Research on deep learning method in ship hull form optimization

Waves will cause added resistance and alter the course for a ship. Therefore, this paper presents a multi-objective optimization for ship design in nolinear waves using the Deep Belief Network (DBN) method. The optimization is performed in nolinear waves for the DTMB5512 ship with three variables. The MOPSO is selected as the optimal algorithm. To improve calculation efficiency of the CFD based optimization, a DBN method is proposed to approximate the total resistance coefficient Ctw and heave transfer function TF3 and pitch transfer function TF5. The optimization results show that the optimization method in this study can optimize the DTMB5512 ship with a reduction in its total resistance and heave and pitch transfer functions. The optimization objective values were reduced about 4.05% and 0.63% and 1.01%, respectively.