Combination of Computational Fluid Dynamics, Adaptive Neuro-Fuzzy Inference System, and Genetic Algorithm for Predicting Discharge Coefficient of Rectangular Side Orifices
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VIEW THE REPLYPublication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 7
Abstract
Side orifices are used to divide and adjust flow into aeration ponds, sedimentation reservoirs, flocculation units, and other hydraulic and environmental areas. In this study, the discharge coefficients of side orifices are estimated using the adaptive neuro-fuzzy inference system (ANFIS) and a hybrid of ANFIS and a genetic algorithm (ANFIS-GA). The genetic algorithm is used to optimize the membership function of ANFIS. To predict the discharge coefficient, the ratio of the main channel width to the side orifice length (), the ratio of the side orifice height to its length (), the ratio of the flow depth in the main channel to the side orifice length () and the Froude number () are considered. Eleven different models are introduced for each of the ANFIS and ANFIS-GA models to calculate the discharge coefficient. The side orifice discharge is simulated using computational fluid dynamics (CFD). To model the flow field turbulence, the standard and renormalization-group (RNG) turbulence models are used. According to the CFD model results, the RNG turbulence model simulates the flow field turbulence with more accuracy. By analyzing the results of the ANFIS, ANFIS-GA and CFD models, the ANFIS-GA model is introduced as the best model in terms of , , , and .
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References
FLOW-3D Version 10.0 [Computer Software]. Flow Science, Santa Fe, NM.
Aydin, M. C., and Emiroglu, M. E. (2013). “Determination of capacity of labyrinth side weir by CFD.” Flow. Meas. Instrument., 29, 1–8.
Azimi, H., Hadad, H., Shokati, Z., and Salimi, M. S. (2015). “Discharge and flow field of the circular channel along the side weir.” Can. J. Civ. Eng., 42(4), 273–280.
Azimi, H., and Shabanlou, S. (2015). “The flow pattern in triangular channels along the side weir for subcritical flow regime.” Flow. Meas. Instrument., 46, 170–178.
Azimi, H., Shabanlou, S., and Salimi, M. S. (2014). “Free surface and velocity field in a circular channel along the side weir in supercritical flow conditions.” Flow. Meas. Instrument., 38, 108–115.
Bagheri, S., Kabiri-Samani, A. R., and Heidarpour, H. (2014). “Discharge coefficient of rectangular sharp-crested side weirs. Part I: Traditional weir equation.” Flow. Meas. Instrument., 35, 109–115.
Ebtehaj, I., and Bonakdari, H. (2014). “Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers.” Water. Resour. Manage., 28(13), 4765–4779.
Ebtehaj, I., Bonakdari, H., Khoshbin, F., and Azimi, H. (2015a). “Pareto genetic design of group method of data handling type neural network for prediction discharge coefficient in rectangular side orifices.” Flow. Meas. Instrument., 41, 67–74.
Ebtehaj, I., Bonakdari, H., Zaji, A. H., Azimi, H., and Khoshbin, F. (2015b). “GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs.” Eng. Sci. Technol. Int. J., 18(4), 746–757.
Ebtehaj, I., Bonakdari, H., Zaji, A. H., Azimi, H., and Sharifi, A. (2015c). “Gene expression programming to predict the discharge coefficient in rectangular side weirs.” Appl. Soft. Comput., 35, 618–628.
Fazlic, L. B., Avdagic, Z., and Besic, I. (2015). “GA-ANFIS expert system prototype for detection of tar content in the manufacturing process.” 38th Int. Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), IEEE, New York, 1194–1199.
Ghodsian, M. (2003). “Flow through side sluice gate.” J. Irrig. Drain. Eng., 458–463.
Hirt, C. W., and Nichols, B. D. (1981). “Volume of fluid (VOF) method for the dynamics of free boundaries.” J. Comput. Phys., 39(1), 201–225.
Holland, J. H. (1975). Adaptation in natural and artificial system, University of Michigan Press, Ann Arbor, MI.
Hussein, A., Ahmad, Z., and Asawa, G. L. (2010). “Discharge characteristics of sharp-crested circular side orifices in open channels.” Flow. Meas. Instrument., 21(3), 418–424.
Hussein, A., Ahmad, Z., and Asawa, G. L. (2011). “Flow through sharp-crested rectangular side orifices under free flow condition in open channels.” Agric. Water. Manage., 98(10), 1536–1544.
Hussein, A., Ahmad, Z., and Ojha, C. S. P. (2014). “Analysis of flow through lateral rectangular orifices in open channels.” Flow. Meas. Instrument., 36, 32–35.
Jang, J. S. R. (1993). “ANFIS: Adaptive-network-based fuzzy inference system.” IEEE Trans. Syst. Man Cybern., 23(3), 665–685.
Jang, J. S. R., Sun, C. T., and Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, NJ.
Khoshbin, F., Bonakdari, H., Ashraf Talesh, S. H., Ebtehaj, I., Zaji, A. H., and Azimi, H. (2016). “Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs.” Eng. Optimize., 48(6), 933–948.
Kisi, O., Kim, S., and Shiri, J. (2013). “Estimation of dew point temperature using neuro-fuzzy and neural network techniques.” Theor. Appl. Climatol., 114(3–4), 365–373.
Kiyak, E., and Yavuz, H. S. (2014). “Modelling and comparison of compressor performance parameters by using ANFIS.” Adv. Mater. Res., 1016, 710–715.
Ojha, C. S. P., and Subbaiah, D. (1997). “Analysis of flow through lateral slot.” J. Irrig. Drain. Eng., 402–405.
Ramamurthy, A. S., and Udoyara, S. T., and Rao, M. V. J. (1987). “Weir orifice units for uniform flow distribution.” J. Environ. Eng., 155–166.
Ramamurthy, A. S., Udoyara, S. T., and Serraf, S. (1986). “Rectangular lateral orifices in open channel.” J. Environ. Eng., 292–300.
Sanikhani, H., and Kisi, O. (2012). “River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches.” Water. Resour. Manage., 26(6), 1715–1729.
Yazar, I., Kiyak, E., and Caliskan, F. (2014). “A new approach for compressor and turbine performance map modeling by using ANFIS structure.” Progress in exergy, energy, and the environment, Springer, Berlin, 541–551.
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©2017 American Society of Civil Engineers.
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Received: Sep 21, 2016
Accepted: Jan 5, 2017
Published online: Mar 15, 2017
Published in print: Jul 1, 2017
Discussion open until: Aug 15, 2017
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