Turbulent Flow Modeling at Tunnel Spillway Concave Bends and Prediction of Pressure using Artificial Neural Network

Document Type : Research Paper

Authors

Department of Civil Engineering , Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

Abstract

A tunnel spillway is one of the spillway types in which a high free surface flow velocity is established. The pressure increases in concave vertical bends due to the rotational acceleration and the nature of irregularities in the turbulent flow. Physical models are the best tools to analyze this phenomenon. The number of the required physical models to cover all practical prototype condition analysis is so large that makes it impractical in terms of placement and costs. Therefore, the FLOW-3D software has been chosen to analyze and produce a database of turbulent flow in tunnels concave bends covering all possible practical alternatives. Various tunnels with different discharges and geometries have been simulated by this software. The numerical results were verified with the experimental ones of the constructed physical model of Alborz Dam tunnel spillway, and a satisfactory agreement was obtained. Dimensional analysis is used to group the involved variables of the problem into dimensionless parameters. These parameters are utilized in the artificial neural network simulation. The results showed a correlation coefficient R2=0.95 between the dimensionless parameters obtained by the Flow-3D software and those predicted by the neaural network which leads to the conclusion that the artificial neural network based on the database obtained by the turbulent flow modeling in this regard is a powerful tool for pressure prediction.

Keywords


  1. Kim, D. G., & Park, J. H. (2005). Analysis of flow structure over ogee-spillway in consideration of scale and roughness effects by using CFD model. KSCE Journal of Civil Engineering, 9(2), 161-169.
  2. Sabbagh-Yazdi, S. R., Rostami, F., & Mastorakis, N. E. (2008, March). Simulation of self-aeration at steep chute spillway flow using VOF technique in a 3D finite volume software. In Am. Conf. on Appl. Maths. Harvard, Mass, 24-28.
  3. Nohani, E. (2015). Numerical simulation of the flow pattern on morning glory spillways. International Journal of Life Sciences, 9(4): 28-31.
  4. Parsaie, A., Dehdar-Behbahani, S., & Haghiabi, A. H. (2016). Numerical modeling of cavitation on spillway’s flip bucket. Frontiers of Structural and Civil Engineering, 10(4), 438-444.
  5. Teuber, K., Broecker, T., Bay´on, A., N¨utzmann, G. and Hinkelmann, R. (2019) ‘CFD-modelling of free surface flows in closed conduits’, Progress in Computational Fluid Dynamics, 19(6), 368–380.
  6. Ghazanfari-Hashemi, R.S., Namin, M.M., Ghaeini-Hessaroeyeh, M. and Fadaei-Kermani, E., 2020. A Numerical Study on Three-Dimensionality and Turbulence in Supercritical Bend Flow. International Journal of Civil Engineering, 18(3), 381-391.
  7. Sha, H. F., Wu, S. Q., & Zhou, H. (2009). Flow characteristics in a circular-section bend of high head spillway tunnel. Advances in Water Science, (6), 14.
  8. Liu, Z., Zhang, D., Zhang, H., & Wu, Y. (2011). Hydraulic characteristics of converse curvature section and aerator in high-head and large discharge spillway tunnel. Science China Technological Sciences, 54(1), 33-39.
  9. Zheng, Q. W., Luo, S. J., & Zhang, F. X. (2012). The Effect of Concave Types on the Hydraulic Characteristics in Spillway Tunnels with High-Speed Velocity. China Rural Water and Hydropower, 4.
  10. Hongmin, G. U. O., Jiang, L. I., Shan, Q. I. N., & Yang, X. I. E. (2014). Three-Dimensional Numerical Simulation on Spillway Tunnel of Pankou Hydropower Station. Water Resources and Power, (4), 22.
  11. Wan, W., Liu, B., & Raza, A. (2018). Numerical Prediction and Risk Analysis of Hydraulic Cavitation Damage in a High-Speed-Flow Spillway. Shock and Vibration, 2018.
  12. Wei, W., Deng, J. and Xu, W. (2020). Numerical investigation of air demand by the free surface tunnel flows. Journal of Hydraulic Research, 1-8.
