Planning craft, whose weight is predominantly supported by hydrodynamic lift, has the advantages of lower resistance and faster speed. It is widely used in various scenarios such as entertainment, rescue and military applications. The amplitudes of the pitch and heave in the sliding state during the sailing of the planning craft are higher than those of the traditional displacement ships, especially, periodic exit and entry similar as jumping phenomena will occur when the planning craft travels in waves. Compared with traditional displacement ships, the planning craft has larger amplitudes of the pitch and heave when it travels at the sliding state, especially, periodic exit and entry similar as jumping phenomena will occur when it travels in waves. The resistance, propulsion and seakeeping performance of a deep-V planning craft (generic prismatic planning hull, GPPH) are numerically simulated by using a self-developed dynamic overset grid technology combined with the open-source CFD library OpenFOAM. The comparison between the numerical calculation results and the model test results shows that the dynamic overset grid technology can better capture the large pitch and heave of the planning craft. The periodic exit and entry of the hull generate large periodic slamming pressure on the bottom of the ship, and the numerical calculations can also provide reasonable predictions of the pressure.

The accurate prediction of the added resistance in waves has an important impact on the power correction of the full-scale ship, and it needs to be properly eliminated to accurately evaluate whether the speed at the agreed power meets the requirements. The MEPC 79 meeting held in December 2022 officially recognized the ITTC 2021 version of the “Preparation, Conduct and Analysis of Speed/Power Trials” as a statutory procedure, and the SNNM method for the prediction of the added resistance of ships in waves of arbitrary heading was introduced to fill the gap of current prediction methods. The influences of the period, wave direction, wave spectrum and the shape of RAO curve on the SNNM method have been examined in detail for the power correction in sea trials.

Ship unsinkability is an important performance to evaluate the ship survivability, and it is also a key index for optimizing the subdivision strategy. However, the high cost is still a difficulty restricting the practical application of the unsinkability optimization. More effective methods will be provided with the continuous application of machine learning technology. The optimization of unsinkability subdivision is solved by using the particle swarm optimization (PSO) algorithm based on reinforcement learning. And the optimization system development and interface design is implemented for the integrated machine learning module and unsinkability design module. The effect of different parameter settings in the algorithm on the optimization efficiency is also discussed. The analysis of the optimal solution shows that this method can effectively find out the optimal subdivision scheme. It can provide a basis for formulating scientific subdivision strategies.