## 6 Model Performance Evaluation and Optimization
### Hidden Layer Size Optimization  
After determining the depth of the hidden layer, it is necessary to further optimize its size, that is, the number of channels (D) in Figure 6. The number of hidden layer channels of the first downsampling component in the selected UNet network for comparison is set to 16, 32, and 128. The number of hidden layer channels in the subsequent downsampling component is twice the model width of the previous layer. The same training set and validation set are used, and the comparison of different models is shown in Table 4. When naming the model, UNet_3-3-16 represents that the number of hidden layer channels of the first downsampling component is 16.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Settings** & **UNet\_2-2** & **UNet\_3-2** & **UNet\_4-2** & **UNet\_5-2** & **UNet\_6-2** \\ \hline MAE & 0.2313 & 0.2038 & 0.1942 & 0.2190 & 0.1593 \\ Parameter quantity & \(1.016\times 10^{6}\) & \(4.262\times 10^{6}\) & \(1.724\times 10^{7}\) & \(6.915\times 10^{7}\) & \(2.768\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance comparison—number of downsampling components.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Settings** & **UNet\_2-3** & **UNet\_3-3** & **UNet\_4-3** & **UNet\_5-3** & **UNet\_6-3** \\ \hline MAE & 0.1801 & 0.1175 & 0.1696 & 0.1456 & 0.1234 \\ Parameter quantity & \(1.533\times 10^{6}\) & \(6.401\times 10^{6}\) & \(2.587\times 10^{7}\) & \(1.037\times 10^{8}\) & \(4.152\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance comparison—number of convolutions.  
Figure 10: Comparison of pressure field prediction effects of four typical shapes. The first column is the predicted value. (**a1**) The ellipse with AR = 0.71. (**b1**) The hexagon with AR = 0.56 R = 0.3. (**c1**) The rectangle with AR = 0.71. (**d1**) The diamond with AR = 0.71. The second column is the true value. (**a2**) The ellipse with AR = 0.71. (**b2**) The hexagon with AR = 0.56 R = 0.3. (**c2**) The rectangle with AR = 0.71. (**d2**) The diamond with AR = 0.71.  
The analysis and prediction results show that the larger part of the prediction error for the ellipse, hexagon, and diamond generally exists in the negative pressure area that deviates far from the shape, and the predicted pressure is larger than the pressure field calculated by CFD. The larger part of the error of the rectangular section exists in the positive pressure area that deviates far from the shape, and the predicted pressure is smaller than that calculated by CFD. The authors believe that this phenomenon is mainly caused by two factors. First, when designing the loss function, the aerostatic performance is considered to be more closely related to the pressure field near the wall. Therefore, in order to predict the wall pressure value more accurately, the loss function gives more weight to the prediction accuracy close to the wall and has more relaxed requirements for the prediction value of the grid points farther away from the wall of the bluff body section. Secondly, the improved UNet model is still too simple, and it is necessary to use a more complex generative neural network in subsequent research. However, the improved Unet network can still achieve rapid prediction of the wall pressure and third-component force coefficient to a certain extent, as will be discussed in subsequent chapters.