Objective In the realm of ship design, the hull form plays a crucial role in determining a ship's navigation performance. However, existing hull form design methods suffer from significant drawbacks, such as long design cycles and low knowledge reusability. This not only increases design costs but also restricts the innovation and development of ship design. Therefore, it is of great urgency to develop a rapid and intelligent hull form design method.
Methods To address these issues, this research focuses on the Yangtze River cargo ship type and leverages the knowledge engineering theory. A comprehensive knowledge base is constructed, which includes a knowledge graph of hull form parameters, parent ship examples, and design rules. The knowledge graph depicts the mapping relationships between various hull form parameters and total resistance performance under different conditions, while the parent ship examples provide practical references, and the design rules summarize the influencing laws of hull form geometric parameters on ship resistance. Based on this knowledge base, a hybrid reasoning model for hull forms is designed. This model combines case - based reasoning (CBR), model - based reasoning (MBR), and rule - based reasoning (RBR) to fully utilize the knowledge in the knowledge base and obtain more accurate hull form design parameters.
Results The research takes the hull form design of a 13,000 DWT bulk carrier in the Yangtze River as an example. Through the intelligent reasoning of the knowledge base, a series of hull form geometric parameters are obtained. After considering the layout constraints and parameter coupling, a parametric hull form scheme is generated. By substituting this scheme into the parametric geometric model, a geometric model of the ship is created. The total resistance of the initial ship type and the reasoning ship type is calculated using the full - viscous flow CFD software STAR - CCM +. The results show that compared with the initial ship type, the reasoning ship type achieves a drag reduction effect of 3.06%. Further analysis reveals that the reduction in total resistance is mainly due to the change in residual resistance. The optimized bow and stern shapes of the reasoning ship type result in a more uniform pressure distribution, reducing the pressure difference and thus the resistance.
Conclusion In conclusion, the knowledge - driven intelligent design method of hull forms proposed in this study can significantly improve the efficiency and quality of hull form design. It can quickly generate reasoning hull form schemes that meet design requirements, which has important engineering application value. Looking ahead, future research will focus on two aspects. One is to establish knowledge bases for other ship performances such as wake flow and conduct multi - performance collaborative reasoning research. The other is to explore the generative design technology of hull forms, which combines artificial intelligence and optimization techniques to break through the traditional experience - based design model and create innovative ship types. This will further promote the development of the ship design industry.
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