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Hydrodynamic inline force prediction on vertical cylinders: a comparative study of neural network and its adaptive wavelets (wavenets)

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Abstract

The design of the neural network model and its adaptive wavelets (wavelet networks and wavenets) was used to estimate the wave-induced hydrodynamic inline force acting on a vertical cylinder. The data used to calibrate and validate the models were obtained from an experiment. In the brain, wavelet neural networks (WNNs) use wavelets to activate their hidden layers of neurons. In WNNs, both the position and dilation of the wavelets are optimized along with the weights. In one special approach to this kind of network construction, the position and dilation of the wavelets are fixed and only the weights of the network are optimized. In the present study, the neural network procedure and the above mentioned approach were employed to design a WNN, a so-called wavenet, using feed-forward neural network topology and its training method. Then, a comparison of these two methods was made. Numerical results demonstrate that both networks are capable of predicting hydrodynamic inline force. Furthermore, the combination of the neural network concept and the wavelet theory i.e. wavenet provides a more robust tool rather than standard feed-forward neural network, considering its more appropriate ability to predict any other data which the network had not experienced before. The results of this study can contribute to reducing the errors in future efforts to predict hydrodynamic inline force using WNNs, and thus improve the reliability of that prediction in comparison to the ANN and other methods. Therefore, this method can be applied to relevant engineering projects with satisfactory results.

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References

  1. Koterayama W, Matsumoto N (1982) Experimental study of hydrodynamic forces and wave forces exerted on a truss. Ocean Eng 9(3):221–234

    Article  Google Scholar 

  2. Cook GR, Simiu E (1989) Hydrodynamic forces on vertical cylinders and the light hill correction. Ocean Eng 16(4):355–372

    Article  Google Scholar 

  3. Chakrabarti SK, Cotter DC (1984) Hydrodynamic coefficients of a mooring tower. J Energy Resource Technol 106:449–458

    Google Scholar 

  4. Nakamura S, Saito K, Takagi M (1986) On the increased damping of a moored body during low-frequency motions in waves. 5th international OMAE symposium, Tokyo

  5. Koterayama W, Nakamura M (1988) Hydrodynamic forces acting on a vertical circular cylinder oscillating with a very low frequency in waves. Ocean Eng 15(3):271–287

    Article  Google Scholar 

  6. Morison JR, O’Brien MP, Johnson JW, Schaff SA (1950) The force on a submerged cylinder near a plane boundary. J Trans Am Inst Min Eng 189:149–154

    Google Scholar 

  7. Morison JR, Johnson JW, O’Brien M (1953) Experimental studies on wave force on piles. In: Proceedings of the fourth conference on coastal engineering. ASCE, New York

  8. Mac Camy RC, Fuchs RA (1954) Wave force on piles: a diffraction theory. Tech. Memo. 69, US Army Corps of Engineers, Beach Erosion Board

  9. Sarpkaya T (1976) Forces on cylinders near a plane boundary in a sinusoidally oscillating fluid. J Fluids Eng 98:499–505

    Article  Google Scholar 

  10. Sundaravadivelu R, Sundar V, Rao TS (1997) Technical note: wave forces and moments on an intake well. Ocean Eng 26:363–380

    Article  Google Scholar 

  11. Armenio V (1998) Dynamic loads on submerged bodies in a viscous numerical wave tank at small KC numbers. Ocean Eng 25(10):881–905

    Article  Google Scholar 

  12. Zhu G, Borthwick AGL, Taylor ER (2001) A finite element model of interaction between viscous free surface waves and submerged cylinders. J Ocean Eng 28:989–1008

    Article  Google Scholar 

  13. Zou J-F, Huang Y-O, Yin X-Y, Ren AL (2002) Simulation of gravity currents using VOF model. J China Ocean Eng 16:525–536

    Google Scholar 

  14. Zhao M, Teng B, Tan L (2004) A finite element solution of wave force on submerged horizontal circular cylinder. J China Ocean Eng 18:335–346

    Google Scholar 

  15. Deo MC, Naidu CS (1999) Real time wave forecasting using neural networks. Ocean Eng 26:191–203

    Article  Google Scholar 

  16. Agrawal JD, Deo MC (2002) On-line wave prediction. Mar Struct 15:57–74

    Article  Google Scholar 

  17. Makarynskyy O (2004) Improving wave predictions with artificial neural networks. Ocean Eng 31:709–724

    Article  Google Scholar 

  18. Makarynskyy O, Pires-Silva AA, Makarynskyy D, Ventura-Soares C (2005) Artificial neural networks in wave predictions at the west coast of Portugal. Comput Geosci 31:415–424

    Article  Google Scholar 

  19. Kazeminezhad MH, Etemad-Shahidi A, Mousavi SJ (2005) Application of fuzzy inference system in the prediction wave parameters. Ocean Eng 32:1709–1725

    Article  Google Scholar 

  20. Mahjoobi J, Etemad-Shahidi A, Kazeminezhad MH (2008) Hindcasting of wave parameters using different soft computing methods. Appl Ocean Res 30:28–36

