In the field of ship and ocean engineering, fluid dynamics analysis plays a crucial role in the design and optimization of marine structures. With the development of big data technologies, data-driven methods have gradually become mainstream in fluid dynamics analysis. However, existing methods have limitations in handling complex nonlinear relationships. Therefore, this paper introduced complex network science into the field of fluid dynamics, redefined the transient flow field and simplified its description through clustering methods, thus effectively captured key structures and features. Additionally, network theory was applied to describe the fluid flow process, revealing important patterns and interactions within it. Finally, a model for unsteady hydrodynamic coefficients was constructed, enabling accurate prediction and description of unsteady behaviors in fluid dynamics. This research integrates network science with fluid dynamics, providing new ideas and tools for solving complex fluid dynamics problems, particularly in the context of ship and ocean engineering applications.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |