Abstract
Owing to the significance of velocity distribution, several laboratory and simulation studies were done on the velocity distribution in open channels. Laboratory and field studies show that the maximum value of velocity through narrow open flumes takes place beneath the water surface knowing as the velocity dip mechanism. This velocity dip is a highly complex feature of flow in narrow channels, and no expression can evaluate it exactly. This study introduces a hybrid Metaheuristic model to estimate the velocity distribution within the narrow open canals. To optimize the linear and nonlinear parameters of adaptive neuro-fuzzy inference systems (ANFIS) models, singular decomposition value (SVD) and genetic algorithm (GA) are employed. In order to increase the flexibility of the model for an optimal design, two different objective functions are used, and the superior optimal point using the Pareto curve is estimated. To evaluate the accuracy of the hybrid ANFIS-GA/SVD model, the velocity distribution in three hydraulic circumstances is compared to the measured values. ANFIS-GA/SVD predicts velocity distribution with reasonable accuracy and estimates the velocity dip value with high precision. The Root Mean Squared Error (RMSE) for depths, D = 0.65 m, D = 0.91 m, and D = 1.19 m is calculated 0.052, 0.044, and 0.053, respectively. According to the numerical model results, the ANFIS-GA/SVD simulated the velocity distribution in depth of D = 0.91 m more accurately than other ones. Also, almost 94%, 96% and 88% predicted velocities for D = 0.65 m, D = 0.91 m, and D = 1.19 m, respectively that are modeled by the ANFIS-GA/SVD algorithm show an error of less than 10% for all measured data in the entire cross-section.
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References
Schlichting, H.: Boundary Layer Theory, 7th edn. McGraw-Hill book Company (1979)
Cebeci, T.: Analysis of Turbulent Flows, 2nd edn. Elsevier Ltd., Oxford, U.K. (2004)
Kirkgoz, M.S.: Turbulent velocity profiles for smooth and rough open channel flow. J. Hydraul. Eng. 115(11), 1543–1561 (1989)
Keulegan, G.H.: Laws of turbulent flow in open channels. J. National Bureau Stand. 21(6), 707–741 (1938)
Nikuradse, J.: Laws of Flow in Rough Pipes, Tech. Memorandum 1292, National Advisory Committee for Aeronautics, Washington, DC. (1950)
Cardoso, A.H., Graf, W.H., Gust, G.: Uniform flow in a smooth open channel. J. Hydraul. Res. 27(5), 603–616 (1989)
Nezu, I., Nakagawa, H.: Turbulent Open-Channel Flows. CRC Press, Taylor and Francis Group, Balkema, Rotterdam, IAHR Monograph (1993)
Kirkgoz, M.S., Ardiçlioglu, M.: Velocity profiles of developing and developed open channel flow. J. Hydraul. Eng. 123(12), 1099–1105 (1997)
Coles, D.: The law of the wake in the turbulent boundary layer. J. Fluid Mech. 1(2), 191–226 (1956)
Nezu, I., Rodi, W.: Open-channel flow measurements with a Laser Doppler Anemometer. J. Hydraul. Eng. 112(5), 335–355 (1986)
Guo, J., Julian, P., Meroney, R.N.: Modified wall wake law for zero pressure gradient turbulent boundary layers. J. Hydraul. Res. 43(4), 421–430 (2005)
Castro-Orgaz, O.: Hydraulics of developing chute flow. J. Hydraul. Res. 47(2), 185–194 (2009)
Yang, S.Q., Tan, S.K., Lim, S.Y.: Velocity distribution and dip phenomenon in smooth uniform open channel flow. J. Hydraul. Eng. 130(12), 1179–1186 (2004)
Hu, Y.F., Wan, W.Y., Cai, F.K., Mao, G., Xie, F.: Velocity distribution in narrow and deep rectangular open channels. J. Zhejiang Univ. (Eng. Sci.) 42(1), 183–187 (2008)
Bonakdari, H., Larrarte, F., Lassabatere, L., Joannis, C.: Turbulent velocity profile in fully-developed open channel flows. Environ. Fluid Mech. 8(1), 1–17 (2008)
Absi, R.: An ordinary differential equation for velocity distribution and dip-phenomenon in open channel flows. J. Hydraul. Res. 49(1), 82–89 (2011)
Gac, J.M.: A large eddy based lattice-Boltzmann simulation of velocity distribution in an open channel flow with rigid and flexible vegetation. Acta Geophys. 62(1), 180–198 (2013). https://doi.org/10.2478/s11600-013-0178-1
Fullard, L.A., Wake, G.C.: An analytical series solution to the steady laminar flow of a Newtonian fluid in a partially filled pipe, including the velocity distribution and the dip phenomenon. IMA J. Appl. Math. 80(6), 1890–1901 (2015)
Yang, S.-Q.: Depth-averaged shear stress and velocity in open-channel flows. J. Hydraul. Eng. 136(11), 952–958 (2010)
