Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 158 countries worldwide.
Numerical Simulation and Prediction for Steep Water Gravity Waves of Arbitrary Uniform Depth using Artificial Neural Network
Mostafa Abdeen, Samir Abohadima
Pages - 23 - 40     |    Revised - 31-03-2011     |    Published - 04-04-2011
Volume - 5   Issue - 1    |    Publication Date - March / April 2011  Table of Contents
Steep Water Gravity Waves, Nonlinear permanent progressive wave, Numerical simulation, Artificial Neural Network
Nonlinear permanent progressive wave is one of the most important applications in water waves. In this study, analytic formulation of the steep water gravity waves is presented. Abohadima and Isobe [1] showed that Cokelet solution [2] is the most accurate among many other solutions. Due to the nonlinearity of analytic equations, the need to numeric simulation is raised up. In the current paper, consequence numerical models, using one of the artificial intelligence techniques, are designed to simulate and then predict the non linear properties of permanent steep water waves. Artificial Neural Network (ANN), one of the artificial intelligence techniques, is introduced in the current paper to simulate and predict the wave celerity, momentum, energy and other wave integral properties for any permanent waves in water of arbitrary uniform depth. The ANN results presented in the current study showed that ANN technique, with less effort, is very efficiently capable of simulating and predicting the non linear properties of permanent steep water waves.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Libsearch 
6 Scribd 
7 SlideShare 
8 PdfSR 
1 Abohadima, S., Isobe, M., “Limiting criteria of permanent progressive waves”, Coastal Eng., Vol. 44/3, pp. 231-237, 2002.
2 Cokelet, E.D., “Steep gravity waves in water of arbitrary uniform depth”, Phil. Trans. R. Soc. London A286, pp. 183-230, 1977.
3 Dean, R.G., “Stream function representation of nonlinear ocean waves”, J. Geophys. Res. Vol. 70, pp. 4561-4572, 1965.
4 Dean, R.G., “Evolution and development of water wave theories for engineering application”, Vols. I and II, Special Rep. No. 1, US Army Coastal Engin. Res. Center, Fort Belvoir, Virginia, 1974.
5 Chaplin, J.R., “Developments of stream function wave theory”, Coastal Eng., Vol. 3, pp. 179-205, 1980.
6 Rienecker M. M. and Fenton J. D., “A Fourier approximation methods for steady water waves”, J. Fluid Mech., Vol. 104, pp. 119-137, 1981
7 Longuet-Higgins, M. S. and Fenton, J.D., “On the mass, momentum, energy and circulation of a solitary wave II”, Proc. R. Soc. London A340, 471-493, 1974.
8 Schwartz, L.W., “Computer extension and analytic continuation of Stokes’s expansion for gravity waves”, J. Fluid Mech. Vol. 62, pp. 553-578, 1974.
9 Longuet-Higgins, M. S., “Integral properties of periodic gravity waves of finite amplitude”, Proc. R. Soc. London A342, 157-174, 1975.
10 Yamada, H. and Shiotani, T., “On the highest water wave of permanent type”, Bull. Disas. Prev. Res. Inst. Kyoto Univ., Vol. 18, Part 2, No. 135, pp. 1-22, 1968.
11 Minns, “Extended Rainfall-Runoff Modeling Using Artificial Neural Networks”, Proc. of the 2nd Int. Conference on Hydroinformatics, Zurich, Switzerland, 1996.
12 Kheireldin, K. A., “Neural Network Application for Modeling Hydraulic Characteristics of Severe Contraction”, Proc. of the 3rd Int. Conference, Hydroinformatics, Copenhagen - Denmark August 24-26, 1998.
13 Abdeen, M. A. M., “Neural Network Model for predicting Flow Characteristics in Irregular Open Channel”, Scientific Journal, Faculty of Engineering-Alexandria University, 40 (4), pp. 539-546, Alexandria, Egypt, 2001.
14 Allam, B. S. M., “Artificial Intelligence Based Predictions of Precautionary Measures for building adjacent to Tunnel Rout during Tunneling Process” Ph.D., 2005.
15 Abdeen, M. A. M., “Development of Artificial Neural Network Model for Simulating the Flow Behavior in Open Channel Infested by Submerged Aquatic Weeds”, Journal of Mechanical Science and Technology, KSME Int. J., Vol. 20, No. 10, Soul, Korea, 2006.
16 Mohamed, M. A. M., “Selection of Optimum Lateral Load-Resisting System Using Artificial Neural Networks”, M. Sc. Thesis, Faculty of Engineering, Cairo University, Giza, Egypt, 2006.
17 Abdeen, M. A. M., “Predicting the Impact of Vegetations in Open Channels with Different Distributaries’ Operations on Water Surface Profile using Artificial Neural Networks”, Journal of Mechanical Science and Technology, KSME Int. J., Vol. 22, pp. 1830-1842, Soul, Korea, 2008.
18 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” Engineering, 2, June 2010, 408-419, Scientific Research Organization, 2010.
19 Hodhod, H. and Abdeen, M. A. M., “Concrete Mix Design Method Based on Experimental Data Base and Predicting the Concrete Behavior Using ANN Technique” Engineering, 2, Augest 2010, 559-572, Scientific Research Organization, 2010.
20 Shin, Y., “NeuralystTM User’s Guide”, “Neural Network Technology for Microsoft Excel”, Cheshire Engineering Corporation Publisher, 1994
Associate Professor Mostafa Abdeen
Faculty of Eng., Cairo University - Egypt
Associate Professor Samir Abohadima
Faculty of Eng., Cairo University - Egypt