PERFORMANCE PREDICTIONS OF AIR-COOLED CONDENSERS HAVING CIRCULAR AND ELLIPTIC CROSS-SECTIONS WITH ARTIFICIAL NEURAL NETWORKS

Abstract

In this study, mathematical models of air cooled condensers with circular and elliptic cross-sections were developed and performances were evaluated with artificial neural networks. Air velocity, orientation angle and ambient temperature were used as the input to the neural network structure while heat transfer rate to the air was used as the output. The data sets were generated from high fidelity, computationally inefficient expensive three dimensional computational fluid dynamics simulations. It was observed that artificial neural network model replaces computational fluid dynamics model and based on the mathematical model with artificial neural network, elliptic condensers perform better in terms of heat transfer compared to circular ones.

Keywords

• Refrigeration,Elliptic,Computational Fluid Dynamics,Artificial Neural Network

References

1. [1] Liu, P., Duan, H., Zhao, W. (2009).Numerical investigation of hot air recirculation of air-cooled condensers at a large power plant. Applied Thermal Engineering, 29,1927–1934.
2. [2] Sun, L., Yang, L., Shao, L.L.,Zhang, C.L. (2015). Overall thermal performance oriented numerical comparison between elliptical and circular finned-tube condensers. International Journal of Thermal Sciences, 89, 234–244.
3. [3] Shabanian, S., Rahimi, M., Shahhosseini, M., Alsairafi, A. (2011). Cfd and experimental studies on heat transfer enhancement in an air cooler equipped with different tube inserts. International Communications in Heat and Mass Transfer,38, 383–390.
4. [4] Zhang, Z., Yang, J., Wang, Y. (2015). A favorable face velocity distribution and a v-frame cell for power plant air-cooled condensers. Applied Thermal Engineering, 87, 1–9.
5. [5] Li, X.D., Yang, L., Xu, Y., Yang, Y. (2013). Numerical simulation on flow and heat transfer of fin structure in air-cooled heat exchanger. Applied Thermal Engineering, 59, 77–86.
6. [6] Melo, C. Hermes, C.J. (2009). A heat transfer correlation for natural draft wire-and-tube condensers. International Journal of Refrigeration, 32, 546–555.
7. [7] Tagliafico, L., Tanda, G. (1997). Radiation and natural convection heat transfer from wire-and-tube heat exchangers in refrigeration appliances. International Journal of Refrigeration, 20, 461–469.
8. [8] Gupta, J., Gopal, M.R. (2008). Modeling of hot-wall condensers for domestic refrigerators. International Journal of Refrigeration, 31, 979–988.

9. [9] Bansal, P., Chin, T. (2003). Modelling and optimisation of wire and tube condenser. International Journal of Refrigeration, 26,601–613.
10. [10] Mohammed, H., Abbas, A. K., Sheriff, J. (2013). Influence of geometrical parameters and forced convective heat transfer in transversely corrugated circular tubes. International Communications in Heat and Mass Transfer, 44, 116–126.
11. [11] Hussain, S. H., Hussein, A. K., Mohammed, R. N. (2012). Studying the effects of a longitudinal magnetic field and discrete isoflux heat source size on natural convection inside a tilted sinusoidal corrugated enclosure. Computers and Mathematics with Applications, 64, 476–488.
12. [12] Nasrin, R., Alim, M., Chamkha, A. J. (2012). Combined convection flow in triangular wavy chamber filled with water-cuo nanofluid: Effect of viscosity models. International Communications in Heat and Mass Transfer, 39, 1226–1236.
13. [13] Selimefendigil, F., Oztop, H. F. (2016). Numerical study of forced convection of nanofluid flow over a backward facing step with a corrugated bottom wall in the presence of different shaped obstacles. Heat Transfer Engineering, 37, 1280–1292.
14. [14] Selimefendigil, F., Chamkha, A. J. (2016). Magnetohydrodynamics mixed convection in a lid-driven cavity having a corrugated bottom wall and filled with a non-newtonian power-law fluid under the influence of an inclined magnetic field. J. Thermal Sci. Eng. Appl., 8, 021023.
15. [15] Ibrahim, E., Moawed, M. (2009). Forced convection and entropy generation from elliptic tubes with longitudinal fins. Energy Conversion and Management, 50, 1946–1954.
16. [16] Elsayed, A.O, Ibrahim, E. Z., Elsayed, S. A. (2003). Free convection from a constant heat flux elliptic tube. Energy Conversion and Management, 44, 2445–2453.
17. [17] Selimefendigil, F., Oztop, H. F. (2014). Estimation of mixed convection heat transfer of rotating cylinder in a vented cavity subjected to nanofluid by using generalized neural networks. Numerical Heat Transfer, Part A, 65, 165–185.
18. [18] Aminossadati, S., Kargar, A., Ghasemi, B. (2012). Adaptive network based fuzzy inference system analysis of mixed convection in a two sided lid-driven cavity filled with a nanofluid. International Journal of Thermal Sciences, 52, 102–111.
19. [19] Selimefendigil, F., Oztop, H. F. (2013). Identification of forced convection in pulsating flow at a backward facing step with a stationary cylinder subjected to nanofluid. International Communications in Heat and Mass Transfer, 45, 111–121.
20. [20] Liang, J., Du, R. (2007). Model-based fault detection and diagnosis of hvac systems using support vector machine method. International Journal of Refrigeration, 30, 1104–1114.
21. [21] Selimefendigil, F., Oztop, H. F. (2012). Fuzzy-based estimation of mixed convection heat transfer in a square cavity in the presence of an adiabatic inclined fin. International Communications in Heat and Mass Transfer, 39, 1639–1646.
22. [22] Varol, Y., Oztop, H. F., Avci, E. (2008). Estimation of thermal and flow fields due to natural convection using support vector machines (svm) in a porous cavity with discrete heat sources. International Communications in Heat and Mass Transfer, 35, 928–936.
23. [23] Kalogirou, S. (1999). Applications of artificial neural networks in energy systems a review. Energy Conversion and Management, 40, 1073–1087.
24. [24] Sahin, A. S. (2011). Performance analysis of single-stage refrigeration system with internal heat exchanger using neural network and neuro-fuzzy. Renewable Energy, 36, 2747-2752.
25. [25] Ertunc, H. M., Hosoz, M. (2008). Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system. International Journal of Refrigeration, 31, 1426–1436.
26. [26] Markatos, N. (1989). Computational fluid flow capabilities and software. Ironmaking Steelmaking, 16, 266273.
27. [27] Versteeg, H., Malalasekera, W. (1999). An Introduction to Computational Fluid Dynamics: The Finite Volume Method. Harlow: Addison- Wesley, Longman Limited.
28. [28] Pha, D.T. (1995). Neural Networks for Identification, Prediction and Control. Springer.
29. [29] Juditsky, A., Hjalmarsson, H., Beneviste, A., Delyon, B., Ljung, L., Sjoberg, J., Zhang, Q. (1995). Nonlinear black-box models in system identification: mathematical foundations. Automatica, 31, 1725–1750.