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MHD Stability of Streaming Jet Using Artificial Intelligence Technique

Published online by Cambridge University Press:  09 August 2012

Mostafa A. M. Abdeen  and
Alfaisal A. Hasan
Mostafa A. M. Abdeen*
Affiliation:
Department of Engineering Mathematics and Physics, Faculty of Engineering, Cairo University, Giza 12211, Egypt
Alfaisal A. Hasan
Affiliation:
Basic and Applied Sciences Department, College of Engineering and Technology, Arab Academy for Science & Technology and Maritime Transport (AASTMT), Elhorria, Cairo, Egypt
*
*Corresponding author (Mostafa_a_m_abdeen@hotmail.com)
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Abstract

Mathematical formulation for Magnetohydrodynamic (MHD) stability of a streaming cylindrical model penetrated by varying transverse magnetic field is presented. Eigen value relation is derived and discussed analytically. In the current paper, Artificial Neural Network (ANN) model, one of the artificial intelligence techniques, is developed to simulate the stability of streaming jet penetrated by magnetic field. The ANN results presented in the current study showed that ANN technique, with less effort and time, is very efficiently capable of simulating and predicting the effect of magnetic field variation and axial exterior field on the stability of the streaming jet. The influence of magnetic field has a stabilizing effect for all short and long wavelengths. However the streaming is strongly destabilizing.

Keywords

MagnetohydrodynamicStreaming jetMagnetic fieldNumerical simulationArtificial neural network
Type
Articles
Information
Journal of Mechanics , Volume 28 , Issue 3 , September 2012 , pp. 453 - 459
Copyright
Copyright © The Society of Theoretical and Applied Mechanics, R.O.C. 2012

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References

REFERENCES

1. Rayleigh, J., The Theory of Sound, Vols I & II, Dover Publications, N.Y. (1945).Google Scholar
2. Chandrasekhar, S., Hydrodynamic and Hydromagnetic Stability, Dover (1981).Google Scholar
3. Cheng, L. Y., “Instability of a Gas Jet in Liquid,” Physics of Fluids, 28, pp. 2614–2617 (1985).Google Scholar
4. Kendall, J., “Experiments on Annular Liquid Jet Instability and on the Formation of Liquid Shells,” Physics of Fluids, 29, pp. 2086–2094 (1986).CrossRefGoogle Scholar
5. Drazin, P. and Reid, W., Hydrodynamic Stability, Cambridge University Press (2004).Google Scholar
6. Radwan, A. E., “Effect of Magnetic Fields on the Capillary Instability of an Annular Liquid Jet,” Journal of Magnetism and Magnetic Materials, 72, pp. 219–232 (1988).CrossRefGoogle Scholar
7. Radwan, A. E., “Large Hydromagnetic Axisymmetric Instability of a Streaming Gas Cylinder Surrounded by Bounded Fluid with Non Uniform Field,” Kyungpook Mathematical Journal, 47, pp. 455–471 (2007).Google Scholar
8. Radwan, A. E. and Hasan, A., “Axisymmetric Electrogravitational Stability of Fluid Cylinder Ambient with Transverse Varying Oscillating Field,” IAENG International Journal of Applied Mathematics, 38, pp. 113–120 (2008).Google Scholar
9. Radwan, A. E. and Hasan, A., “Magnetohydrodynamic Stability of Self-gravitational Fluid Cylinder,” Applied Mathematical Modelling, 33, pp. 2121–2131 (2009).Google Scholar
10. Hasan, A., “Electrogravitational Stability of Oscillating Streaming Fluid Cylinder,” Physica B, 406, pp. 234–240 (2011).CrossRefGoogle Scholar
11. Kheireldin, K. A., “Neural Network Application for Modeling Hydaulic Characteristics of Sever Contraction,” Proceeding of the 3rd International Conference, Hydroinformatics, Copenhagen – Denmark, pp. 41–48 (1998).Google Scholar
12. Allam, B. S. M., “Artificial Intelligence Based Predictions of Precautionary Measures for Building Adjacent to Tunnel Rout during Tunneling Process,” Ph.D. Thesis, Faculty of Engineering, Cairo University, Egypt (2005)Google Scholar
13. Mohamed, M. A. M., “Selection of Optimum Lateral Load-Resisting System using Artificial Neural Networks,” M. Sc. Thesis, Faculty of Engineering, Cairo University, Egypt (2006).Google Scholar
14. Abdeen, M. A. M., “Predicting the Impact of Vegetations in Open Channels with Different Distributaries' Operation on Water Surface Profile using Artificial Neural Networks,” Journal of Mechanical Science and Technology, KSME International Journal, Korea, 22, pp. 1830–1842 (2008).Google Scholar
15. Abdeen, M. A. M. and Hodhod, H., “Experimental Investigation and Development of Artificial Neural Network Model for the Properties of Locally Produced Light Weight Aggregate Concrete. Scientific Research Organization,” Scientific Research Organization, Engineering, 2, pp. 408–419 (2010).Google Scholar
16. Gaafar, M. S., Abdeen, M. A. M. and Marzouk, S. Y., “Structure Investigation and Simulation of Acoustic Properties of Some Tellurite Glasses using Artificial Intelligence Technique,” Journal of Alloys and Compounds, Elsevier, 509, pp. 3566–3575 (2011).CrossRefGoogle Scholar
17. Shin, Y., NeuralystTM User's Guide, Neural Network Technology for Microsoft Excel, Cheshire Engineering Corporation Publisher (1994).Google Scholar
18. Abramowitz, M. and Stegun, I., Handbook of Mathematical Functions, Dover Publications, N.Y. (1970).Google Scholar