Research on Intelligent Fluid Mechanics Model in High-Efficiency Propeller Design

With the growing demand for efficient ship propulsion systems, the efficiency of propeller design is particularly important. This paper studies the intelligent fluid mechanics model in the design of efficient propellers, and proposes a new fluid mechanics modeling method by combining convolutional neural network (CNN) technology. This method establishes a nonlinear mapping relationship between propeller performance and fluid dynamics by training a large amount of propeller design data and fluid mechanics simulation data, thereby improving the efficiency and accuracy of the design. Convolutional neural networks are used to automatically extract and predict propeller design parameters, and the complex characteristics in fluid mechanics are captured through hierarchical convolution operations. In the model simulation, the propeller performance under different design parameters is predicted by combining numerical simulation with experimental data. The model has outstanding performance in flow field prediction, thrust coefficient calculation, and energy consumption optimization. Especially in the design optimization stage, the model can converge quickly and give a reliable design solution.