Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks

Engineering Applications of Computational Fluid Mechanics

Volumn 9, Issue 1, 2015, Pages 355-369

  Gholami, Azadeh ^a   Bonakdari, Hossein ^a   Zaji, Amir Hossein ^a   Akhtari, Ali Akbar ^a  

^a RAZI UNIVERSITY   (Iran)

Author keywords

Artificial neural network; Computational fluid dynamics; Curved open channel; Experimental modeling; Velocity field

Indexed keywords

EID: 84947074002     PISSN: 19942060     EISSN: 1997003X     Source Type: Journal    
DOI: 10.1080/19942060.2015.1033808     Document Type: Article

References (55)

1. Anwar, H. O. (1986). Turbulent structure in a river bed. Journal of Hydraulic Engineering, 112(8), 657-669.
2. Baghalian, S., Bonakdari, H., Nazari, F., & Fazli, M. (2012). Closed-form solution for flow field in curved channels in comparison with experimental and numerical analyses and artificial neural network. Engineering Applications of Computational Fluid Mechanics, 6(4), 514-526.
3. Balen, W. V., Uijttewaal, W. S. J., & Blanckaert, K. (2010). Large-eddy simulation of a curved open-channel flow over topography. Physics of Fluids, 22(7), 1-18.
4. Bilhan, O., Emiroglu, M. E., & Kisi, O. (2010). Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel. Advances in Engineering Software, 41(6), 831-837.
5. Bilhan, O., Emiroglu, M. E., & Kisi, O. (2011). Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels. Advances in Engineering Software, 42(4), 208-214.
6. Blanckaert, K., & Graf, W. H. (2001). Mean flow and turbulence in open channel bend. Journal of Hydraulic Engineering, 127(10), 835-847.
7. Bodnar, T., & Prihoda, J. (2006). Numerical simulation of turbulent free-surface flow in curved channel. Flow, Turbulence and Combustion, 76, 429-442.
8. Bonakdari, H., Baghalian, S., Nazari, F., & Fazli, M. (2011). Numerical analysis and prediction of the velocity field in curved open channel using artificial neural network and genetic algorithm. Engineering Applications of Computational Fluid Mechanics, 5(3), 384-396.
9. Chau, K. W., Wu, C. L., & Li, Y. S. (2005). Comparison of several flood forecasting models in Yangtze river. Journal of Hydrologic Engineering, 10(6), 485-491.
10. Cobaner, M., Unal, B., & Kisi, O. (2009). Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. Journal of hydrology, 367(1), 52-61.
11. Dawson, C. W., & Wilby, R. L. (1998). An artificial neural network approach to rainfall-runoff modeling. Hydrological Sciences Journal, 43(1), 47-66.
12. DeMarchis, M., & Napoli, E. (2006). 3D numerical simulation of curved open channel flows. Proceedings of the International Conference on Water Resources, Hydraulics & Hydrology, May 11-13, Chalkida, Evia Island, Greece, 86-91.
13. DeVriend, H. J., & Geoldof, H. J. (1983). Main flow velocity in short river bends. Journal of Hydraulic Engineering, 109(7), 991-1011.
14. Dursun, O. F., Kaya, N., & Firat, M. (2012). Estimating discharge coefficient of semi-elliptical side weir using ANFIS. Journal of Hydrology, 426, 55-62.
15. Ebtehaj, I., & Bonakdari, H. (2013). Evaluation of sediment transport in sewer using artificial neural network. Engineering Applications of Computational Fluid Mechanics, 7(3), 382-392.
16. Emiroglu, M. E., Bilhan, O., & Kisi, O. (2011). Neural network for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel. Expert Systems with Applications, 38(1), 867-874.
17. Fausett, L. (1994). Fundamental of neural networks: Architecture, algorithms and applications. New Jersey, USA: Prentice Hall.
18. Ferguson, R. I., Parsons, D. R., Lane, S. N., & Hardy, R. J. (2003). Flow in meander bends with recirculation at the inner bank. Journal of Water Resources Research, 39(11), 1-13.
19. Fluent Manual (2005). Manual and user guide of fluent software. New York, USA: Fluent Inc.
20. Ghamry, H. K., & Steffler, P. M. (2003). Effect of applying different distribution shapes for velocities and pressure on simulation of curved open channels. Journal of Hydraulic Engineering, 128(11), 969-982.
21. Gholami, A., Akhtari, A. A., Minatour, Y., Bonakdari, H., & Javadi, A. A. (2014). Experimental and numerical study on velocity fields and water surface profile in a stronglycurved 90° open channel bend. Engineering Applications of Computational Fluid Mechanics, 8(3), 447-461.
22. Huang, S. L., Jia, Y. F., Chan, H. C., & Wang, S. S. Y. (2009). Three dimensional numerical modeling of secondary flows in a wide curved channel. Journal of Hydrodynamics, 21(6), 758-766.
23. Jahangirzadeh, A., Shamshirband, S., Petkovic, D., Basser, H., Sedaghat, A., Akib, S., & Karami, H. (2014). Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups. Journal of Coastal Conservation. doi:10.1007/s11852-014-0357-5.
24. Kalteh, A. M. (2008). Rainfall-runoff modeling using artificial neural networks (ANNs): modeling and understanding. Caspian Journal of Environmental Science, 6(1), 53-58.
25. Kimura, I., Uijttewaal, W. S. J., & Balen, W. (2008). Application of the non-linear k . A model for simulation curved open channel flows. Proceedings of River Flow, September 3-5 Cesme-izmir, Turkey, 1, 99-108.
