The main focus of the present study is to utilize the artifi cial neural network (ANN) in predicting the natural convection from horizontal isothermal cylinders arranged in vertical and inclined arrays. The effects of the vertical separation spacing to the cylinder diameter ratio (P y /d), horizontal separation spacing to the cylinder diameter ratio (P x /d) and Rayleigh number (Ra) variation on the average heat transfer from the arrays are considered via this prediction. The training data for optimizing the ANN structure is based on available experimental data. The Levenberg-Marquardt back propagation algorithm is used for ANN training. The proposed ANN is developed using MATLAB functions. For the best ANN structure obtained in this investigation, the mean relative errors of 0.027% and 0.482% were reached for the training and test data, respectively. The results show that the predicted values are very close to the experimental ones.

An appropriate pre-processing algorithm in classification is not only of great importance with respect to classifier choice, but also would be more crucial. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The aim of this approach is finding a transformation matrix causes classes to be more discriminable by transforming data into the new space and consequently, increases the classification accuracy. This transformation matrix is computed through two methods based on linear discrimination. In the first method, we use class independent LDA to increase classification accuracy by finding a transformation that maximizes the between-class scatter and minimizes within-class scatter using a transformation matrix. Because LDA cannot obtain optimal transformation, in the second method, Harmony Search is used to increase performance of LDA. Obtained results show that utilization of these pre-processing causes increasing the accuracy of different classifiers.

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Purpose Present research focus on using solar energy as a renewable option for office buildings in different climatic conditions in Iran. To seeking a way to use clean solar energy and reduce current expense in buildings an investigation carried out. Nine office buildings in various climatic regions selected as case studies. Through a precise examination, buildings specifications, energy demand and climate information carried out. In the first step based on the buildings type and hot water demand, solar water heater systems designed for each case. In the second step, a cost-benefit analysis is done to detriment the economic aspects of implement aforementioned type of solar system. A cost-benefit analysis is done from saving energy and return time of investment point of view. Results indicate that solar water heater with low investment about US$500 and payback time between 2 and 5 years can be noticed as a desirable renewable option in case studies. Furthermore, analysis reveals that thermal load of building is more effective on fuel saving in building, while solar radiation intensity has more effective on the payback in solar water heater utilization. Design/methodology/approach In this study based on thermal load of nine building office and radiation of different part of Kermnashah province, the possibility of solar water system is investigated. Findings Analyses reveal that the thermal load of building is more effective on fuel saving, while solar radiation intensity has more effective on the payback in solar water heater utilization. The main originality goes back to consideration of different meteorological conditions in solar water heater selection.

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