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1 International Journal of Applied Mechanics and Engineering

1 2011

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ANAFIS and neural network for modeling and prediction of ship squat in shallow waters: a new approach

Zeraatgar H., Akbari Vakilabadi K., Salmalian K., Haghi A. K.

International Journal of Applied Mechanics and Engineering

2011

Vol. 16, no 1

211-231

The reduction of the distance between ship floor and seabed, while the ship is moving forward, is called squat. In this research, squat is determined for vessels with Series-60 hull forms in various depths by experimental methods and then different numerical methods are employed for squat modeling. For this reason, a set of facilities for testing the ship movement in shallow waters is prepared. A series of models of the vessel is manufactured and many tests are carried out. The aim of the present study is to demonstrate the usefulness of an adaptive-network-based fuzzy inference system (ANFIS) for modeling and predicting the squat parameter for ships in shallow waters. It is also shown how dimensionless squat (S*) varies with the variation of important parameters, namely: block coefficient (CB), dimensionless distance between the seabed and ship floor […] and hydraulic Froude Number (Fnh). The results obtained through the ANFIS are also compared with those of a multiple linear regression and GMDH-type neural network with multi-layered feed forward back propagation algorithm. The results show that the ANFIS-based squat has higher predictability function than other numerical methods.