Hybrid CFD-ANN Scheme for Air Flow and Heat Transfer Across In-Line Flat Tubes Array

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Ataalah Hussain Jassim
M.M. Rahman
Khalaf Ibrahim Hamada
M. Ishak
Tahseen Ahmad Tahseen

Abstract

Flat tubes are vital components of various technical applications including modern heat exchangers, thermal power plants, and automotive radiators. This paper presents the hybridization of computational fluid dynamic (CFD) and artificial neural network (ANN) approach to predict the thermal-hydraulic characteristics of in-line flat tubes heat exchangers. A 2D steady state and an incompressible laminar flow in a tube configuration are considered for numerical analysis. Finite volume technique and body-fitted coordinate system are used to solve the Navier–Stokes and energy equations. The Reynolds number based on outer hydraulic diameter varies between 10 and 320. Heat transfer coefficient and friction are analyzed for various tube configurations including transverse and longitudinal pitches. The numerical results from CFD analysis are used in the training and testing of the ANN for predicting thermal characteristics and friction factors. The predicted results revealed a satisfactory performance, with the mean relative error ranging from 0.39% to 5.57%, the root-mean-square error ranging from 0.00367 to 0.219, and the correlation coefficient (R2) ranging from 99.505% to 99.947%. Thus, this study verifies the effectiveness of using ANN in predicting the performance of thermal-hydraulic systems in engineering applications such as heat transfer modeling and fluid flow in tube bank heat exchangers.

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References

Žukauskas A. Heat transfer from tubes in crossflow. Advanced Heat Transfer 1972; 8: 93–158. DOI: https://doi.org/10.1016/S0065-2717(08)70038-8

Benarji N, Balaji C, Venkateshan SP. Unsteady fluid flow and heat transfer over a bank of flat tubes. Heat Mass Transfer 2007; 44(4): 445–461. DOI: https://doi.org/10.1007/s00231-007-0256-5

Kaptan Y, Buyruk E, Ecder A. Numerical investi-gation of fouling on cross-flow heat exchanger tubes with conjugated heat transfer approach. International Communication Heat and Mass Transfer 2008; 35(9): 1153–1158. DOI: https://doi.org/10.1016/j.icheatmasstransfer.2008.05.005

Incropera FP, Dewitt DP, Bergman TL, Lavine AS. Fundamentals of heat and mass transfer. 7th ed. John Wiley & Sons, Inc: USA; 2011.

Min JC, Webb RL. Numerical analyses of effects of tube shape on performance of a finned tube heat exchanger. Journal Enhanced Heat Transfer 2004; 11: 63–76. DOI: https://doi.org/10.1615/JEnhHeatTransf.v11.i1.50

Webb RL, Kim N-H. Principle of enhanced heat transfer. 2nd ed. Taylor & Francis: New York; 2005.

Tahseen TA, Ishak M, Rahman MM. An overview on thermal and fluid flow characteristics in a plain plate finned and unfinned-tube banks heat exchanger. Renewable & Sustainable Energy Reviews 2015; 43: 363–380. DOI: https://doi.org/10.1016/j.rser.2014.10.070

Atayılmaz ŞÖ, Demir H, Ağra Ö, Application of artificial neural networks for prediction of natural convection from a heated horizontal cylinder. International Communication Heat and Mass Transfer 2010; 37(1): 68–73. DOI: https://doi.org/10.1016/j.icheatmasstransfer.2009.08.009

Ermis K, Erek A, 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 2007; 50(15–16): 3163–3175. DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2006.12.017

Fadare DA, Fatona AS. Artificial neural network modeling of heat transfer in a staggered cross-flow tube-type heat exchanger. Pacific Journal of Science and Technology 2008; 9(2): 317–323.

