Computing the Pressure Drop of Nanofluid Turbulent Flows in a Pipe Using an Artificial Neural Network Model

Mahmoud S. Youssef, Ayman A. Aly, El-Shafei B. Zeidan

Department of Mechanical Engineering, Faculty of Engineering, Taif University, Al-Hawiah, Saudi Arabia.
Mechanical Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt.
Mechanical Power Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.

DOI: 10.4236/ojfd.2012.24013                

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.

Keywords

Artificial Neural Networks (ANNs); Turbulent Flow; Nanofluids; Pressure Drop

Conflicts of Interest

The authors declare no conflicts of interest.

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