To promote lightweight design in very large crude carriers (VLCCs) with double longitudinal bulkheads, this study conducts a detailed investigation of the topological configuration of their transverse frames and the factors influencing it. Rather than presenting only the final optimized structures, the research systematically elucidates the complete formation process and evolutionary patterns of transverse frame topology optimization.

The research adopts the variable density method. The optimization model constrains the structural volume fraction, with the objective of minimizing structural strain energy. For multi-load case analysis, the analytic hierarchy process (AHP) is applied to determine the weight coefficients based on the initial strain energy of each load case. Load cases include combinations of deck loads, external water pressure, and liquid cargo pressure. A systematic investigation is conducted into the influence of mesh size, compartment length, and longitudinal bulkhead position on the evolutionary patterns of the topological configuration under multiple load cases. To ensure objective and reliable evaluation, the intersection over union (IoU) metric is employed to quantitatively measure the similarity between configurations.

Liquid cargo pressure is identified as the primary factor driving the formation of horizontal struts between the double longitudinal bulkheads, which enhance structural stability and load-bearing capacity. Mesh size mainly influences the finer details of the optimized configuration; a mesh size equivalent to the longitudinal spacing achieves a balance between computational efficiency and structural clarity, yielding practical designs. Compartment length significantly influences the priority of horizontal strut formation. When the length reaches three times the frame spacing, the horizontal struts form preferentially even at low volume fractions, becoming the main load-bearing path and resulting in a configuration that is both stable and representative of the full compartment model. The position of the longitudinal bulkheads governs the significance and timing of bracket formation. When the central tank width ratio is below 37%, the structure prioritizes horizontal strut reinforcement, with brackets emerging as auxiliary supports at a volume fraction of 0.10. Beyond this threshold, however, brackets appear at a lower volume fraction of 0.05 and serve as essential load-bearing components in conjunction with the horizontal struts.

This study clarifies the fundamental load-bearing paths and their evolutionary patterns in the transverse frames of double longitudinal bulkhead VLCCs, and provides practical guidance for both modeling and design. For modeling, a computational model with a mesh size equal to the longitudinal spacing and a compartment length of three frame spaces is recommended to balance accuracy and efficiency. For design, a central tank width ratio of 37% is identified as a critical threshold that dictates the structural role of brackets. These findings provide both a solid theoretical foundation and practical reference for the optimal and lightweight design of VLCC transverse frames.

This study addresses the limitations of existing deep learning-based ship cross-section topology optimization methods, which are restricted to single-section ship structures. A fast topology optimization method for general ship cross-sections is proposed.

The proposed method employs automated parametric modeling and computation techniques to construct a large-scale, structurally diverse dataset of ship cross-section static analysis and topology optimization results. This dataset enables deep supervised learning to train the neural network to rapidly generate reasonable topology optimization configurations for various ship cross-sections. Furthermore, to address the challenge of directly applying neural network predictions in engineering analysis, an algorithm was developed to automatically reconstruct finite element models from the binarized density tensor output by the neural network, thus overcoming the limitations of element removal methods and ensuring mechanical consistency between network predictions and traditional iterative calculation results.

Experimental results demonstrate that applying the proposed method to predict the topological configuration of various ship cross-sections reduces computation time by two orders of magnitude, with an average prediction accuracy exceeding 90%. Sampling inspection results indicate that the finite element models reconstructed based on the network predictions avoid stress concentration, with less than 3% deviation in mechanical performance compared to traditional iterative calculations, further verifying the reliability of the proposed method.

The proposed method provides a general solution for the rapid topology optimization of ship cross-sections, reduces ship design costs, and possesses significant engineering value.

This paper proposes a hull transverse web topology optimization method based on UNet for application in the optimization design of complex ship structures.

Taking the transverse web of a very large crude carrier (VLCC) as the research object, a UNet topology optimization surrogate model is first created according to optimization mathematical principles. The finite element grid physical quantity is then mapped to the tensor to obtain the dataset for model training. Finally, the intersection over union (IoU) method is used to evaluate the training results, and the method is compared with the solid isotropic material with penalization (SIMP) method in terms of topology configuration.

The results show that this method can quickly output the material layout of the design domain, and compared with SIMP topology optimization, it can obtain the topology configuration more efficiently.

The proposed topology optimization method can provide a new design method for ship transverse web structures.