Islamoglu Y, Kurt A. Heat transfer analysis using ANNs with experimental data for air flowing in corrugated channels. International Journal of Heat Mass and Transfer 2004; 47(6–7): 361–365. DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2003.07.031

Beigzadeh R, Rahimi M. Prediction of heat transfer and flow characteristics in helically coiled tubes using artificial neural networks. International Communication Heat and Mass Transfer 2012; 39(8): 1279–1285. DOI: https://doi.org/10.1016/j.icheatmasstransfer.2012.06.008

Tahseen TA, Ishak M, Rahman MM. Heat transfer and pressure drop prediction in an in-line flat tube bundle by radial basis function network. International Journal of Automotive & Mechanical Engineering 2015; 10: 2003-2015. DOI: https://doi.org/10.15282/ijame.10.2014.17.0168

Bejan A. Convection heat transfer, 2nd ed. USA: John Wiley & Sons Inc.; 2004.

Tannehill JC, Anderson DA, Pletcher RH. Computational fluid mechanics and heat transfer. Second ed. USA: Taylor & Francis; 1997.

Thompson JR, Warsi ZUA, Martin CW. Numerical grid generation, foundations and applications. USA: North-Holland; 1997.

El-Shaboury AMF, Ormiston SJ. Analysis of laminar forced convection of air crossflow in in-line tube banks with nonsquare arrangements. Numerical Heat Transfer, Part A: Applications 2005; 48(2): 99–126. DOI: https://doi.org/10.1080/10407780590945452

Holman JP. Heat Transfer, 10th ed., USA: McGraw-Hill Companies, Inc.; 2010.

Ferziger JH, Perić M. Computational methods for fluid dynamics. 3rd ed. USA: Springer-Verlag Berlin Heidelberg; 1999. DOI: https://doi.org/10.1007/978-3-642-98037-4

Versteeg HK, Malalasekera W. An introduction to computational fluid dynamics the finite volume method. 2nd ed. England: Pearson Education India; 2007.

Bahaidarah HMS, Anand NK, Chen HC. A numerical study of fluid flow and heat transfer over a bank of flat tubes. Numerical Heat Transfer, Part A: Applications 2005; 48(4): 359–385. DOI: https://doi.org/10.1080/10407780590957134

Tahseen TA, Ishak M, Rahman MM. Performance predictions of laminar heat transfer and pressure drop in an in-line flat tube bundle using an adaptive Neuro-Fuzzy Inference System (ANFIS) model. International Communications in Heat and Mass Transfer 2014; 50: 85–97. DOI: https://doi.org/10.1016/j.icheatmasstransfer.2013.11.007

Kalogirou SA. Applications of artificial neural-networks for energy systems. Applied Energy 2000; 67(1): 17–35. DOI: https://doi.org/10.1016/S0306-2619(00)00005-2

Nasr GE, Badr EA, Joun C. Backpropagation neural networks for modeling gasoline consumption. Energy Conversion and Management 2003; 44(6): 893–905. DOI: https://doi.org/10.1016/S0196-8904(02)00087-0

Kvalseth TO. Cautionary note about R2. American Statistician 1985; 139: 279–85. DOI: https://doi.org/10.1080/00031305.1985.10479448

Hayati M, Rezaei A, Seifi M. Prediction of the heat transfer rate of a single layer wire-on-tube type heat exchanger using ANFIS. International Journal of Refrigeration 2009; 32(8): 1914–1917. DOI: https://doi.org/10.1016/j.ijrefrig.2009.05.012

Akdag U, Komur MA, Ozguc AF. Estimation of heat transfer in oscillating annular flow using artifical neural networks. Advances in Engineering Software 2009; 40(9): 864–870. DOI: https://doi.org/10.1016/j.advengsoft.2009.01.010

Junqi D, Jiangping C, Zhijiu C, Yimin Z, Wenfeng Z, Heat transfer and pressure drop correlations for the wavy fin and flat tube heat exchangers. Applied Thermal Engineering 2007; 27(11): 2066–2073. DOI: https://doi.org/10.1016/j.applthermaleng.2006.11.012

Koronaki I, Rogdakis E, Kakatsiou T. Thermod-ynamic analysis of an open cycle solid desiccant cooling system using artificial neural network. Energy Conversion and Management 2012; 60: 152–160. DOI: https://doi.org/10.1016/j.enconman.2012.01.022

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