Parameter Optimization and Temperature Prediction of Friction Stir Welding for Aluminum Alloy; Experiment, Simulation

One of the most efficient methods for joining of aluminum alloys is friction stir welding  (FSW) process. In FSW, welding parameters and tool geometry affect the weld strength. Heat is generated by friction between the tool and the workpiece, is important to predict and identify the mechanical and micro-structural changes. In this study, first using the Taguchi approach a design of experiment technique to set the optimal process parameters is investigated. It is shown that with increasing the shoulder diameter, the tensile strength increases and with increasing the tool rotational speed the tensile strength decreases. The traverse speed has less effect. Moreover temperature distribution is investigated experimentally. Results are compared with the software based on finite element method, analytical method, and analytical-empirical method. The capabilities, weaknesses, and accuracy of each method are discussed and suggestion is given

Study of Swarm Behavior in Modeling and Simulation of Cluster Formation in Nanofluids

Modeling the multiagents cooperative systems inspired from biological self-organized systems in the context of swarm model has been under great considerations especially in the field of the cooperation of multi robots. These models are trying to optimize the behavior of artificial multiagent systems by introducing a consensus, which is a mathematical model between the agents as an intelligence property for each member of the swarm set. The application of this novel approach in the modeling of nonintelligent multi agents systems in the field of cohesion and cluster formation of nanoparticles in nanofluids has been investigated in this study. This goal can be obtained by applying the basic swarm model for agents that are more mechanistic by considering their physical properties such as their mass, diameter, as well as the physical properties of the flow. Clustering in nanofluids is one of the major issues in the study of its effects on heat transfer. Study of the cluster formation dynamics in nanofluids using the swarm model can be useful in controlling the size and formation time of the clusters as well as designing appropriate microchannels, which the nanoparticles are plunged into

Phase change material solidification in a finned cylindrical shell thermal energy storage: An approximate analytical approach

Results are reported of an investigation of the solidification of a phase change material (PCM) in a cylindrical shell thermal energy storage with radial internal fins. An approximate analytical solution is presented for two cases. In case 1, the inner wall is kept at a constant temperature and, in case 2, a constant heat flux is imposed on the inner wall. In both cases, the outer wall is insulated. The results are compared to those for a numerical approach based on an enthalpy method. The results show that the analytical model satisfactory estimates the solid-liquid interface. In addition, a comparative study is reported of the solidified fraction of encapsulated PCM for different geometric configurations of finned storage having the same volume and surface area of heat transfer

Experimental and drying kinetics study on millet particles by a pulsating fluidized bed dryer

This research studies experimentally the drying of foxtail millet in a pulsation-assisted fluidized bed. The effects of temperature and pulsating flow frequency on millet drying are examined. The experiments are conducted at temperatures of 40 °C, 50 °C, and 60 °C for three pulsating frequencies of 0.5, 1, and 2.5 Hz and continuous flow. The best result is obtained for drying with a frequency of 1 Hz. It shows that the pulsating flow is more effective at 50 °C as compared to other temperatures. Four reliable semi-empirical models are used for predicting the moisture reduction during drying process. Among the fitted dynamic models, the model that has the maximum correlation coefficient (R2) and minimum sum of squares of error (SSE) and root mean squared error (RMSE) and well able to predict the behavior of millet drying in the whole process was chosen