M. S. Youssef

Work place: Mechanical Engineering Department, Faculty of Engineering, Taif University, Al-Haweiah, P.O. Box 888, Saudi Arabia

E-mail: youssef2056@yahoo.com

Website:

Research Interests: Computational Engineering, Engineering

Biography

Dr. M. S. Youssef is the secretary of College Accreditation Steering Committee, College of Engineering, Taif University, Saudi Arabia since 2010 until now. He is also the chairman of Mechanical Engineering Program Committee, College of Engineering, Taif University, since 2010 until now. Dr. Youssef graduated in Mech. Eng. Dept., Assiut University, Egypt in 1983 and worked as Demonstrator and got his M. Sc. in 1989 from the same department.  He got his Ph. D. from Nagoya Institute of Technology, Japan in 1994. Since 1995 He rejoined with Mech. Eng. Dept. of Assiut University as an Assistant Professor and promoted to Associate Professor in 2003. Dr. Youssef is currently on sabbatical leave of Assiut University. His research interest areas are turbulence modeling and computational fluid dynamics, development of numerical modeling techniques for the treatment of turbulent flows, liquid atomization and spray systems, and Nanofluids and their Modeling and Applications.

Author Articles
Artificial Neural Network Turbulent Modeling for Predicting the Pressure Drop of Nanofluid

By M. S. Youssef Ayman A. Aly

DOI: https://doi.org/10.5815/ijitcs.2013.11.02, Pub. Date: 8 Oct. 2013

An Artificial Neural Network (ANN) model was developed to predict the pressure drop of titanium dioxide-water (TiO2-water). The model was developed based on experimentally measured data. Experimental measurements of fully developed turbulent flow in pipe at different particle volumetric concentrations, nanoparticle diameters, nanofluid temperature and Reynolds number were used to construct the proposed model. The ANN model was validated by comparing the predicted results with the experimental measured data at different experimental conditions. It was shown that, the present ANN model performed well in predicting the pressure drop of TiO2-water nanofluid under different flow conditions with a high degree of accuracy.

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