ANN: Prediction of Heat Transfer and Flow Characteristics of a Tube with Modified Twisted Tapes
Year 2020,
Volume: 4 Issue: 1, 12 - 20, 30.06.2020
Aslı Durmuşoğlu
,
Sibel Güneş
,
Emrah Özaşır
Abstract
In this paper, we present an Artificial Neural Networks (ANNs) model which is prone to accurately estimate the friction factor and nusselt number of a tube with modified twisted tapes. Numerical analysis were realized using the tapes with six different rates of pitch length of twisted tape to inner diameter of tube (y/D=3, 4, 5) in a range of Reynolds number 8000 to 24000 under uniform heat flux conditions. The ANN model was improved and validated using a databank containing numerical datasets. The back propagation algorithm is recognized to be the most extensive learning method for ANN. This algorithm is used for training and testing of the network. The results of the ANNs were demonstrated superior performance to adapt the numerical datas. Value of the coefficient of multiple determination were obtained. The R2 values were found 0,9994 for nusselt number and 0,9995 for friction factor.
References
- [1] X. Zhang, Z. Liu and W. Liu, “Numerical studies on heat transfer and friction factor characteristics of a tube fitted with helical screw-tape without core-rod inserts,” International Journal of Heat and Mass Transfer, vol. 60, pp.490-498, 2013.
- [2] H. Gül and D. Evin, “Heat Transfer Enhancement in a Circular Tubes Using Helical Swirl Generator Insert at the Entrance,” International Jounal of Thermal Sciences, vol.46, pp. 1297-1303, 2007.
- [3] P. Murugesan, K. Mayilsamy and S. Suresh, “Turbulent Heat Transferand Pressure Drop in Tube Fitted with Square-cut Twisted Tape,” , Chinese Journal of Chemical Engineering, vol. 18, no. 4, pp. 609-617, 2010.
- [4] S. Güneş, V. Özceyhan and O. Büyükalaca, “Heat transfer enhancementin a tube with equilateral triangle cross sectioned coiled wire inserts,” Experimental Thermal and Fluid Science, vol. 34, no. 6, pp. 684-691, 2010.
- [5] R. Kamali, and A. R. Binesh, “ The Importance of Rib Shape Effects on the Local Heat Transfer and Flow Friction Characteristics of Square Ducts with Ribbed Internal Surfaces,” International Communications in Heat and Mass Transfer, vol. 35, no. 8, pp. 1032-1040, 2008.
- [6] S. Eimsa-ard, P. Nivesrangsan, S. Chokphoemphun and P. Promvonge, “Influence of combined non-uniform wire coil and twisted tape inserts on thermal performance characteristics,” International Communications in Heat and Mass Transfer , vol. 37, no. 7, pp. 850-856, 2010.
- [7] P. Sivashanmugam and P. K. Nagarajan, “Studies on Heat Transfer and Friction Factor Characteristics of Laminar Flow Through a Circular Tube Fitted with Right And Left Helical Screw-Tape Inserts,” Experimental Thermal and Fluid Science, vol. 32, pp. 192-197, 2007.
- [8] Kalogirou, S.A., “Applications of artificial neuralnetworks for energy systems”, Applied Energy, vol. 67, pp. 17-35, 2000.
- [9] S. Kmet, P. Sincak, P. Stehlik, “Artificial neural network for creep behavior predictions of a parallel-lay aramid rope under varying stresses”, Int. J. Exp. Mech., vol. 47, pp. 121–128, 2011.
- [10] S.P. Kyung, H.K. Soung, “Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review”, Artif. Intell. Eng., vol. 12, pp. 121–134, 1998.
- [11] K.W. Chau, “A review on integration of artificial intelligence into water quality modelling”, Mar. Pollut. Bull., vol. 52, pp. 726–733, 2006.
- [12] S. Sanaye, H. Hajabdollahi, “Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm”, Appl. Energy, vol. 87, pp. 1893–1902, 2010.
- [13] K. Varshney, P.K. Panigrahi, “Artificial neural network control of a heat exchanger in a closed flow air circuit”, Appl. Soft Comput., vol. 5, pp. 441–465, 2005.
- [14] T.N. Verma, P. Nashine, D. V. Singh, S. Singh and D. Panwar, “ANN: Prediction of an experimental heat transfer analysis of concentric tube heat exchanger with corrugated inner tubes,” Applied Thermal Engineering, vol. 120, pp. 219-227, 2017.
- [15] M. Hojjat, “Nanofluids as coolant in a shell and tube heat exchanger: ANN modeling and multi-objective optimization,” Applied Mathematics and Computation, vol 365, issue C,1-15, 2020.
- [16] M. Luy, U. Saray, “Wind speed estimation for missing wind data with three different backpropagation algorithms,” Energy Education Science and Technology Part A: Energy Science and Research, vol. 30, no. 1, pp. 45–54, 2012.
- [17] Göçmen, E , Derse, O . “Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey,” International Scientific and Vocational Studies Journal , 2 (2), 20-30, 2018.
