Heat transfer analysis using artificial neural networks of the spirally fluted tubes
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Published:
Aug 7, 2018
Keywords:
Artificial neural network spirally fluted tube eat transfer and pressure drop
Main Article Content
Abstract
The optimal artificial neural network model for predicting the heat transfer coefficient and friction factor of the spirally fluted tube is considered. The experiment, nine test sections with different characteristic parameters of: helical rib height-to-diameter, /di = 0.12, 0.15, 0.19 and helical rib pitch-todiameter, p/di = 1.05, 0.78, 0.63 are tested. The developed artificial neural network model shows the mean square error (MSE) of 0.0123 and the correlation coefficient (R) of 1.00 in modeling of overall experimental data set. The predicted results obtained from the optimize ANN model are verified with the testing experimental data and good agreement is obtained with errors of ±2.5%, ±15% for the Nusselt number and friction factor, respectively. In addition, the optimal ANN model results are found to be more accurate than the predicted results obtained from the published correlation.
Article Details
How to Cite
Naphon, P., & Arisariyawong, T. (2018). Heat transfer analysis using artificial neural networks of the spirally fluted tubes. Journal of Research and Applications in Mechanical Engineering, 4(2), 135–147. Retrieved from https://ph01.tci-thaijo.org/index.php/jrame/article/view/138630
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RESEARCH ARTICLES
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References
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[3] Islamoglu, Y. A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger use of an artificial neural network model, Applied Thermal Engineering, Vol. 23, 2003, pp. 243-249.
[4] Wang, W.J., Zhao, L.X. and Zhang, C.L. Generalized neural network correlation for flow boiling heat transfer of R22 and its alternative refrigerants inside horizontal smooth tubes, International Journal of Heat and Mass Transfer, Vol. 49, 2006, pp. 2458-2465.
[5] Scalabrin, G., Condosta, M. and Marchi, P. Mixtures flow boiling: modeling heat transfer through artificial neural networks, International Journal of Thermal Sciences, Vol. 45, pp. 2006, pp. 664-680.
[6] Yigit, K.S. and Ertunc, H.M. Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks, International Communications in Heat and Mass Transfer, Vol. 33, 2006, pp. 898-907.
[7] Zdaniuk, G.J., Chamra, L.M. and Walters, D.K., Correlating heat transfer and friction in helically-finned tubes using artificial neural networks, International Journal of Heat and Mass Transfer, Vol. 50, 2007, pp. 4713-4723.
[8] Ermis, K., Erek, A. and Dincer, I. Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network, International Journal of Heat and Mass Transfer, Vol. 50, 2007, pp. 3163-3175.
[9] Xie, G.N., Wang, Q.W., Zeng, M. and Luo, L.Q. Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach, Applied Thermal Engineering, Vol. 27, 2007, pp. 1096-1104.
[10] Kurt, H., Atik, K., Özkaymak, M. and Recebli, Z. Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network, International Journal of Thermal Sciences, Vol. 47, 2008, pp. 192-200.
[11] Tahavvor, A.R. and Yaghoubi, M., Natural cooling of horizontal cylinder using Artificial Neural Network (ANN), International Communications in Heat and Mass Transfer, Vol. 35, 2008, pp. 1196-1203.
[12] Xie, G., Sunden, B., Wang, Q. and Tang, L. Performance predictions of laminar and turbulent heat transfer and fluid flow of heat exchangers having large tube-diameter and large tube-row by artificial neural networks, International Journal of Heat and Mass Transfer, Vol. 52, 2009, pp. 2484-2497.
[13] Taymaz, I. and Islamoglu, Y., Prediction of convection heat transfer in converging–diverging tube for laminar air flowing using back-propagation neural network, International Communications in Heat and Mass Transfer, Vol. 36, 2009, pp. 614-617.
[14] Alizadehdakhel, A., Rahimi, M., Sanjari, J. and Alsairafi, A.A. CFD and artificial neural network modeling of two-phase flow pressure drop, International Communications in Heat and Mass Transfer, Vol. 36, 2009, pp. 850-856.
[15] Gao, M., Sun, F.Z., Zhou, S.J., Shi, Y.T., Zhao, Y.B. and Wang, N.H. Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions, International Journal of Thermal Sciences, Vol. 48, 2009, pp. 583-589.
[16] Bar, N., Bandyopadhyay, T.K., Biswas, M.N. and Das, S.K. Prediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components, Journal of Petroleum Science and Engineering, Vol. 71, 2010, pp. 187-194.
[17] Kumar, A. and Balaji, C. ANN based estimation of heat generation from multiple protruding heat sources on a vertical plate under conjugate mixed convection, International Journal of Thermal Sciences, Vol. 50, 2011, pp. 532-543.
[18] Wu, J., Zhang, G., Zhang, Q., Zhou, J. and Wang, Y. Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter, Energy and Buildings, Vol. 43, 2011, pp. 1685-1693.
[19] Reza, B. and Masoud, R. Prediction of heat transfer and flow characteristics in helically coiled tubes using artificial neural networks, International Communications in Heat and Mass Transfer, Vol. 39, 2012, pp. 1279-1285.
[20] Khairul, M.A., Hossain, A., Saidur, R. and Alim, M.A. Prediction of heat transfer performance of CuO/water nanofluids flow in spirally corrugated helically coiled heat exchanger using fuzzy logic technique, Computers & Fluids, Vol. 100, 2014, pp. 123-129.
[21] Haykin, S. Neural Networks, A Comprehensive Foundation, New Jersey, 1994.
[22] Naphon, P., Nuchjapo, M. and Kurujareon, J. Heat transfer coefficient and friction factor of the horizontal double tube helical ribs, Energy Conversion and Management, Vol. 47, 2006, pp. 3031-3044.
[23] Hosoz, M., Ertunc, H.M. and Bulgurcu, H. Performance prediction of a cooling tower using artificial neural network, Energy Conversion and Management, Vol. 48, 2007, pp. 1349-1359.
[24] Du, K.I. and Swamy, M.N.S. Neural Networks in a Soft Computing Framework, Springer, London, 2006.
[25] Terrence, L.F. Feed Forward Neural Network Methodology, Springer, New York, 1999.
[26] Reed, R. Pruning algorithms-a survey, IEEE Trans. Neural Network, Vol. 4, 1993, pp. 740-747.
[27] Xin, F. Basic Theory and Method of Neural Net Intelligence, Southwest Jiaotong University Press, Chengdu, 2000 (in Chinese).
[28] Lu, Z.F. Thermodynamic brine-bulb temperature: another air state parameter, Heating Ventilation Air Conditioning, Vol. 31, 2001, pp. 77-79 (in Chinese).
[29] Majumdar, A.K., Singhal, A.K. and Spalding, D.B. Numerical modeling of wet cooling tower-Part 1: mathematical and physical models, Journal of Heat and Mass transfer, Vol. 105, 1983, pp. 728-735.
[30] Xie, Q.S. Neural Net Method in Mechanical Engineering, China Machine Press, Beijing, 2003 (in Chinese).
[31] Yao, Y.B. and Wang, J.L. Research on Raising “BP” Network Training Speed, Heilongjiang Electric Technology, Vol. 1, 2002, pp. 4-6 (in Chinese).