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Conventional resistance prediction method of proxy models takes main scale ratios, ship form coefficients, and other similar parameters as inputs. Compared to CFD calculations, in which the complete hull form is used as input, prediction method with lower information density of proxy models results in lower prediction accuracy. In this paper, a high-dimensional, high-precision resistance prediction method was proposed for ship hulls using 4108 sets of complete hull geometry feature tensors as input and employing neural networks as proxy models. The total resistance coefficient of the ship was taken as the output. Dimensionless treatment of the hull forms was conducted at first and feature tensors were extracted as inputs. Next, a neural network model was constructed, comprising input layers, hidden layers, and an output layer. Finally, the feature tensors of the hull forms and the corresponding total resistance coefficients were fed into the neural network, and the model was trained using error back propagation until the loss function converges. The research findings in this paper can provide theoretical and technical support for high-dimensional proxy model-based resistance performance prediction.
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| 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 |
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