Moreover, the model can predict deposition rates for non-stoichiometric gas compositions.