ORIGINAL_ARTICLE
THERMAL PERFORMANCE EVALUATION OF A PROPOSED POINT-FOCUS SOLAR COLLECTOR FOR LOW POWER APPLICATIONS
In this study, thermal performance of a proposed point-focus solar collector for lowpower applications was estimated under different operating variables. For this purpose, theoreticalanalysis was employed with varying relevant parameters, using a set of thermodynamics andenergy equations, i.e., ambient temperature, beam solar insolation, wind speed, wind incidenceangle and wall temperature of the absorber. The results show decreasing trend of the windincidence angle along with increasing the convective heat loss coefficient as the highest relatedvalues obtained under head-on wind flow, but the wall temperature of the absorber exertsnegligible influence. The maximum thermal efficiency of 79.68% was obtained in August with theside-on wind flow of 4.9⁄ and an ambient temperature of 29.2 when the absorber walltemperature has a minimum value of 150.
https://ijstm.shirazu.ac.ir/article_1971_58d8911143613aa280e25ec8be652cd5.pdf
2014-05-01
263
268
10.22099/ijstm.2014.1971
Solar energy
point-focus solar collector
low power application
thermal performance
ORIGINAL_ARTICLE
PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS
Information about the temperature of drilling hole during the drilling process isimportant in work-piece quality and tools life aspects. In this study temperature of the drilling holeis determined using Artificial Neural Networks according to certain points’ temperature of thework piece and two parameters, drill diameter and ambient temperature. To achieve this aim, twodimensionalmodel of work piece is provided; then by Computational Heat Transfer simulationsbased on Finite Volume Method, temperature in different nodes of the work piece is specified.Obtained results are used for training and testing the neural network. Temperature of specifiedpoints, drill diameter and ambient temperature are selected as inputs of the network andtemperature of drilling hole is considered as an output data. Also, for comparison, temperature isobtained experimentally. Comparison between numerical results and experimental data shows thatneural network can be used more efficiently to determine temperature of hole in a drilling process.
https://ijstm.shirazu.ac.ir/article_1972_05b4b7c6703327a815da22cdba5eca7f.pdf
2014-05-01
269
274
10.22099/ijstm.2014.1972
Temperature of drilling hole
Artificial Neural Network
Levenberg-Marquardt