Performance prediction of hydrofoil- supported catamarans using experiment and ANNs

Using hydrofoils between two hulls of catamarans, hydrofoil supported catamaran (HYSUCAT), is one of the best way to improve the hydrodynamic characteristics of this vessels. In the present study, the hydrodynamic performance of three different hydrofoils of NACA 16, EPPLER 874 and Gottingen 11k are evaluated initially and experimentally via model tests. Afterward, the hydrodynamic performance of these hydrofoils is predicted by using appropriate artificial neural networks (ANNs). For this purpose, the total resistance, effective power, sinkage and trim of HYSUCAT is predicted under different Froude number (Fr) and hydrofoil type. According to the results achieved from the model tests, a significant decrease in total resistance and trim is observed using hydrofoils in the considered catamaran, where Gottingen 11k shows more effects on hydrodynamic performance of HYSUCAT compared to the other two hydrofoils. In addition, maximum mean square errors (MSE) of ANNs output in prediction of total resistance, effective power, sinkage and trim is achieved 0.000683, 0.000155, 0.000454 and 0.00688, respectively. Moreover, an equation is proposed to predict the hydrodynamic performance of the HYSUCAT using ANNs weights and bias.