Abstract
An approach based on artificial neural network (ANN) is used to develop predictive relations between hydrodynamic inline force on a vertical cylinder and some effective parameters. The data used to calibrate and validate the ANN models are obtained from an experiment. Multilayer feed-forward neural networks that are trained with the back-propagation algorithm are constructed by use of three design parameters (i.e. wave surface height, horizontal and vertical velocities) as network inputs and the ultimate inline force as the only output. A sensitivity analysis is conducted on the ANN models to investigate the generalization ability (robustness) of the developed models, and predictions from the ANN models are compared to those obtained from Morison equation which is usually used to determine inline force as a computational method. With the existing data, it is found that least square method (LSM) gives less error in determining drag and inertia coefficients of Morison equation. With regard to the predicted results agreeing with calculations achieved from Morison equation that used LSM method, neural network has high efficiency considering its convenience, simplicity and promptitude. The outcome of this study can contribute to reducing the errors in predicting hydrodynamic inline force by use of ANN and to improve the reliability of that in comparison with the more practical state of Morison equation. Therefore, this method can be applied to relevant engineering projects with satisfactory results.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agnieszka, H., Ralf, K. and Hanz, D. N., 2009. Wind-wave variability in a shallow tidal sea-spectral modeling combined with neural network methods, Coast. Eng., 56(7): 759–772.
Agrawal, J. D. and Deo, M. C., 2002. On-line wave prediction, Mar. Struct., 15(1): 57–74.
Chakrabarti, S. K., 1981. Hydrodynamic coefficients for a vertical tube in an array, Appl. Ocean Res., 3(1): 2–12.
Chakrabarti, S. K., 1982. Inline and transverse forces on a tube array in tandem with waves, Appl. Ocean Res., 4(1): 25–32.
Chakrabarti, S. K., 1987. Hydrodynamics of Offshore Structures, Computational Mechanics Publications, New York.
Chakrabarti, S. K., 2001. Hydrodynamics of Offshore Structures, Computational Mechanics Publications, New York.
Chakrabarti, S. K., Wolbert, A. L. and Tam, W. A., 1977. Wave force on inclined tubes, Coast. Eng., 1, 149–165.
Chakrabarti, S. K. and Cotter, D. C., 1984. Hydrodynamic coefficients of a mooring tower, J. Energy Resour. Technol., 106(2): 183–190.
Chaplin, J. R., 1984. Nonlinear forces on a horizontal cylinder beneath waves, J. Fluid Mech., 147, 449–464.
Cook, G. R. and Simiu, E., 1989. Hydrodynamic forces on vertical cylinders and the light hill correction, Ocean Eng., 16(4): 355–372.
Deo, M. C. and Naidu, C. S., 1999. Real time wave forecasting using neural network, Ocean Eng., 26(3): 191–203.
Etemad-Shahidi, A., and Mahjoobi, J., 2009. Comparison between M5-model tree and neural networks for prediction of significant wave height in Lake Superio, Ocean Eng., 36(15–16): 1175–1181.
Fausett, L., 1994. Fundamentals of Neural Networks Architectures, Algorithms and Applications, Prentice Hall, United States.
Fenton, J. D., 1985. A fifth-order stokes theory for steady waves, J. Waterw. Port Coast. Ocean Eng., ASCE, 111(2): 216–234.
Gaur, S. and Deo, M. C., 2008. Real-time wave forecasting using genetic programming, Ocean Eng., 35(11–12): 1166–1172.
Haykin, S., 1994. Neural Networks, Macmillan College Publishing Company, Inc. United States.
Isaacson, M., 1979. Nonlinear Inertia Forces on Bodies, J. Waterw. Port Coast. Ocean Div., ASCE, 105(3): 213–227.
Jain, P. and Deo, M. C., 2006. Neural networks in ocean engineering, Int. J. Ships Offshore Struct., 1(1): 25–35.
Kazeminezhad, M. H., Etemad-Shahidi, A. and Mousavi, S. J., 2005. Application of fuzzy inference system in the prediction wave parameters, Ocean Eng., 32(14–15): 1709–1725.
Koterayama, W. and Matsumoto, N., 1982. Experimental study of hydrodynamic forces and wave forces exerted on a truss, Ocean Eng., 9(3): 221–234.
Koterayama, W. and 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.
Li, Y. C. and Ye, Z. F., 1990. Random wave and current force on vertical cylinder, Appl. Ocean Res., 12(3): 126–133.
