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Thermal energy storing technologies are a new approach in reducing energy costs, managing demand side, pick shaving and increasing portion of renewable energies in energy production. In spite of lots of advantages of thermal energy storage techniques, there are still major challenges in the path of Latent heat thermal storages (LHTS). One of the challenges is the low charge and discharge rate of heat transfer in LHTS. In the current study charging rate of a shell and tube LHTS is numerically studied by enthalpy-porosity numerical technique. Exact positioning of the heat transfer tubes and thermal fins has great impact on the natural convection flows. In this study effect of increasing heat transfer tubes (HTF), lower positioning of tubes in four tubes configuration, changing upper tubes distance and using interconnected axial fins has been studied and compared to each other. Moreover, velocity and temperature contours have been analyzed. Results demonstrated that increasing number of tubes could not solve the slowing rate of charging at the end of process and tubes need to be positioned lower in the tube. In addition, it was observed that heat transfer axial fins can decelerate convection flows and develop stationary areas inside the shell. Prediction results revealed by lowering tubes and closing them to the shell wall, introduced in this article, it is possible to decrease charging time of 0.95 of storage capacity to one fourth of similar time in a one tube LHTS.

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In plasma cutting, a noble gas at high speed is blown from the nozzle and ionized with the help of a frequency spark at high voltage and an electric arc is created which cause the gas changes to the plasma state. Plasma cutting is an ideal process for cutting of the hard metals. In this research, the effect of the input parameters and their optimization in plasma cutting of AISI 309 stainless steel were studied. By conducting the different experimental tests, the effect of input parameters including amperage, gas pressure and the cutting speed of torch on the three output parameters of the width of cut (Kerf), heat-affected zone (HAZ) and surface roughness (Ra) were investigated. Analysis of the results showed that the amperage, cutting speed and gas pressure have the highest impact on the output parameters, respectively. The artificial neural network (ANN)-genetic algorithm was used to predict and optimize the output parameters. The results indicate that the artificial neural networks model trained by the genetic algorithm are able to predict the output parameters accurately. Finally, the optimization of output parameters to achieve the best cutting conditions was carried out using the genetic algorithm. The artificial neural network models were considered as the objective function and also, the parameters of the heat-affected zone, surface roughness, and the width of cut were introduced as inputs of the algorithm. According to results, a combination of the neural network and genetic algorithm is an efficient method for optimization of the plasma cutting process. This method can be easily modified and utilized for other advanced cutting methods.