Resistance is one of the important performance indicators of ships. In this paper, a prediction method based on the Radial Basis Function neural network (RBFNN) is proposed to predict the resistance of a 13500 transmission extension unit (13500TEU) container ship at different drafts. The predicted draft state in the known range is called interpolation prediction; otherwise, it is extrapolation prediction. First, ship features are extracted to make the resistance R_t prediction. The resistance prediction results show that the performance of the RBFNN is significantly better than the other four machine learning models, backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Then, the ship data is processed in a dimensionless manner, and the models mentioned above are used to predict the total resistance coefficient C_t of the container ship. The prediction results show that the RBFNN prediction model still performs well. Good results can be obtained by RBFNN in interpolation prediction, even when using part of dimensionless features. Finally, the accuracy of the prediction method based on RBFNN is greatly improved compared with the modified admiralty coefficient.

Resistance serves as a critical performance metric for ships. Swift and accurate resistance prediction can enhance ship design efficiency. Currently, methods for determining ship resistance encompass model tests, estimation techniques, and computational fluid dynamics (CFDs) simulations. There is a need to improve the prediction speed or accuracy of these methods. Machine learning is gradually emerging as a method applied in the field of ship research. This study aims to investigate ship resistance prediction methods utilizing machine learning across various datasets. This study proposes two methods: employing stacking ensemble learning to enhance resistance prediction accuracy with identical ship samples and utilizing various ship resistance prediction models for accurate resistance prediction through transfer learning. Initially focusing on container ships as the research subject, the stacking ensemble learning model outperforms the basic machine learning model, the Holtrop and Mennen method, and the updated Guldhammer and Harvald method based on comparative prediction results. Subsequently, the container ship resistance prediction model achieves precise resistance prediction for bulk carriers. This study offers dependable guidance for applying machine learning in predicting ship hydrodynamic performance.

In this paper, the ditching performance of a seaplane model on calm water and a uniform water current coupled with wind was numerically investigated. The overset grid technique was applied to treat the large amplitude of the body motions of the seaplane without leading to mesh distortion. The effects of the initial velocity and the initial pitch angle on the slamming loads and motion responses were investigated for the seaplane’s ditching on calm water. A good agreement with the experimental data on the velocity and angle was obtained. Besides ditching on calm water without the water current and wind, three more-complicated conditions were adopted, including the seaplane’s ditching on calm water with wind, a water current without wind, and a water current coupled with wind. The accelerations and impact pressures of the seaplane can be influenced by the wind or current. Water splashing and overwashing could be observed during the water entry process, with water overtopping the seaplane head or nose and flowing over the body surface. It can be concluded that the relative motion between the water and the seaplane model should be carefully controlled to avoid possible damages caused by the occurrence of overwashing.