Predicting the Thermal Changes of a Fluid Storage Tank of a Solar Dryer Using Artificial Neural Network and Computational Fluid Dynamics Method

Document Type : Research Paper


1 Assistant Professor, Department of Biosystem Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2 M.Sc. Student, Department of Biosystem Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran


In this research, to increase the performance of parabolic solar collector, PCM phase change materials were used inside the fluid storage tank. The effect of fluid flow rate at three levels of 1, 2.5 and 5 l/min and PCM mass at two levels of 1.5 and 3.2 kg on output temperature and thermal efficiency of the collector and tank efficiency using experimental methods and CFD and ANN were evaluated and compared. Drying efficiency changed from 21.11 to 25.20% and collector from 62.9 to 64.03. The collector efficiency error of CFD and ANN methods varied from 5.31 to 7.4% and 1.22 to 3.84%, respectively. According to the statistical data and the time spent, it was found that the ANN method can be used to predict the thermal behavior of the system more accurately and less time than the CFD method.


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