The Hangzhou-Ningbo Canal is of great significance to the economy and material circulation in the eastern Zhejiang region. This study focuses on the automatic modeling and hull form optimization of container vessels sailing in the Hangzhou-Ningbo Canal by coupling CAESES with computational fluid dynamics (CFD). First, the hull structure is deformed based on semi-parametric modeling. A new hull form is iteratively generated according to the geometric parameters given by the optimization algorithm. In each iteration, the hydrodynamic performance of the hull model is solved by using CFD software to provide geometric parameters for the next iteration. The optimal hull form is eventually identified in the geometric configuration space through this iterative process. The results show that the optimized hull form achieves a drag reduction of 5.8%, and the pressure distribution on the hull surface is significantly improved, with a reduced negative pressure area and less stress concentration. This study can provide theoretical and technical support for the hull form optimization of inland container vessels.

To address the issues of high training costs and low efficiency in conventional manual hull modeling methods, an intelligent software for 3D geometric hull model generation from 2D lines plan has been developed, enhancing the automation of modeling and production efficiency. This modeling software, which is developed and designed using python, simulates the human visual system to extract, analyze and interpret the hull lines information from 2D drawings in units of pixel clusters. First, it reads, identifies and processes 2D lines plan (JPEG or PNG format) obtained from screenshots or scans, and maps the 2D profile points to 3D point cloud data according to the spatial relationships among the three views. Then, it generates the hull surface through information densification, thereby simplifying the modeling process from 2D plans to 3D models. Finally, accuracy verification results show that the modeling software based on computer vision technology can quickly convert the 2D lines plan in image form into the 3D hull models, demonstrating its reliability.