This paper describes the application of computational fluid dynamics rather than a towing tank test for the prediction of hydrodynamic derivatives using a RANS-based solver. Virtual captive model tests are conducted, including an oblique towing test and circular motion test for a bare model scale KVLCC2 hull, to obtain linear and nonlinear hydrodynamic derivatives in the 3rd-order MMG model. A static drift test is used in a convergence study to verify the numerical accuracy. The computed hydrodynamic forces and derivatives are compared with the available captive model test data, showing good agreement overall. Simulations of standard turning and zigzag manoeuvres are carried out with the computed hydrodynamic derivatives and are compared with available experimental data. The results show an acceptable level of prediction accuracy, indicating that the proposed method is capable of predicting manoeuvring motions.

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.

The stability of navigation in waves is crucial for ships, and the effect of the waves on navigation stability is complicated. Hence, the LSTM neural network technique is applied to predict the course changing of a ship in different wave conditions, where K-means clustering analysis is used for the category of the ship’s navigation data to improve the quality of the database. In this paper, the effect of the initial database obtained by the K-means clustering analysis on prediction accuracy is studied first. Then, different input features are used to establish the database to train the neural network, and the influence of the database by different input features on the accuracy of the navigation prediction is discussed and analyzed. Finally, multi-task learning is used to make the neural network better predict the navigation in various wave conditions. Using the improved neural network model, the course of an autopilot ship in waves is predicted, and the results show that the current database and the neural network model are accurate enough for the course prediction of the autopilot ship in waves.

Based on open-source code OpenFOAM, the turn and zigzag maneuvers of the trimaran with hull attitude free and fixed are simulated at different Froude numbers in calm water. By the simulation results, the effect of hull attitude on the maneuver characteristics of the trimaran is analyzed and discussed. Two trimaran models are used in this paper for simulation. First, the grid uncertainty test about the turn and zigzag maneuver in calm water is carried out, and the computed results of turn and zigzag maneuvers in calm water are compared with the experimental results to validate the numerical method. Then, the simulation result with free hull attitude is compared with that with fixed hull attitude. The result demonstrates that the hull attitude has significant effect on the maneuverability of the trimaran, especially when the Froude number gets larger and the longitudinal position of the side hulls is in the middle of the center hull.