Iranian Journal of Science and Technology Transactions of Mechanical Engineering
Volume:36 Issue: 2, 2012

This paper focuses on the development of an efficient distributed collaborative optimization method for the design of remote sensing small satellite mission in low earth orbit (LEO). The satellite mission requirement involves the duration in which the satellite is able to take images, send data to the ground station and the amount of information it can store. Conventionally, all at once methods are used in satellite mission analysis, however, design optimization of such systems are multidisciplinary task with multiple conflicting objectives such as cost, performance and reliability. The approach adopted in this paper is based on a distributed collaborative optimization (CO) framework. In this approach, the design optimization problem is divided into two levels; namely system and discipline levels. The discipline level optimization involves payload, power, mission and launch subsystems. The objective function of the system level is to minimize the resolution of the satellite imaging payload subject to equality constraints. The use of equality constraints at the system level in CO to represent the disciplinary feasible regions introduces numerical and computational difficulties, as the discipline level optima are non-smooth and noisy functions of the system level optimization parameters. As a result of these difficulties, derivative-based optimization techniques cannot be used for the system level optimization. To address these difficulties a robust optimization algorithm, genetic algorithms (GA), are used at the system level, whilst at the discipline level efficient gradient based techniques are utilized. The results show that distributed CO framework using GA has the same level of accuracy as with the conventional all at once approaches, while providing a potential approach for solving complex multidisciplinary design problems such as the design of satellite systems.

A full three-dimensional, single phase computational fluid dynamics model of a proton exchange membrane fuel cell (PEMFC) with both the gas distribution flow channels and the Membrane Electrode Assembly (MEA) has been developed. A single set of conservation equations which are valid for the flow channels, gas-diffusion electrodes, catalyst layers, and the membrane region are developed and numerically solved using a finite volume based computational fluid dynamics technique. In this research, some important parameters such as variation of oxygen and water mass fraction, liquid water activity and the membrane protonic conductivity have been presented at the entry and exit regions of the cell. The numerical results indicated that, at lower cell voltage (0.6v) which corresponds to higher current density, the hydrogen and oxygen consumption and, accordingly water production is high. Finally the numerical results of the proposed CFD model are compared with the available experimental data that represent good agreement.

this study, the flow around a rural building for both supported and surface mounted cases has been investigated. For this purpose experimental studies are performed. Hot wire anemometry was used to measure the stream-wise velocity in the wind tunnel. The experimental data depict that no recirculation zone exists in front of the supported building and the reattachment length behind this building decreases compared with that for the surface mounted building. Turbulent intensity was also measured and its variation around both building models is compared.

In this experimental study, the Diesel fuel blends with different percentages of Ethanol and Methanol with a constant percentage (5%) of n-Butanol were used. These percentages were 5%, 10% and 15% for Ethanol and Methanol. The Diesel fuel blends were designated as Z5E5D90, Z5E10D85, Z5E15D80 and Z5M5D90, Z5M10D85, Z5M15D80. The experiments were carried out on a three cylinder, four stroke, direct fuel injection compression ignition engine on different engine speeds at constant load, while engine performance and exhaust gas emissions of Carbon Monoxide (CO) and Hydrocarbons (HC) were also examined. The engine speed was varied from 800 rpm to 1800 rpm with an increment of 200 rpm. During the experimentation the times for consumption of 50 ml of fuel were recorded. The values of Brake Power, Brake Specific Fuel Consumption and Brake Thermal Efficiency were calculated by using the formulas. The experimental outcomes were analyzed, and showed an increase in Brake Specific Fuel Consumption and decrease in Brake Thermal Efficiency and exhaust gas emissions of Carbon Monoxide (CO) and Hydrocarbons (HC) with the use of different Diesel fuel blends.

Toward green educational building development, windows are important design elements as the source of natural lighting and heating in classrooms. The amount of natural lighting and net heating received by a classroom in a year depends on the school location, weather conditions, as well as the window orientation and size. Schools in Iran consume a considerable amount of energy which is mostly supplied using nonrenewable fossil fuel resources. This energy consumption can be reduced through a well-designed daylighting approach. In this paper, in order to investigate the effects of window characteristics on construction and operational costs of schools, by varying the Window-to-Wall Ratio (WWR) and window orientation, 288 daylighting scenarios are generated for a typical standard classroom in a warm-dry climatic zone in central Iran. The DOE-2 software is utilized to estimate annual gas and electric consumption, for the generated scenarios over a period of 50 years. Considering the operation and construction cost, the best window facing and optimal range of WWR in each orientation is determined for the studied standard classroom. The results of simulated daylighting scenarios are then used to train regression based Support Vector Machines (SVMs) in order to show the feasibility of applying the Support Vector Regression (SVR) as an artificial intelligent system. The obtained results show that SVR as an architectural assistant performs well and the SVR-based predictor can rapidly, easily and accurately predict the operational and construction cost of a classroom just by determining the window size and installation face.