Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents involve small ships (vessels with gross tonnage of less than 10 tons). Stability calculations are critical for assessing the risk of capsizing incidents and evaluating a ship’s seaworthiness. Despite the high frequency of capsizing accidents involving them, small ships are generally exempt from adhering to stability regulations, thus remaining systemically exposed to the risk of capsizing. Moreover, the absence of essential design documents complicates direct ship stability calculations. This study utilizes hull form feature data—obtained from the general arrangement of small ships—as input for a deep learning model. The model is structured as a multilayer neural network and aims to infer hydrostatic curves, which are required data for stability calculations.

Global ship analysis is conducted using a finite element model (FE model) for ship design and construction, which involves structural, motion, and vibration analyses. It is crucial to examine the structural safety of the hull and motion response. In the ship FE model used in global ship analysis, weight distribution is employed to adjust the light weight and center of gravity (COG), which are required to perform the analysis. Further, the FE model needs to satisfy the required longitudinal shear force (SF) and bending moment (BM) under the loading conditions of the ship. Moreover, the SF and BM in the ship Trim and Stability data are utilized to perform shear force tuning (SFT) and bending moment tuning (BMT) for the ship FE model. This ensures the ship model exhibits curves of the SF and BM that coincide with those of the ship. The SFT and BMT for the ship FE model are time-consuming and costly. Thus, to address these limitations, we propose an effective and accurate algorithm and program for SFT and BMT. Accordingly, we developed a C#-based algorithm to tune the weight, SF, BM, and COG of the ship FE model to the required target value. Finally, the accuracy of the newly developed algorithm was analyzed and compared by applying it to the shuttle tanker FE model under the ballast and full load conditions. Accuracy was within tolerance in both loading conditions. The average errors of SF and BM were smaller in the ballast condition than in the full load condition, and the errors were smaller at the bow than at the stern.

Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents occur in small ships (vessels with gross tonnage less than 10 tons). Stability calculations are critical for assessing the risk of capsizing incidents and evaluating a ship's seaworthiness. Despite the high frequency of capsizing accidents involving small ships, they are generally exempt from adhering to stability regulations, thus remaining systemically exposed to the risk of capsizing. Moreover, the absence of essential design documents complicates direct ship stability calculations. This study utilizes hull form feature data—obtained from the general arrangement of small ships—as input for a deep learning model. The model is structured as a multilayer neural network and aims to infer hydrostatic curves, which is required data for stability calculations.

This paper develops a two-dimensional fully Lagrangian meshless fluid–structure interaction solver by integrating the Moving Particle Semi-implicit (MPS) method with continuum mechanics to investigate the nonlinear interaction between waves and floating bodies. The stability and accuracy of the proposed model are validated through several benchmark cases. Furthermore, the solver is employed to analyze the dynamic response of a flat plate floating body in waves. The numerically generated waves exhibit a minimum error of approximately −0.5% and a period consistent with theoretical values, maintaining a smooth and continuous free surface. Due to the inherent limitations of the two-dimensional wave-floating body simulation, the Root Mean Square Error (RMSE) of the interaction results ranges from 5.4% to 15.2%. These findings indicate that the proposed method provides a valuable reference for the design and analysis of floating structures in ocean engineering.