Artificial neural network ability in evaluation of random wave-induced inline force on a vertical cylinder |
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| Authors: | M A Lotfollahi-Yaghin A Pourtaghi B Sanaaty A Lotfollahi-Yaghin |
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| Institution: | Department of Naval Architecture and Ocean Engineering, Osaka University, Japan |
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| 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. |
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| Keywords: | neural network random waves Morison equation hydrodynamics inline force |
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