The evaluation of ship strength during the hull structure design stage is essential for structural safety. Additionally, plate is one of the basic structural members of the hull, it is important to analyze and predict the ultimate strength. The curved plate used for shipbuilding must be able to withstand repeated axial loads and complex loads, and stability needs to be confirmed through ultimate strength analysis. In general situations, the magnitude of the transverse compression is smaller than that of the longitudinal and combined loads, but transverse compression causes different physical behaviors from the longitudinal load state, which affects the ultimate strength, so study on the ultimate strength of the curved-plate under transverse compression is essential. Therefore, in this paper, a curved plate under transverse compressive load was selected as a subject, and the ultimate strength of the curved plate under the corresponding compression condition was predicted using a deep learning model. To obtain the training data for the deep-learning model, 4050 cases were selected and analyzed using the ANSYS. The accuracy of the model was verified by comparing the results predicted by the model and empirical equations with those of the FEM analysis. The study shows that it is possible to consider the ultimate strength more efficiently in the initial design stage of the ship.

Plates and curved plates are essential components in ship construction. In the design stage, the methods used to evaluate the ultimate strength required to confirm the structural safety of plates include prediction through analytical methods, finite-element analysis (FEA), and empirical formulas. However, with nonlinear buckling, the results of the empirical formula and the FEA differ for small flank angles (1~9). As a result, the prediction of the nonlinear ultimate strength of flank angle (1~9) plates still requires significant computation time and cost. To compensate for this, this study performed an ultimate strength prediction method utilizing a deep neural network together with the 4050 curved plate analysis. In addition, this paper presents the analysis results of the nonlinear finite-element method and the geometric shape and ratio of curved plates as training data. Based on the results of this study, designers can more efficiently design appropriate curved plate members by considering the ultimate strength.

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.

Cylindrical bodies generate flow-induced noise when exposed to external flows, which can be predicted numerically using Computational Fluid Dynamics (CFD) combined with the Ffowcs Williams–Hawkings (FW–H) Equation. Accurate prediction, however, requires turbulence models such as Detached Eddy Simulation (DES) with fine spatial resolution and small time steps, in addition to time-dependent surface pressure data and receiver arrangements. These requirements greatly increase computational costs and limit the applicability of such methods during the design stage. To address this challenge, a Deep Neural Network (DNN) model was developed to predict flow-induced noise around a cylinder. Training data were generated from CFD cases using cylinder geometry and inflow velocity as design variables, with multiple receivers arranged in a polar coordinate system. Acoustic signals were computed using Farassat’s Formulation 1A, the time-domain surface solution of the FW–H Equation. The DNN was trained with design variables, receiver coordinates, and octave-band center frequencies as inputs, while the Sound Pressure Level (SPL) served as the output. Model performance was evaluated using the adjusted coefficient of determination (${\mathrm{R}}_{\mathrm{adj}}^2$) and the root mean squared error (RMSE). In addition, interpolation capability was tested by varying receiver spacing to examine robustness under sparse data conditions. The results confirm that the proposed framework provides accurate and computationally efficient predictions suitable for early-stage design.