OJFD  Vol.2 No.4 , December 2012
Computing the Pressure Drop of Nanofluid Turbulent Flows in a Pipe Using an Artificial Neural Network Model
ABSTRACT
In this study, an Artificial Neural Network (ANN) model to predict the pressure drop of turbulent flow of titanium dioxide-water (TiO2-water) is presented. Experimental measurements of TiO2-water under fully developed turbulent flow regime in pipe with different particle volumetric concentrations, nanoparticle diameters, nanofluid temperatures and Reynolds numbers have been used to construct the proposed ANN model. The ANN model was then tested by comparing the predicted results with the measured values at different experimental conditions. The predicted values of pressure drop agreed almost completely with the measured values.

Cite this paper
M. Youssef, A. Aly and E. Zeidan, "Computing the Pressure Drop of Nanofluid Turbulent Flows in a Pipe Using an Artificial Neural Network Model," Open Journal of Fluid Dynamics, Vol. 2 No. 4, 2012, pp. 130-136. doi: 10.4236/ojfd.2012.24013.
References
[1]   W. Yu, D. M. France, S. U. S. Choi and J. L. Routbort, “Review and Assessment of Nanofluid Technology for Transportation and Other Applications,” Energy Systems Division, Argonne National Laboratory, DuPage, 2007. doi:10.2172/919327

[2]   W. Duangthongsuk and S. Wongwises, “An Experimental Study on the Heat Transfer and Pressure Drop of TiO2-Water Nanofluids Flowing under a Turbulent Regime,” International Journal of Heat and Mass Transfer, Vol. 53, No. 1-3, 2010, pp. 334-344. doi:10.1016/j.ijheatmasstransfer.2009.09.024

[3]   W. Duangthongsuk and S. Wongwises, “Heat Transfer Enhancement and Pressure Drop Characteristics of TiO2-Water Nanofluid in a Double-Tube Counter Flow Heat Exchanger,” International Journal of Heat and Mass Transfer, Vol. 52, No. 7-8, 2009, pp. 2059-2067. doi:10.1016/j.ijheatmasstransfer.2008.10.023

[4]   Y. He, Y. Jin, H. Chen, Y. Ding, D. Cang and H. Lu, “Heat Transfer and Flow Behavior of Aqueous Suspensions of TiO2 Nanoparticles (Nanofluids) Flowing Upward through a Vertical Pipe,” International Journal of Heat and Mass Transfer, Vol. 50, No. 11-12, 2007, pp. 2272-2281.

[5]   T.-P. Teng, Y.-H. Hung, C.-S. Jwo, C.-C. Chen and L.-Y. Jeng, “Pressure Drop of TiO2 Nanofluid in Circular Pipes,” Particuology, Vol. 9, No. 5, 2011, pp. 486-491. doi:10.1016/j.partic.2011.05.001

[6]   A. R Sajadi and M. H. Kazemi, “Investigation of Turbulent Convective Heat Transfer and Pressure Drop of TiO2/Water Nanofluid in Circular Tube,” International Communications in Heat and Mass Transfer, Vol. 38, No. 10, 2011, pp. 1474-1478. doi:10.1016/j.icheatmasstransfer.2011.07.007

[7]   S. M. Fotukain and M. N. Esfahany, “Experimental Study of Turbulent Convective Heat Transfer and Pressure Drop of Dilute CuO/Water Nanofluid inside a Circular Tube,” International Communications in Heat and Mass Transfer, Vol. 37, No. 2, 2010, pp. 214-219. doi:10.1016/j.icheatmasstransfer.2009.10.003

[8]   R. S. Vajjha, D. K. Das and D. P. Kulkarni, “Development of New Correlations for Convective Heat Transfer and Friction Factor in Turbulent Regime for Nanofluids,” International Journal of Heat and Mass Transfer, Vol. 53, No. 21-22, 2010, pp. 4607-4618. doi:10.1016/j.ijheatmasstransfer.2010.06.032

[9]   M. H. Fard, M. N. Esfahany and M. R. Talaie, “Numerical Study of Convective Heat Transfer of Nanofluids in a Circular Tube Two-Phase Model versus Single-Phase Model,” International Communications in Heat and Mass Transfer, Vol. 37, No. 1, 2010, pp. 91-97. doi:10.1016/j.icheatmasstransfer.2009.08.003

[10]   H. Demir, A., S. Dalkilic, N. A. Kürekci, W. Duangthongsuk and S. Wongwise, “Numerical Investigation on the Single Phase Forced Convection Heat Transfer Characteristics of TiO2 Nanofluids in a Double-Tube Counter Flow Heat Transfer,” International Communications in Heat and Mass Transfer, Vol. 38, No. 2, 2011, pp. 218-228. doi:10.1016/j.icheatmasstransfer.2010.12.009

[11]   P. K. Namburu, D. K. Das, K. M. Tanguturi and R. S. Vajjha, “Numerical Study of Turbulent Flow and Heat Transfer Characteristics of Nanofluids Considering Variable Properties,” International Journal of Thermal Sciences, Vol. 48, No. 2, 2009, pp. 290-302. doi:10.1016/j.ijthermalsci.2008.01.001

[12]   S. Kondaraju, E. K. Jin and J. S. Lee, “Direct Numerical Simulation of Thermal Conductivity of Nanofluids: The Effect of Temperature Two-Way Coupling and Coagulation of Particles,” International Journal of Heat and Mass Transfer, Vol. 53, No. 5-6, 2010, pp. 862-869. doi:10.1016/j.ijheatmasstransfer.2009.11.038

[13]   K. L. Hsu, H. V. Gupta and S. Sorooshian, “Artificial Neural Network Modeling of the Rainfall-Runoff Process,” Water Resources Research, Vol. 31, No. 10, 1995, pp. 2517-2530. doi:10.1029/95WR01955

[14]   H. Kurt and M. Kayfeci, “Prediction of Thermal Conductivity of Ethylene Glycol-Water Solutions by Using Artificial Neural Networks,” Applied Energy, Vol. 86, No. 10, 2009, pp. 2244-2248. doi:10.1016/j.apenergy.2008.12.020

[15]   A. A. Aly, “Flow Rate Control of Variable Displacement Piston Pump with Pressure Compensation Using Neural Network,” Journal of Engineering Science, Vol. 33, No. 1, 2007, pp. 199-209

 
 
Top