Shiraz University Iranian Journal of Science and Technology Transactions of Mechanical Engineering 2228-6187 38 38M1+ 2014 05 01 THERMAL PERFORMANCE EVALUATION OF A PROPOSED POINT-FOCUS SOLAR COLLECTOR FOR LOW POWER APPLICATIONS 263 268 1971 10.22099/ijstm.2014.1971 EN Journal Article 2013 10 20 In 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􀔨. Solar energy point-focus solar collector low power application thermal performance https://ijstm.shirazu.ac.ir/article_1971_58d8911143613aa280e25ec8be652cd5.pdf
Shiraz University Iranian Journal of Science and Technology Transactions of Mechanical Engineering 2228-6187 38 38M1+ 2014 05 01 PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS 269 274 1972 10.22099/ijstm.2014.1972 EN Journal Article 2013 01 04 Information 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. Temperature of drilling hole Artificial Neural Network Levenberg-Marquardt https://ijstm.shirazu.ac.ir/article_1972_05b4b7c6703327a815da22cdba5eca7f.pdf