Lotfollahi-Yaghin, M. A. and Sanaaty, B., 2008. A new method in determining random wave-induced inline force, World Applied Sciences Journal, 3(4): 674–683.
Liu, S. X, Li, Y. C. and Li, G. W, 2007. Wave current forces on the pile group of base foundation for the east sea bridge, Journal of Hydrodynamics, Ser. B, 19(6): 661–670.
Mahjoobi, J., Etemad-Shahidi, A. and Kazeminezhad, M. H., 2008. Hindcasting of wave parameters using different soft computing methods, Appl. Ocean Res., 30(1): 28–36.
Makarynskyy, O., 2004. Improvingwave predictionswith artificial neural network, Ocean Eng., 31(5–6): 709–724.
Makarynskyy, O., Pires-Silva, A. A., Makarynskyy, D. and Ventura-Soares, C., 2005. Artificial neural networks in wave predictions at the west coast of Portugal, Computers and Geosciences, 31(4): 415–424.
More, A. and Deo, M. C., 2003. Forecasting wind with neural networks, Mar. Struct., 16(1): 35–49.
Morison, J. R., O’Brien, M. P., Johnson, J. W. and Schaff, S. A., 1950. The force on a submerged cylinder near a plane boundary, Transactions of the American Institute of Mining Engineers, 189, 149–154.
Mackwood, P. R., 1993. Wave and Current Flows around Circular Cylinders at Large Scale, LIP Project 10D.
van Gent, M., R. A., van den Boogaard, H., F. P., Pozueta, B. and Medina, J. R., 2007. Neural network modeling of wave overtopping at coastal structures, Coast. Eng., 54(8): 586–593.
Naghipour, M., 1996. The Accuracy of Hydrodynamic Force Prediction for Offshore Structures and Morison’s Equation, Ph.D Thesis, Heriot Watt University.
Nakamura, S., Saito, K. and Takagi, M., 1986. On the increased damping of a moored body during low-frequency motions in waves, J. Offshore Mech. Arct. Eng., 110(4): 348–354.
Patterson, D. W., 1996. Artificial Neural Network, Theory and Applications, Prentice Hall. Singapore.
Philippe, D. W., 1997. Neural Network Models, Springer-Verlag, London.
Rumelhart, D. E., Hinton, G. E. and Williams, R.J., 1986. Learning representations by back propagation errors, Nature, 323, 533–536.
Sarpkaya, T., 1976. Vortex shedding and resistance in harmonic flow about smooth and rough circular cylinders, Proceedings of the International Conference on the Behavior of Offshore Structures, BOSS ′76, 1, 220–235.
Sarpkaya, T., 1977. Vortex shedding and resistance in harmonic flow about smooth and rough cylinders at high Reynolds numbers, Monterey: Naval Postgraduate School, Report No. NPS-59SL76021.
Sarpkaya, T., 1986. Force on a circular cylinder in viscous oscillatory flow at low Keulegan-Carpenter numbers, J. Fluid Mech., 165, 61–71.
Sarpkaya, T. and Isaacson, M., 1981. Mechanics of Wave Forces on Offshore Structures, New York: Van Nostrand Reinhold, ISBN 0442254024.
Sumer, B. M. and Fredsoe, J., 2006. Hydrodynamics around cylindrical structures, Advanced Series on Ocean Engineering, 26 (revised ed.), World Scientific, ISBN 9812700390.
Tsai, C. P., Lin, C. and Shen, J., 2002. Neural network for wave forecasting among multi-stations, Ocean Eng., 29(13): 1683–1695.
Vengatesan, V., Varyani, K. S. and Barltrop, N., 2000. An experimental investigation of hydrodynamic coefficients for a vertical truncated rectangular cylinder due to regular and random waves, Ocean Eng., 27(3): 291–313.
Wasserman, P. D., 1989. Neural Computing, Theory and Practice, Van Nostrand Reinhold, United States.
Wolfram, J. and Naghipour, M., 1999. On the estimation of Morison force coefficients and their predictive accuracy for very rough circular cylinders, Appl. Ocean Res., 21(6): 311–328.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lotfollahi-Yaghin, M.A., Pourtaghi, A., Sanaaty, B. et al. Artificial neural network ability in evaluation of random wave-induced inline force on a vertical cylinder. China Ocean Eng 26, 19–36 (2012). https://doi.org/10.1007/s13344-012-0002-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13344-012-0002-8