ANN: Prediction of Heat Transfer and Flow Characteristics of a Tube with Modified Twisted Tapes
Year 2020,
Volume: 4 Issue: 1, 12 - 20, 30.06.2020
Aslı Durmuşoğlu
,
Sibel Güneş
,
Emrah Özaşır
Abstract
In this paper, we present an Artificial Neural Networks (ANNs) model which is prone to accurately estimate the friction factor and nusselt number of a tube with modified twisted tapes. Numerical analysis were realized using the tapes with six different rates of pitch length of twisted tape to inner diameter of tube (y/D=3, 4, 5) in a range of Reynolds number 8000 to 24000 under uniform heat flux conditions. The ANN model was improved and validated using a databank containing numerical datasets. The back propagation algorithm is recognized to be the most extensive learning method for ANN. This algorithm is used for training and testing of the network. The results of the ANNs were demonstrated superior performance to adapt the numerical datas. Value of the coefficient of multiple determination were obtained. The R2 values were found 0,9994 for nusselt number and 0,9995 for friction factor.
References
- [1] X. Zhang, Z. Liu and W. Liu, “Numerical studies on heat transfer and friction factor characteristics of a tube fitted with helical screw-tape without core-rod inserts,” International Journal of Heat and Mass Transfer, vol. 60, pp.490-498, 2013.
- [2] H. Gül and D. Evin, “Heat Transfer Enhancement in a Circular Tubes Using Helical Swirl Generator Insert at the Entrance,” International Jounal of Thermal Sciences, vol.46, pp. 1297-1303, 2007.
- [3] P. Murugesan, K. Mayilsamy and S. Suresh, “Turbulent Heat Transferand Pressure Drop in Tube Fitted with Square-cut Twisted Tape,” , Chinese Journal of Chemical Engineering, vol. 18, no. 4, pp. 609-617, 2010.
- [4] S. Güneş, V. Özceyhan and O. Büyükalaca, “Heat transfer enhancementin a tube with equilateral triangle cross sectioned coiled wire inserts,” Experimental Thermal and Fluid Science, vol. 34, no. 6, pp. 684-691, 2010.
- [5] R. Kamali, and A. R. Binesh, “ The Importance of Rib Shape Effects on the Local Heat Transfer and Flow Friction Characteristics of Square Ducts with Ribbed Internal Surfaces,” International Communications in Heat and Mass Transfer, vol. 35, no. 8, pp. 1032-1040, 2008.
- [6] S. Eimsa-ard, P. Nivesrangsan, S. Chokphoemphun and P. Promvonge, “Influence of combined non-uniform wire coil and twisted tape inserts on thermal performance characteristics,” International Communications in Heat and Mass Transfer , vol. 37, no. 7, pp. 850-856, 2010.
- [7] P. Sivashanmugam and P. K. Nagarajan, “Studies on Heat Transfer and Friction Factor Characteristics of Laminar Flow Through a Circular Tube Fitted with Right And Left Helical Screw-Tape Inserts,” Experimental Thermal and Fluid Science, vol. 32, pp. 192-197, 2007.
- [8] Kalogirou, S.A., “Applications of artificial neuralnetworks for energy systems”, Applied Energy, vol. 67, pp. 17-35, 2000.
- [9] S. Kmet, P. Sincak, P. Stehlik, “Artificial neural network for creep behavior predictions of a parallel-lay aramid rope under varying stresses”, Int. J. Exp. Mech., vol. 47, pp. 121–128, 2011.
- [10] S.P. Kyung, H.K. Soung, “Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review”, Artif. Intell. Eng., vol. 12, pp. 121–134, 1998.
- [11] K.W. Chau, “A review on integration of artificial intelligence into water quality modelling”, Mar. Pollut. Bull., vol. 52, pp. 726–733, 2006.
- [12] S. Sanaye, H. Hajabdollahi, “Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm”, Appl. Energy, vol. 87, pp. 1893–1902, 2010.
- [13] K. Varshney, P.K. Panigrahi, “Artificial neural network control of a heat exchanger in a closed flow air circuit”, Appl. Soft Comput., vol. 5, pp. 441–465, 2005.
- [14] T.N. Verma, P. Nashine, D. V. Singh, S. Singh and D. Panwar, “ANN: Prediction of an experimental heat transfer analysis of concentric tube heat exchanger with corrugated inner tubes,” Applied Thermal Engineering, vol. 120, pp. 219-227, 2017.
- [15] M. Hojjat, “Nanofluids as coolant in a shell and tube heat exchanger: ANN modeling and multi-objective optimization,” Applied Mathematics and Computation, vol 365, issue C,1-15, 2020.
- [16] M. Luy, U. Saray, “Wind speed estimation for missing wind data with three different backpropagation algorithms,” Energy Education Science and Technology Part A: Energy Science and Research, vol. 30, no. 1, pp. 45–54, 2012.
- [17] Göçmen, E , Derse, O . “Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey,” International Scientific and Vocational Studies Journal , 2 (2), 20-30, 2018.