  13. Xu, W., Dang, Y., Li, G., Shao, J. and Chen, G. (2007) 'Three-dimensional numerical simulation of the bi-tunnel spillway flow [J] ', Journal of Hydroelectric Engineering, 1, 56-60.
  14. Huang, H.Y., Gong, A.M., Qiu, Y. and Wangliang, Z.A. (2015) ' 3D Numerical Simulation and Experimental Analysis of Spillway Tunnel' In Applied Mechanics and Materials. Trans Tech Publications Ltd. 723, 171-175.
  15. Li, S., Zhang, J. M., Xu, W. L., Chen, J. G., Peng, Y., Li, J. N., & He, X. L. (2016). Simulation and experiments of aerated flow in curve-connective tunnel with high head and large discharge. International Journal of Civil Engineering, 14(1), 23-33.
  16. Shilpakar, R., Hua, Z., Manandhar, B., Shrestha, N., Zafar, M. R., Iqbal, T., & Hussain, Z. (2017, August). Numerical simulation on tunnel spillway of Jingping-I hydropower project with four aerators. In IOP Conference Series: Earth and Environmental Science. 82, 012013.
  17. Song, C. C., & Zhou, F. (1999). Simulation of free surface flow over spillway. Journal of Hydraulic Engineering, 125(9), 959-967.
  18. Fais, L.M.C.F., Filho, J.G.D., Genovez, A.I.B. (2015). Geometry influence and discharge curve correction in morning glory spillways. Proceedings of the 36th IAHR World Congress.
  19. Falvey, H. T. (1990). Cavitation in chutes and spillways. Denver: US Department of the Interior, Bureau of Reclamation. 49-57.
  20. Chaudhry, M. H. (2007). Open-channel flow. Springer Science & Business Media.
  21. Novak, P., Moffat, A. I. B., Nalluri, C., & Narayanan, R. (2007). Hydraulic structures. Fourth Edition, Taylor & Francis, New York , 246–265.
  22. Jorabloo, M., Maghsoodi, R., Sarkardeh, H., & Branch, G. (2011). 3D simulation of flow over flip buckets at dams. Journal of American Science, 7(6), 931-936.
  23. Khani, S., Moghadam, M. A., & Nikookar, M. (2017). Pressure Fluctuations Investigation on the Curve of Flip Buckets Using Analytical and Numerical Methods. Vol. 03(04), 165-171.
  24. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
  25. Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
  26. Wu,C.L. Huang, B. Xie, C.B. (2008) . Comparison of calculation methods for irrigation district water inlet, China Rural Water and Hydropower ,5 (71) ,74–77.
  27. Qiu,J. Huang, B.S. . Lai, G.W. (2002). Research and application of discharge coefficient of wide crest weir, China Rural Water and Hydropower ,9 ,41–42.
  28. Xiang, H.Q .Ba,D.D. Liu, J.J. (2012) . Acquiring of curved practical weir flow coefficient by curve-fitting based on Matlab, Hydropower Energy Sci. 3 ,97–99.
  29. Ye,Y.T. He,J.J.(2013).Experimental study on hydraulic calculation of discharge under plane gate on broad-crested weir, J. Water Resour. Archit. Eng. 11 (2), 138–141.
  30. Salmasi, F., Yıldırım, G., Masoodi, A., & Parsamehr, P. (2013). Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques. Arabian Journal of Geosciences, 6(7), 2709-2717.
  31. Noori, R.; Hooshyaripor, F. (2014). Effective prediction of scour downstream of ski-jump buckets using artificial neural networks. Water Resour. 41, 8–18.
  32. Flow-Science. (2014). FLOW-3D user manual. version11. In: Flow Science Santa Fe, NM.
  33. Yakhot, V. S. A. S. T. B. C. G., Orszag, S. A., Thangam, S., Gatski, T. B., & Speziale, C. G. (1992). Development of turbulence models for shear flows by a double expansion technique. Physics of Fluids A: Fluid Dynamics, 4(7), 1510-1520.
  34. Report on the hydraulic model of Alborz dam reservoir. (2001). Iran Water Research Institute
  35. Lippman, R. (1987). An introduction to computing with neural nets. IEEE Assp magazine, 4(2), pp.4-22.
  36. Baylar, A., Ozgur, K.I.S.I. and Emiroglu, M.E. (2009). Modeling air entrainment rate and aeration efficiency of weirs using ANN approach. Gazi University Journal of Science, 22(2), 107-116.
  37. Maureen, C. and Caudill, M. (1989). Neural network primer: Part I. AI Expert, 2(12), p.1987.