THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

Authors of this Paper

External Links

online first only

Modeling of overall heat transfer coefficient of a concentric double pipe heat exchanger with limited experimental data by using curve fitting and artificial neural network combination

ABSTRACT
The modeling accuracy of artificial neural networks was evaluated by using limited heat exchanger data acquired experimentally. The artificial neural networks were used for predicting the overall heat transfer coefficient of a concentric double pipe heat exchanger where oil flowed inside the inner tube while the water flowed in the outer tube. In the cases of parallel and counter flows, the experimental data were collected by testing heat exchanger in wide range of operating conditions. Curve fitting and artificial neural network combination was used for the estimation of the overall heat transfer coefficient to compensate the experimental errors in the data. The curve fitting was used to detect the trend and generate data points between the experimentally collected points. The artificial neural network was trained better from the generated data set. The feed forward type artificial neural network was trained by using the Levenberg-Marquardt algorithm. Two backpropagation network type artificial neural network algorithms were also used, and their performance were compared with the estimation of the Levenberg-Marquardt algorithm. The average estimation error between the predictions and the experimental data were in the range of 1.31e-4 to 4.35e-2%. The study confirmed that curve fitting and artificial neural network combination could be used effectively to estimate the overall heat transfer coefficient of heat exchanger.
KEYWORDS
PAPER SUBMITTED: 2017-12-06
PAPER REVISED: 2018-02-28
PAPER ACCEPTED: 2018-03-12
PUBLISHED ONLINE: 2018-04-28
DOI REFERENCE: https://doi.org/10.2298/TSCI171206111K
REFERENCES
  1. Shah, R.K., Seculic, D.P., Fundamentals of Heat Exchanger Design, John Wiley & Sons, Inc., Hoboken, New Jersey, 2003
  2. Cengel, Y.A., Heat and mass transfer, McGraw-Hill, New York, 2007
  3. Holman, J.P., Heat transfer, McGraw-Hill, New York, 2010
  4. Patrascioiu, C., Radulescu, S., Modeling and simulation of the double tube heat exchanger case studies, Advances in Fluid Mechanics & Heat & Mass Transfer, (2012), pp. 35-41.
  5. 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, 23 (2003), pp. 243-249
  6. Islamoglu, Y., Kurt, A., Heat transfer analysis using ANNs with experimental data for air flowing in corrugated channels, Int. Journal Heat Mass Transfer, 47 (2004), pp. 1361-1365
  7. Pacheco-Vega, A., et al., Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data, Int. Journal Heat and Mass Transfer, 44 (2001), 5, pp. 763-770
  8. Mohanraj, M., et al., Applications of artificial neural networks for thermal analysis of heat exchangers- A review, International Journal of Thermal Sciences, 90 (2015), pp. 150-172
  9. Díaz, G., et al., Dynamic prediction and control of heat exchangers using artificial neural networks, Int. Heat and Mass Transfer, 44 (2001), 9, pp. 1671-1679.
  10. Esfe, M.H., Designing a neural network for predicting the heat transfer and pressure drop characteristics of Ag/water nanofluids in a heat exchanger, Applied Thermal Engineering, 126,(2017), 5, pp. 559-565.
  11. Naphon, P., et al., Artificial Neural Network Analysis on the Heat Transfer and Friction Factor of the Double Tube with Spring Insert, Int. J. Applied Engineering Research, 11 (2016), 5, pp. 3542-3549.
  12. Fadare, D.A., Fatona, A.S., Artificial neural network modeling of heat transfer in a staggered cross-flow tube-type heat exchanger, The Pacific Journal of Science and Technology, 9 (2008), 2, pp. 317-323
  13. Islamoglu, Y., Performance prediction for non-adiabatic capillary tube suction-line heat exchanger: an artificial neural network approach, Energy Conversion and Management, 46 (2005), pp. 223-232
  14. Shabiulla, A.M., Sivaprakasam, S., Experimental investigation and neural modeling of water-butanol system in a spiral plate heat exchanger, Int. J. Application or Innovation in Engineering, 2 (2013), 9, pp. 125-135.
  15. Xie, G.N., et al., Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach, Applied Thermal Engineering, 27 (2007), pp. 1096-1104
  16. Colorado, D., et al., Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution, Energy, 36 (2011), pp. 854-863
  17. Patra, S.R., et al., Artificial Neural Network Model for Intermediate Heat Exchanger of Nuclear Reactor, Int. J. Computer Applications, 1 (2010), 26, pp. 63-69.
  18. Erdogan, A., Colpan, C.O., Performance assessment of shell and tube heat exchanger based subcritical and supercritical organic Rankine cycles, Thermal Science, DOI Reference: 10.2298/TSCI171101019E
  19. Ali, M., et al., Parametric investigation of a counter-flow heat and mass exchanger based on maisotsenko cycle, Thermal Science, DOI Reference: 10.2298/TSCI160808296A
  20. Direk, M., and Kelesoglu, A., Performance analysis of automotive air conditioning system with an internal heat exchanger using r1234yf under different evaporation and condensation temperatures, Thermal Science, DOI Reference: 10.2298/TSCI170125215D
  21. Yang, W., et al., Two-region simulation model of vertical U-tube ground heat exchanger and its experimental verification, Applied Energy, 86, (2009), pp. 2005-2012
  22. Hosseini, M.J., et al., A combined experimental and computational study on the melting behavior of a medium temperature phase change storage material inside shell and tube heat exchanger, International Communications in Heat and Mass Transfer, 39 (2012), pp. 1416-1424
  23. Jamali, A., et al., Optimization of a novel carbon dioxide cogeneration system using artificial neural network and multi-objective genetic algorithm, Applied Thermal Engineering, 64 (2014), pp. 293-306
  24. Seber, A.F., Wild, C.J., Nonlinear Regression, John Wiley & Sons, Inc., New Jersey, 2003
  25. Haykin, S., Neural Networks and learning Machines, Pearson Education Inc., New Jersey, 2009
  26. Kocyigit, N., Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network, Int. J. Refrigeration, 50 (2015), pp. 69-79
  27. Danfos Heat exchangers, files.danfoss.com/technicalinfo/dila/01/DKRCC.PD.FD0.A8.02.pdf E-book
  28. Hosoz, M., et. al, An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower, Expert Systems with Applications, 38 (2011), pp.14148-14155.
  29. Barbeau, E.J., Polynomials, Springer, New York, 2003
  30. MathWorks, www.mathworks.com/help/stats/anova1.html
  31. Mohanraj, M., et al., Applications of artificial neural networks for refrigeration, air conditioning and heat pump systems, Renewable and Sustainable Energy Reviews, 16 (2012) pp.1340-1358