Shiraz UniversityIranian Journal of Science and Technology Transactions of Mechanical Engineering2228-61873838M1+20140501THERMAL PERFORMANCE EVALUATION OF A PROPOSED POINT-FOCUS SOLAR COLLECTOR FOR LOW POWER APPLICATIONS263268197110.22099/ijstm.2014.1971ENJournal Article20131020In this study, thermal performance of a proposed point-focus solar collector for low<br />power applications was estimated under different operating variables. For this purpose, theoretical<br />analysis was employed with varying relevant parameters, using a set of thermodynamics and<br />energy equations, i.e., ambient temperature, beam solar insolation, wind speed, wind incidence<br />angle and wall temperature of the absorber. The results show decreasing trend of the wind<br />incidence angle along with increasing the convective heat loss coefficient as the highest related<br />values obtained under head-on wind flow, but the wall temperature of the absorber exerts<br />negligible influence. The maximum thermal efficiency of 79.68% was obtained in August with the<br />side-on wind flow of 4.9⁄ and an ambient temperature of 29.2 when the absorber wall<br />temperature has a minimum value of 150.Shiraz UniversityIranian Journal of Science and Technology Transactions of Mechanical Engineering2228-61873838M1+20140501PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS269274197210.22099/ijstm.2014.1972ENJournal Article20130104Information about the temperature of drilling hole during the drilling process is<br />important in work-piece quality and tools life aspects. In this study temperature of the drilling hole<br />is determined using Artificial Neural Networks according to certain points’ temperature of the<br />work piece and two parameters, drill diameter and ambient temperature. To achieve this aim, twodimensional<br />model of work piece is provided; then by Computational Heat Transfer simulations<br />based on Finite Volume Method, temperature in different nodes of the work piece is specified.<br />Obtained results are used for training and testing the neural network. Temperature of specified<br />points, drill diameter and ambient temperature are selected as inputs of the network and<br />temperature of drilling hole is considered as an output data. Also, for comparison, temperature is<br />obtained experimentally. Comparison between numerical results and experimental data shows that<br />neural network can be used more efficiently to determine temperature of hole in a drilling process.