Heat Transfer Prediction for Helical Baffle Heat Exchangers with Experimental Data by Radial Basis Function Neural Networks

Bin Jiang; Xiao Ling Yang; Lu Hong Zhang

doi:10.4028/www.scientific.net/AMR.452-453.1441

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 452-453Heat Transfer Prediction for Helical Baffle Heat...

Heat Transfer Prediction for Helical Baffle Heat Exchangers with Experimental Data by Radial Basis Function Neural Networks

Abstract:

In this paper an artificial neural network (ANN) is used to correlate experimentally determined heat transfer rate of non-continuous helical baffle heat exchangers. First the heat exchangers with three helical angles were experimentally investigated under different inlet volumetric flow rate and temperature. The commonly implemented radial-basis function (RBF) neural network is applied to develop a prediction model based on the limited experimental data. Compared with correlations, the RBF network exhibits superiority in accuracy. The satisfactory results suggest the RBF network might be used to predict the thermal performance of shell-and-tube heat exchangers with helical baffles.

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Periodical:

Advanced Materials Research (Volumes 452-453)

Pages:

1441-1445

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.452-453.1441

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Online since:

January 2012

Authors:

Bin Jiang, Xiao Ling Yang, Lu Hong Zhang

Keywords:

Artificial Neural Network (ANN), Heat Transfer Rate, Helical Baffle, Radial Basis Function (RBF), Shell-and-Tube Heat Exchanger

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References

[1] J. Lutcha, J. Nemcansky, Performance improvement of tubular heat exchangers by helical baffles, Chemical engineering research & design, 68 (1990) 263-270.

Google Scholar

[2] D. Kral, P. Stehlik, H. Van Der Ploeg, B.I. Master, Helical baffles in shell-and-tube heat exchangers, Part I: Experimental verification, Heat Transfer Engineering, 17 (1996) 93-101.

DOI: 10.1080/01457639608939868

Google Scholar

[3] B. Peng, Q. Wang, C. Zhang, G. Xie, L. Luo, Q. Chen, M. Zeng, An experimental study of shell-and-tube heat exchangers with continuous helical baffles, Journal of Heat Transfer, 129 (2007) 1425.

DOI: 10.1115/1.2754878

Google Scholar

[4] Y.G. Lei, Y.L. He, R. Li, Y.F. Gao, Effects of baffle inclination angle on flow and heat transfer of a heat exchanger with helical baffles, Chemical Engineering and Processing, 47 (2008) 2336-2345.

DOI: 10.1016/j.cep.2008.01.012

Google Scholar

[5] J.F. Zhang, B. Li, W.J. Huang, Y.G. Lei, Y.L. He, W.Q. Tao, Experimental performance comparison of shell-side heat transfer for shell-and-tube heat exchangers with middle-overlapped helical baffles and segmental baffles, Chemical Engineering Science, 64 (2009).

DOI: 10.1016/j.ces.2008.12.018

Google Scholar

[6] G. Chen, Q. Wang, Experimental and Numerical Studies of Shell-and-Tube Heat Exchangers With Helical Baffles, in, ASME, (2009).

Google Scholar

[7] G. Diaz, M. Sen, K. Yang, R.L. McClain, Simulation of heat exchanger performance by artificial neural networks, Hvac&R Research, 5 (1999) 195-208.

DOI: 10.1080/10789669.1999.10391233

Google Scholar

[8] A. Pacheco-Vega, G. D¨ªaz, M. Sen, K. Yang, R.L. McClain, Heat rate predictions in humid air-water heat exchangers using correlations and neural networks, Journal of Heat Transfer, 123 (2001) 348.

DOI: 10.1115/1.1351167

Google Scholar

[9] C. Shen, G.Y. Cao, X.J. Zhu, Nonlinear modeling of MCFC stack based on RBF neural networks identification, Simulation Modelling Practice and Theory, 10 (2002) 109-119.

DOI: 10.1016/s1569-190x(02)00064-3

Google Scholar

[10] A. Garg, P. Sastry, M. Pandey, U. Dixit, S. Gupta, Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor, Nuclear engineering and design, 237 (2007) 230-239.

DOI: 10.1016/j.nucengdes.2006.06.008

Google Scholar

[11] G.N. Xie, Q.W. Wang, M. Zeng, L.Q. Luo, Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach, Applied Thermal Engineering, 27 (2007) 1096-1104.

DOI: 10.1016/j.applthermaleng.2006.07.036

Google Scholar

[12] N. Vaziri, A. Hojabri, A. Erfani, M. Monsefi, B. Nilforooshan, Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study, Nuclear engineering and design, 237 (2007) 377-385.

DOI: 10.1016/j.nucengdes.2006.05.005

Google Scholar

[13] G. Xie, B. Sunden, Q. Wang, L. Tang, 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, 52 (2009).

DOI: 10.1016/j.ijheatmasstransfer.2008.10.036

Google Scholar