Email this article 
(Faculty of Civil and Environmental Engineering, Institute of Technology Bandung, Jalan Ganesa No. 10, Bandung 40132)*corresponding author
AbstractThe resistance to flow in an open channel is associated with the value of the Darcy-Weisbach friction factor f. For natural channels with a movable bed, the f value depends on the grain size of the bed materials and the bedforms, such as ripple, dune, or anti-dune. The total resistance to flow is the sum of the resistance due to grain roughness and bedform. Several researchers have proposed several graphs to determine the friction factor value due to the bedforms. Still, using these graphs requires graphical interpolation, which is inconvenient and difficult to apply to the flow and sediment transport calculation. This study proposes two explicit equations, ANN models 1 and 2, to compute the friction factor due to the bedform based on artificial neural networks (ANN) procedure. The data used to build the equations were obtained by digitizing the graph proposed by Alan and Kennedy. The explicit ANN equations are in the form of a series of hyperbolic tangent functions. The resulting equations can predict the friction factor value due to bedform satisfactorily. KeywordsFriction Factor; Moveable Bed; Open Channel; Explicit Equation; Artificial Neural Networks
|
DOIhttps://doi.org/10.29099/ijair.v6i1.309 |
Article metrics10.29099/ijair.v6i1.309 Abstract views : 660 | PDF views : 107 |
Cite |
Full Text Download
|
References
Subramanya, K., Flow in Open Channels, Tata McGraw-Hill Publishing Company Limited New Delhi, 3rd Ed. 2009. pp. 86-91.
Akan, A. Osman. Ópen Channel Hydraulics; Elsevier. Amsterdam. 2006. pp. 67-74.
Knight, D. W’; Hazlewood, C.; Lamb, R.; Samuels, P. G.; Shiono, K., Practical Channel Hydraulics Roughness, Conveyance and Afflux, CRC Press., Balkema, Leiden, 2nd. 2018. pp. 29-33.
Chadwick, A., J. Mortet and M. Borthwick, (2013) Hydraulics in Civil and Environmental Engineering, 5th Ed, CPC Press, Taylor Francis Group A Spon Book.
Wu, W., 2008. Computational River Dynamics, Taylor & Francis Group, London. pp. 80-81.
Julien, P. Y., 2010. Eroson and Sedimentation, 2nd Edn, Cambridge University Press, Cambridge., pp.123.
Yang, C. T.,1996. Sediment Transport Theory and Practice. The McGraw-Hill Companies, Inc.New York., pp.79-80.
Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedward networks are universal Approximators Neural Networks, 2: 359-366.
Haykin, S., 1999. Neural Networks: A Comprehensive Foundation; Prentice Hall: Englewood Cliffs, NJ, USA.
Haykin, S., 2009. Neural Networks and Learning Machines, 3rd Edn, Pearson Prentice Hall, New York
Ferrari, S. and Stengel, R. F., 2005. Smooth Function Approximation Using Neural Networks. IEEE Transactions on Neural Networks, Vol. 16, No.1, JANUARY 2005
Cahyono, M. The Development of Explicit Equations for Estimating Settling Velocity Based on Artificial Neural Networks Procedure. Hydrology 2022, 9, 98. https://doi.org/10.3390/hydrology9060098
Cahyono, M. Hybrid Models for Solving the Colebrook–White Equation Using Artificial Neural Networks. Fluids 2022, 7, 211. https://doi.org/10.3390/fluids7070211
Chapra, S. C.; Canale, R. P. Numerical Method for Engineer. McGraw Hill Education. New Yorks. 2010. pp.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
________________________________________________________
The International Journal of Artificial Intelligence Research
Organized by: Departemen Teknik Informatika
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung
Email: jurnal.ijair@gmail.com
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.