Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network |
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| Affiliation: | 1. Department of Mechanical Education, Sakarya University, Sakarya, 54187, Turkey;2. Department of Mechanical Engineering, Dokuz Eylul University, Bornova, 35100 Izmir, Turkey;3. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Ont., Canada L1H 7K4;1. Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, P.O. Box 50329, 3603 Limassol, Cyprus;2. Solar & Other Energy Systems Laboratory, “DEMOKRITOS” National Center for Scientific Research, 15310 Agia Paraskevi, Attikis, Greece;1. School of Mechanical Engineering, Shiraz University, Shiraz, Iran;2. School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran;1. Department of Mechanical Engineering, Golestan University, P.O. Box 155, Gorgan, Iran;2. School of Mechanical Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran;3. Department of Mechanical Engineering, K.N.Toosi University of Technology, Tehran, Iran;1. School of Mechanical Engineering, Southwest Jiaotong University, 610031, Chengdu, PR China;2. School of Industrial Technology and Business Studies, Dalarna University, SE-79188, Falun, Sweden |
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| Abstract: | In this study, a feed-forward back-propagation artificial neural network (ANN) algorithm is proposed for heat transfer analysis of phase change process in a finned-tube, latent heat thermal energy storage system. Heat storage through phase change material (PCM) around the finned tube is experimentally studied. A numerical study is performed to investigate the effect of fin and flow parameter by the solving governing equations for the heat transfer fluid, pipe wall and phase change material. Learning process is applied to correlate the total heat stored in different fin types of tubes, various Reynolds numbers and different inlet temperatures. A number of hidden numbers of ANN are trained for the best output prediction of the heat storage. The predicted total heat storage values obtained by an ANN model with extensive sets of non-training experimental data are then compared with experimental measurements and numerical results. The trained ANN model with an absolute mean relative error of 5.58% shows good performance to predict the total amount of heat stored. The ANN results are found to be more accurate than the numerical model results. The present study using ANN approach for heat transfer analysis in phase change heat storage process appears to be significant for practical thermal energy storage applications. |
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