Prediction of asphaltene deposition during turbulent flow using heat transfer approach

In this study, asphaltene deposition from crude oil has been studied experimentally using a test loop and prediction using theoretical study under turbulent flow (Reynolds numbers below 5000). The effects of many parameters such as oil velocity, surface temperature and concentration of flocculated asphaltene on the asphaltene deposition were investigated. The results showed that asphaltene deposition thickness increases with increasing both surface temperature and concentration of flocculated asphaltene and decreasing oil velocity. Thermal approach was used to describe the mechanisms involved in this process and the results of data fitting showed that there are good agreements between the results of the proposed model and the measured asphaltene deposition rates.     

Image encryption method based on improved ECC and modified AES algorithm

Multimedia Tools and Applications - Currently, embedded systems can be found everywhere in quotidian life. In the development of embedded systems, information security is one of the important...     

Fluid flow characterization of liquid�Cliquid mixing in mixer-settler

Agitation in a mixer-settler is one of the most common operations, yet presents one of the greatest challenges in the area of computer simulation. Mixer-settlers typically contain an impeller mounted on a shaft, and optionally can contain baffles. The hydrodynamic characteristics of mixer-settlers have been studied in the present study. The effect of different geometrical parameters on the efficiency characteristics of the system has been investigated. The effects of different width of impellers, impeller speed, inlet velocity, impeller diameter, etc. have been studied. Computational fluid dynamics (CFD) model has been developed to predict the efficiency characteristics. The model has been validated with the help of experimental data for different velocity outlets used in the work. This work has enabled developing efficiency that can produce higher condition than those reported in previous literature. From the CFD simulations results, optimum mixer-settler geometry has been proposed.     

The GA Optimization Performance in the Microstructure and Mechanical Properties of MMNCs

This paper is aimed at development of an effective approach based on the combination of GA, FEM and artificial intelligent methods to determine the optimum conditions of Al Matrix nano composites in terms of microstructure and mechanical properties. Using the stir casting method, alumina nano-particles were incorporated into the Al–Si aluminum alloy. Characterization of the mechanical properties showed that the presence of nano particles significantly improved the hardness and strength of the composites. Then artificial neural network and finite element technique were implemented in order to predict the mechanical properties and genetic algorithms were used for the process conditions optimization. It was revealed that the proposed model is a useful and efficient method to find the optimal process conditions in stir casting.     

Trade-Off Analysis of Energy Consumption and Image Quality for Multihop Wireless Sensor Networks

It has become necessary in recent years to observe and monitor some physical phenomena. This was made possible by the emergence of wireless sensor networks. The main characteristic of such networks is nodes with scarce resources. Given the stringent resource constraints, nodes are limited in energy, memory and computational power. These resource constraints pose serious difficulties for image processing and transmission to the destination. Therefore, image transfer in wireless sensor networks presents major challenge which raises issues related to its representation, its storage and its transmission. Based on wavelet transform an Adaptive Energy Efficient Wavelet Image Compression Algorithm is proposed in order to be suitable for wireless sensor network. In addition, an identification of the wavelet image compression parameters is investigated to analyze the trade-offs between the energy saving, and the image quality. Performance studies indicate that the proposed scheme enabling significant reductions in computation as well as communication energy needed, with minimal degradation in image quality.     

Fatigue behavior of high-entropy alloys: A review

Fatigue failures cost approximately 4% of the United States’ gross domestic product (GDP). The design of highly fatigue-resistant materials is always in demand. Different from conventional strategies of alloy design, high-entropy alloys (HEAs) are defined as materials with five or more principal elements, which could be solid solutions. This locally-disordered structure is expected to lead to unique fatigue-resistant properties. In this review, the studies of the fatigue behavior of HEAs during the last five years are summarized. The four-point-bending high-cycle fatigue coupled with statistical modelling, and the fatigue-crack-growth behavior of HEAs, are reviewed. The effects of sample defects and nanotwins-deformation mechanisms on four-point-bending high-cycle fatigue of HEAs are discussed in detail. The influence of stress ratio and temperature on fatigue-crack-growth characteristics of HEAs is also discussed. HEAs could exhibit comparable or greater fatigue properties, relative to conventional materials. Finally, the possible future work regarding the fatigue behavior of HEAs is suggested.