مجله فناوری اطلاعات در طراحی مهندسی
سال دوم شماره 1 (پاییز 1387)

Titanium hydride powder is used as blowing agent in the production process of metallic foams (especially aluminum metallic foams). As a result of thermal decomposition of TiH2 powder, a massive volume of hydrogen gas releases. The hydrogen evolution, inside molten aluminum, generates bubbles and if the process is properly controlled Al foam will be produced. Determination of the conditions, rate and mechanism of decomposition process is very important for reaching to an acceptable product. In this research, the online measurement of hydrogen evolution, at temperatures range of 673- 1273K, was used for studying the decomposition kinetics of titanium hydride. The results showed that at temperatures range of 673-873K, the decomposition of TiH2 powder is under control of internal diffusion of hydrogen atoms along the titanium oxide (Ti2O3) layer. At higher temperatures, 973-1073K, the TiH2 powder is decomposed at two stages. At the first stage, the equation of velocity is parabolic and the decomposition process under control of internal diffusion. After hydrogen gas pressure reaches to a constant level, the second stage starts. In this stage, the rate equation of decomposition of TiH2 powder will be of zero order, at first, which changes to a parabolic equation later. Titanium hydride decomposes at two stages TiH2→TiHX→α-Ti. The first stage is under control of diffusion of hydrogen atoms in the TiHX layer and the second stage is under control of the chemical reaction. The activation energy is 59.112 kJ/mole approximately, at the first stage, and 81.388 kJ/mole at the second stage, respectively.

In this study, Al-ZrO2 composites were produced via Vortex method. ZrO2 particles with 1 micron average diameter (as reinforcement) and Al-356 (as metal matrix) were used for production of these composites. The molten composites were stirred for 13 minutes, and then cast into a metallic mold. The effects of ZrO2 volume percents and casting temperatures on microstructure of composites for q different states were investigated. The results revealed a homogenous distribution of ZrO2 particles within the metal matrix in all samples. Although, ZrO2 agglomerates were observed within the matrix, they were distributed homogenously.

Due to economic reasons as well as specifications required for different applications, high hardness together with good erosion resistance is required for gold coatings. In this research nano sized diamond particles together with cobalt and gold were code posited on copper substrate. This composite coating could be used for electrical and decorative applications. The results show that micro hardness and wear resistance of gold - cobalt coating increases with the content of nano diamond particles in coating electrolyte. Addition of 12 g/l nano diamond to the electrolyte increases the coating hardness and wears resistance to about 25% and 400% compared with the nano- diamond content.

Heat transfer and flow of nanofluids in a right circular tube in laminar regime were simulated numerically. Aluminum oxide () was used as a nano-particle and Water as the base fluid. Although nanofluids are two- phase mixtures, since the nanofluids used for the purpose of heat transfer enhancement are usually dilute solid-liquid mixtures, under assumptions that there exists no motion slip between the discontinuous phase of the dispersed ultra fine particles and the continuous liquid and the local thermal equilibrium between the nanoparticles and the fluid, the nanofluids can be treated as the common pure fluids. Results show that heat transfer coefficients of nanofluids are significantly higher than those of base fluid. This is a function of nanoparticle concentration and Reynolds number. The results also show that wall shear stresses of nanofluids increase drastically as compared to the base fluid, and increase would higher for more concentrated nano fluids.

In this paper regarding the fact that the collector current and therefore Base current are needed for a very short time, in order to drive large capacitive load, using BiCMOS technology, we have used the existing capacitor between source and gate of MOS transistors to do so. This method has increased the speed of the circuit in comparison to its similar circuits, and it needs very low static power dissipation. In this design, remarkable reduction of the number of transistors has lead to a significant improvement in chip area.

Enterprise architecture process contains three phases namely information technology strategic planning enterprise architecture planning and execution of the enterprise architecture. In this process each phase is pre-requisite for another one. If the enterprise architecture planning was performed incorrectly enterprise architecture execution fails and therefore wasting the time and money is faced. To avoid this definition and deh1ion of a method to evaluate the enterprise architecture planning before performing the plan is required. Our proposed approach evaluates the process of extracting main components of enterprise architecture plan in details; including current state architecture target state architecture and transitional strategy. We can refer to the enterprise architecture certification and increasing the enterprise architecture maturity as the main benefits of this method. In addition our method discovers the strengths and weaknesses of an enterprise architecture plan so that architects can improve it.

One of the most dangerous symptoms of Type 1 diabetes is the frequent and great oscillation of blood glucose level that can lead the patient to unconscious and coma states. So being able to predict and finally prevent these symptoms would help the diabetic patients. This paper attempts to use Elman neural networks to predict the blood glucose levels in type1 diabetic patients. Data set used in this paper consists of the protocol of 3 Iranian type1 Diabetic women and includes parameters such as type and dosage of injected insulin, the period of time (in hour) between two consecutive measurements of the blood glucose level, carbohydrate intake, exercise and the blood glucose level measured at start of the given period of time. Finally we concluded that the use of Recurrent Neural Network such as Elman with great reduction of prediction error and also reduction of number of layers and neurons used in the construction of Neural Networks as compared with other method can be an appropriate model to predict the long term blood glucose levels in type 1 diabetes.