In response to the complexity and time demands of conventional methods for estimating the hydrodynamic coefficients, this study aims to revolutionize ship maneuvering analysis by utilizing automatic identification system (AIS) data and the Support Vector Regression (SVR) algorithm. The AIS data were collected and processed to remove outliers and impute missing values. The rate of turn (ROT), speed over ground (SOG), course over ground (COG) and heading (HDG) in AIS data were used to calculate the rudder angle and ship velocity components, which were then used as training data for a regression model. The accuracy and efficiency of the algorithm were validated by comparing SVR-based estimated hydrodynamic coefficients and the original hydrodynamic coefficients of the Mariner class vessel. The validated SVR algorithm was then applied to estimate the hydrodynamic coefficients for real ships using AIS data. The turning circle test wassimulated from calculated hydrodynamic coefficients and compared with the AIS data. The research results demonstrate the effectiveness of the SVR model in accurately estimating the hydrodynamic coefficients from the AIS data. In conclusion, this study proposes the viability of employing SVR model and AIS data for accurately estimating the hydrodynamic coefficients. It offers a practical approach to ship maneuvering prediction and control in the maritime industry.

Investigating ship motion under real conditions is vital for evaluating the seakeeping performance, particularly in the design process stage. This study examined the influence of the principal particulars of a trawler on its seakeeping performance. The wave conditions in the Bering Sea are investigated using available data. The length-to-beam (L/B) and beam-to-draft (B/T) ratios of the ship are changed by 10% for the numerical simulation. The response amplitude operator (RAO) motion, root mean square (RMS) value and sensitivity analysis are calculated to evaluate the influence of the trawler dimensions on ship motions. The peak RAO motion affected the ship motions noticeably because of the resonance at the natural frequency. The L/B and B/T ratios are important geometric parameters of a ship that significantly influence its RMS motion, particularly in the case of roll and pitch. The change in the B/T ratio has a good seakeeping performance based on a comparison of the roll and pitch with the seakeeping criteria. The present results provide insights into the seakeeping performance of ships due to the influence of the principal dimensions in the design stage.

When a submarine moves near the free surface, the lift and drag characteristics that act on it are different compared to when in deep water; for example, waves on the free surface cause submarine motions that are not seen in deep water conditions and lead to changes in speed, fuel efficiency, safety, and maneuverability. To accurately predict the maneuverability of a submarine, it is necessary to consider how both maneuvering and seakeeping performance are affected by free-surface effects during the design stage. In this study, the unified maneuvering and seakeeping dynamic model is proposed. In the maneuvering performance analysis, hydrodynamic forces in the horizontal plane were calculated using STAR-CCM+. In the seakeeping performance analysis, the 6-DOF motions of the submarine and the mean wave drift forces in the horizontal plane were calculated using Ansys AQWA. Since the maneuvering motion component has a relatively long period and the seakeeping motion component has a relatively short period, the unified maneuvering and seakeeping dynamic model for a submarine moving near the surface was established using a two-time-scale approach. Using the established unified maneuvering and seakeeping dynamic model, turning circle simulations were performed in both calm water and in waves. In calm water, there were no significant differences as depth was varied. However, in irregular waves, significant differences were found in the trajectories and motion variables as depth varied. These findings underscore the necessity of accounting for sea surface conditions when operating near the free surface to ensure safety and avoid potentially hazardous scenarios during submarine operations.

Effective abnormal behavior detection in ship operations is essential for ensuring navigational safety and operational efficiency in marina ports. This study presents a hybrid method that integrates statistical analysis and neural network modeling to detect abnormal behavior based on data obtained through leisure boat sea trials. Detection criteria were established based on ship motion characteristics, operating area conditions, and the properties of the sea trial data. The method combines Rayda’s criterion and standard deviation thresholds to identify sudden changes in measured data, while a Long Short-Term Memory (LSTM) network is used to predict normal ship behavior. Deviations between predicted and measured values were evaluated using three thresholds (levels 1, 2, and 3), with level 3 effectively isolating the most significant abnormal data (representing 2–10% of the data). The proposed method is capable of successfully identifying sudden acceleration or deceleration, unusual course changes, extended stationary periods, deviations from expected routes, complex maneuvers, and track continuity issues. The results demonstrate that the proposed hybrid method can reliably distinguish abnormal ship behaviors based on real sea trial data. To separate true abnormalities from false alarms or sensor and environmental noise, its practical application on a real ship is planned as future work. This study provides a foundation for intelligent ship monitoring systems and supports the development of autonomous and semi-autonomous navigation technologies.