In this work, we have developed a data-driven artificial intelligence (AI) solution to assist the ship hull design process. Specifically, we have developed and implemented an AI-based multiple-input neural network model to realize the real-time prediction of the total resistance of the ship hull structure while avoiding the inconsistent estimates from different types of design input parameters. It is demonstrated that the developed AI-based machine learning algorithm as a prediction tool can assist the ship hull design process by accurately providing the total resistance of ship hulls in real time. Moreover, we have conducted design tasks to validate the proposed method, and the validation results show that a well-trained artificial neural network model can avoid the problem of different sensitivities due to the different degrees of influence of the input parameters on the output parameter. The proposed AI-based data-driven solution provides a real-time hydrodynamic performance calculation, which can predict the hyperdynamic performances of ship hulls based on their geometry modification parameters. This approach gives a consistent prediction in terms of accuracy when facing different geometry modification parameters, and it in turn provides a fast and accurate AI-based method to assist ship hull design to achieve an optimum forecast accuracy in the entire design space, making an advance to artificial intelligence assist design in naval architecture engineering.

Sailing in oblique stern waves causes a ship to make sharp turns and uncontrollable course deviation, which is accompanied by a large heel and sometimes leads to capsizing. Studying the control algorithm in oblique stern waves is imperative because an excellent controller scheme can improve the ship’s course-keeping stability. This paper uses the Maneuvering Modelling Group (MMG) method based on hydrodynamic derivatives and the Computational Fluid Dynamics (CFD)-based self-navigation simulation to simulate ship navigation in waves. This study examines the effect of proportion-integral-derivative (PID) controller schemes on the stability of course maintenance based on hydrodynamic derivatives and 3DOF MMG methods. Then, the optimized PID control parameters are used to simulate the ship’s 6DOF self-propulsion navigation in oblique waves using the CFD method. The nonlinear phenomena during the process, such as side-hull emergency, slamming, and green water, are considered. This study found that the range of the control bandwidth should be optimized based on the ship's heading and wave parameters.