Lassabatere, L., Pu, J.H., Bonakdari, H., Joannis, C., Larrarte, F.: Velocity distribution in open channel flows: analytical approach for the outer region. J. Hydraul. Eng. 139(1), 37–43 (2012)
Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Azimi, H., Shabanlou, S., Ebtehaj, I., Bonakdari, H., Kardar, S.: Combination of computational fluid dynamics, adaptive neuro-fuzzy inference system, and genetic algorithm for predicting discharge coefficient of rectangular side orifices. J. Irrig. Drain. Eng. 04017015 (2017)
Gholami, A., et al.: A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS. Eng. Geol. 239, 298–309 (2018)
Marzbanrad, J., Jamali, A.: Design of ANFIS networks using hybrid genetic and SVD methods for modeling and prediction of rubber engine mount stiffness. Int. J. Automot. Technol. 10(2), 167–174 (2009)
Azimi, H., Bonakdari, H., Ebtehaj, I., Shabanlou, S., Ashraf Talesh, S.H., Jamali, A.: A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth. Sādhanā 44(7), 1–14 (2019). https://doi.org/10.1007/s12046-019-1153-6
Bonakdari, H., et al.: Pareto design of multiobjective evolutionary neuro-fuzzy system for predicting scour depth around bridge piers. In: Water Engineering Modeling and Mathematic Tools, pp. 491–517. Elsevier (2021)
Ebtehaj, I., et al.: Pareto multiobjective bioinspired optimization of neuro-fuzzy technique for predicting sediment transport in sewer pipe. In: Soft Computing Techniques in Solid Waste and Wastewater Management, pp. 131–144. Elsevier (2021)
Khoshbin, F., Bonakdari, H., Ashraf Talesh, S.H., Ebtehaj, I., Zaji, A.H., Azimi, H.: 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. Optim. 48(6), 933–948 (2016)
Ebtehaj, I., Bonakdari, H., Zaji, A.H., Azimi, H., Khoshbin, F.: 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 (2015)
Azimi, H., Bonakdari, H., Ebtehaj, I., Michelson, D.G.: A combined adaptive neuro-fuzzy inference system–firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed. Neural Comput. Appl. 29(6), 249–258 (2016). https://doi.org/10.1007/s00521-016-2560-9
Azimi, H., Bonakdari, H., Ebtehaj, I., Talesh, S.H.A., Michelson, D.G., Jamali, A.: Evolutionary Pareto optimization of an ANFIS network for modeling scour at Pile groups in clear water condition. Fuzzy Sets Syst. 319, 50–69 (2017)
Bonakdari, H., Gharabaghi, B., Ebtehaj, I.: Extreme learning machines in predicting the velocity distribution in compound narrow channels. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) SAI 2018. AISC, vol. 857, pp. 119–128. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01177-2_9
Bonakdari, H., Gharabaghi, B., Ebtehaj, I.: A highly efficient gene expression programming for velocity distribution at compound sewer channel. In: The 38th IAHR World Congress from September 1st to 6th, Panama City, Panama, pp. 2019–0221. (2019)
Bonakdari, H., Zaji, A.H., Gharabaghi, B., Ebtehaj, I., Moazamnia, M.: More accurate prediction of the complex velocity field in sewers based on uncertainty analysis using extreme learning machine technique. ISH J. Hydraul. Eng. 26(4), 409–420 (2020)
Bonakdari, H., Qasem, S.N., Ebtehaj, I., Zaji, A.H., Gharabaghi, B., Moazamnia, M.: An expert system for predicting the velocity field in narrow open channel flows using self-adaptive extreme learning machines. Measurement 151, 107202 (2020)
Larrarte, F.: Velocity fields within sewers: an experimental study. Flow Meas. Instrum. 17(5), 282–290 (2006)
Bonakdari, H.: Modelisation des écoulements en conllecteur d’assainissement-application à la conception de points de mesures. Ph.D. Thesis, University of Caen, Caen, France (2006)
Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. In: Bauer, F.L. (eds.) Linear algebra, HDBKAUCO, vol. 2, pp. 134–151. Springer, Heidelberg (1971). https://doi.org/10.1007/978-3-662-39778-7_10
Ebtehaj, I., Bonakdari, H., Zaji, A.H., Azimi, H., Sharifi, A.: Gene expression programming to predict the discharge coefficient in rectangular side weirs. Appl. Soft Comput. 35, 618–628 (2015)
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Bonakdari, H., Azimi, H., Ebtehaj, I., Gharabaghi, B., Jamali, A., Talesh, S.H.A. (2022). Estimation of Velocity Field in Narrow Open Channels by a Hybrid Metaheuristic ANFIS Network. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-10461-9_1
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