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.

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.

Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea.

The fall pipe rock dumping technique is extensively employed to create protection embankments around submarine cables, mitigating distortion and breakage resulting from bottom scouring. During the rock dumping operation, intricate interactions among the pipeline, rocks, and water currents can affect the stability and efficiency of the fall pipe system. This research proposed a method employing the fluid–structure interaction to analyze the interactions between the pipeline, rocks, and water currents. The paper begins with the design of an innovative fall pipe rock dumping system and presents a theoretical analysis of the applied model testing approach. The simulation parameters were determined according to the geometric, Froude, and Strouhal similarity criteria. A thorough numerical analysis was performed to investigate the hydrodynamic properties of the rockfall pipeline under fluid–structure interaction. The research examined the settling of rocks during rockfall, along with the forces and movements associated with the deposition process. The results show that the rockfall pipeline experienced vortex-induced vibrations (VIVs) caused by ocean currents during operation. The maximum settling velocity of the rocks throughout the rockfall process reached 2.2 m/s, with a final stable velocity of 1.5 m/s. These simulation results offer critical insights for improving the design and functionality of the rockfall pipeline, thereby enhancing the protection of underwater infrastructure.

Accurate prediction of drifting trajectories is essential for improving the operational efficiency of maritime search and rescue (SAR), particularly within the complex geomorphological settings of island and reef regions, such as those in the South China Sea. This study investigates the drift characteristics of life rafts under varying loading conditions across both open-sea and island–reef regions. Comprehensive field experiments were conducted over 15 days in the waters around the Wanshan Archipelago, using advanced instruments to collect wind, current, and drift trajectory data. Based on these observations, two models—the AP98 leeway model and a BP neural network model—were developed and validated. The results show that the AP98 model performs better in open-sea conditions, whereas the BP neural network provides more accurate predictions in island and reef areas with complex environmental factors. A Monte Carlo simulation was also integrated to enhance the robustness of drift area predictions. These findings offer valuable insights into life raft drift behavior in complex marine environments and provide technical support for improving SAR operations in island–reef regions.