Experimental and Artificial Neural Network Evaluation of Frost Formation on Square Finned Tube under Natural Convection

Abstract

In this study, a test setup is constructed to investigate the frost formation on a square-finned tube under natural convection conditions experimentally. Accordingly, the impacts of relative moisture, fin surfaces, and air temperature on the frost thickness on the first, middle, and last fins are analyzed in detail. Despite the widespread use of square-finned tubes in industrial equipment, few investigations have been conducted on these fins. Due to the obstructed air path, the experimental results show that frost growth starts from the fin tips and not the pipe surface. It is also found that an increase in the mean refrigeration temperature reduces the frost porosity, but increasing relative humidity enhances frost deposition considerably. Various artificial neural networks are developed and tested using experimental data to choose the most accurate model (341 points). The final appropriate model was able to forecast the behavior of frost growth using an input layer, two hidden layers, and an output layer (4-10-10-9). For this model, the statistical error test of coefficient of determination and mean square error gave values of 0.9892 and 0.000297, respectively. Eventually, a series of valuable dimensionless correlations are developed and presented to evaluate the frost thickness on square fins.

Additional information

Notes on contributors

Soroush Abadi Iranagh

Soroush Abadi Iranagh is a Ph.D. candidate in mechanical engineering in the Department of Mechanical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran. He is interested in heat and mass transfer phenomena, especially in frost formation and refrigeration systems. Other research interests of him are artificial neural networks and biomechanics.

Ali Reza Tahavvor

Ali Reza Tahavvor is an Associate Professor of mechanical engineering in the Department of Mechanical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran. He received his B.S. degree in mechanical engineering from Shiraz University in 2000 and his M.S. degree from Tabriz University in 2002. He received his Ph.D. from the University of Shiraz in 2009. He is interested in heat and mass transfer phenomena, especially in the frost formation process. He has some publications on the application of soft computing in convective heat and mass transfer. Other research interests of him are soft computing techniques, computational fluid dynamics, and computational heat transfer.

Mahmood Yaghoubi

Mahmood Yaghoubi is a Professor at the Mechanical Engineering School of Shiraz University. He obtained his B.S. and M.S. degrees from Shiraz University, Shiraz, Iran in 1971 and 1973, respectively, and his Ph.D. from the School of Mechanical Engineering, Purdue University, West Lafayette, Indiana, USA, in 1978. His research areas include heat transfer, solar thermal power plant, computational fluid dynamics, and engineering education. He is a Fellow of the Academy of Sciences I.R. Iran.

Mohammad Mehdi Tavakol

Mohammad Mehdi Tavakol is an Assistant Professor at the Mechanical Engineering Department of Islamic Azad University, Shiraz Branch, since 2014. He obtained his B.S. M.S. and Ph.D. degrees in Mechanical Engineering from Shiraz University in 2005, 2009, and 2014, respectively. His research areas include multiphase flows, experimental and computational fluid dynamics, computational wind engineering and renewable energy. He has published 30 peer-reviewed papers in scientific journals and international conferences.