Surface deformation of the hull is a prerequisite for the optimization of the ship shape. Surface deformation method based on the radial basis function interpolation is suitable for the optimization of the hydrodynamic performance of the ship. This paper mainly improved the method of obtaining the support radius in radial basis interpolation, and proposed a dynamic method for obtaining the support radius. It considered the influence of the distribution of variable points and the change of coordinates on the support radius. On this basis, the improved radial basis interpolation method was applied to the ship hydrodynamic performance multi-disciplinary comprehensive optimization platform developed by the authors’ team. The Series 60 ship type was used as the research object to complete the ship type optimization study under the given constraints and the feasibility of the method was proved.
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In the realm of ship design, the hull form plays a crucial role in determining a ship's hydrodynamic performance. However, existing hull form design methods have significant drawbacks, such as lengthy design cycles and poor reusability of design knowledge. This not only increases design costs but also hinders innovation in ship design. Therefore, there is an urgent need to develop a rapid and intelligent hull form design method.
To address these issues, this research focuses on the Yangtze River cargo ships and leverages the knowledge engineering principles. A comprehensive knowledge base is constructed, including a knowledge graph of hull form parameters, parent ship cases, and design rules; the knowledge graph depicts the mapping relationships between various hull form parameters and total resistance performance under different conditions; the parent ship cases provide practical references, while the design rules summarize how geometric parameters affect ship resistance. Based on this knowledge base, a hybrid reasoning model for hull form design has been developed. This model integrates case-based reasoning (CBR), model-based reasoning (MBR), and rule-based reasoning (RBR) to fully utilize the knowledge base and derive more accurate hull form design parameters.
This research uses the hull form design of a 13 000 DWT bulk carrier operating on the Yangtze River as a case study. A set of hull form geometric parameters is obtained through intelligent reasoning based on the knowledge base. Taking into account spatial layout constraints and parameter interactions, a parametric hull form design scheme is generated. By inputting this scheme into the parametric geometric model, a geometric model of the ship is created. The total resistance of both the initial ship type and the optimized hull form is calculated using STAR-CCM +, a CFD software based on fully viscous flow. The results show that, compared with the initial ship type, the optimized hull form achieves a 3.06% reduction in total resistance. Furthermore, analysis reveals that the reduction is primarily attributed to a decrease in residual resistance. The optimized bow and stern shapes contribute to a more uniform pressure distribution, thereby reducing pressure differentials and overall resistance.
In conclusion, the knowledge-driven intelligent method for hull form design proposed in this study can significantly improve the efficiency and quality of hull form design. It can quickly generate hull form schemes derived through reasoning that meet design requirements, demonstrating strong potential for engineering applications.
With the vigorous development of artificial intelligence, knowledge-based engineering (KBE), as an engineering technology discipline that studies the intelligent processing of knowledge, has gradually become a research hotspot, and KBE-based ship design method have also received increasing attention. This article systematically summarizes and analyzes the application and development of KBE in the field of ship design in three aspects: overall conceptual ship design, hull structure design, and ship layout design. It also introduces two typical cases of the KBE application in ship hull form design and shipyard-institute collaborative design. Meanwhile, it summarizes and refines the main problems and constraints in the KBE application, and looks forward to the promotion of KBE development by large language model (LLM) technology. Through KBE technology, ship design can be intelligentized, relevant specifications and requirements can be quickly invoked, and the existing ship design knowledge that has been fully proven through practice can be stored and derived, thereby improving design efficiency and shortening the design cycle. However, the full application of KBE techniques still faces numerous challenges and requires further exploration and improvement.
Aiming at the complexity of the navigation environment for shipping on the Yangtze River, as well as the high energy consumption and serious pollution of some ship types, there is an urgent need to carry out research on green and intelligent ship types. In this study, a knowledge graph is applied to the hull form design method so as to adapt to the green and intelligent technological development requirements in the optimization of ship types.
The knowledge graph and inference model of a Yangtze River bulk cargo ship hull form are introduced, and a knowledge graph-based hull form design process is proposed for the first time to complete the rapid design of an excellent hull form for a 6 000-ton Yangtze River bulk cargo ship. The hull form is then compared with that of a ship designed by conventional methods.
Drag reduction effect of 2.39% is obtained compared with the conventionally designed hull form.
The results of this study verify the effectiveness of the proposed knowledge graph-based hull form design method.
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