    Article  Google Scholar 

  21. Gaur S, Deo MC (2008) Real-time wave forecasting using genetic programming. Ocean Eng 35:1166–1172

    Article  Google Scholar 

  22. Etemad-Shahidi A, Mahjoobi J (2009) Comparison between M5-model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Eng 36:1175–1181

    Article  Google Scholar 

  23. Jain P, Deo MC (2006) Neural networks in ocean engineering. Int J Ships Offshore Struct 1:25–35

    Article  Google Scholar 

  24. More A, Deo MC (2003) Forecasting wind with neural networks. Mar Struct 16:35–49

    Article  Google Scholar 

  25. Lotfollahi-Yaghin MA, Sanaaty B (2008) A new method in determining random wave-induced inline force. World Appl Sci J 3(4):674–683

    Google Scholar 

  26. Lotfollahi-Yaghin MA, Pourtaghi A, Sanaaty B, Lotfollahi-Yaghin A (2012) Artificial neural network ability in evaluation of random wave induced inline force on a vertical cylinder. China Ocean Eng 26(1):19–36

    Article  Google Scholar 

  27. Chakrabarti SK, Cotter DC (1984) Hydrodynamic coefficients of a mooring tower. J Energy Resource Technol 106:449–458

    Google Scholar 

  28. Mackwood PR (1993) Wave and current flows around circular cylinders at large scale. LIP Project 10D

  29. Wolfram J, Naghipour M (1999) On the estimation of Morison force coefficients and their predictive accuracy for very rough circular cylinders. Appl Ocean Res 21:311–328

    Article  Google Scholar 

  30. Chakrabarti SK (1981) Hydrodynamic coefficients for a vertical tube in an array. Appl Ocean Res 3(1):2–12

    Article  Google Scholar 

  31. Chakrabarti SK (1987) Hydrodynamics of offshore structures. Computational Mechanics Publications, New York

    Google Scholar 

  32. Isaacson M, (1979) Nonlinear Inertia Forces on Bodies Journal of the Waterway Port Coastal and Ocean Division, Vol. 105, No. 3, pp. 213-227

  33. Chaplin JR (1984) Nonlinear forces on a horizontal cylinder beneath waves. J Fluid Mech 147:449–464

    Article  Google Scholar 

  34. Sarpkaya T, Isaacson M (1981) Mechanics of wave forces on offshore structures. Van Nostrand Reinhold, New York. ISBN 0442254024

    Google Scholar 

  35. Sumer BM, Fredsoe J (2006) Hydrodynamics around cylindrical structures. Advanced Series on Ocean Engineering, vol 26 (revised ed.). World Scientific, ISBN 9812700390

  36. Kim C-Y, Bae G-J, Hong SW, Park CH, Moonb HK, Shin HS (2001) Neural network based prediction of ground surface settlements due to tunneling. Comput Geotech 28:517–547

    Article  Google Scholar 

  37. Kartam N, Flood I (1998) Artificial neural networks for civil engineers: fundamentals and applications. ASCE

  38. Rumelhart DE, Hinton GE, McClellend JL (1986) A general framework for parallel distribution processing parallel distribution processing, vol 1. MIT Press, Cambridge

    Google Scholar 

  39. Hertz J, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley, California

    Google Scholar 

  40. Lekutai G (1997) Adaptive self-tuning neuro wavelet network controllers, Blacksburg

  41. Thuillard M (2000) A review of wavelet networks, wavenet, fuzzy wavenets and their applications. ESIT, Aachen, pp 5–16

    Google Scholar 

  42. Oussar Y, Dreyfus G (2000) Initialization by selection for wavelet network training. Neurocomputing 34:131–143

    Article  MATH  Google Scholar 

  43. Gholizadeh S, Salajegheh E, Torkzadeh P (2008) Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network, J Sound Vibr, 312, 316–331

    Google Scholar 

  44. Tsai RT, Leu WM, Chen LY, Hu NT (2002) A reversibly dissociable ternary complex formed by XpsL, XpsM and XpsN of the Xanthomonas campestris pv. campestris type II secretion apparatus. Biochem J 367:865–871

    Article  Google Scholar 

  45. Philippe DW (1997) Neural network models. Springer, London

    MATH  Google Scholar 

  46. Hecht-Nielson R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international joint conference on neural networks, vol 3, New York, pp 11–14

  47. Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resource Res 30(2):457–481

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    A. Pourtaghi & M. A. Lotfollahi-Yaghin

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  1. A. Pourtaghi
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  2. M. A. Lotfollahi-Yaghin
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Correspondence to A. Pourtaghi.

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Pourtaghi, A., Lotfollahi-Yaghin, M.A. Hydrodynamic inline force prediction on vertical cylinders: a comparative study of neural network and its adaptive wavelets (wavenets). J Mar Sci Technol 18, 418–434 (2013). https://doi.org/10.1007/s00773-013-0218-1

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  • DOI: https://doi.org/10.1007/s00773-013-0218-1

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