26. Kisi, O. (2008). The potential of different ANN techniques in evapotranspiration modeling. Hydrological Processes, 22(14), 2449-2460.
27. Kisi, O., Emiroglu, M. E., Bilhan, O., & Guven, A. (2012). Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches. Expert Systems with Applications, 39(3), 3454-3460.
28. Leschziner, M. A., & Rodi, W. (1979). Calculation of strongly curved open channel flow. Journal of the Hydraulics Division, 105(10), 1297-1314.
29. Lien, H. C., Hsieh, T. Y., Yang, J. C., & Yeh, K. C. (1999). Bend flow simulation using 2D depth-averaged model. Journal of Hydraulic Engineering, 125(10), 1097-1108.
30. Lippman, R. P. (1987). An introduction to computing with neural nets. ASSP Magazine IEEE, 4(2), 4-22.
31. Lu, W. Z., Zhang, W. S., Cui, C. Z., & Leung, A. Y. T. (2004). A numerical analysis of free-surface flow in curved open channel with velocity-pressure-free-surface correction. Computational Mechanics, 33, 215-224.
32. MATLAB (2009). User's Guide Version 7.8, R2009a. Math-Works Co., USA.
33. Mohd Zahit, A. (2007). The application of the artificial neural network model for river water quality classification with emphasis on the impact of land use activities: A case study from several catchments in Malaysia, Ph.D Thesis, University of Nottingham, Nottingham, UK.
34. Molls, T., & Chaudhry, M. H. (1995). Depth-averaged openchannel flow model. Journal of Hydraulic Engineering, 121(6), 453-465.
35. Naji, M. A., Ghodsian, M., Vaghefi, M., & Panahpur, N. (2010). Experimental and numerical simulation of flow in a 90° bend. Flow Measurement and Instrumentation, 21(3), 292-298.
36. Ottevanger, W., Blanckaert, K., & Uijttewaal, W. S. J. (2012). Processes governing the flow redistribution in sharp river bends. Geomorphology, 163-164, 45-55.
37. Rahimzadeh, H., Maghsoodi, R., Sarkardeh, H., & Tavakkol, S. (2012). Simulating flow over circular spillways by using different turbulence models. Engineering Applications of Computational Fluid Mechanics, 6(1), 100-109.
38. Ramamurthy, A., Han, S., & Biron, P. (2013). Three-dimensional simulation parameters for 90° open channel bend flows. Journal of Computing in Civil Engineering, 27(3), 282-291.
39. Rajurkar, M. P., Kothyari, U. C., & Chaube, U. C. (2004). Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology, 285, 96-113.
40. Rozovskii, I. L. (1961). Flow of water in bends of open channels. Academy of Sciences of the Ukrainian SSR, Israel Program for Science Translation, Jerusalem, 1-233.
41. Sahu, M., Jana, S., Agarwal, S., & Khatua, K. K. (2011). Point form velocity prediction in meandering open channel using artificial neural network. 2nd International Conference on Environmental Science and Technology, 6, IACSIT Press, Singapore, 209-212.
42. Shamshirband, S., Gocic, M., Petkovic, D., Javidnia, H., Hafizah, S., Hamid, A., Qasem, S. N. (2015). Clustering project management for drought regions determination: A case study in Serbia. Agricultural and Forest Meteorology, 200(15), 57-65.
43. Steffler, P. M., Rajartnam, N., & Peterson, A. W. (1985). Water surface change of channel curvature. Journal of Hydraulic Engineering, 111(5), 866-870.
44. Stoesser, T., Ruether, N., & Olsen, N. R. B. (2010). Calculation of primary and secondary flow and boundary shear stresses in a meandering channel. Advances in Water Resources, 33, 158-170.
45. Wang, W. C., Chau, K. W., Cheng, C. T., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374(3-4) 294-306.
46. Wang,W. C., Xu, D. M., Chau, K.W., & Lei, G. J. (2014). Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resources Management, 28(12), 4183-4200.
47. Wu, C. L., & Chau, K. W. (2010). Data-driven models for monthly streamflow time series prediction. Engineering Applications of Artificial Intelligence, 23(8), 1350-1367.
48. Wu, C. L., Chau, K.W., & Li, Y. S. (2008). River stage prediction based on a distributed support vector regression. Journal of Hydrology, 358(1-2) 96-111.
49. Yakhot, V., & Orszag, S. A. (1986). Renormalization group analysis of turbulence I: basic theory. Journal of Scientific Computing, 1, 3-51.
50. Ye, J., & McCorquodale, J. A. (1998). Simulation of curved open channel flows by 3D hydrodynamic model. Journal of Hydraulic Engineering, 124(7), 687-698.
51. Yuhang, Z., & Wenxin, H. (2009). Application of artificial neural network to predict the friction factor of open channel flow. Communications in Nonlinear Science and Numerical Simulation, 14, 2373-2378.
52. Zaji, A. H., & Bonakdari, H. (2015). Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions. Flow Measurement and Instrumentation, 41, 81-89.
53. Zeng, J., Constantinescu, G., Blanckaert, K., & Weber, L. (2008). Flow and bathymetry in sharp open-channel bends: experiments and predictions. Water resources research, 42(1-2) 1-12.
54. Zhang, M. L., & Shen, Y. M. (2008). Three dimensional simulation of meandering river based on 3-D k - ε (RNG) turbulence model. Journal of Hydrodynamics, 20(4), 448-455.
55. Zhou, G.,Wang, H., Shao, X., & Jia, D. D. (2006). 2-D numerical simulation of flow in a curved open channel. Advances in Water Resources and Hydraulic Engineering, 3, 871-876.